A Control Chart Approach for Representing and Mining Data Streams with Shape Based Similarity

Conference · Wed Jan 01 00:00:00 EST 2014

OSTI ID:1148872

Omitaomu, Olufemi A ^[1]

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1. ORNL

The mining of data streams for online condition monitoring is a challenging task in several domains including (electric) power grid system, intelligent manufacturing, and consumer science. Considering a power grid application in which thousands of sensors, called the phasor measurement units, are deployed on the power grid network to continuously collect streams of digital data for real-time situational awareness and system management. Depending on design, each sensor could stream between ten and sixty data samples per second. The myriad of sensory data captured could convey deeper insights about sequence of events in real-time and before major damages are done. However, the timely processing and analysis of these high-velocity and high-volume data streams is a challenge. Hence, a new data processing and transformation approach, based on the concept of control charts, for representing sequence of data streams from sensors is proposed. In addition, an application of the proposed approach for enhancing data mining tasks such as clustering using real-world power grid data streams is presented. The results indicate that the proposed approach is very efficient for data streams storage and manipulation.

• Research Organization: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: DE-AC05-00OR22725
• OSTI ID: 1148872
• Resource Relation: Conference: 2014 Industrial and Systems Engineering Research Conference, Montreal, Canada, 20140531, 20140531
• Country of Publication: United States
• Language: English

Related Subjects

systems health management
big data
data stream mining
control chart concept