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data</title> <meta name="description" content="Search results for: web log data"> <meta name="keywords" content="web log data"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="web log data"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 25114</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: web log data</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25114</span> Data Transformations in Data Envelopment Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mansour%20Mohammadpour">Mansour Mohammadpour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data transformation refers to the modification of any point in a data set by a mathematical function. When applying transformations, the measurement scale of the data is modified. Data transformations are commonly employed to turn data into the appropriate form, which can serve various functions in the quantitative analysis of the data. This study addresses the investigation of the use of data transformations in Data Envelopment Analysis (DEA). Although data transformations are important options for analysis, they do fundamentally alter the nature of the variable, making the interpretation of the results somewhat more complex. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20transformation" title="data transformation">data transformation</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20envelopment%20analysis" title=" data envelopment analysis"> data envelopment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=undesirable%20data" title=" undesirable data"> undesirable data</a>, <a href="https://publications.waset.org/abstracts/search?q=negative%20data" title=" negative data"> negative data</a> </p> <a href="https://publications.waset.org/abstracts/192236/data-transformations-in-data-envelopment-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192236.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">20</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25113</span> Processing Big Data: An Approach Using Feature Selection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nikat%20Parveen">Nikat Parveen</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Ananthi"> M. Ananthi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Big data is one of the emerging technology, which collects the data from various sensors and those data will be used in many fields. Data retrieval is one of the major issue where there is a need to extract the exact data as per the need. In this paper, large amount of data set is processed by using the feature selection. Feature selection helps to choose the data which are actually needed to process and execute the task. The key value is the one which helps to point out exact data available in the storage space. Here the available data is streamed and R-Center is proposed to achieve this task. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=key%20value" title=" key value"> key value</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=retrieval" title=" retrieval"> retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=performance" title=" performance"> performance</a> </p> <a href="https://publications.waset.org/abstracts/74596/processing-big-data-an-approach-using-feature-selection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74596.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">341</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25112</span> Applications of Big Data in Education</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Faisal%20Kalota">Faisal Kalota</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Big Data and analytics have gained a huge momentum in recent years. Big Data feeds into the field of Learning Analytics (LA) that may allow academic institutions to better understand the learners’ needs and proactively address them. Hence, it is important to have an understanding of Big Data and its applications. The purpose of this descriptive paper is to provide an overview of Big Data, the technologies used in Big Data, and some of the applications of Big Data in education. Additionally, it discusses some of the concerns related to Big Data and current research trends. While Big Data can provide big benefits, it is important that institutions understand their own needs, infrastructure, resources, and limitation before jumping on the Big Data bandwagon. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20analytics" title=" learning analytics"> learning analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=analytics" title=" analytics"> analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data%20in%20education" title=" big data in education"> big data in education</a>, <a href="https://publications.waset.org/abstracts/search?q=Hadoop" title=" Hadoop "> Hadoop </a> </p> <a href="https://publications.waset.org/abstracts/27525/applications-of-big-data-in-education" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27525.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">426</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25111</span> Analysis of Big Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sandeep%20Sharma">Sandeep Sharma</a>, <a href="https://publications.waset.org/abstracts/search?q=Sarabjit%20Singh"> Sarabjit Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As per the user demand and growth trends of large free data the storage solutions are now becoming more challenge-able to protect, store and to retrieve data. The days are not so far when the storage companies and organizations are start saying 'no' to store our valuable data or they will start charging a huge amount for its storage and protection. On the other hand as per the environmental conditions it becomes challenge-able to maintain and establish new data warehouses and data centers to protect global warming threats. A challenge of small data is over now, the challenges are big that how to manage the exponential growth of data. In this paper we have analyzed the growth trend of big data and its future implications. We have also focused on the impact of the unstructured data on various concerns and we have also suggested some possible remedies to streamline big data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=unstructured%20data" title=" unstructured data"> unstructured data</a>, <a href="https://publications.waset.org/abstracts/search?q=volume" title=" volume"> volume</a>, <a href="https://publications.waset.org/abstracts/search?q=variety" title=" variety"> variety</a>, <a href="https://publications.waset.org/abstracts/search?q=velocity" title=" velocity"> velocity</a> </p> <a href="https://publications.waset.org/abstracts/5372/analysis-of-big-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5372.