Work

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

Year: 2015

Type: article

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.

Source: Mobile Information Systems

DOI: 10.1155/2015/818307

Authors: Cang-hong Jin, Ze-min Liu, Ming-hui Wu, Jing Ying

Institution: Zhejiang University

Language: English

Topic: Advanced Database Systems and Queries

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,550

Funders: National Natural Science Foundation of China, Natural Science Foundation of Zhejiang Province