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This approach aims at eliminating any human effort before the actual exploitation of data, but the problem is only delayed since preparing and querying a data lake is usually a hard task. We address this problem by introducing Kayak, a framework that helps data scientists in the definition and optimization of pipelines of data preparation. Since in many cases approximations of the results, which can be computed rapidly, are enough informative, Kayak allows the users to specify their needs in terms of accuracy over performance and produces previews of the outputs satisfying such requirement. In this way, the pipeline is executed much faster and the process of data preparation is shortened. We discuss the design choices of Kayak including execution strategies, optimization techniques, scheduling of operations, and metadata management. 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c-article-identifiers--cite-list"> <li class="c-article-identifiers__item"> <span class="c-chapter-book-details__meta"> pp 474–489</span> </li> <li class="c-article-identifiers__item c-article-identifiers__item--cite"> <a href="#citeas" data-track="click" data-track-action="cite this chapter" data-track-category="chapter body" data-track-label="link">Cite this conference paper</a> </li> </ul> <div class="app-article-masthead__buttons" data-track-context="masthead"> </div> </div> <div class="app-article-masthead__brand app-article-masthead__brand--no-border app-article-masthead__conference-link"> <a href="/book/10.1007/978-3-319-91563-0" class="app-article-masthead__conference-link app-article-masthead__journal-link" data-track="click" data-track-action="book homepage" data-track-label="link"> <picture> <source type="image/webp" media="(min-width: 768px)" width="120" height="182" srcset="https://media.springernature.com/w120/springer-static/cover-hires/book/978-3-319-91563-0?as=webp, 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class="c-context-bar__title"> KAYAK: A Framework for Just-in-Time Data Preparation in a Data Lake </div> </div> </div> <article lang="en"> <div class="c-article-header"> <header> <div class="app-overview-section"> <ul class="c-article-author-list c-article-author-list--short" data-test="authors-list" data-component-authors-activator="authors-list"><li class="c-article-author-list__item"><a data-test="author-name" data-track="click" data-track-action="open author" data-track-label="link" href="#auth-Antonio-Maccioni" data-author-popup="auth-Antonio-Maccioni" data-corresp-id="c1">Antonio Maccioni<svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-mail-medium"></use></svg></a><sup class="u-js-hide"><a href="#Aff15">15</a></sup> & </li><li class="c-article-author-list__item"><a data-test="author-name" data-track="click" data-track-action="open author" data-track-label="link" href="#auth-Riccardo-Torlone" data-author-popup="auth-Riccardo-Torlone">Riccardo Torlone</a><sup class="u-js-hide"><a href="#Aff16">16</a></sup> </li></ul> <div class="app-overview-section__separator app-overview-section__book-series"> <div class="app-book-series-listing"> <div> <svg class="app-book-series-listing__icon" width="24" height="24" aria-hidden="true" focusable="false"><use href="#icon-eds-i-book-series-medium"></use></svg> </div> <div> <p data-test="series-link"> <span class="app-book-series-listing__description">Part of the book series:</span> <a href="https://www.springer.com/series/558" data-track="click" data-track-action="open book series" data-track-label="link">Lecture Notes in Computer Science</a> ((LNISA,volume 10816)) </p> </div> </div> </div> <div class="app-book-series-listing app-overview-section__conf-series"> <div> <svg class="app-book-series-listing__icon" width="24" height="24" aria-hidden="true" focusable="false"><use 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class="c-article-section__title js-section-title js-c-reading-companion-sections-item"><span class="c-article-section__title-number"> </span>Abstract</h2><div class="c-article-section__content" id="Abs1-content"><p>A data lake is a loosely-structured collection of data at large scale that is usually fed with almost no requirement of data quality. This approach aims at eliminating any human effort before the actual exploitation of data, but the problem is only delayed since preparing and querying a data lake is usually a hard task. We address this problem by introducing <span class="u-small-caps">Kayak</span>, a framework that helps data scientists in the definition and optimization of pipelines of data preparation. Since in many cases approximations of the results, which can be computed rapidly, are enough informative, <span class="u-small-caps">Kayak</span> allows the users to specify their needs in terms of accuracy over performance and produces previews of the outputs satisfying such requirement. In this way, the pipeline is executed much faster and the process of data preparation is shortened. We discuss the design choices of <span class="u-small-caps">Kayak</span> including execution strategies, optimization techniques, scheduling of operations, and metadata management. With a set of preliminary experiments, we show that the approach is effective and scales well with the number of datasets in the data lake.</p></div></div></section><div class="c-article-section__content c-article-section__content--separator"><p>This work has been supported by the European Commission under the grant agreement number 774571 – Project PANTHEON.</p></div> <div data-test="cobranding-download"> <div class="note test-pdf-link" id="cobranding-and-download-availability-text"> <div class="c-article-access-provider" data-component="provided-by-box"> <p class="c-article-access-provider__text c-article-access-provider__text--chapter"> You have full access to this open access chapter,  <a href="/content/pdf/10.1007/978-3-319-91563-0_29.pdf?pdf=inline%20link" class="c-pdf-download__link" id="js-body-chapter-download" style="display: inline; padding:0px!important;" target="_blank" rel="noopener" data-track="content_download" data-track-context="article body" data-track-type="conference paper PDF download" data-track-action="Pdf download" data-track-label="inline link" download>Download conference paper PDF</a> <svg width="24" height="24" focusable="false" role="img" aria-hidden="true" class="c-download-pdf-icon-large"> <use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-download-medium"></use> </svg> </p> </div> </div> </div> <section aria-labelledby="inline-recommendations" data-title="Inline Recommendations" class="c-article-recommendations" data-track-component="inline-recommendations"> <h3 class="c-article-recommendations-title" id="inline-recommendations">Similar content being viewed by others</h3> <div class="c-article-recommendations-list"> <div class="c-article-recommendations-list__item"> <article class="c-article-recommendations-card" itemscope itemtype="http://schema.org/ScholarlyArticle"> <div class="c-article-recommendations-card__img"><img src="https://media.springernature.com/w92h120/springer-static/cover-hires/book/978-3-319-67262-5?as=webp" loading="lazy" alt=""></div> <div class="c-article-recommendations-card__main"> <h3 class="c-article-recommendations-card__heading" itemprop="name headline"> <a class="c-article-recommendations-card__link" itemprop="url" href="https://link.springer.com/10.1007/978-3-319-67262-5_10?fromPaywallRec=false" data-track="select_recommendations_1" data-track-context="inline recommendations" data-track-action="click recommendations inline - 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3" data-track-label="10.1007/978-1-4842-8233-5_14">Adaptive Query Execution </a> </h3> <div class="c-article-meta-recommendations" data-test="recommendation-info"> <span class="c-article-meta-recommendations__item-type">Chapter</span> <span class="c-article-meta-recommendations__date">© 2022</span> </div> </div> </article> </div> </div> </section> <script> window.dataLayer = window.dataLayer || []; window.dataLayer.push({ recommendations: { recommender: 'semantic', model: 'specter', policy_id: 'NA', timestamp: 1732351409, embedded_user: 'null' } }); </script> <div class="main-content"> <h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="keywords">Keywords</h2><ul class="c-article-subject-list"><li class="c-article-subject-list__subject"><span><a href="/search?query=Data%20lake&facet-discipline="Computer%20Science"" data-track="click" data-track-action="view keyword" data-track-label="link">Data lake</a></span></li><li class="c-article-subject-list__subject"><span><a href="/search?query=Data%20preparation&facet-discipline="Computer%20Science"" data-track="click" data-track-action="view keyword" data-track-label="link">Data preparation</a></span></li><li class="c-article-subject-list__subject"><span><a href="/search?query=Big%20data&facet-discipline="Computer%20Science"" data-track="click" data-track-action="view keyword" data-track-label="link">Big data</a></span></li><li class="c-article-subject-list__subject"><span><a href="/search?query=Schema-on-read&facet-discipline="Computer%20Science"" data-track="click" data-track-action="view keyword" data-track-label="link">Schema-on-read</a></span></li></ul> <section data-title="Introduction"><div class="c-article-section" id="Sec1-section"><h2 id="Sec1" class="c-article-section__title js-section-title js-c-reading-companion-sections-item"><span class="c-article-section__title-number">1 </span>Introduction</h2><div class="c-article-section__content" id="Sec1-content"><p>In traditional business intelligence, activities such as modeling, extracting, cleaning, and transforming data are necessary but they also make the data analysis an endless process. In response to that, big data-driven organizations are adopting an agile strategy that dismisses any pre-processing before the actual exploitation of data. This is done by maintaining a repository, called “data lake”, for storing any kind of raw data in its native format. A dataset in the lake is a file, either collected from internal applications (e.g., logs or user-generated data) or from external sources (e.g., open data), that is directly stored on a (distributed) file system without going through an ETL process.</p><p>Unfortunately, reducing the engineering effort upfront just delays the traditional issues of data management since this approach does not eliminate the need of, e.g., data quality and schema understanding. Therefore, a long process of <i>data-preparation</i> (a.k.a. <i>data wrangling</i>) is required before any meaningful analysis can be performed [<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 6" title="Deng, D., Fernandez, R.C., Abedjan, Z., Wang, S., Stonebraker, M., Elmagarmid, A.K., Ilyas, I.F., Madden, S., Ouzzani, M., Tang, N.: The data civilizer system. In: CIDR (2017)" href="#ref-CR6" id="ref-link-section-d20310988e865">6</a>, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 13" title="Heudecker, N., White, A.: The data lake fallacy: all water and little substance. Gartner Report G 264950 (2014)" href="#ref-CR13" id="ref-link-section-d20310988e868">13</a>, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 23" title="Terrizzano, I., Schwarz, P.M., Roth, M., Colino, J.E.: Data wrangling: the challenging journey from the wild to the lake. In: CIDR (2015)" href="#ref-CR23" id="ref-link-section-d20310988e871">23</a>]. This process typically consists of pipelines of operations such as: source and feature selection, exploratory analysis, data profiling, data summarization, and data curation. A number of state-of-the-art applications can support these activities, including: (i) data and metadata catalogs, for selecting the appropriate datasets [<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 1" title="CKAN: The open source data portal software. 