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">548</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25110</span> Research of Data Cleaning Methods Based on Dependency Rules</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yang%20Bao">Yang Bao</a>, <a href="https://publications.waset.org/abstracts/search?q=Shi%20Wei%20Deng"> Shi Wei Deng</a>, <a href="https://publications.waset.org/abstracts/search?q=WangQun%20Lin"> WangQun Lin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper introduces the concept and principle of data cleaning, analyzes the types and causes of dirty data, and proposes several key steps of typical cleaning process, puts forward a well scalability and versatility data cleaning framework, in view of data with attribute dependency relation, designs several of violation data discovery algorithms by formal formula, which can obtain inconsistent data to all target columns with condition attribute dependent no matter data is structured (SQL) or unstructured (NoSQL), and gives 6 data cleaning methods based on these algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20cleaning" title="data cleaning">data cleaning</a>, <a href="https://publications.waset.org/abstracts/search?q=dependency%20rules" title=" dependency rules"> dependency rules</a>, <a href="https://publications.waset.org/abstracts/search?q=violation%20data%20discovery" title=" violation data discovery"> violation data discovery</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20repair" title=" data repair"> data repair</a> </p> <a href="https://publications.waset.org/abstracts/31348/research-of-data-cleaning-methods-based-on-dependency-rules" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31348.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">564</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25109</span> Mining Big Data in Telecommunications Industry: Challenges, Techniques, and Revenue Opportunity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hoda%20A.%20Abdel%20Hafez">Hoda A. Abdel Hafez</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mining big data represents a big challenge nowadays. Many types of research are concerned with mining massive amounts of data and big data streams. Mining big data faces a lot of challenges including scalability, speed, heterogeneity, accuracy, provenance and privacy. In telecommunication industry, mining big data is like a mining for gold; it represents a big opportunity and maximizing the revenue streams in this industry. This paper discusses the characteristics of big data (volume, variety, velocity and veracity), data mining techniques and tools for handling very large data sets, mining big data in telecommunication and the benefits and opportunities gained from them. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mining%20big%20data" title="mining big data">mining big data</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data" title=" big data"> big data</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=telecommunication" title=" telecommunication"> telecommunication</a> </p> <a href="https://publications.waset.org/abstracts/41412/mining-big-data-in-telecommunications-industry-challenges-techniques-and-revenue-opportunity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41412.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">409</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25108</span> JavaScript Object Notation Data against eXtensible Markup Language Data in Software Applications a Software Testing Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Theertha%20Chandroth">Theertha Chandroth</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a comparative study on how to check JSON (JavaScript Object Notation) data against XML (eXtensible Markup Language) data from a software testing point of view. JSON and XML are widely used data interchange formats, each with its unique syntax and structure. The objective is to explore various techniques and methodologies for validating comparison and integration between JSON data to XML and vice versa. By understanding the process of checking JSON data against XML data, testers, developers and data practitioners can ensure accurate data representation, seamless data interchange, and effective data validation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=XML" title="XML">XML</a>, <a href="https://publications.waset.org/abstracts/search?q=JSON" title=" JSON"> JSON</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20comparison" title=" data comparison"> data comparison</a>, <a href="https://publications.waset.org/abstracts/search?q=integration%20testing" title=" integration testing"> integration testing</a>, <a href="https://publications.waset.org/abstracts/search?q=Python" title=" Python"> Python</a>, <a href="https://publications.waset.org/abstracts/search?q=SQL" title=" SQL"> SQL</a> </p> <a href="https://publications.waset.org/abstracts/170435/javascript-object-notation-data-against-extensible-markup-language-data-in-software-applications-a-software-testing-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170435.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">140</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25107</span> Using Machine Learning Techniques to Extract Useful Information from Dark Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nigar%20Hussain">Nigar Hussain</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is a subset of big data. Dark data means those data in which we fail to use for future decisions. There are many issues in existing work, but some need powerful tools for utilizing dark data. It needs sufficient techniques to deal with dark data. That enables users to exploit their excellence, adaptability, speed, less time utilization, execution, and accessibility. Another issue is the way to utilize dark data to extract helpful information to settle on better choices. In this paper, we proposed upgrade strategies to remove the dark side from dark data. Using a supervised model and machine learning techniques, we utilized dark data and achieved an F1 score of 89.48%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=dark%20data" title=" dark data"> dark data</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=heatmap" title=" heatmap"> heatmap</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title=" random forest"> random forest</a> </p> <a href="https://publications.waset.org/abstracts/191942/using-machine-learning-techniques-to-extract-useful-information-from-dark-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/191942.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">28</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25106</span> Multi-Source Data Fusion for Urban Comprehensive Management</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bolin%20Hua">Bolin Hua</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In city governance, various data are involved, including city component data, demographic data, housing data and all kinds of business data. These data reflects different aspects of people, events and activities. Data generated from various systems are different in form and data source are different because they may come from different sectors. In order to reflect one or several facets of an event or rule, data from multiple sources need fusion together. Data from different sources using different ways of collection raised several issues which need to be resolved. Problem of data fusion include data update and synchronization, data exchange and sharing, file parsing and entry, duplicate data and its comparison, resource catalogue construction. Governments adopt statistical analysis, time series analysis, extrapolation, monitoring analysis, value mining, scenario prediction in order to achieve pattern discovery, law verification, root cause analysis and public opinion monitoring. The result of Multi-source data fusion is to form a uniform central database, which includes people data, location data, object data, and institution data, business data and space data. We need to use meta data to be referred to and read when application needs to access, manipulate and display the data. A uniform meta data management ensures effectiveness and consistency of data in the process of data exchange, data modeling, data cleansing, data loading, data storing, data analysis, data search and data delivery. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multi-source%20data%20fusion" title="multi-source data fusion">multi-source data fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20comprehensive%20management" title=" urban comprehensive management"> urban comprehensive management</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20fusion" title=" information fusion"> information fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=government%20data" title=" government data"> government data</a> </p> <a href="https://publications.waset.org/abstracts/42478/multi-source-data-fusion-for-urban-comprehensive-management" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42478.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">393</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25105</span> Reviewing Privacy Preserving Distributed Data Mining</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sajjad%20Baghernezhad">Sajjad Baghernezhad</a>, <a href="https://publications.waset.org/abstracts/search?q=Saeideh%20Baghernezhad"> Saeideh Baghernezhad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays considering human involved in increasing data development some methods such as data mining to extract science are unavoidable. One of the discussions of data mining is inherent distribution of the data usually the bases creating or receiving such data belong to corporate or non-corporate persons and do not give their information freely to others. Yet there is no guarantee to enable someone to mine special data without entering in the owner’s privacy. Sending data and then gathering them by each vertical or horizontal software depends on the type of their preserving type and also executed to improve data privacy. In this study it was attempted to compare comprehensively preserving data methods; also general methods such as random data, coding and strong and weak points of each one are examined. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=distributed%20data%20mining" title=" distributed data mining"> distributed data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=privacy%20protection" title=" privacy protection"> privacy protection</a>, <a href="https://publications.waset.org/abstracts/search?q=privacy%20preserving" title=" privacy preserving"> privacy preserving</a> </p> <a href="https://publications.waset.org/abstracts/28876/reviewing-privacy-preserving-distributed-data-mining" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28876.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">525</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25104</span> The Right to Data Portability and Its Influence on the Development of Digital Services</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Roman%20Bieda">Roman Bieda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The General Data Protection Regulation (GDPR) will come into force on 25 May 2018 which will create a new legal framework for the protection of personal data in the European Union. Article 20 of GDPR introduces a right to data portability. This right allows for data subjects to receive the personal data which they have provided to a data controller, in a structured, commonly used and machine-readable format, and to transmit this data to another data controller. The right to data portability, by facilitating transferring personal data between IT environments (e.g.: applications), will also facilitate changing the provider of services (e.g. changing a bank or a cloud computing service provider). Therefore, it will contribute to the development of competition and the digital market. The aim of this paper is to discuss the right to data portability and its influence on the development of new digital services. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20portability" title="data portability">data portability</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20market" title=" digital market"> digital market</a>, <a href="https://publications.waset.org/abstracts/search?q=GDPR" title=" GDPR"> GDPR</a>, <a href="https://publications.waset.org/abstracts/search?q=personal%20data" title=" personal data"> personal data</a> </p> <a href="https://publications.waset.org/abstracts/77312/the-right-to-data-portability-and-its-influence-on-the-development-of-digital-services" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77312.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">473</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25103</span> Recent Advances in Data Warehouse</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fahad%20Hanash%20Alzahrani">Fahad Hanash Alzahrani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes some recent advances in a quickly developing area of data storing and processing based on Data Warehouses and Data Mining techniques, which are associated with software, hardware, data mining algorithms and visualisation techniques having common features for any specific problems and tasks of their implementation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20warehouse" title="data warehouse">data warehouse</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20discovery%20in%20databases" title=" knowledge discovery in databases"> knowledge discovery in databases</a>, <a href="https://publications.waset.org/abstracts/search?q=on-line%20analytical%20processing" title=" on-line analytical processing"> on-line analytical processing</a> </p> <a href="https://publications.waset.org/abstracts/63299/recent-advances-in-data-warehouse" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63299.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">404</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25102</span> How to Use Big Data in Logistics Issues</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mehmet%20Akif%20Aslan">Mehmet Akif Aslan</a>, <a href="https://publications.waset.org/abstracts/search?q=Mehmet%20Simsek"> Mehmet Simsek</a>, <a href="https://publications.waset.org/abstracts/search?q=Eyup%20Sensoy"> Eyup Sensoy </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Big Data stands for today’s cutting-edge technology. As the technology becomes widespread, so does Data. Utilizing massive data sets enable companies to get competitive advantages over their adversaries. Out of many area of Big Data usage, logistics has significance role in both commercial sector and military. This paper lays out what big data is and how it is used in both military and commercial logistics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=logistics" title=" logistics"> logistics</a>, <a href="https://publications.waset.org/abstracts/search?q=operational%20efficiency" title=" operational efficiency"> operational efficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20management" title=" risk management"> risk management</a> </p> <a href="https://publications.waset.org/abstracts/27245/how-to-use-big-data-in-logistics-issues" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27245.