Data Management

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</div> </div> </li> <li data-gt-secondary="Foundational ML & Algorithms"> <div class="navigation__sub__columns__desktop"> <h2 class="headline-6 navigation__sub__columns__heading">Foundational ML & Algorithms</h2> <ul> <li> <a class="navigation__sub__columns__list-link caption js-drawer-link" href="http://research.google/research-areas/algorithms-and-theory/" > Algorithms & Theory </a> </li> <li> <a class="navigation__sub__columns__list-link caption js-drawer-link" href="http://research.google/research-areas/data-management/" > Data Management </a> </li> <li> <a class="navigation__sub__columns__list-link caption js-drawer-link" href="http://research.google/research-areas/data-mining-and-modeling/" > Data Mining & Modeling </a> </li> <li> <a class="navigation__sub__columns__list-link caption js-drawer-link" href="http://research.google/research-areas/information-retrieval-and-the-web/" > Information Retrieval & the Web </a> </li> <li> <a class="navigation__sub__columns__list-link caption 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Software Systems </a> </li> </ul> </div> <div class="navigation__sub__columns__mobile"> <button class="glue-header__link js-sub-nav-target" data-panel="nested" role="menuitem" aria-haspopup="true"> Computing Systems & Quantum AI <span class="icon icon--caret"></span> </button> <div class="navigation__nested-sub js-sub-nav-parent"> <div class="navigation__sub__mobile-heading"> <button class="glue-header__link js-sub-nav-close-mobile" role="menuitem" aria-haspopup="true"> <span class="sr-text">Back to</span> <span class="icon icon--caret"></span> Computing Systems & Quantum AI <span class="sr-text">menu</span> </button> <hr/> </div> <ul> <li role="menuitem"> <a href="http://research.google/research-areas/distributed-systems-and-parallel-computing/" class="navigation__sub__columns__mobile__link" > Distributed Systems & Parallel鈥–omputing <span> </span> </a> </li> <li role="menuitem"> <a href="http://research.google/research-areas/hardware-and-architecture/" 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<svg class="glue-icon glue-icon--24px" role="presentation" aria-hidden="true"> <use href="/gr/static/assets/icons/glue-icons.svg#close"></use> </svg> </button> </div> </div> </header> <main id="page-content"> <div class="research-area-detail"> <section class="basic-hero bhoig --theme-dark " data-gt-id="basic_hero" data-gt-component-name=""> <div class="glue-page"> <div class="glue-grid"> <div class="bhoig__image-wrapper glue-grid__col--span-2 glue-grid__col--span-3-md glue-grid__col--span-2-lg"> <div class="bhoig__image-bg" style="background-color: var(--glue-grey-900); "> <span class="icon icon--data-management" style="" ></span> </div> </div> <div class="bhoig__breadcrumb-wrapper glue-grid__col--span-10 glue-grid__col--span-9-md glue-grid__col--span-10-lg"> <nav class="glue-breadcrumbs" aria-label="Breadcrumbs"> <ol class="glue-breadcrumbs__list"> <li class="glue-breadcrumbs__item"> <a class="glue-breadcrumbs__link attribution" href="/">Home</a> <svg role="presentation" aria-hidden="true" class="glue-icon "> <use href="/gr/static/assets/icons/glue-icons.svg#chevron-right"></use> </svg> </li> <li class="glue-breadcrumbs__item"> <a class="glue-breadcrumbs__link attribution" href="/research-areas/">Research areas</a> <svg role="presentation" aria-hidden="true" class="glue-icon "> <use href="/gr/static/assets/icons/glue-icons.svg#chevron-right"></use> </svg> </li> </ol> </nav> </div> <h1 class="headline-1 bhoig__headline glue-grid__col--span-10 glue-grid__col--span-9-md glue-grid__col--span-10-lg">Data Management</h1> <div class="basic-hero__description bhoig__description glue-grid__col--span-10 glue-grid__col--span-9-md glue-grid__col--span-10-lg"> <p data-block-key="5c7l1">Google is deeply engaged in Data Management research across a variety of topics with deep connections to Google products. We are building intelligent systems to discover, annotate, and explore structured data from the Web, and to surface them creatively through Google products, such as Search (e.g., <a href="http://googleresearch.blogspot.com/2014/09/introducing-structured-snippets-now.html">structured snippets</a>, Docs, and many others). The overarching goal is to create a plethora of structured data on the Web that maximally help Google users consume, interact and explore information. Through those projects, we study various cutting-edge data management research issues including information extraction and integration, large scale data analysis, effective data exploration, etc., using a variety of techniques, such as information retrieval, data mining and machine learning.</p><p data-block-key="fqu6a">A major research effort involves the management of structured data within the enterprise. The goal is to discover, index, monitor, and organize this type of data in order to make it easier to access high-quality datasets. This type of data carries different, and often richer, semantics than structured data on the Web, which in turn raises new opportunities and technical challenges in their management.</p><p data-block-key="3ak61">Furthermore, Data Management research across Google allows us to build technologies that power Google's largest businesses through scalable, reliable, fast, and general-purpose infrastructure for large-scale data processing as a service. Some examples of such technologies include <a href="http://research.google/pubs/f1-a-distributed-sql-database-that-scales/">F1</a>, the database serving our ads infrastructure; <a href="http://research.google/pubs/mesa-geo-replicated-near-real-time-scalable-data-warehousing/">Mesa</a>, a petabyte-scale analytic data warehousing system; and <a href="http://research.google/pubs/dremel-interactive-analysis-of-web-scale-datasets/">Dremel</a>, for petabyte-scale data processing with interactive response times. Dremel is available for external customers to use as part of Google Cloud鈥檚 <a href="https://cloud.google.com/bigquery/" target="_blank" rel="noopener noreferrer">BigQuery</a>.