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A Survey of Data Partitioning and Sampling Methods to Support Big Data Analysis

Big Data Mining and Analytics 2020年第2期3卷 85-101页

作者： Mohammad Sultan Mahmud Joshua zhexue Huang Salman Salloum tamer z.emara Kuanishbay Sadatdiynov National Engineering Laboratory for Big Data System Computing Technology Shenzhen UniversityUniversityShenzhen 518060China Big Data Institute College of Computer Science and Software EngineeringShenzhen UniversityShenzhen 518060China.

Computer clusters with the shared-nothing architecture are the major computing platforms for big data processing and *** cluster computing,data partitioning and sampling are two fundamental strategies to speed up the computation of big data and increase *** this paper,we present a comprehensive survey of the methods and techniques of data partitioning and sampling with respect to big data processing and *** start with an overview of the mainstream big data frameworks on Hadoop *** basic methods of data partitioning are then discussed including three classical horizontal partitioning schemes:range,hash,and random *** partitioning on Hadoop clusters is also discussed with a summary of new strategies for big data partitioning,including the new Random Sample Partition(RSP)distributed *** classical methods of data sampling are then investigated,including simple random sampling,stratified sampling,and reservoir *** common methods of big data sampling on computing clusters are also discussed:record-level sampling and blocklevel ***-level sampling is not as efficient as block-level sampling on big distributed *** the other hand,block-level sampling on data blocks generated with the classical data partitioning methods does not necessarily produce good representative samples for approximate computing of big *** this survey,we also summarize the prevailing strategies and related work on sampling-based approximation on Hadoop *** believe that data partitioning and sampling should be considered together to build approximate cluster computing frameworks that are reliable in both the computational and statistical respects.

关键词： big data analysis data partitioning data sampling distributed and parallel computing approximate computing

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