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">641</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25101</span> Implementation of an IoT Sensor Data Collection and Analysis Library</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jihyun%20Song">Jihyun Song</a>, <a href="https://publications.waset.org/abstracts/search?q=Kyeongjoo%20Kim"> Kyeongjoo Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Minsoo%20Lee"> Minsoo Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the development of information technology and wireless Internet technology, various data are being generated in various fields. These data are advantageous in that they provide real-time information to the users themselves. However, when the data are accumulated and analyzed, more various information can be extracted. In addition, development and dissemination of boards such as Arduino and Raspberry Pie have made it possible to easily test various sensors, and it is possible to collect sensor data directly by using database application tools such as MySQL. These directly collected data can be used for various research and can be useful as data for data mining. However, there are many difficulties in using the board to collect data, and there are many difficulties in using it when the user is not a computer programmer, or when using it for the first time. Even if data are collected, lack of expert knowledge or experience may cause difficulties in data analysis and visualization. In this paper, we aim to construct a library for sensor data collection and analysis to overcome these problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clustering" title="clustering">clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=DBSCAN" title=" DBSCAN"> DBSCAN</a>, <a href="https://publications.waset.org/abstracts/search?q=k-means" title=" k-means"> k-means</a>, <a href="https://publications.waset.org/abstracts/search?q=k-medoids" title=" k-medoids"> k-medoids</a>, <a href="https://publications.waset.org/abstracts/search?q=sensor%20data" title=" sensor data"> sensor data</a> </p> <a href="https://publications.waset.org/abstracts/82893/implementation-of-an-iot-sensor-data-collection-and-analysis-library" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/82893.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">378</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25100</span> Government (Big) Data Ecosystem: Definition, Classification of Actors, and Their Roles </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Syed%20Iftikhar%20Hussain%20Shah">Syed Iftikhar Hussain Shah</a>, <a href="https://publications.waset.org/abstracts/search?q=Vasilis%20Peristeras"> Vasilis Peristeras</a>, <a href="https://publications.waset.org/abstracts/search?q=Ioannis%20Magnisalis"> Ioannis Magnisalis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Organizations, including governments, generate (big) data that are high in volume, velocity, veracity, and come from a variety of sources. Public Administrations are using (big) data, implementing base registries, and enforcing data sharing within the entire government to deliver (big) data related integrated services, provision of insights to users, and for good governance. Government (Big) data ecosystem actors represent distinct entities that provide data, consume data, manipulate data to offer paid services, and extend data services like data storage, hosting services to other actors. In this research work, we perform a systematic literature review. The key objectives of this paper are to propose a robust definition of government (big) data ecosystem and a classification of government (big) data ecosystem actors and their roles. We showcase a graphical view of actors, roles, and their relationship in the government (big) data ecosystem. We also discuss our research findings. We did not find too much published research articles about the government (big) data ecosystem, including its definition and classification of actors and their roles. Therefore, we lent ideas for the government (big) data ecosystem from numerous areas that include scientific research data, humanitarian data, open government data, industry data, in the literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data%20ecosystem" title=" big data ecosystem"> big data ecosystem</a>, <a href="https://publications.waset.org/abstracts/search?q=classification%20of%20big%20data%20actors" title=" classification of big data actors"> classification of big data actors</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data%20actors%20roles" title=" big data actors roles"> big data actors roles</a>, <a href="https://publications.waset.org/abstracts/search?q=definition%20of%20government%20%28big%29%20data%20ecosystem" title=" definition of government (big) data ecosystem"> definition of government (big) data ecosystem</a>, <a href="https://publications.waset.org/abstracts/search?q=data-driven%20government" title=" data-driven government"> data-driven government</a>, <a href="https://publications.waset.org/abstracts/search?q=eGovernment" title=" eGovernment"> eGovernment</a>, <a href="https://publications.waset.org/abstracts/search?q=gaps%20in%20data%20ecosystems" title=" gaps in data ecosystems"> gaps in data ecosystems</a>, <a href="https://publications.waset.org/abstracts/search?q=government%20%28big%29%20data" title=" government (big) data"> government (big) data</a>, <a href="https://publications.waset.org/abstracts/search?q=public%20administration" title=" public administration"> public administration</a>, <a href="https://publications.waset.org/abstracts/search?q=systematic%20literature%20review" title=" systematic literature review"> systematic literature review</a> </p> <a href="https://publications.waset.org/abstracts/119739/government-big-data-ecosystem-definition-classification-of-actors-and-their-roles" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/119739.