Distributed Adaptive Model Rules for mining big data streams

• Type: Article in International Conference Proceedings Book
• Date: 2015

Authors

Vu, AT (Author) Other; De Francisci Morales, G (Author) Other; João Gama (Author) FEP; Bifet, A (Author) Other

Conference proceedings International

Title: Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014

Pages: 345-353

2nd IEEE International Conference on Big Data, IEEE Big Data 2014

27 October 2014 through 30 October 2014

Indexing

ISI Web of Knowledge - 12 Citations

Scopus - 20 Citations

Other information

Authenticus ID: P-00A-7ZB

DOI: 10.1109/bigdata.2014.7004251

Abstract (EN): Decision rules are among the most expressive data mining models. We propose the first distributed streaming algorithm to learn decision rules for regression tasks. The algorithm is available in samoa (Scalable Advanced Massive Online Analysis), an open-source platform for mining big data streams. It uses a hybrid of vertical and horizontal parallelism to distribute Adaptive Model Rules (AMRules) on a cluster. The decision rules built by AMRules are comprehensible models, where the antecedent of a rule is a conjunction of conditions on the attribute values, and the consequent is a linear combination of the attributes. Our evaluation shows that this implementation is scalable in relation to CPU and memory consumption. On a small commodity Samza cluster of 9 nodes, it can handle a rate of more than 30000 instances per second, and achieve a speedup of up to 4.7x over the sequential version. © 2014 IEEE.

Language: English

Type (Professor's evaluation): Scientific