 http://ckan.org/
 
 . Accessed Nov 2017" href="#ref-CR1" id="ref-link-section-d20310988e875">1</a>, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 5" title="Bhardwaj, A.P., Deshpande, A., Elmore, A.J., Karger, D.R., Madden, S., Parameswaran, A.G., Subramanyam, H., Wu, E., Zhang, R.: Collaborative data analytics with DataHub. PVLDB 8(12), 1916–1927 (2015)" href="#ref-CR5" id="ref-link-section-d20310988e878">5</a>, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 10" title="Halevy, A.Y., Korn, F., Noy, N.F., Olston, C., Polyzotis, N., Roy, S., Whang, S.E.: Goods: organizing Google’s datasets. In: SIGMOD (2016)" href="#ref-CR10" id="ref-link-section-d20310988e881">10</a>, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 12" title="Hellerstein, J.M., Sreekanti, V., Gonzalez, J.E., Dalton, J., Dey, A., Nag, S., Ramachandran, K., Arora, S., Bhattacharyya, A., Das, S., Donsky, M., Fierro, G., She, C., Steinbach, C., Subramanian, V., Sun, E.: Ground: a data context service. In: CIDR (2017)" href="#ref-CR12" id="ref-link-section-d20310988e884">12</a>]; (ii) tools for full-text indexing, for providing keyword search and other advanced search capabilities [<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 9" title="Hai, R., Geisler, S., Quix, C.: Constance: an intelligent data lake system. In: SIGMOD, pp. 2097–2100 (2016)" href="#ref-CR9" id="ref-link-section-d20310988e887">9</a>, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 10" title="Halevy, A.Y., Korn, F., Noy, N.F., Olston, C., Polyzotis, N., Roy, S., Whang, S.E.: Goods: organizing Google’s datasets. In: SIGMOD (2016)" href="#ref-CR10" id="ref-link-section-d20310988e890">10</a>]; (iii) data profilers, for collecting meta-information from datasets [<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 6" title="Deng, D., Fernandez, R.C., Abedjan, Z., Wang, S., Stonebraker, M., Elmagarmid, A.K., Ilyas, I.F., Madden, S., Ouzzani, M., Tang, N.: The data civilizer system. In: CIDR (2017)" href="#ref-CR6" id="ref-link-section-d20310988e894">6</a>, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 9" title="Hai, R., Geisler, S., Quix, C.: Constance: an intelligent data lake system. In: SIGMOD, pp. 2097–2100 (2016)" href="#ref-CR9" id="ref-link-section-d20310988e897">9</a>, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 16" title="Papenbrock, T., Bergmann, T., Finke, M., Zwiener, J., Naumann, F.: Data profiling with metanome. PVLDB 8(12), 1860–1863 (2015)" href="#ref-CR16" id="ref-link-section-d20310988e900">16</a>]; (iv) processing engines like Spark [<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 24" title="Zaharia, M., Xin, R.S., Wendell, P., Das, T., Armbrust, M., Dave, A., Meng, X., Rosen, J., Venkataraman, S., Franklin, M.J., Ghodsi, A., Gonzalez, J., Shenker, S., Stoica, I.: Apache spark: a unified engine for big data processing. Commun. ACM 59(11), 56–65 (2016)" href="#ref-CR24" id="ref-link-section-d20310988e903">24</a>] in conjunction with data science notebooks such as Jupyter<sup><a href="#Fn1"><span class="u-visually-hidden">Footnote </span>1</a></sup> or Zeppelin<sup><a href="#Fn2"><span class="u-visually-hidden">Footnote </span>2</a></sup>, for executing the analysis and visualize the results. In such scenario, data preparation is an involved, fragmented and time-consuming process, thus preventing analysis on-the-fly over the lake.</p><p>In this framework, we propose a system, called <span class="u-small-caps">Kayak</span>, supporting data scientists in the definition, execution and, most importantly, optimization of data preparation pipelines in a data lake<sup><a href="#Fn3"><span class="u-visually-hidden">Footnote </span>3</a></sup>. With <span class="u-small-caps">Kayak</span> data scientists can: (i) define pipelines composed by <i>primitives</i> implementing common data preparation activities and (ii) specify, for each primitive, their time <i>tolerance</i> in waiting for the result. This represents a mechanism to trade-off between performance and accuracy of primitives’ results. Indeed, these primitives involve hard-to-scale algorithms that prevent analysis on-the-fly over new datasets [<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 14" title="Ilyas, I.F., Markl, V., Haas, P.J., Brown, P., Aboulnaga, A.: CORDS: automatic discovery of correlations and soft functional dependencies. In: SIGMOD, pp. 647–658 (2004)" href="#ref-CR14" id="ref-link-section-d20310988e958">14</a>, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" title="Papenbrock, T., Bergmann, T., Finke, M., Zwiener, J., Naumann, F.: Data profiling with metanome. PVLDB 8(12), 1860–1863 (2015)" href="#ref-CR16" id="ref-link-section-d20310988e961">16</a>,<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" title="Papenbrock, T., Ehrlich, J., Marten, J., Neubert, T., Rudolph, J., Schönberg, M., Zwiener, J., Naumann, F.: Functional dependency discovery: an experimental evaluation of seven algorithms. PVLDB 8(10), 1082–1093 (2015)" href="#ref-CR17" id="ref-link-section-d20310988e961_1">17</a>,<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 18" title="Papenbrock, T., Naumann, F.: A hybrid approach to functional dependency discovery. In: SIGMOD, pp. 821–833 (2016)" href="#ref-CR18" id="ref-link-section-d20310988e964">18</a>], but often an approximate result is informative enough to move forward to the next action in the pipeline, with no need to wait for an exact result. <span class="u-small-caps">Kayak</span> takes into account the tolerances by producing quick <i>previews</i> of primitive’s results, when necessary. In this way, the pipelines are executed much faster and the time for data preparation is shortened.</p><p>On the practical side, each primitive in a pipeline is made of a series of tasks implementing built-in, atomic operations of data preparation. Each task can be computed incrementally via a number of steps, each of which can return previews to the user. <span class="u-small-caps">Kayak</span> orchestrates the overall execution process by scheduling and computing the various steps of a pipeline according to several optimization strategies that balance the accuracy of results with the given time constraints. Another important feature of <span class="u-small-caps">Kayak</span> is its ability to collect automatically, in a metadata catalog, different relationships among datasets, which can be used later to implement advanced analytics. The catalog also keeps the profile of each dataset and provides a high-level view of the content of the data lake.</p><p>We have verified the effectiveness of our approach with the first implementation of <span class="u-small-caps">Kayak</span> and tested its scalability when the number and size of datasets in the lake increase.</p><p>Currently, the system is used in operation within the PANTHEON project whose aim is supporting precision farming: it is charge of collecting and managing heterogeneous data coming from terrestrial and aerial robots moving in plantations, as well as from ground sensors and weather stations located nearby.</p><p>The rest of the paper is organized as follows. In Sect. <a data-track="click" data-track-label="link" data-track-action="section anchor" href="#Sec2">2</a> we provide an overview of our approach. In Sect. <a data-track="click" data-track-label="link" data-track-action="section anchor" href="#Sec3">3</a> we illustrate how <span class="u-small-caps">Kayak</span> models data and, in Sect. <a data-track="click" data-track-label="link" data-track-action="section anchor" href="#Sec4">4</a>, we describe our strategy for executing primitives. In Sect. <a data-track="click" data-track-label="link" data-track-action="section anchor" href="#Sec5">5</a> we discuss our experimental results and, in Sect. <a data-track="click" data-track-label="link" data-track-action="section anchor" href="#Sec8">6</a>, some related works. Finally, in Sect. <a data-track="click" data-track-label="link" data-track-action="section anchor" href="#Sec9">7</a>, we draw some conclusions and sketch future works.</p></div></div></section><section data-title="Overview of the Approach"><div class="c-article-section" id="Sec2-section"><h2 id="Sec2" class="c-article-section__title js-section-title js-c-reading-companion-sections-item"><span class="c-article-section__title-number">2 </span>Overview of the Approach</h2><div class="c-article-section__content" id="Sec2-content"><p>This section provides an overview, from a high-level perspective, of the main features of the system.</p><p><b>Pipelines, Primitives and Tasks.</b> <span class="u-small-caps">Kayak</span> is a framework that lies between users/applications and the file system where data is stored. It exposes a series of <i>primitives</i> for data preparation, some of which are reported in Table <a data-track="click" data-track-label="link" data-track-action="table anchor" href="#Tab1">1</a>. For example, a data scientist can use primitive P<span class="mathjax-tex">$_5$</span> to find interesting ways to access a dataset. Each primitive is composed of a sequence of <i>tasks</i> that are reused across primitives (e.g., P<span class="mathjax-tex">$_6$</span> is split into T<span class="mathjax-tex">$_b$</span>, T<span class="mathjax-tex">$_c$</span>, T<span class="mathjax-tex">$_w$</span>, while primitive P<span class="mathjax-tex">$_7$</span> uses T<span class="mathjax-tex">$_c$</span> only). A task is atomic and consists of operations that can be executed either directly within <span class="u-small-caps">Kayak</span> or involving external tools [<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 16" title="Papenbrock, T., Bergmann, T., Finke, M., Zwiener, J., Naumann, F.: Data profiling with metanome. PVLDB 8(12), 1860–1863 (2015)" href="#ref-CR16" id="ref-link-section-d20310988e1124">16</a>, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 24" title="Zaharia, M., Xin, R.S., Wendell, P., Das, T., Armbrust, M., Dave, A., Meng, X., Rosen, J., Venkataraman, S., Franklin, M.J., Ghodsi, A., Gonzalez, J., Shenker, S., Stoica, I.: Apache spark: a unified engine for big data processing. Commun. ACM 59(11), 56–65 (2016)" href="#ref-CR24" id="ref-link-section-d20310988e1127">24</a>], as shown in Table <a data-track="click" data-track-label="link" data-track-action="table anchor" href="#Tab2">2</a>.</p><div class="c-article-table" data-test="inline-table" data-container-section="table" id="table-1"><figure><figcaption class="c-article-table__figcaption"><b id="Tab1" data-test="table-caption">Table 1. Example of primitives in <span class="u-small-caps">Kayak</span>.