A Framework for Distributed Cleaning of Data Streams | Zendy

Saul Gill | Zendy; Brian Lee | Zendy

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A Framework for Distributed Cleaning of Data Streams

Author(s) -

Saul Gill,

Brian Lee

Publication year - 2015

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2015.05.156

Subject(s) - computer science , data stream mining , internet of things , streams , quality (philosophy) , data quality , real time computing , data mining , environmental data , the internet , variable (mathematics) , database , embedded system , world wide web , computer network , metric (unit) , philosophy , operations management , mathematical analysis , mathematics , epistemology , political science , law , economics

Vast and ever increasing quantities of data are produced by sensors in the Internet of Things (IoT). The quality of this data can be very variable due to problems with sensors, incorrect calibration etc. Data quality can be greatly enhanced by cleaning the data before it reaches its end user. This paper reports on the construction of a distributed cleaning system (DCS) to clean data streams in real-time for an environmental case-study. A combination of declarative and statistical model based cleaning methods are applied and initial results are reported

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