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">162</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25099</span> Government Big Data Ecosystem: A Systematic Literature Review</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Syed%20Iftikhar%20Hussain%20Shah">Syed Iftikhar Hussain Shah</a>, <a href="https://publications.waset.org/abstracts/search?q=Vasilis%20Peristeras"> Vasilis Peristeras</a>, <a href="https://publications.waset.org/abstracts/search?q=Ioannis%20Magnisalis"> Ioannis Magnisalis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data that is high in volume, velocity, veracity and comes from a variety of sources is usually generated in all sectors including the government sector. Globally public administrations are pursuing (big) data as new technology and trying to adopt a data-centric architecture for hosting and sharing data. Properly executed, big data and data analytics in the government (big) data ecosystem can be led to data-driven government and have a direct impact on the way policymakers work and citizens interact with governments. In this research paper, we conduct a systematic literature review. The main aims of this paper are to highlight essential aspects of the government (big) data ecosystem and to explore the most critical socio-technical factors that contribute to the successful implementation of government (big) data ecosystem. The essential aspects of government (big) data ecosystem include definition, data types, data lifecycle models, and actors and their roles. We also discuss the potential impact of (big) data in public administration and gaps in the government data ecosystems literature. As this is a new topic, we did not find specific articles on government (big) data ecosystem and therefore focused our research on various relevant areas like humanitarian data, open government data, scientific research data, industry data, etc. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=applications%20of%20big%20data" title="applications of big data">applications of big data</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data" title=" big data"> big data</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data%20types.%20big%20data%20ecosystem" title=" big data types. big data ecosystem"> big data types. big data ecosystem</a>, <a href="https://publications.waset.org/abstracts/search?q=critical%20success%20factors" title=" critical success factors"> critical success factors</a>, <a href="https://publications.waset.org/abstracts/search?q=data-driven%20government" title=" data-driven government"> data-driven government</a>, <a href="https://publications.waset.org/abstracts/search?q=egovernment" title=" egovernment"> egovernment</a>, <a href="https://publications.waset.org/abstracts/search?q=gaps%20in%20data%20ecosystems" title=" gaps in data ecosystems"> gaps in data ecosystems</a>, <a href="https://publications.waset.org/abstracts/search?q=government%20%28big%29%20data" title=" government (big) data"> government (big) data</a>, <a href="https://publications.waset.org/abstracts/search?q=literature%20review" title=" literature review"> literature review</a>, <a href="https://publications.waset.org/abstracts/search?q=public%20administration" title=" public administration"> public administration</a>, <a href="https://publications.waset.org/abstracts/search?q=systematic%20review" title=" systematic review"> systematic review</a> </p> <a href="https://publications.waset.org/abstracts/116280/government-big-data-ecosystem-a-systematic-literature-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/116280.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">228</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25098</span> A Machine Learning Decision Support Framework for Industrial Engineering Purposes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anli%20Du%20Preez">Anli Du Preez</a>, <a href="https://publications.waset.org/abstracts/search?q=James%20Bekker"> James Bekker</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data is currently one of the most critical and influential emerging technologies. However, the true potential of data is yet to be exploited since, currently, about 1% of generated data are ever actually analyzed for value creation. There is a data gap where data is not explored due to the lack of data analytics infrastructure and the required data analytics skills. This study developed a decision support framework for data analytics by following Jabareen’s framework development methodology. The study focused on machine learning algorithms, which is a subset of data analytics. The developed framework is designed to assist data analysts with little experience, in choosing the appropriate machine learning algorithm given the purpose of their application. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Data%20analytics" title="Data analytics">Data analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=Industrial%20engineering" title=" Industrial engineering"> Industrial engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=Machine%20learning" title=" Machine learning"> Machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=Value%20creation" title=" Value creation"> Value creation</a> </p> <a href="https://publications.waset.org/abstracts/116912/a-machine-learning-decision-support-framework-for-industrial-engineering-purposes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/116912.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">168</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25097</span> Providing Security to Private Cloud Using Advanced Encryption Standard Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Annapureddy%20Srikant%20Reddy">Annapureddy Srikant Reddy</a>, <a href="https://publications.waset.org/abstracts/search?q=Atthanti%20Mahendra"> Atthanti Mahendra</a>, <a href="https://publications.waset.org/abstracts/search?q=Samala%20Chinni%20Krishna"> Samala Chinni Krishna</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Neelima"> N. Neelima</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In our present world, we are generating a lot of data and we, need a specific device to store all these data. Generally, we store data in pen drives, hard drives, etc. Sometimes we may loss the data due to the corruption of devices. To overcome all these issues, we implemented a cloud space for storing the data, and it provides more security to the data. We can access the data with just using the internet from anywhere in the world. We implemented all these with the java using Net beans IDE. Once user uploads the data, he does not have any rights to change the data. Users uploaded files are stored in the cloud with the file name as system time and the directory will be created with some random words. Cloud accepts the data only if the size of the file is less than 2MB. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cloud%20space" title="cloud space">cloud space</a>, <a href="https://publications.waset.org/abstracts/search?q=AES" title=" AES"> AES</a>, <a href="https://publications.waset.org/abstracts/search?q=FTP" title=" FTP"> FTP</a>, <a href="https://publications.waset.org/abstracts/search?q=NetBeans%20IDE" title=" NetBeans IDE"> NetBeans IDE</a> </p> <a href="https://publications.waset.org/abstracts/139365/providing-security-to-private-cloud-using-advanced-encryption-standard-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139365.