</b></figcaption><div class="u-text-right u-hide-print"><a class="c-article__pill-button" data-test="table-link" data-track="click" data-track-action="view table" data-track-label="button" rel="nofollow" href="/chapter/10.1007/978-3-319-91563-0_29/tables/1" aria-label="Full size table 1"><span>Full size table</span><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-right-small"></use></svg></a></div></figure></div> <div class="c-article-table" data-test="inline-table" data-container-section="table" id="table-2"><figure><figcaption class="c-article-table__figcaption"><b id="Tab2" data-test="table-caption">Table 2. Example of tasks in <span class="u-small-caps">Kayak</span>.</b></figcaption><div class="u-text-right u-hide-print"><a class="c-article__pill-button" data-test="table-link" data-track="click" data-track-action="view table" data-track-label="button" rel="nofollow" href="/chapter/10.1007/978-3-319-91563-0_29/tables/2" aria-label="Full size table 2"><span>Full size table</span><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-right-small"></use></svg></a></div></figure></div> <p>A <i>pipeline</i> is a composition of primitives that is representable as a DAG (direct acyclic graph). As an example, Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="#Fig1">1</a> shows a pipeline composed by six primitives: P<span class="mathjax-tex">$_1$</span> inserts a new dataset in the lake and P<span class="mathjax-tex">$_4$</span> generates a profiles for it; then P<span class="mathjax-tex">$_8$</span> processes the dataset with a machine learning algorithm while P<span class="mathjax-tex">$_7$</span> identifies possible relationships with another dataset. Eventually, P<span class="mathjax-tex">$_5$</span> produces a query recommendation. Users can mark the primitives in the pipeline with a <i>watchpoint</i> to inspect some intermediate result. For example, in Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="#Fig1">1</a> we have defined a watchpoint on P<span class="mathjax-tex">$_7$</span>, P<span class="mathjax-tex">$_5$</span>, P<span class="mathjax-tex">$_3$</span>, and P<span class="mathjax-tex">$_8$</span>.</p><p>Note that, we assume here that the output of a primitive is not directly used as input of the following primitive; they rather communicate indirectly by storing data in the lake or metadata in a catalog. Primitives can be <i>synchronous</i> when they do not allow the execution of a subsequent primitive before its completion, or <i>asynchronous</i>, when can be invoked and executed concurrently.</p><div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-1" data-title="Fig. 1."><figure><figcaption><b id="Fig1" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 1.</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><a class="c-article-section__figure-link" data-test="img-link" data-track="click" data-track-label="image" data-track-action="view figure" href="/chapter/10.1007/978-3-319-91563-0_29/figures/1" rel="nofollow"><picture><source type="image/webp" srcset="//media.springernature.com/lw685/springer-static/image/chp%3A10.1007%2F978-3-319-91563-0_29/MediaObjects/454396_1_En_29_Fig1_HTML.gif?as=webp"><img aria-describedby="Fig1" src="//media.springernature.com/lw685/springer-static/image/chp%3A10.1007%2F978-3-319-91563-0_29/MediaObjects/454396_1_En_29_Fig1_HTML.gif" alt="figure 1" loading="lazy"></picture></a></div><div class="c-article-section__figure-description" data-test="bottom-caption" id="figure-1-desc"><p>Example of a data preparation pipeline.</p></div></div><div class="u-text-right u-hide-print"><a class="c-article__pill-button" data-test="chapter-link" data-track="click" data-track-label="button" data-track-action="view figure" href="/chapter/10.1007/978-3-319-91563-0_29/figures/1" data-track-dest="link:Figure1 Full size image" aria-label="Full size image figure 1" rel="nofollow"><span>Full size image</span><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-right-small"></use></svg></a></div></figure></div> <p><b>Metadata Management.</b> <span class="u-small-caps">Kayak</span> extracts metadata from datasets explicitly, with ad-hoc primitives (e.g., <span class="mathjax-tex">$P_4$</span>), or implicitly, when a primitive needs some metadata and uses the corresponding profiling task (e.g., <span class="mathjax-tex">$T_a$</span> in <span class="mathjax-tex">$P_1$</span>). Metadata are organized in a set of predefined attributes and are stored in a catalog so that they can be accessed by any task.</p><p>Specifically, <span class="u-small-caps">Kayak</span> collects <i>intra-dataset</i> and <i>inter-dataset</i> metadata. Intra-dataset metadata form the profile associated with each single dataset, which includes descriptive, statistical, structural and usage metadata attributes. Inter-dataset metadata specify relationships between different datasets or between attributes belonging to different datasets. They include integrity constraints (e.g., inclusion dependencies) and other properties proposed by ourselves, such as <i>joinability</i> (<span class="mathjax-tex">$\varOmega $</span>) and <i>affinity</i> (<span class="mathjax-tex">$\varPsi $</span>) between datasets. Inter-dataset metadata are represented graphically, as shown in Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="#Fig2">2</a>(a) and (b). Intuitively, joinability measures the mutual percentage of common values between attributes of two datasets, whereas affinity measures the semantic strength of a relationship according to some external knowledge. The <i>affinity</i> is an adaptation, to data lakes, of the entity complement proposed by Sarma et al. [<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 21" title="Sarma, A.D., Fang, L., Gupta, N., Halevy, A.Y., Lee, H., Wu, F., Xin, R., Yu, C.: Finding related tables. In: SIGMOD (2012)" href="#ref-CR21" id="ref-link-section-d20310988e2229">21</a>].</p><p><b>Time-to-Action and Tolerance of the User.</b> Let us call <i>time-to-action</i> the amount of time elapsing between the submission of a primitive in a pipeline and the instant in which a data scientist is able to take an informed decision on how to move forward to the next step of the pipeline. To shorten primitive computation when unnecessarily long, we let the data scientist specify a <i>tolerance</i>. A high tolerance is set by the data scientist who does not want to wait for long and believes that an approximate result is enough informative. On the contrary, a low tolerance is specified when the priority is on accuracy. For instance, in the pipeline of Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="#Fig1">1</a>, primitives <span class="mathjax-tex">$P_4$</span> and <span class="mathjax-tex">$P_7$</span> have been specified with a tolerance of 60% and 35%, respectively.</p><p><b>Incremental Execution for Reducing Time-to-Action.</b> In <span class="u-small-caps">Kayak</span>, primitives can be executed incrementally and produce a sequence of <i>previews</i> along their computation. A primitive is decomposed into a series of tasks. Each task can be computed as a sequence of steps that returns the previews. A preview is an approximation of the exact result of the task and it is, therefore, computed much faster. Two strategies of incremental execution exist. A <i>greedy</i> strategy aims at reducing the time-to-action by producing a quick preview first, and then updating the user with refined previews within her tolerance. Alternatively, a <i>best-fit</i> strategy aims at giving the best accuracy according to the given tolerance. It generates only the most accurate preview that fits the tolerance of the user.</p><p><b>Confidence of Previews.</b> Each preview comes with a <i>confidence</i> indicating the uncertainty on the correctness of the result with a value between 0 and 1. A confidence is 0 when the result is random and it is 1 when it is exact. A sequence of previews is always produced with an increasing confidence so that the user is always updated with more accurate results and metadata are updated with increasingly valuable information.</p><p><b>Extensibility.</b> <span class="u-small-caps">Kayak</span> provides a set of built-in, atomic tasks that can be easily extended for implementing new functionalities. Specifically, tasks implementing common activities of data preparation and therefore can be used by different primitives. For instance, referring to Table <a data-track="click" data-track-label="link" data-track-action="table anchor" href="#Tab2">2</a>, task T<span class="mathjax-tex">$_b$</span> is used by three primitives. In addition, a new task can be defined by the users, who needs to specify also the cost model for the computation of the task and all the possible ways to approximate it, as we will show next.</p></div></div></section><section data-title="Modeling a Data Lake"><div class="c-article-section" id="Sec3-section"><h2 id="Sec3" class="c-article-section__title js-section-title js-c-reading-companion-sections-item"><span class="c-article-section__title-number">3 </span>Modeling a Data Lake</h2><div class="c-article-section__content" id="Sec3-content"><p>In this section, we discuss on how data and metadata are represented and managed in our framework. Let us start with some basic notions.</p> <h3 class="c-article__sub-heading" id="FPar1">Definition 1</h3> <p><b>(Dataset).</b> A dataset <i>D</i>(<i>X</i>, <i>C</i>, <i>R</i>) has a name <i>D</i>, and is composed by a set <i>X</i> of <i>attributes</i>, a set <i>C</i> of <i>data objects</i>, and a <i>profile</i> <i>R</i>. Each data object in <i>C</i> is a set of attribute-value pairs, with attributes taken from <i>X</i>. The profile <i>R</i> is a set of attribute-value pairs, with attributes taken from a predefined set <i>M</i> of <i>metadata attributes</i>.</p> <p>A metadata attribute of a dataset <i>D</i> can refer to either the whole dataset or to an attribute of <i>D</i>. We use the dot notation to distinguish between the two cases. For instance, if <i>D</i> is a dataset involving an attribute <span class="mathjax-tex">$ ZipCode$</span>, the profile of <i>D</i> can include the pairs <span class="mathjax-tex">$ \langle D{.}CreationDate:11/11/2016\rangle $</span> and <span class="mathjax-tex">$ \langle ZipCode{.}unique:true\rangle $</span>. For simplicity, we assume that each dataset is stored in a file and therefore we often blur the distinction between dataset and file.</p> <h3 class="c-article__sub-heading" id="FPar2">Definition 2</h3> <p><b>(Data Lake).</b> A data lake <span class="mathjax-tex">$\mathcal {D}$</span> is a collection of datasets having distinct names.</p> <p>Differently from a profile of a dataset, inter-dataset metadata capture relationships between different datasets and between attributes of different datasets. They are represented as graphs and are introduced next.</p><p><b>Affinity.</b> In the affinity graph of a data lake <span class="mathjax-tex">$\mathcal {D}$</span>, the nodes represent the attributes of the datasets in <span class="mathjax-tex">$\mathcal {D}$</span> and an edge between two attributes represents the presence of some time-independent relationship between them (e.g., the fact that they refer to the same real-world entity). Edges can have weights that measure the “strength” of the relationship.