Parallel, distributed, and differential processing system for human activity sensing flows
Authors: 
Takamichi Toda, Sozo Inoue, Lin LiAuthors Info & Claims
Takamichi Toda, Sozo Inoue, Lin LiAuthors Info & ClaimsPages 689 - 700
Abstract
In this paper, we propose a parallel distributed processing system for data-analytic project including human activity sensing flows, which manages dependency among data and analytic programs, and re-execute updated programs and dependent programs for updated data/programs. In the system, a data analyzer can specify the dependency and parts for requiring distributed parallel processing using Hadoop Streaming, and they can be processed only for updated and the dependent part, with flexibly selecting parallel or sequential execution on the fly. The specification can also specify repeated execution of a single program with different data, while their dependencies are checked separately at execution. We describe the mathematical model, the system design, the usage, and the experimental result applying to the essential process in human activity sensing.
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Index Terms
- Parallel, distributed, and differential processing system for human activity sensing flows
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Published In
September 2013
1608 pages
ISBN:9781450322157
DOI:10.1145/2494091
- General Chairs:
- Friedemann Mattern,
- Silvia Santini,
- Program Chairs:
- John F. Canny,
- Marc Langheinrich,
- Jun Rekimoto
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Published: 08 September 2013
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UbiComp '13: The 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing
September 8 - 12, 2013
Zurich, Switzerland
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UbiComp '13 Adjunct Paper Acceptance Rate 254 of 399 submissions, 64%;
Overall Acceptance Rate 764 of 2,912 submissions, 26%
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