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">206</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25096</span> Business Intelligence for Profiling of Telecommunication Customer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rokhmatul%20Insani">Rokhmatul Insani</a>, <a href="https://publications.waset.org/abstracts/search?q=Hira%20Laksmiwati%20Soemitro"> Hira Laksmiwati Soemitro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Business Intelligence is a methodology that exploits the data to produce information and knowledge systematically, business intelligence can support the decision-making process. Some methods in business intelligence are data warehouse and data mining. A data warehouse can store historical data from transactional data. For data modelling in data warehouse, we apply dimensional modelling by Kimball. While data mining is used to extracting patterns from the data and get insight from the data. Data mining has many techniques, one of which is segmentation. For profiling of telecommunication customer, we use customer segmentation according to customer’s usage of services, customer invoice and customer payment. Customers can be grouped according to their characteristics and can be identified the profitable customers. We apply K-Means Clustering Algorithm for segmentation. The input variable for that algorithm we use RFM (Recency, Frequency and Monetary) model. All process in data mining, we use tools IBM SPSS modeller. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=business%20intelligence" title="business intelligence">business intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=customer%20segmentation" title=" customer segmentation"> customer segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20warehouse" title=" data warehouse"> data warehouse</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a> </p> <a href="https://publications.waset.org/abstracts/46969/business-intelligence-for-profiling-of-telecommunication-customer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46969.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">483</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25095</span> Imputation Technique for Feature Selection in Microarray Data Set</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Younies%20Saeed%20Hassan%20Mahmoud">Younies Saeed Hassan Mahmoud</a>, <a href="https://publications.waset.org/abstracts/search?q=Mai%20Mabrouk"> Mai Mabrouk</a>, <a href="https://publications.waset.org/abstracts/search?q=Elsayed%20Sallam"> Elsayed Sallam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Analysing DNA microarray data sets is a great challenge, which faces the bioinformaticians due to the complication of using statistical and machine learning techniques. The challenge will be doubled if the microarray data sets contain missing data, which happens regularly because these techniques cannot deal with missing data. One of the most important data analysis process on the microarray data set is feature selection. This process finds the most important genes that affect certain disease. In this paper, we introduce a technique for imputing the missing data in microarray data sets while performing feature selection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DNA%20microarray" title="DNA microarray">DNA microarray</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=missing%20data" title=" missing data"> missing data</a>, <a href="https://publications.waset.org/abstracts/search?q=bioinformatics" title=" bioinformatics"> bioinformatics</a> </p> <a href="https://publications.waset.org/abstracts/21839/imputation-technique-for-feature-selection-in-microarray-data-set" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21839.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">574</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25094</span> PDDA: Priority-Based, Dynamic Data Aggregation Approach for Sensor-Based Big Data Framework</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lutful%20Karim">Lutful Karim</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20S.%20Al-kahtani"> Mohammed S. Al-kahtani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sensors are being used in various applications such as agriculture, health monitoring, air and water pollution monitoring, traffic monitoring and control and hence, play the vital role in the growth of big data. However, sensors collect redundant data. Thus, aggregating and filtering sensors data are significantly important to design an efficient big data framework. Current researches do not focus on aggregating and filtering data at multiple layers of sensor-based big data framework. Thus, this paper introduces (i) three layers data aggregation and framework for big data and (ii) a priority-based, dynamic data aggregation scheme (PDDA) for the lowest layer at sensors. Simulation results show that the PDDA outperforms existing tree and cluster-based data aggregation scheme in terms of overall network energy consumptions and end-to-end data transmission delay. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=tree%20topology" title=" tree topology"> tree topology</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20aggregation" title=" data aggregation"> data aggregation</a>, <a href="https://publications.waset.org/abstracts/search?q=sensor%20networks" title=" sensor networks"> sensor networks</a> </p> <a href="https://publications.waset.org/abstracts/47419/pdda-priority-based-dynamic-data-aggregation-approach-for-sensor-based-big-data-framework" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47419.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">345</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25093</span> Control the Flow of Big Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shizra%20Waris">Shizra Waris</a>, <a href="https://publications.waset.org/abstracts/search?q=Saleem%20%20Akhtar"> Saleem Akhtar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Big data is a research area receiving attention from academia and IT communities. In the digital world, the amounts of data produced and stored have within a short period of time. Consequently this fast increasing rate of data has created many challenges. In this paper, we use functionalism and structuralism paradigms to analyze the genesis of big data applications and its current trends. This paper presents a complete discussion on state-of-the-art big data technologies based on group and stream data processing. Moreover, strengths and weaknesses of these technologies are analyzed. This study also covers big data analytics techniques, processing methods, some reported case studies from different vendor, several open research challenges and the chances brought about by big data. The similarities and differences of these techniques and technologies based on important limitations are also investigated. Emerging technologies are suggested as a solution for big data problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer" title="computer">computer</a>, <a href="https://publications.waset.org/abstracts/search?q=it%20community" title=" it community"> it community</a>, <a href="https://publications.waset.org/abstracts/search?q=industry" title=" industry"> industry</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data" title=" big data"> big data</a> </p> <a href="https://publications.waset.org/abstracts/124479/control-the-flow-of-big-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/124479.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">194</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25092</span> High Performance Computing and Big Data Analytics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Branci%20Sarra">Branci Sarra</a>, <a href="https://publications.waset.org/abstracts/search?q=Branci%20Saadia"> Branci Saadia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Because of the multiplied data growth, many computer science tools have been developed to process and analyze these Big Data. High-performance computing architectures have been designed to meet the treatment needs of Big Data (view transaction processing standpoint, strategic, and tactical analytics). The purpose of this article is to provide a historical and global perspective on the recent trend of high-performance computing architectures especially what has a relation with Analytics and Data Mining. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=high%20performance%20computing" title="high performance computing">high performance computing</a>, <a href="https://publications.waset.org/abstracts/search?q=HPC" title=" HPC"> HPC</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data" title=" big data"> big data</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20analysis" title=" data analysis"> data analysis</a> </p> <a href="https://publications.waset.org/abstracts/15079/high-performance-computing-and-big-data-analytics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15079.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">520</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25091</span> A Landscape of Research Data Repositories in Re3data.org Registry: A Case Study of Indian Repositories</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Prashant%20Shrivastava">Prashant Shrivastava</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this study is to explore re3dat.org registry to identify research data repositories registration workflow process. Further objective is to depict a graph for present development of research data repositories in India. Preliminarily with an approach to understand re3data.org registry framework and schema design then further proceed to explore the status of research data repositories of India in re3data.org registry. Research data repositories are getting wider relevance due to e-research concepts. Now available registry re3data.org is a good tool for users and researchers to identify appropriate research data repositories as per their research requirements. In Indian environment, a compatible National Research Data Policy is the need of the time to boost the management of research data. Registry for Research Data Repositories is a crucial tool to discover specific information in specific domain. Also, Research Data Repositories in India have not been studied. Re3data.org registry and status of Indian research data repositories both discussed in this study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=research%20data" title="research data">research data</a>, <a href="https://publications.waset.org/abstracts/search?q=research%20data%20repositories" title=" research data repositories"> research data repositories</a>, <a href="https://publications.waset.org/abstracts/search?q=research%20data%20registry" title=" research data registry"> research data registry</a>, <a href="https://publications.waset.org/abstracts/search?q=re3data.org" title=" re3data.org"> re3data.org</a> </p> <a href="https://publications.waset.org/abstracts/84974/a-landscape-of-research-data-repositories-in-re3dataorg-registry-a-case-study-of-indian-repositories" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84974.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">324</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25090</span> A Study of Cloud Computing Solution for Transportation Big Data Processing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ilgin%20G%C3%B6ka%C5%9Far">Ilgin Gökaşar</a>, <a href="https://publications.waset.org/abstracts/search?q=Saman%20Ghaffarian"> Saman Ghaffarian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The need for fast processed big data of transportation ridership (eg., smartcard data) and traffic operation (e.g., traffic detectors data) which requires a lot of computational power is incontrovertible in Intelligent Transportation Systems. Nowadays cloud computing is one of the important subjects and popular information technology solution for data processing. It enables users to process enormous measure of data without having their own particular computing power. Thus, it can also be a good selection for transportation big data processing as well. This paper intends to examine how the cloud computing can enhance transportation big data process with contrasting its advantages and disadvantages, and discussing cloud computing features. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=cloud%20computing" title=" cloud computing"> cloud computing</a>, <a href="https://publications.waset.org/abstracts/search?q=Intelligent%20Transportation%20Systems" title=" Intelligent Transportation Systems"> Intelligent Transportation Systems</a>, <a href="https://publications.waset.org/abstracts/search?q=ITS" title=" ITS"> ITS</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20data%20processing" title=" traffic data processing"> traffic data processing</a> </p> <a href="https://publications.waset.org/abstracts/32308/a-study-of-cloud-computing-solution-for-transportation-big-data-processing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32308.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">467</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25089</span> Harmonic Data Preparation for Clustering and Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Asheibi">Ali Asheibi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The rapid increase in the size of databases required to store power quality monitoring data has demanded new techniques for analysing and understanding the data. One suggested technique to assist in analysis is data mining. Preparing raw data to be ready for data mining exploration take up most of the effort and time spent in the whole data mining process. Clustering is an important technique in data mining and machine learning in which underlying and meaningful groups of data are discovered. Large amounts of harmonic data have been collected from an actual harmonic monitoring system in a distribution system in Australia for three years. This amount of acquired data makes it difficult to identify operational events that significantly impact the harmonics generated on the system. In this paper, harmonic data preparation processes to better understanding of the data have been presented. Underlying classes in this data has then been identified using clustering technique based on the Minimum Message Length (MML) method. The underlying operational information contained within the clusters can be rapidly visualised by the engineers. The C5.0 algorithm was used for classification and interpretation of the generated clusters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=harmonic%20data" title=" harmonic data"> harmonic data</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/80801/harmonic-data-preparation-for-clustering-and-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80801.