</p><p>Specifically, we consider a <i>domain-knowledge</i> affinity <span class="mathjax-tex">$\varOmega (D_1.A_i, D_2.A_j)$</span> that measures the “semantic” affinity between attributes <span class="mathjax-tex">$D_1.A_i$</span> and <span class="mathjax-tex">$D_2.A_j$</span>, which is computed by taking advantage of some existing external knowledge base (such as a domain ontology). The value assigned by <span class="mathjax-tex">$\varOmega (D_1.A_i, D_2.A_j)$</span> ranges in [0, 1] (i.e. 0 when there is no affinity and 1 when the affinity is maximum). Here, we take inspiration from the notion of <i>entity complement</i> proposed by Sarma et al. [<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 21" title="Sarma, A.D., Fang, L., Gupta, N., Halevy, A.Y., Lee, H., Wu, F., Xin, R., Yu, C.: Finding related tables. In: SIGMOD (2012)" href="#ref-CR21" id="ref-link-section-d20310988e2538">21</a>]. However, different kinds of affinity can be used such as those based on text classification.</p><p>We can now define the graph representing the affinity of the attributes <span class="mathjax-tex">$\mathcal {A}$</span> of the datasets in the data lake <span class="mathjax-tex">$\mathcal {D}$</span>.</p> <h3 class="c-article__sub-heading" id="FPar3">Definition 3</h3> <p><b>(Affinity Graph of Attributes).</b> The affinity graph of attributes in <span class="mathjax-tex">$\mathcal {D}$</span> is an undirected and weighted graph <span class="mathjax-tex">$G_\mathcal {A}^{\varOmega } = (N_{\mathcal {A}}, E_{\mathcal {A}}^{\varOmega })$</span> where <span class="mathjax-tex">$N_{\mathcal {A}}$</span> contains a node for each attribute <i>A</i> in <span class="mathjax-tex">$\mathcal {A}$</span> and <span class="mathjax-tex">$E_{\mathcal {A}}^{\varOmega }$</span> contains an edge <span class="mathjax-tex">$(A_1, A_2, \varOmega (A_1,A_2))$</span> for each pair of attributes <span class="mathjax-tex">$A_1$</span> and <span class="mathjax-tex">$A_2$</span> in <span class="mathjax-tex">$\mathcal {A}$</span> such that <span class="mathjax-tex">$\varOmega (A_1, A_2)>0$</span>.</p> <p>The notion of affinity between attributes can be used to define the affinity between two datasets <span class="mathjax-tex">$D_1$</span> and <span class="mathjax-tex">$D_2$</span>.</p> <h3 class="c-article__sub-heading" id="FPar4">Definition 4</h3> <p><b>(Affinity of Datasets).</b> Let <span class="mathjax-tex">$X_1$</span> and <span class="mathjax-tex">$X_2$</span> be the set of attributes of the datasets <span class="mathjax-tex">$D_1$</span> and <span class="mathjax-tex">$D_2$</span>, respectively, and let <span class="mathjax-tex">$\hat{X}=X_1\times X_2$</span>. The affinity between <span class="mathjax-tex">$D_1$</span> and <span class="mathjax-tex">$D_2$</span>, denoted by <span class="mathjax-tex">$\varOmega (D_1, D_2)$</span>, is defined as follows:</p><div id="Equ1" class="c-article-equation"><div class="c-article-equation__content"><span class="mathjax-tex">$$\varOmega (D_1, D_2) = \sum _{(A_j, A_k)\in \hat{X}}{\varOmega (A_j, A_k)}$$</span></div></div> <p>Analogously, we can define an affinity graph of datasets.</p> <h3 class="c-article__sub-heading" id="FPar5">Definition 5</h3> <p><b>(Affinity Graph of Datasets).</b> The affinity graph of datasets for <span class="mathjax-tex">$\mathcal {D}$</span> is an undirected and weighted graph <span class="mathjax-tex">$G_\mathcal {D}^{\varOmega } = (N_{\mathcal {D}}, E_{\mathcal {D}}^{\varOmega })$</span> where <span class="mathjax-tex">$N_{\mathcal {D}}$</span> contains a node for each dataset <i>D</i> in <span class="mathjax-tex">$\mathcal {D}$</span> and <span class="mathjax-tex">$E_{\mathcal {D}}$</span> contains an edge <span class="mathjax-tex">$(D_1, D_2, \varOmega (D_1, D_2) )$</span> for each pair of dataset <span class="mathjax-tex">$D_1$</span> and <span class="mathjax-tex">$D_2$</span> in <span class="mathjax-tex">$\mathcal {D}$</span> such that <span class="mathjax-tex">$\varOmega (D_1, D_2)>0$</span>.</p> <p>We are clearly interested in highly affine relationships. Therefore, we consider a simplified version of the graph where the edges have weights higher than a threshold <span class="mathjax-tex">$\tau $</span>, defined by the user. This is equivalent to consider irrelevant affinities below <span class="mathjax-tex">$\tau _{\varOmega }$</span>. An example of affinity graph of attributes is reported in Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="#Fig2">2</a>(a).</p><div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-2" data-title="Fig. 2."><figure><figcaption><b id="Fig2" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 2.</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><a class="c-article-section__figure-link" data-test="img-link" data-track="click" data-track-label="image" data-track-action="view figure" href="/chapter/10.1007/978-3-319-91563-0_29/figures/2" rel="nofollow"><picture><source type="image/webp" srcset="//media.springernature.com/lw685/springer-static/image/chp%3A10.1007%2F978-3-319-91563-0_29/MediaObjects/454396_1_En_29_Fig2_HTML.gif?as=webp"><img aria-describedby="Fig2" src="//media.springernature.com/lw685/springer-static/image/chp%3A10.1007%2F978-3-319-91563-0_29/MediaObjects/454396_1_En_29_Fig2_HTML.gif" alt="figure 2" loading="lazy"></picture></a></div><div class="c-article-section__figure-description" data-test="bottom-caption" id="figure-2-desc"><p>Inter-dataset metadata.</p></div></div><div class="u-text-right u-hide-print"><a class="c-article__pill-button" data-test="chapter-link" data-track="click" data-track-label="button" data-track-action="view figure" href="/chapter/10.1007/978-3-319-91563-0_29/figures/2" data-track-dest="link:Figure2 Full size image" aria-label="Full size image figure 2" rel="nofollow"><span>Full size image</span><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-right-small"></use></svg></a></div></figure></div> <p><b>Joinability.</b> Another way to relate attributes and dataset is simply based on the existence of common values. We introduce the concept of <i>joinability</i> for this purpose.</p> <h3 class="c-article__sub-heading" id="FPar6">Definition 6</h3> <p><b>(Joinability).</b> Given two attributes <span class="mathjax-tex">$A_i$</span> and <span class="mathjax-tex">$A_j$</span> belonging to the datasets <span class="mathjax-tex">$D_1$</span> and <span class="mathjax-tex">$D_2$</span>, respectively, their joinability <span class="mathjax-tex">$\varPsi $</span> is defined as</p><div id="Equ2" class="c-article-equation"><div class="c-article-equation__content"><span class="mathjax-tex">$$\varPsi (D_{1}.A_i, D_{2}.A_j)=\frac{2\cdot |\pi _{A_{i}}(D_1 \bowtie _{A_{i}=A_{j}} D_2)|}{(|\pi _{A_{i}}(D_1)| + |\pi _{A_{j}}(D_2)|)}$$</span></div></div> <p>The joinability measures the mutual percentage of tuples of <span class="mathjax-tex">$D_1$</span> that join with tuples of <span class="mathjax-tex">$D_2$</span> on <span class="mathjax-tex">$D_1.A_i$</span> and <span class="mathjax-tex">$D_2.A_j$</span>, and vice versa. This notion enjoys interesting properties, which we can discuss by considering the example in Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="#Fig3">3</a>.</p><div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-3" data-title="Fig. 3."><figure><figcaption><b id="Fig3" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 3.</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><a class="c-article-section__figure-link" data-test="img-link" data-track="click" data-track-label="image" data-track-action="view figure" href="/chapter/10.1007/978-3-319-91563-0_29/figures/3" rel="nofollow"><picture><source type="image/webp" srcset="//media.springernature.com/lw685/springer-static/image/chp%3A10.1007%2F978-3-319-91563-0_29/MediaObjects/454396_1_En_29_Fig3_HTML.gif?as=webp"><img aria-describedby="Fig3" src="//media.springernature.com/lw685/springer-static/image/chp%3A10.1007%2F978-3-319-91563-0_29/MediaObjects/454396_1_En_29_Fig3_HTML.gif" alt="figure 3" loading="lazy"></picture></a></div><div class="c-article-section__figure-description" data-test="bottom-caption" id="figure-3-desc"><p>Tabular datasets.</p></div></div><div class="u-text-right u-hide-print"><a class="c-article__pill-button" data-test="chapter-link" data-track="click" data-track-label="button" data-track-action="view figure" href="/chapter/10.1007/978-3-319-91563-0_29/figures/3" data-track-dest="link:Figure3 Full size image" aria-label="Full size image figure 3" rel="nofollow"><span>Full size image</span><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-right-small"></use></svg></a></div></figure></div> <div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-4" data-title="Fig. 4."><figure><figcaption><b id="Fig4" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 4.</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><a class="c-article-section__figure-link" data-test="img-link" data-track="click" data-track-label="image" data-track-action="view figure" href="/chapter/10.1007/978-3-319-91563-0_29/figures/4" rel="nofollow"><picture><source type="image/webp" srcset="//media.springernature.com/lw685/springer-static/image/chp%3A10.1007%2F978-3-319-91563-0_29/MediaObjects/454396_1_En_29_Fig4_HTML.gif?as=webp"><img aria-describedby="Fig4" src="//media.springernature.com/lw685/springer-static/image/chp%3A10.1007%2F978-3-319-91563-0_29/MediaObjects/454396_1_En_29_Fig4_HTML.gif" alt="figure 4" loading="lazy"></picture></a></div><div class="c-article-section__figure-description" data-test="bottom-caption" id="figure-4-desc"><p>Business logic of the framework.</p></div></div><div class="u-text-right u-hide-print"><a class="c-article__pill-button" data-test="chapter-link" data-track="click" data-track-label="button" data-track-action="view figure" href="/chapter/10.1007/978-3-319-91563-0_29/figures/4" data-track-dest="link:Figure4 Full size image" aria-label="Full size image figure 4" rel="nofollow"><span>Full size image</span><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-right-small"></use></svg></a></div></figure></div> <p>The maximum joinability, (e.g., <span class="mathjax-tex">$\varPsi (D_{1}.A_1, D_{2}.A_2)=1$</span>), is when each value of one attribute matches a value of the other attribute. If the result of the join between the two attributes is empty, the joinability is 0 (e.g., <span class="mathjax-tex">$\varPsi (D_{2}.A_2, D_{4}.A_4)=0$</span>). A joinability in (0, 1) means that there are several matching values. The joinability takes also into account, for both attributes, the number of distinct values that do not match. For the dataset in Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="#Fig3">3</a> we have: <span class="mathjax-tex">$\varPsi (D_{1}.A_1, D_{2}.A_2)>\varPsi (D_{1}.A_1, D_{3}.A_3) > \varPsi (D_{1}.A_1, D_{4}.A_4)$</span>.</p><p>Similarly to the property of affinity, we can build a joinability graph of attributes and a joinability graph of datasets, where we represent only those edges whose joinability is higher or equal than a threshold parameter <span class="mathjax-tex">$\tau _{\varPsi }$</span>. An example of a joinability graph is reported in Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="#Fig2">2</a>(b).</p></div></div></section><section data-title="Incremental Execution of Primitives"><div class="c-article-section" id="Sec4-section"><h2 id="Sec4" class="c-article-section__title js-section-title js-c-reading-companion-sections-item"><span class="c-article-section__title-number">4 </span>Incremental Execution of Primitives</h2><div class="c-article-section__content" id="Sec4-content"><p>In this section, we describe the incremental execution of primitives, a mechanism that allows users to obtain previews of a result at an increasing level of accuracy.</p><p><b>Basic Idea.</b> Users submit a primitive over an input <i>I</i>, which is typically a set of datasets. As we can see from Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="#Fig4">4</a>, a primitive is composed of one or more tasks of type <i>T</i>. Each task type is associated with one or more <i>steps</i>. A step is an operation that is able to return a result for <i>T</i> over <i>I</i>. The result of a step can be either exact or approximate. We use <i>t</i> for indicating the step that computes the exact result <i>r</i> for <i>t</i> for <i>T</i> over <i>I</i> (i.e., <span class="mathjax-tex">$r = t(I)$</span>). We use <span class="mathjax-tex">$s_i^{T}$</span> for indicating the <i>i</i>-th approximate step of <i>T</i>, which returns a preview <span class="mathjax-tex">$p_i = s_i^T(I)$</span>. Therefore, a preview <span class="mathjax-tex">$p_i$</span> is an approximation of <i>r</i>.</p><p>We have several types of approximate steps, corresponding to different ways to approximate a task. For instance, some step reduces the input <i>I</i> (e.g., sampling), while other steps apply heuristics over <i>I</i>. In our framework, we have components that support the approximate steps, as shown in Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="#Fig4">4</a>. The list of steps <span class="mathjax-tex">$S_T$</span> for a task type <i>T</i> is declared in the definition of <i>T</i>. For simplicity, the following discussion considers a primitive with a single task type <i>T</i>, but this generalizes easily to primitives with many tasks. The incremental execution of a task type <i>T</i> over <i>I</i> is a sequence of <i>m</i> steps <span class="mathjax-tex">$s_1^T, \ldots , s_m^T $</span>, where possibly the last step is the exact task <i>t</i> (i.e. <span class="mathjax-tex">$t=s_m^T$</span>).</p><p>Each preview comes with a <i>confidence</i>, indicating the uncertainty on the correctness of the result with a number between 0 and 1. A confidence is 0 when the result is random and it is 1 when the result is exact. <span class="u-small-caps">Kayak</span> computes the confidence executing a function embedded in the step definition. This function considers the confidence associated with the input of the step, e.g., the confidence of a metadata attribute. Note that, since previews are produced with an increasing confidence, metadata are stored with increasingly precise information and the user is always updated with more accurate primitive results.</p><p>In addition, each step is associated with a cost function that estimates its computational time over an input <i>I</i>, i.e. <span class="mathjax-tex">$cost(s_i^T, I)$</span>. We have defined the cost functions using the Big-<span class="mathjax-tex">$\varTheta $</span> time complexity. All other time-based measures like the load of the system and the tolerance have to be comparable with the cost and are therefore expressed in terms of the same virtual time. Moreover, we want to underline that there is no formal relationship between the cost of a step and the confidence of the preview it produces.</p><p><b>Optimization.</b> <span class="u-small-caps">Kayak</span> needs to find a suitable incremental execution for a primitive, that is the order of the execution of steps. To this aim, <span class="u-small-caps">Kayak</span> takes into account the tolerance of the user and the current workload. The tolerance is fixed by the user for each of the primitives in the pipeline. The workload is given by the sum of the costs of the steps of the primitives to be executed.</p><p>In our framework, we devised several incremental execution strategies and further strategies can be defined. For example, we have a so-called <i>best-fit</i> strategy that tries to generate only the most accurate preview that fits within the tolerance. This tends to limit the overall delay while still reducing the time-to-action according to the user’s tolerance. Another strategy is the so-called <i>greedy</i> strategy that aims at minimizing the time-to-action and to update the user with subsequent previews. However, due to lack of space, we do not detail any strategy.</p><p><b>Step Dependency.</b> At the end of the step generation, we set dependencies to enforce a correct execution of primitives composed of many tasks. Since we allow for a parallel execution of steps, a DAG of dependencies is considered. In the DAG, each node <span class="mathjax-tex">$T_i$</span> is a task type and each edge <span class="mathjax-tex">$(T_i, T_j)$</span> represents a dependency of a task <span class="mathjax-tex">$T_j$</span> (the destination node) from another task <span class="mathjax-tex">$T_i$</span> (the source node). A dependency indicates that <span class="mathjax-tex">$T_j$</span> can start its execution only after <span class="mathjax-tex">$T_i$</span> is completed.</p><p>In <span class="u-small-caps">Kayak</span>, we do not have a centralized representation of the DAG, but dependencies are set within each task. For example, in Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="#Fig5">5</a>(a) we have the DAG for the primitive <span class="mathjax-tex">$P_5$</span> that is composed of four tasks <span class="mathjax-tex">$T_b, T_c, T_d$</span> and <span class="mathjax-tex">$T_q$</span>, as in Table <a data-track="click" data-track-label="link" data-track-action="table anchor" href="#Tab2">2</a>. The task <span class="mathjax-tex">$T_q$</span> is the last task of the primitive and uses metadata provided by <span class="mathjax-tex">$T_b, T_c$</span> and <span class="mathjax-tex">$T_d$</span>. For this reason, <span class="mathjax-tex">$T_q$</span> has a dependency with every other task. In addition, there is a dependency between <span class="mathjax-tex">$T_d$</span> and <span class="mathjax-tex">$T_b$</span>. This means that <span class="mathjax-tex">$T_b$</span> and <span class="mathjax-tex">$T_c$</span> can execute before the others, possibly in parallel.</p><div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-5" data-title="Fig. 5."><figure><figcaption><b id="Fig5" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 5.</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><a class="c-article-section__figure-link" data-test="img-link" data-track="click" data-track-label="image" data-track-action="view figure" href="/chapter/10.1007/978-3-319-91563-0_29/figures/5" rel="nofollow"><picture><source type="image/webp" srcset="//media.springernature.com/lw685/springer-static/image/chp%3A10.1007%2F978-3-319-91563-0_29/MediaObjects/454396_1_En_29_Fig5_HTML.gif?as=webp"><img aria-describedby="Fig5" src="//media.springernature.com/lw685/springer-static/image/chp%3A10.1007%2F978-3-319-91563-0_29/MediaObjects/454396_1_En_29_Fig5_HTML.gif" alt="figure 5" loading="lazy"></picture></a></div><div class="c-article-section__figure-description" data-test="bottom-caption" id="figure-5-desc"><p>Dependencies among tasks of a primitive.</p></div></div><div class="u-text-right u-hide-print"><a class="c-article__pill-button" data-test="chapter-link" data-track="click" data-track-label="button" data-track-action="view figure" href="/chapter/10.1007/978-3-319-91563-0_29/figures/5" data-track-dest="link:Figure5 Full size image" aria-label="Full size image figure 5" rel="nofollow"><span>Full size image</span><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-right-small"></use></svg></a></div></figure></div> <p>When a primitive is executed in incremental mode, the step generation phase produces a DAG that considers every single step. Let us suppose that <span class="mathjax-tex">$T_b, T_c$</span> and <span class="mathjax-tex">$T_q$</span> are executed incrementally in two steps each, while <span class="mathjax-tex">$T_d$</span> is executed in a single step. Figure <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="#Fig5">5</a>(b) shows the resulting DAG for this primitive execution. We set a dependency between two subsequent steps of the same task, such as <span class="mathjax-tex">$(s_1^b,s_2^b)$</span>, to preserve the increase of the confidence of results. If this dependency is not set, then a preview might overwrite another preview with higher confidence.</p><p>We also need to set the inter-task dependencies. In this case, we set a dependency between the first generated steps of the two tasks. Using the example of <span class="mathjax-tex">$P_5$</span>, the dependency <span class="mathjax-tex">$(T_c, T_q)$</span> is established by the dependency <span class="mathjax-tex">$(s_1^c,t_1^q)$</span>. No inter-task dependency between following steps is considered. The reason behind this decision is, again, aimed at reducing the time-to-action.</p><p>There are two side effects that motivate this decision. The former is when, for example, <span class="mathjax-tex">$t_1^c$</span> and <span class="mathjax-tex">$t_2^c$</span> terminate before <span class="mathjax-tex">$t_1^q$</span> has started. It follows that <span class="mathjax-tex">$t_1^q$</span> will use metadata produced by <span class="mathjax-tex">$t_2^c$</span>, resulting in a higher confidence. The latter side effect is when <span class="mathjax-tex">$t_1^q$</span> is computed between <span class="mathjax-tex">$t_1^c$</span> and <span class="mathjax-tex">$t_2^c$</span>. The final result of <span class="mathjax-tex">$t_1^q$</span> will be less accurate but the time-to-action is minimized. However, the user is notified of the fact that more accurate metadata is present for, possibly, refining the result of the primitive she just launched.</p><p><b>Scheduling of Steps.