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Mobile Information Systems
Volume 2015, Article ID 818307, 18 pages
http://dx.doi.org/10.1155/2015/818307
Research Article

FastFlow: Efficient Scalable Model-Driven Framework for Processing Massive Mobile Stream Data

1College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
2Department of Computer Science and Engineering, Zhejiang University City College, Hangzhou 310015, China

Received 18 January 2015; Revised 15 April 2015; Accepted 28 April 2015

Academic Editor: Laurence T. Yang

Copyright © 2015 Cang-hong Jin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Massive stream data mining and computing require dealing with an infinite sequence of data items with low latency. As far as we know, current Stream Processing Engines (SPEs) cannot handle massive stream data efficiently due to their inability of horizontal computation modeling and lack of interactive query. In this paper, we detail the challenges of stream data processing and introduce FastFlow, a model-driven infrastructure. FastFlow differs from other existing SPEs in terms of its user-friendly interface, support of complex operators, heterogeneous outputs, extensible computing model, and real-time deployment. Further, FastFlow includes optimizers to reorganize the execution topology for batch query to reduce resource cost rather than executing each query independently.