</p> </div> <div class="bhoig__cta glue-grid__col--span-10 glue-grid__col--span-9-md glue-grid__col--span-10-lg"> </div> </div> </div> </section> <section class="offset-two-up"> <div class="glue-page glue-grid"> <div class="offset-two-up__left-col glue-grid__col glue-grid__col--span-4-sm glue-grid__col--span-12-md glue-grid__col--span-3-lg"> <h3 class="offset-two-up__headline headline-3">Recent Publications</h3> <div class="offset-two-up__cta-below-description"> <a class="glue-button glue-button--medium-emphasis" href="http://research.google/pubs/?category=data-management" > <span class="js-gt-item-id">See More</span> </a> </div> </div> <div class="glue-grid__col glue-grid__col--span-4-sm glue-grid__col--span-12-md glue-grid__col--span-9-lg"> <div class="publications-list --theme- row-card-list" data-hot-swap="pub-list" data-gt-id="publications_list" data-gt-component-name=""> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/vortex-a-stream-oriented-storage-engine-for-big-data-analytics/ > Vortex: A Stream-oriented Storage Engine For Big Data Analytics </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/pavanedara/"> Pavan Edara </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Jonathan Forbes </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Bigang Li </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> SIGMOD (2024) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentvortex-is-an-exabyte-scale-structu tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentvortex-is-an-exabyte-scale-structu" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">Vortex is an exabyte scale structured storage system built for streaming and batch analytics. It supports high-throughput batch and stream ingestion. For the user, it supports both batch-oriented and stream-based processing on the ingested data.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/vortex-a-stream-oriented-storage-engine-for-big-data-analytics/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/discovering-datasets-on-the-web-scale-challenges-and-recommendations-for-google-dataset-search/ > Discovering Datasets on the Web Scale: Challenges and Recommendations for Google Dataset Search </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/105399/"> Katrina Sostek </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Daniel Russell </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/105380/"> Tesh Goyal </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/tarfahalrashed/"> Tarfah Alrashed </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Stella Dugall </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/natalyanoy/"> Natasha Noy </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Harvard Data Science Review (2024) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentwith-the-rise-of-open-data-in-the tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentwith-the-rise-of-open-data-in-the" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">With the rise of open data in the last two decades, more datasets are online and more people are using them for projects and research. But how do people find datasets? We present the first user study of Google Dataset Search, a dataset-discovery tool that uses a web crawl and open ecosystem to find datasets. Google Dataset Search contains a superset of the datasets in other dataset-discovery tools鈥攁 total of 45 million datasets from 13,000 sources. We found that the tool addresses a previously identified need: a search engine for datasets across the entire web, including datasets in other tools. However, the tool introduced new challenges due to its open approach: building a mental model of the tool, making sense of heterogeneous datasets, and learning how to search for datasets. We discuss recommendations for dataset-discovery tools and open research questions.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/discovering-datasets-on-the-web-scale-challenges-and-recommendations-for-google-dataset-search/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/automatic-histograms-leveraging-language-models-for-text-dataset-exploration/ > Automatic Histograms: Leveraging Language Models for Text Dataset Exploration </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/106150/"> Emily Reif </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/108114/"> Crystal Qian </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/105507/"> James Wexler </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/minsukkahng/"> Minsuk Kahng </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA '24), ACM, Honolulu, HI, USA (2024), pp. 9 </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentmaking-sense-of-unstructured-text tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentmaking-sense-of-unstructured-text" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">Making sense of unstructured text datasets is perennially difficult, yet increasingly relevant with Large Language Models. Data practitioners often rely on dataset summaries, especially distributions of various derived features. Some features, like toxicity or topics, are relevant to many datasets, but many interesting features are domain specific, e.g., instruments and genres for a music dataset, or diseases and symptoms for a medical dataset. Accordingly, data practitioners often run custom analyses for each dataset, which is cumbersome and difficult, or use unsupervised methods. We present AutoHistograms, a visualization tool leveraging LLMs. AutoHistograms automatically identifies relevant entity-based features, visualizes their distributions, and allows the user to interactively query the dataset for new categories of entities. In a user study with (n=10) data practitioners, we observe that participants were able to quickly onboard to AutoHistograms, use the tool to identify actionable insights, and conceptualize a broad range of applicable use cases. We also describe a variety of usage scenarios from different types of users to highlight how this app can provide value in many different contexts. Finally, we present a quantitative evaluation of the tool. Together, this tool and user study contribute to the growing field of LLM-assisted sensemaking tools. </span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/automatic-histograms-leveraging-language-models-for-text-dataset-exploration/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/chain-of-table-evolves-tables-in-the-llm-reasoning-chain-for-table-understanding/ > Chain-of-Table: Evolves Tables in the LLM Reasoning Chain for Table Understanding </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> Zilong Wang </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Hao Zhang </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Chun-Liang Li </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/106772/"> Julian Eisenschlos </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/vincentperot/"> Vincent Perot </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/108469/"> Zifeng Wang </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/lesly/"> Lesly Miculicich </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/yasuhisafujii/"> Yasuhisa Fujii </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Jingbo Shang </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/107149/"> Chen-Yu Lee </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/105803/"> Tomas Pfister </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> ICLR (2024) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contenttable-based-reasoning-with-large-l tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contenttable-based-reasoning-with-large-l" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">Table-based reasoning with large language models (LLMs) is a promising direction to tackle many table understanding tasks, such as table-based question answering and fact verification. Compared with generic reasoning, table-based reasoning requires the extraction of underlying semantics from both free-form questions and semi-structured tabular data. Chain-of-Thought and its similar approaches incorporate the reasoning chain in the form of textual context, but it is still an open question how to effectively leverage tabular data in the reasoning chain. We propose the Chain-of-Table framework, where tabular data is explicitly used in the reasoning chain as a proxy for intermediate thoughts. Specifically, we guide LLMs using in-context learning to iteratively generate operations and update the table to represent a tabular reasoning chain. LLMs can therefore dynamically plan the next operation based on the results of the previous ones. This continuous evolution of the table forms a chain, showing the reasoning process for a given tabular problem. The chain carries structured information of the intermediate results, enabling more accurate and reliable predictions. Chain-of-Table achieves new state-of-the-art performance on WikiTQ, FeTaQA, and TabFact benchmarks across multiple LLM choices.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/chain-of-table-evolves-tables-in-the-llm-reasoning-chain-for-table-understanding/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/biglake-bigquerys-evolution-toward-a-multi-cloud-lakehouse/ > BigLake: BigQuery鈥檚 Evolution toward a Multi-Cloud Lakehouse </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> Justin Levandoski </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Garrett Casto </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Mingge Deng </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Rushabh Desai </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/pavanedara/"> Pavan Edara </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Thibaud Hottelier </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Amir Hormati </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/anoopjohnson/"> Anoop Johnson </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Jeff Johnson </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Dawid Kurzyniec </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/sammcveety/"> Sam McVeety </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Prem Ramanathan </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Gaurav Saxena </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Vidya Shanmugam </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Yuri Volobuev </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> SIGMOD (2024) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentbigquerys-cloud-native-disaggrega tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentbigquerys-cloud-native-disaggrega" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">BigQuery鈥檚 cloud-native disaggregated architecture has allowed Google Cloud to evolve the system to meet several customer needs across the analytics and AI/ML workload spectrum. A key customer requirement for BigQuery centers around the unification of data lake and enterprise data warehousing workloads. This approach combines: (1) the need for core data management primitives, e.g., security, governance, common runtime metadata, performance acceleration, ACID transactions, provided by an enterprise data warehouses coupled with (2) harnessing the flexibility of the open source format and analytics ecosystem along with new workload types such as AI/ML over unstructured data on object storage. In addition, there is a strong requirement to support BigQuery as a multi-cloud offering given cloud customers are opting for a multi-cloud footprint by default. This paper describes BigLake, an evolution of BigQuery toward a multi-cloud lakehouse to address these customer requirements in novel ways. We describe three main innovations in this space. We first present BigLake tables, making open-source table formats (e.g., Apache Parquet, Iceberg) first class citizens, providing fine-grained governance enforcement and performance acceleration over these formats to BigQuery and other open-source analytics engines. Next, we cover the design and implementation of BigLake Object tables that allow BigQuery to integrate AI/ML for inferencing and processing over unstructured data. Finally, we present Omni, a platform for deploying BigQuery on non-GCP clouds, focusing on the infrastructure and operational innovations we made to provide an enterprise lakehouse product regardless of the cloud provider hosting the data.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/biglake-bigquerys-evolution-toward-a-multi-cloud-lakehouse/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/are-we-cobblers-without-shoes-making-computer-science-data-fair/ > Are we cobblers without shoes? Making Computer Science data FAIR </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/natalyanoy/"> Natasha Noy </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Carole Goble </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Communications of ACM, 66 (1) (2023) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentna-a-viewpoint-article tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentna-a-viewpoint-article" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> 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