Cloud based predictive analytics: Text classification, recommender systems and decision support

Klavdiya Hammond, Aparna S. Varde

Research output: Contribution to conferencePaper

8 Scopus citations

Abstract

This paper presents a detailed study of technologies based on Hadoop and MapReduce available over the cloud for large-scale data mining and predictive analytics. Although some studies may have shown that cloud technologies relying on the MapReduce framework do not perform as well as parallel database management systems, e.g., with ad hoc queries and interactive applications, MapReduce has still been widely used by many organizations for big data storage and analytics. A number of MapReduce based tools are broadly available over the cloud. In this work we explore the Apache Hive data warehousing solution and particularly its Mahout data mining libraries for predictive analytics. We present results in the context of text classification, recommender systems and decision support. We develop prototype tools in these areas and discuss our outcomes from the study useful to researchers and other professionals in cloud computing and application domains. To the best of our knowledge, ours is among the first few in-depth studies on Mahout with application prototypes available for use.

Original languageEnglish
Pages607-612
Number of pages6
DOIs
StatePublished - 1 Jan 2013
Event2013 13th IEEE International Conference on Data Mining Workshops, ICDMW 2013 - Dallas, TX, United States
Duration: 7 Dec 201310 Dec 2013

Other

Other2013 13th IEEE International Conference on Data Mining Workshops, ICDMW 2013
CountryUnited States
CityDallas, TX
Period7/12/1310/12/13

Keywords

  • Cloud computing
  • Data mining
  • Mahout
  • Predictive analytics

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    Hammond, K., & Varde, A. S. (2013). Cloud based predictive analytics: Text classification, recommender systems and decision support. 607-612. Paper presented at 2013 13th IEEE International Conference on Data Mining Workshops, ICDMW 2013, Dallas, TX, United States. https://doi.org/10.1109/ICDMW.2013.95