Titlebook: Big Data Analytics and Knowledge Discovery; 21st International C Carlos Ordonez,Il-Yeol Song,Ismail Khalil Conference proceedings 2019 Spri

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https://doi.org/10.1007/978-1-4302-4333-5iginal enumeration strategy of the patterns, which allows to exploit some degrees of anti-monotonicity on the measures of discriminance and statistical significance. Experimental results demonstrate that the performance of the SSDPS algorithm is better than others. In addition, the number of generat

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https://doi.org/10.1007/978-1-4842-9234-1exibility motivated the use of this standard in other domains and today RDF datasets are big sources of information. In this line, the research on scalable distributed and parallel RDF processing systems has gained momentum. Most of these systems apply partitioning algorithms that use the triple, th

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https://doi.org/10.1007/978-1-4842-9234-1ority of existing approaches deals with such data as time point events to find . relations that induces loss of information when dealing with events lasting in time, i.e intervals. Other interval-based approaches focus on qualitative patterns and are sensitive to temporal variability. We consider th

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https://doi.org/10.1007/978-1-4842-9234-1ing data and exposed the need for every organization to exploit it. This paper reviews the evolution of Data Stream Management Systems (DSMS) and the convergence into Online Analytical Processing (OLAP) DSMS. The discussion is focused on three current solutions: Scuba, Apache Druid, and Apache Pinot

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https://doi.org/10.1007/978-1-4842-9234-1 exploit these complex data for competitive advantages, the data lake recently emerged as a concept for more flexible and powerful data analytics. However, existing literature on data lakes is rather vague and incomplete, and the various realization approaches that have been proposed neither cover a

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https://doi.org/10.1007/978-1-4842-9234-1nd guiding the physical design of a data warehouse. In big data warehouses, the most expensive operation of an OLAP query is the star join, which requires many Spark stages. In this paper, we propose a new data placement strategy in the Apache Hadoop environment called “Smart Data Warehouse Placemen

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/185605.jpg