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">247</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25088</span> Linguistic Summarization of Structured Patent Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=E.%20Y.%20Igde">E. Y. Igde</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Aydogan"> S. Aydogan</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20E.%20Boran"> F. E. Boran</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Akay"> D. Akay </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Patent data have an increasingly important role in economic growth, innovation, technical advantages and business strategies and even in countries competitions. Analyzing of patent data is crucial since patents cover large part of all technological information of the world. In this paper, we have used the linguistic summarization technique to prove the validity of the hypotheses related to patent data stated in the literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20sets" title=" fuzzy sets"> fuzzy sets</a>, <a href="https://publications.waset.org/abstracts/search?q=linguistic%20summarization" title=" linguistic summarization"> linguistic summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=patent%20data" title=" patent data"> patent data</a> </p> <a href="https://publications.waset.org/abstracts/74491/linguistic-summarization-of-structured-patent-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74491.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">272</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25087</span> Proposal of Data Collection from Probes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Kebisek">M. Kebisek</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20Spendla"> L. Spendla</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Kopcek"> M. Kopcek</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Skulavik"> T. Skulavik</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In our paper we describe the security capabilities of data collection. Data are collected with probes located in the near and distant surroundings of the company. Considering the numerous obstacles e.g. forests, hills, urban areas, the data collection is realized in several ways. The collection of data uses connection via wireless communication, LAN network, GSM network and in certain areas data are collected by using vehicles. In order to ensure the connection to the server most of the probes have ability to communicate in several ways. Collected data are archived and subsequently used in supervisory applications. To ensure the collection of the required data, it is necessary to propose algorithms that will allow the probes to select suitable communication channel. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=communication" title="communication">communication</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20network" title=" computer network"> computer network</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20collection" title=" data collection"> data collection</a>, <a href="https://publications.waset.org/abstracts/search?q=probe" title=" probe"> probe</a> </p> <a href="https://publications.waset.org/abstracts/11742/proposal-of-data-collection-from-probes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11742.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">360</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25086</span> A Review on Big Data Movement with Different Approaches</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nay%20Myo%20Sandar">Nay Myo Sandar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the growth of technologies and applications, a large amount of data has been producing at increasing rate from various resources such as social media networks, sensor devices, and other information serving devices. This large collection of massive, complex and exponential growth of dataset is called big data. The traditional database systems cannot store and process such data due to large and complexity. Consequently, cloud computing is a potential solution for data storage and processing since it can provide a pool of resources for servers and storage. However, moving large amount of data to and from is a challenging issue since it can encounter a high latency due to large data size. With respect to big data movement problem, this paper reviews the literature of previous works, discusses about research issues, finds out approaches for dealing with big data movement problem. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Big%20Data" title="Big Data">Big Data</a>, <a href="https://publications.waset.org/abstracts/search?q=Cloud%20Computing" title=" Cloud Computing"> Cloud Computing</a>, <a href="https://publications.waset.org/abstracts/search?q=Big%20Data%20Movement" title=" Big Data Movement"> Big Data Movement</a>, <a href="https://publications.waset.org/abstracts/search?q=Network%20Techniques" title=" Network Techniques"> Network Techniques</a> </p> <a href="https://publications.waset.org/abstracts/167936/a-review-on-big-data-movement-with-different-approaches" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167936.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">86</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25085</span> Optimized Approach for Secure Data Sharing in Distributed Database</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Mateen">Ahmed Mateen</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhu%20Qingsheng"> Zhu Qingsheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Bilal"> Ahmad Bilal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the current age of technology, information is the most precious asset of a company. Today, companies have a large amount of data. As the data become larger, access to data for some particular information is becoming slower day by day. Faster data processing to shape it in the form of information is the biggest issue. The major problems in distributed databases are the efficiency of data distribution and response time of data distribution. The security of data distribution is also a big issue. For these problems, we proposed a strategy that can maximize the efficiency of data distribution and also increase its response time. This technique gives better results for secure data distribution from multiple heterogeneous sources. The newly proposed technique facilitates the companies for secure data sharing efficiently and quickly. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ER-schema" title="ER-schema">ER-schema</a>, <a href="https://publications.waset.org/abstracts/search?q=electronic%20record" title=" electronic record"> electronic record</a>, <a href="https://publications.waset.org/abstracts/search?q=P2P%20framework" title=" P2P framework"> P2P framework</a>, <a href="https://publications.waset.org/abstracts/search?q=API" title=" API"> API</a>, <a href="https://publications.waset.org/abstracts/search?q=query%20formulation" title=" query formulation"> query formulation</a> </p> <a href="https://publications.waset.org/abstracts/87386/optimized-approach-for-secure-data-sharing-in-distributed-database" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/87386.pdf" target="_blank" class="btn btn-primary 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