</b> Dependencies are used for guaranteeing the consistency of primitives’ results but they do not suffice to reduce the time-to-action of tasks coming from different primitives. To avoid a random order of execution, we use a step scheduling mechanism where the order of execution is done with respect to a priority assigned to each step. Steps with higher priority are executed first, while low priority steps are treated like processes to execute in background when the system is inactive. The priority function is defined as follows:</p><div id="Equ3" class="c-article-equation"><div class="c-article-equation__content"><span class="mathjax-tex">$$ priority(s) = \frac{1}{cost(s)} + freshness(s) + completeness(s)$$</span></div></div><p>where:</p><ul class="u-list-style-dash"> <li> <p>The <i>cost</i> is used to favor shorter steps, with the aim of reducing the time-to-action. It is given by the same function used in the previous sections.</p> </li> <li> <p>The <i>freshness</i> is used to avoid starvation. It uses the creation time of steps (with older steps having higher freshness). Let us explain the motivation behind this factor with an example. Let us consider the submission of a heavy task of type T<span class="mathjax-tex">$_c$</span> followed by the submission of many shorter tasks of type T<span class="mathjax-tex">$_a$</span>. If we consider only the cost factor, the task T<span class="mathjax-tex">$_c$</span> will never be executed and it will starve in the queue.</p> </li> <li> <p>The <i>completeness</i> is used to balance the time-to-action across different primitives. It considers how many steps have already been instantiated for the task type. For instance, the completeness gives an advantage to the first step of task T<span class="mathjax-tex">$_c$</span> over the second step of another task T<span class="mathjax-tex">$_a$</span>. In fact, if we use only cost and freshness some step for T<span class="mathjax-tex">$_c$</span> might not fulfill its time-to-action objective.</p> </li> </ul> <p>Note that our scheduling mechanisms do not conflict with mechanisms of cluster resource managers (e.g., Apache Mesos or Apache Yarn) used by data processing engines. We decide when a step can start its execution, while they schedule jobs of data processing only once their corresponding step has already started.</p><p><b>Use Case: Incremental Computation of Joinability.</b> We now show the incremental execution of the task type T<span class="mathjax-tex">$_c$</span> that computes the joinability of a dataset against the rest of the datasets in the lake (see Definition <a data-track="click" data-track-label="link" data-track-action="subsection anchor" href="#FPar6">6</a>). Since the data lake <span class="mathjax-tex">$\mathcal {D}$</span> can have a large number of datasets with many attributes, this task is computationally expensive and does not scale with the size of <span class="mathjax-tex">$\mathcal {D}$</span>. Since this task is used by many primitives, it is often convenient to execute incrementally. Below we list the techniques used to generate previews of T<span class="mathjax-tex">$_c$</span>.</p> <ol class="u-list-style-none"> <li> <span class="u-custom-list-number">1.</span> <p><i>Joinability over frequent items.</i> Our least accurate step for T<span class="mathjax-tex">$_c$</span> is given by a heuristic that takes the most frequent items present in the domain of two attributes along with the number of occurrences. This information is present in our metadata catalog and it is collected by other profiling tasks. We then compute the intersection between the two sets that allows us to determine the joinability of a small portion of the two attribute’s domains in a constant time. The confidence is computed by considering the percentage of the coverage that the frequent items have over the entire domain.</p> </li> <li> <span class="u-custom-list-number">2.</span> <p><i>Joinability over a subset of attributes and sampled datasets.</i> This step uses some heuristics that aim at selecting those attributes that are likely to have higher values of joinability against the input dataset. It specifically selects a subset of attributes <span class="mathjax-tex">$Z_i$</span> of the lake to be used in the computation of the joinability against attributes of <span class="mathjax-tex">$D_i$</span>. Then, the datasets which the attributes of <span class="mathjax-tex">$Z_i$</span> belong from are sampled to further reduce the cost of the operation. The approximation of joinability is similar to compute approximate joins [<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 4" title="Agarwal, S., Mozafari, B., Panda, A., Milner, H., Madden, S., Stoica, I.: BlinkDB: queries with bounded errors and bounded response times on very large data. In: EuroSys, pp. 29–42 (2013)" href="#ref-CR4" id="ref-link-section-d20310988e4110">4</a>, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 11" title="Hellerstein, J.M., Haas, P.J., Wang, H.J.: Online aggregation. In: SIGMOD, pp. 171–182 (1997)" href="#ref-CR11" id="ref-link-section-d20310988e4113">11</a>]. The sample rate is chosen dynamically according to the size of the dataset, with lower sample rate for higher dataset size. The selected attributes <span class="mathjax-tex">$Z_i$</span> are those having: (a) overlapping among the most frequent items of the attributes, (b) an inclusion dependency with <span class="mathjax-tex">$D_i$</span> (we check from available metadata without computing T<span class="mathjax-tex">$_e$</span> of Table <a data-track="click" data-track-label="link" data-track-action="table anchor" href="#Tab2">2</a>), (c) high affinity with the attributes of <span class="mathjax-tex">$D_i$</span> as taken from the <i>affinity graph of attributes</i>. The confidence of this level is given by the used sample ratios and the number of attributes that have been selected.</p> </li> <li> <span class="u-custom-list-number">3.</span> <p><i>Joinability over a subset of attributes.</i> This step selects the attributes of the previous case but does not apply any sampling over the datasets.</p> </li> <li> <span class="u-custom-list-number">4.</span> <p><i>Joinability over a subset of sampled datasets.</i> This step selects a set <span class="mathjax-tex">$Y_i$</span> of datasets in <span class="mathjax-tex">$\mathcal {D}$</span> having high affinity with <span class="mathjax-tex">$D_i$</span> by checking the <i>affinity graph of datasets</i>. Then, it computes the joinability between attributes of <span class="mathjax-tex">$D_i$</span> and attributes of datasets in <span class="mathjax-tex">$Y_i$</span>.</p> </li> <li> <span class="u-custom-list-number">5.</span> <p><i>Joinability over a subset of datasets.</i> This step selects the same set <span class="mathjax-tex">$Y_i$</span> of datasets of the previous case but, differently from it, sampling is not applied.</p> </li> <li> <span class="u-custom-list-number">6.</span> <p><i>Joinability over a sampled data lake.</i> This step selects a sample from each of the datasets in <span class="mathjax-tex">$\mathcal {D}$</span> and then it applies the joinability between the attributes of <span class="mathjax-tex">$D_i$</span> and any other attribute in <span class="mathjax-tex">$\mathcal {D}$</span>.</p> </li> </ol> <p>We have described here the steps of the joinability task. The implementation of these steps makes use of other optimizations such as those in presence of inclusion dependencies or those that return zero when data types are different or domain ranges do not overlap. However, we do not discuss them in detail here because they do not deal with the approximation of the exact results.</p></div></div></section><section data-title="Experimental Results"><div class="c-article-section" id="Sec5-section"><h2 id="Sec5" class="c-article-section__title js-section-title js-c-reading-companion-sections-item"><span class="c-article-section__title-number">5 </span>Experimental Results</h2><div class="c-article-section__content" id="Sec5-content"><h3 class="c-article__sub-heading" id="Sec6"><span class="c-article-section__title-number">5.1 </span>Set-Up</h3><p>The architecture of <span class="u-small-caps">Kayak</span> is discussed in [<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 15" title="Maccioni, A., Torlone, R.: Crossing the finish line faster when paddling the data lake with KAYAK. PVLDB 10(12), 1853–1856 (2017)" href="#ref-CR15" id="ref-link-section-d20310988e4346">15</a>]. <span class="u-small-caps">Kayak</span> is implemented in Java 8 and exploits <i>java.util.concurrent</i> library for the concurrent execution of tasks. The current version limits the use to <i>json</i> and to <i>csv</i> files only. The Metadata Catalog relies on two different database systems, namely MongoDB for the intra-dataset catalog and Neo4j for the inter-dataset catalog. The Queue Manager uses RabbitMQ as a messaging queue system. The User Interface is implemented using JSP pages and servlets on the web application server Tomcat 8. We also rely on external tools such as Spark 2.1 with MLib and SparkSQL add-ons for parallelizing operations on large datasets, and on Metanome<sup><a href="#Fn4"><span class="u-visually-hidden">Footnote </span>4</a></sup> for some of the tasks for which the Metadata Collector is in charge.</p><h3 class="c-article__sub-heading" id="Sec7"><span class="c-article-section__title-number">5.2 </span>Results</h3><p>This section presents the experimentation that was conducted on a cluster of <i>m4.4xlarge</i> machines on Amazon EC2. Each machine is equipped with 16 vCPU, 64 GB and running a 2,3 GHz Intel Xeon with 18 cores. We created a data lake with 200 datasets ranging from hundreds of MBs to few TBs. We have taken datasets from the U.S. open data catalog<sup><a href="#Fn5"><span class="u-visually-hidden">Footnote </span>5</a></sup> and from the NYC Taxi trips<sup><a href="#Fn6"><span class="u-visually-hidden">Footnote </span>6</a></sup>. We have also generated synthetic datasets to create uses cases that were not covered with downloaded datasets.</p><div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-6" data-title="Fig. 6."><figure><figcaption><b id="Fig6" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 6.</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><a class="c-article-section__figure-link" data-test="img-link" data-track="click" data-track-label="image" data-track-action="view figure" href="/chapter/10.1007/978-3-319-91563-0_29/figures/6" rel="nofollow"><picture><source type="image/webp" srcset="//media.springernature.com/lw685/springer-static/image/chp%3A10.1007%2F978-3-319-91563-0_29/MediaObjects/454396_1_En_29_Fig6_HTML.gif?as=webp"><img aria-describedby="Fig6" src="//media.springernature.com/lw685/springer-static/image/chp%3A10.1007%2F978-3-319-91563-0_29/MediaObjects/454396_1_En_29_Fig6_HTML.gif" alt="figure 6" loading="lazy"></picture></a></div><div class="c-article-section__figure-description" data-test="bottom-caption" id="figure-6-desc"><p>Incremental step generation at work.