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13 - Electrical and Electronic Engineering, Metallurgy and Materials

University of Surrey

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Article title

Information Abstraction for Heterogeneous Real World Internet Data

Type

D - Journal article

DOI

10.1109/JSEN.2013.2271562

Title of journal

IEEE Sensors Journal

Article number

Volume number

Issue number

First page of article

3793

ISSN of journal

1558-1748

Year of publication

2013

URL

Number of additional authors

Additional information

This paper describes using time-series analysis, probabilistic machine learning and logical reasoning techniques for processing and interpreting large-scale sensory data. The work addresses one of the key challenges of dealing with world big data sources that provide real world observation and measurement data. A pattern construction method with sliding windows for real world time-series data is developed that creates string patterns from raw sensory data. Results show significant decrease in size of data that is required to be communicated and great potential for large scale and real time event processing and data analysis in dealing with real world big data.

Interdisciplinary

Cross-referral requested

Research group

None

Proposed double-weighted

Double-weighted statement

Reserve for a double-weighted output

Non-English

English abstract