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Reduce, You Say: What NoSQL Can Do for Data Aggregation and BI in Large Repositories Laurent Bonnet 608f91ad94bb86f045ec927d30d54398 lirmm.fr 1488470 12757-1488470 https://orcid.org/0009-0003-0109-1522 Anne Laurent 30cc2fc4136879eb4d7049fc772e10b8 lirmm.fr anne-laurent 21743 17629-21743 https://orcid.org/0000-0003-3708-6429 https://www.idref.fr/075173735 Bénédicte Laurent 165872-0 Michel Sala 0ecf7b0422c4de64526f7bd65c44db45 lirmm.fr 938397 699104-938397 Nicolas Sicard 691487-0 Anne Laurent 30cc2fc4136879eb4d7049fc772e10b8 lirmm.fr 2019-11-01 18:30:52 2026-05-01 10:20:51 2019-11-13 13:50:04 2011-08-29 2019-11-01 contributor Anne Laurent 30cc2fc4136879eb4d7049fc772e10b8 lirmm.fr CCSD lirmm-00803917 https://hal-lirmm.ccsd.cnrs.fr/lirmm-00803917 bonnet:lirmm-00803917 <i>DEXA 2011 - 22nd International Conference on Database and Expert Systems Applications</i>, Aug 2011, Toulouse, France. pp.483-488, <a target="_blank" href="https://dx.doi.org/10.1109/DEXA.2011.71">⟨10.1109/DEXA.2011.71⟩</a> DEXA 2011 - 22nd International Conference on Database and Expert Systems Applications, Aug 2011, Toulouse, France. pp.483-488, ⟨10.1109/DEXA.2011.71⟩ HAL Authorization CNRS - Centre national de la recherche scientifique Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier MEDIA ET INTERACTIONS Mathématiques, Informatique, Physique et Systèmes Université de Montpellier TEST Université de Rennes Université de Montpellier (2015-2021) International No No Yes Yes Reduce, You Say: What NoSQL Can Do for Data Aggregation and BI in Large Repositories Laurent Bonnet 608f91ad94bb86f045ec927d30d54398 lirmm.fr 1488470 12757-1488470 https://orcid.org/0009-0003-0109-1522 Anne Laurent 30cc2fc4136879eb4d7049fc772e10b8 lirmm.fr anne-laurent 21743 17629-21743 https://orcid.org/0000-0003-3708-6429 https://www.idref.fr/075173735 Bénédicte Laurent 165872-0 Michel Sala 0ecf7b0422c4de64526f7bd65c44db45 lirmm.fr 938397 699104-938397 Nicolas Sicard 691487-0 DEXA 2011 - 22nd International Conference on Database and Expert Systems Applications 2011-08-29 2011-09-02 Toulouse France 483-488 2011 10.1109/DEXA.2011.71 English Massive Data Sets Data Aggregation SQL NoSQL MapReduce Read Intensive Write Intensive MongoDB Computer Science [cs]/Databases [cs.DB] Conference papers Conference papers Conference papers

Data aggregation is one of the key features used in databases, especially for Business Intelligence (e.g., ETL, OLAP) and analytics/data mining. When considering SQL databases, aggregation is used to prepare and visualize data for deeper analyses. However, these operations are often impossible on very large volumes of data regarding memory-and-timeconsumption. In this paper, we show how NoSQL databases such as MongoDB and its key-value stores, thanks to the native MapReduce algorithm, can provide an efficient framework to aggregate large volumes of data. We provide basic material about the MapReduce algorithm, the different NoSQL databases (read intensive vs. write intensive). We investigate how to efficiently modelize the data framework for BI and analytics. For this purpose, we focus on read intensive NoSQL databases using MongoDB and we show how NoSQL and MapReduce can help handling large volumes of data.

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