</p></div></div><div class="u-text-right u-hide-print"><a class="c-article__pill-button" data-test="chapter-link" data-track="click" data-track-label="button" data-track-action="view figure" href="/chapter/10.1007/978-3-319-91563-0_29/figures/6" data-track-dest="link:Figure6 Full size image" aria-label="Full size image figure 6" rel="nofollow"><span>Full size image</span><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-right-small"></use></svg></a></div></figure></div> <p><b>Effectiveness.</b> In this campaign, we have measured the trade-off between accuracy and time-to-action for the joinability task. It is a fundamental task in our framework that is used by many primitives. The results are in Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="#Fig6">6</a>(a). We have divided the tests in three sections according to the range of the joinability value <span class="mathjax-tex">$\varPsi $</span>: <i>low</i> (0.0 <span class="mathjax-tex">$\le \varPsi \le $</span> 0.35), <i>medium</i> (0.35 <span class="mathjax-tex">$ < \varPsi \le $</span> 0.70) and <i>high</i> (0.70 <span class="mathjax-tex">$< \varPsi \le $</span> 1.0). The time-to-action is represented in terms of percentage of the time for the exact step. Each point of the graph represents the average of six joinability computations (we have six different tests for each of the sections). As we can see, the accuracy is constantly high for low values of joinability. This is due to the fact that a reduced input is already able to show that two attributes do not join well. A similar behavior is for medium values, although accuracy slightly degrades for low time-to-action. High values of joinability are more difficult to approximate with shorter time-to-action than previous sections, but we consider this accuracy still good for many practical situations.</p><p><b>Strategies Comparison.</b> In this campaign, we test the differences between two incremental strategies, that we have briefly mentioned above. Again, we consider the joinability task. Let us consider the delay as the extra time spent on the incremental execution with respect to the non-incremental counterpart. We measure how the time-to-action and the delay vary with respect to the tolerance. Both the measures are taken in percentage with respect to the duration of the exact step. As we can see from the results in Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="#Fig6">6</a>(b), the time-to-action for the greedy strategy is constant because the same short level is executed independently of the user’s tolerance, while for the best-fit strategy the time-to-action increases linearly with the tolerance. However, the time-to-action is always lower than the tolerance due to a fragmentation effect that makes it hard to have the cost of a step that perfectly fits the tolerance. The delay of the greedy strategy is always greater than the delay of the best-fit strategy, because of all the short steps executed at the beginning. The delay for both strategies tends to diminish as the tolerance increases. The delay of the best-fit strategy has an opposite behavior w.r.t. the time-to-action. Indeed, the delay is inversely proportional to the tolerance. This is because as the tolerance increases, the best-fit strategy tends to schedule fewer and fewer steps.</p></div></div></section><section data-title="Related Work"><div class="c-article-section" id="Sec8-section"><h2 id="Sec8" class="c-article-section__title js-section-title js-c-reading-companion-sections-item"><span class="c-article-section__title-number">6 </span>Related Work</h2><div class="c-article-section__content" id="Sec8-content"><p>We divide related work of <span class="u-small-caps">Kayak</span> into categories discussed separately.</p><p><b>Data Catalogs.</b> There are several tools that are used for building repositories of datasets [<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 1" title="CKAN: The open source data portal software. 
 http://ckan.org/
 
 . Accessed Nov 2017" href="#ref-CR1" id="ref-link-section-d20310988e4515">1</a>, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 3" title="Trifacta. 
 https://www.trifacta.com/
 
 . Accessed Nov 2017" href="#ref-CR3" id="ref-link-section-d20310988e4518">3</a>, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 5" title="Bhardwaj, A.P., Deshpande, A., Elmore, A.J., Karger, D.R., Madden, S., Parameswaran, A.G., Subramanyam, H., Wu, E., Zhang, R.: Collaborative data analytics with DataHub. PVLDB 8(12), 1916–1927 (2015)" href="#ref-CR5" id="ref-link-section-d20310988e4521">5</a>, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 10" title="Halevy, A.Y., Korn, F., Noy, N.F., Olston, C., Polyzotis, N., Roy, S., Whang, S.E.: Goods: organizing Google’s datasets. In: SIGMOD (2016)" href="#ref-CR10" id="ref-link-section-d20310988e4524">10</a>, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 12" title="Hellerstein, J.M., Sreekanti, V., Gonzalez, J.E., Dalton, J., Dey, A., Nag, S., Ramachandran, K., Arora, S., Bhattacharyya, A., Das, S., Donsky, M., Fierro, G., She, C., Steinbach, C., Subramanian, V., Sun, E.: Ground: a data context service. In: CIDR (2017)" href="#ref-CR12" id="ref-link-section-d20310988e4527">12</a>]. Basic catalogs like CKAN [<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 1" title="CKAN: The open source data portal software. 
 http://ckan.org/
 
 . Accessed Nov 2017" href="#ref-CR1" id="ref-link-section-d20310988e4531">1</a>] do not consider relationships among datasets and metadata are mostly inserted manually. DataHub [<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 5" title="Bhardwaj, A.P., Deshpande, A., Elmore, A.J., Karger, D.R., Madden, S., Parameswaran, A.G., Subramanyam, H., Wu, E., Zhang, R.: Collaborative data analytics with DataHub. PVLDB 8(12), 1916–1927 (2015)" href="#ref-CR5" id="ref-link-section-d20310988e4534">5</a>] is a catalog that enables collaborative use and analysis of datasets. It includes features like versioning, merging and branching for datasets, similarly to version control systems in the context of software engineering. <span class="u-small-caps">Goods</span> is an enterprise search system for a data lake that is in use at Google [<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 10" title="Halevy, A.Y., Korn, F., Noy, N.F., Olston, C., Polyzotis, N., Roy, S., Whang, S.E.: Goods: organizing Google’s datasets. In: SIGMOD (2016)" href="#ref-CR10" id="ref-link-section-d20310988e4540">10</a>]. It proposes, among the others, a solution with the semi-automatic realization of a metadata catalog, an annotation service, an efficient tracking of the provenance and advanced search features based on full-text indexing. All above catalogs use basic ways to understand relationships among datasets and give little support to users who are unaware of the content of the datasets, though they are not explicitly designed for data preparation and exploration purposes.</p><p><b>Profiling Tools.</b> In data science, tools such as R<sup><a href="#Fn7"><span class="u-visually-hidden">Footnote </span>7</a></sup>, IPython [<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 19" title="Pérez, F., Granger, B.E.: IPython: a system for interactive scientific computing. Comput. Sci. Eng. 9(3), 21–29 (2007)" href="#ref-CR19" id="ref-link-section-d20310988e4560">19</a>], Pandas-profiling<sup><a href="#Fn8"><span class="u-visually-hidden">Footnote </span>8</a></sup> and notebook technologies<sup><a href="#Fn9"><span class="u-visually-hidden">Footnote </span>9</a></sup> are extensively used for data exploration. They mainly compute statistical summaries integrated with some plotting features. More advanced data profiling consists on the discovery of constraints in the data [<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 7" title="Ehrlich, J., Roick, M., Schulze, L., Zwiener, J., Papenbrock, T., Naumann, F.: Holistic data profiling: simultaneous discovery of various metadata. In: EDBT, pp. 305–316 (2016)" href="#ref-CR7" id="ref-link-section-d20310988e4587">7</a>, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 14" title="Ilyas, I.F., Markl, V., Haas, P.J., Brown, P., Aboulnaga, A.: CORDS: automatic discovery of correlations and soft functional dependencies. In: SIGMOD, pp. 647–658 (2004)" href="#ref-CR14" id="ref-link-section-d20310988e4591">14</a>, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 16" title="Papenbrock, T., Bergmann, T., Finke, M., Zwiener, J., Naumann, F.: Data profiling with metanome. PVLDB 8(12), 1860–1863 (2015)" href="#ref-CR16" id="ref-link-section-d20310988e4594">16</a>, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 18" title="Papenbrock, T., Naumann, F.: A hybrid approach to functional dependency discovery. In: SIGMOD, pp. 821–833 (2016)" href="#ref-CR18" id="ref-link-section-d20310988e4597">18</a>]. Metanome, for instance, offers a suite of different algorithms for data profiling [<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 16" title="Papenbrock, T., Bergmann, T., Finke, M., Zwiener, J., Naumann, F.: Data profiling with metanome. PVLDB 8(12), 1860–1863 (2015)" href="#ref-CR16" id="ref-link-section-d20310988e4600">16</a>]. Some of these algorithms run by sharing pieces of computation [<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 7" title="Ehrlich, J., Roick, M., Schulze, L., Zwiener, J., Papenbrock, T., Naumann, F.: Holistic data profiling: simultaneous discovery of various metadata. In: EDBT, pp. 305–316 (2016)" href="#ref-CR7" id="ref-link-section-d20310988e4603">7</a>] or by the aid of approximate techniques [<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 14" title="Ilyas, I.F., Markl, V., Haas, P.J., Brown, P., Aboulnaga, A.: CORDS: automatic discovery of correlations and soft functional dependencies. In: SIGMOD, pp. 647–658 (2004)" href="#ref-CR14" id="ref-link-section-d20310988e4606">14</a>, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 18" title="Papenbrock, T., Naumann, F.: A hybrid approach to functional dependency discovery. In: SIGMOD, pp. 821–833 (2016)" href="#ref-CR18" id="ref-link-section-d20310988e4610">18</a>]. In <span class="u-small-caps">Kayak</span> we have tasks that make use of these algorithms such as for example T<span class="mathjax-tex">$_c$</span> in Table <a data-track="click" data-track-label="link" data-track-action="table anchor" href="#Tab2">2</a>.</p><p><b>Data Wranglers.</b> Schema-on-read data access has opened severe challenges in data wrangling [<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 8" title="Furche, T., Gottlob, G., Libkin, L., Orsi, G., Paton, N.W.: Data wrangling for big data: challenges and opportunities. In: EDBT, pp. 473–478 (2016)" href="#ref-CR8" id="ref-link-section-d20310988e4635">8</a>, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 23" title="Terrizzano, I., Schwarz, P.M., Roth, M., Colino, J.E.: Data wrangling: the challenging journey from the wild to the lake. In: CIDR (2015)" href="#ref-CR23" id="ref-link-section-d20310988e4638">23</a>] and specific tools are aimed at solving this problem [<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2" title="Tamr. 
 http://www.tamr.com/
 
 . Accessed Nov 2017" href="#ref-CR2" id="ref-link-section-d20310988e4641">2</a>, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 3" title="Trifacta. 
 https://www.trifacta.com/
 
 . Accessed Nov 2017" href="#ref-CR3" id="ref-link-section-d20310988e4644">3</a>, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 22" title="Stonebraker, M., Bruckner, D., Ilyas, I.F., Beskales, G., Cherniack, M., Zdonik, S.B., Pagan, A., Xu, S.: Data curation at scale: the data tamer system. In: CIDR (2013)" href="#ref-CR22" id="ref-link-section-d20310988e4647">22</a>]. Data TamR helps in finding insights thanks to novel approaches of data curation and data unification [<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2" title="Tamr. 
 http://www.tamr.com/
 
 . Accessed Nov 2017" href="#ref-CR2" id="ref-link-section-d20310988e4651">2</a>, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 22" title="Stonebraker, M., Bruckner, D., Ilyas, I.F., Beskales, G., Cherniack, M., Zdonik, S.B., Pagan, A., Xu, S.: Data curation at scale: the data tamer system. In: CIDR (2013)" href="#ref-CR22" id="ref-link-section-d20310988e4654">22</a>]. Trifacta is an application for self-service data wrangling providing several tools to the user [<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 3" title="Trifacta. 
 https://www.trifacta.com/
 
 . Accessed Nov 2017" href="#ref-CR3" id="ref-link-section-d20310988e4657">3</a>]. All these systems provide features that can be embedded in <span class="u-small-caps">Kayak</span> to be executed incrementally for minimizing the time-to-action.</p><p><b>Approximate Querying Systems.</b> Another branch of work specifically focuses on approximating analytical query results [<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 4" title="Agarwal, S., Mozafari, B., Panda, A., Milner, H., Madden, S., Stoica, I.: BlinkDB: queries with bounded errors and bounded response times on very large data. In: EuroSys, pp. 29–42 (2013)" href="#ref-CR4" id="ref-link-section-d20310988e4669">4</a>, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 11" title="Hellerstein, J.M., Haas, P.J., Wang, H.J.: Online aggregation. In: SIGMOD, pp. 171–182 (1997)" href="#ref-CR11" id="ref-link-section-d20310988e4672">11</a>, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 20" title="Potti, N., Patel, J.M.: DAQ: a new paradigm for approximate query processing. PVLDB 8(9), 898–909 (2015)" href="#ref-CR20" id="ref-link-section-d20310988e4675">20</a>]. Hellerstein et al. [<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 11" title="Hellerstein, J.M., Haas, P.J., Wang, H.J.: Online aggregation. In: SIGMOD, pp. 171–182 (1997)" href="#ref-CR11" id="ref-link-section-d20310988e4678">11</a>] propose an incremental strategy that aggregates tuples online so that the temporary result of the query is shown to the user, who can decide to interrupt the process anytime. Differently, when computing analytical queries with BlinkDB [<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 4" title="Agarwal, S., Mozafari, B., Panda, A., Milner, H., Madden, S., Stoica, I.: BlinkDB: queries with bounded errors and bounded response times on very large data. In: EuroSys, pp. 29–42 (2013)" href="#ref-CR4" id="ref-link-section-d20310988e4681">4</a>], users are asked the trade-off between time and accuracy in advance, and the system dynamically selects the best sample that allows replying the query under the user’s constraints. This is similar to our best-fit strategy but we do not apply only sampling and we do not consider analytical queries. A critical aspect in all these works is the estimation of the error. To overcome these problems, DAQ [<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 20" title="Potti, N., Patel, J.M.: DAQ: a new paradigm for approximate query processing. PVLDB 8(9), 898–909 (2015)" href="#ref-CR20" id="ref-link-section-d20310988e4685">20</a>] has recently introduced a deterministic approach to approximating analytic queries, where the user is initially provided with an interval that is guaranteed to contain the query result. Then, the interval shrinks as the query answering proceeds, until the convergence to the final answer. All these techniques work well on OLAP queries but since they require the workload in advance, they cannot be applied in our context where the user has usually not accessed the data yet and sampling cannot be the only technique for reducing the workload.</p></div></div></section><section data-title="Conclusion and Future Work"><div class="c-article-section" id="Sec9-section"><h2 id="Sec9" class="c-article-section__title js-section-title js-c-reading-companion-sections-item"><span class="c-article-section__title-number">7 </span>Conclusion and Future Work</h2><div class="c-article-section__content" id="Sec9-content"><p>In this paper, we have presented <span class="u-small-caps">Kayak</span>, a end-to-end framework for data management with a data lake approach. <span class="u-small-caps">Kayak</span> addresses data preparation, a crucial aspect for helping data-driven businesses in their analytics processes. <span class="u-small-caps">Kayak</span> provides a series of primitives for data preparation that can be executed by specifying a tolerance when the user prefers a quick result instead of an exact result. The framework also allows to define pipelines of primitives.</p><p>We have several future work directions in mind. We want to integrate the framework with components for supporting unstructured data, query expansion, and data visualization. We want to introduce a dynamic scheduling for the tasks and the possibility to set a tolerance for an entire pipeline. Finally, we would like to define a declarative language for designing primitives data preparation.</p></div></div></section> </div> <section data-title="Notes" lang="en"><div class="c-article-section" id="notes-section"><h2 id="notes" class="c-article-section__title js-section-title js-c-reading-companion-sections-item"><span class="c-article-section__title-number"> </span>Notes</h2><div class="c-article-section__content" id="notes-content"><ol class="c-article-footnote c-article-footnote--listed"><li class="c-article-footnote--listed__item" id="Fn1"><span class="c-article-footnote--listed__index">1.</span><div class="c-article-footnote--listed__content"><p><a href="http://jupyter.org/">http://jupyter.org/</a>.</p></div></li><li class="c-article-footnote--listed__item" id="Fn2"><span class="c-article-footnote--listed__index">2.</span><div class="c-article-footnote--listed__content"><p><a href="https://zeppelin.apache.org/">https://zeppelin.apache.org/</a>.</p></div></li><li class="c-article-footnote--listed__item" id="Fn3"><span class="c-article-footnote--listed__index">3.</span><div class="c-article-footnote--listed__content"><p>A demo of <span class="u-small-caps">Kayak</span> has been shown in [<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 15" title="Maccioni, A., Torlone, R.: Crossing the finish line faster when paddling the data lake with KAYAK. PVLDB 10(12), 1853–1856 (2017)" href="#ref-CR15" id="ref-link-section-d20310988e943">15</a>].</p></div></li><li class="c-article-footnote--listed__item" id="Fn4"><span class="c-article-footnote--listed__index">4.</span><div class="c-article-footnote--listed__content"><p><a href="https://github.com/HPI-Information-Systems/Metanome">https://github.com/HPI-Information-Systems/Metanome</a>.</p></div></li><li class="c-article-footnote--listed__item" id="Fn5"><span class="c-article-footnote--listed__index">5.</span><div class="c-article-footnote--listed__content"><p><a href="https://www.data.gov/">https://www.data.gov/</a>.</p></div></li><li class="c-article-footnote--listed__item" id="Fn6"><span class="c-article-footnote--listed__index">6.</span><div class="c-article-footnote--listed__content"><p><a href="http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml">http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml</a>.</p></div></li><li class="c-article-footnote--listed__item" id="Fn7"><span class="c-article-footnote--listed__index">7.</span><div class="c-article-footnote--listed__content"><p><a href="https://www.r-project.org/">https://www.r-project.org/</a>.</p></div></li><li class="c-article-footnote--listed__item" id="Fn8"><span class="c-article-footnote--listed__index">8.</span><div class="c-article-footnote--listed__content"><p><a href="https://github.com/JosPolfliet/pandas-profiling">https://github.com/JosPolfliet/pandas-profiling</a>.</p></div></li><li class="c-article-footnote--listed__item" id="Fn9"><span class="c-article-footnote--listed__index">9.</span><div class="c-article-footnote--listed__content"><p><a href="http://zeppelin-project.org/">http://zeppelin-project.org/</a>.</p></div></li></ol></div></div></section><div id="MagazineFulltextChapterBodySuffix"><section aria-labelledby="Bib1" data-title="References"><div class="c-article-section" id="Bib1-section"><h2 id="Bib1" class="c-article-section__title js-section-title js-c-reading-companion-sections-item"><span class="c-article-section__title-number"> </span>References</h2><div class="c-article-section__content" id="Bib1-content"><div data-container-section="references"><ol class="c-article-references" data-track-component="outbound reference" data-track-context="references section"><li class="c-article-references__item js-c-reading-companion-references-item" data-counter="1."><p class="c-article-references__text" id="ref-CR1">CKAN: The open source data portal software. <a href="http://ckan.org/" data-track="click" data-track-action="external reference" data-track-label="http://ckan.org/">http://ckan.org/</a>. 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