A Comprehensive Survey of MapReduce Models for Processing Big Data
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| Argitaratua izan da: | Big Data and Cognitive Computing vol. 9, no. 4 (2025), p. 77 |
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| Egile nagusia: | |
| Beste egile batzuk: | , , |
| Argitaratua: |
MDPI AG
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| Sarrera elektronikoa: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3194489404 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2504-2289 | ||
| 024 | 7 | |a 10.3390/bdcc9040077 |2 doi | |
| 035 | |a 3194489404 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Abdalla Hemn Barzan |u Department of Computer Science, Wenzhou-Kean University, Wenzhou 325015, China; yuezhao@kean.edu | |
| 245 | 1 | |a A Comprehensive Survey of MapReduce Models for Processing Big Data | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Literature Review | ||
| 520 | 3 | |a With the rapid increase in the amount of big data, traditional software tools are facing complexity in tackling big data, which is a huge concern in the research industry. In addition, the management and processing of big data have become more difficult, thus increasing security threats. Various fields encountered issues in fully making use of these large-scale data with supported decision-making. Data mining methods have been tremendously improved to identify patterns for sorting a larger set of data. MapReduce models provide greater advantages for in-depth data evaluation and can be compatible with various applications. This survey analyses the various map-reducing models utilized for big data processing, the techniques harnessed in the reviewed literature, and the challenges. Furthermore, this survey reviews the major advancements of diverse types of map-reduce models, namely Hadoop, Hive, Pig, MongoDB, Spark, and Cassandra. Besides the reliable map-reducing approaches, this survey also examined various metrics utilized for computing the performance of big data processing among the applications. More specifically, this review summarizes the background of MapReduce and its terminologies, types, different techniques, and applications to advance the MapReduce framework for big data processing. This study provides good insights for conducting more experiments in the field of processing and managing big data. | |
| 653 | |a Big Data | ||
| 653 | |a Data mining | ||
| 653 | |a Data processing | ||
| 653 | |a Datasets | ||
| 653 | |a Classification | ||
| 653 | |a Literature reviews | ||
| 653 | |a Batch processing | ||
| 653 | |a Algorithms | ||
| 653 | |a Structured Query Language-SQL | ||
| 653 | |a Software | ||
| 653 | |a Business metrics | ||
| 700 | 1 | |a Kumar, Yulia |u Department of Computer Science and Technology, Kean University, Union, NJ 07083, USA; ykumar@kean.edu | |
| 700 | 1 | |a Zhao, Yue |u Department of Computer Science, Wenzhou-Kean University, Wenzhou 325015, China; yuezhao@kean.edu | |
| 700 | 1 | |a Tosi Davide |u Department of Theoretical and Applied Sciences, University of Insubria, 21100 Varese, Italy; davide.tosi@uninsubria.it | |
| 773 | 0 | |t Big Data and Cognitive Computing |g vol. 9, no. 4 (2025), p. 77 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3194489404/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3194489404/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3194489404/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |