NDLI: Smart Meter Data Analytics: Systems, Algorithms, and Benchmarking

Content Provider	ACM Digital Library
Author	Ilyas, Ihab F. Golab, Lukasz Liu, Xiufeng Jin, Shichao Golab, Wojciech
Copyright Year	2016
Description	Author Affiliation: Technical University of Denmark, Denmark(Jin, Shichao; Ilyas, Ihab F.; Golab, Wojciech; University of waterloo, Ontario, Canada (Golab, Lukasz; Liu, Xiufeng))
Abstract	Smart electricity meters have been replacing conventional meters worldwide, enabling automated collection of fine-grained (e.g., every 15 minutes or hourly) consumption data. A variety of smart meter analytics algorithms and applications have been proposed, mainly in the smart grid literature. However, the focus has been on what can be done with the data rather than how to do it efficiently. In this article, we examine smart meter analytics from a software performance perspective. First, we design a performance benchmark that includes common smart meter analytics tasks. These include offline feature extraction and model building as well as a framework for online anomaly detection that we propose. Second, since obtaining real smart meter data is difficult due to privacy issues, we present an algorithm for generating large realistic datasets from a small seed of real data. Third, we implement the proposed benchmark using five representative platforms: a traditional numeric computing platform (Matlab), a relational DBMS with a built-in machine learning toolkit (PostgreSQL/MADlib), a main-memory column store (“System C”), and two distributed data processing platforms (Hive and Spark/Spark Streaming). We compare the five platforms in terms of application development effort and performance on a multicore machine as well as a cluster of 16 commodity servers.
Starting Page	1
Ending Page	39
Page Count	39
File Format	PDF
ISSN	03625915
e-ISSN	15574644
DOI	10.1145/3004295
Volume Number	42
Issue Number	1
Journal	ACM Transactions on Database Systems (TODS)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-11-21
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Hadoop Smart meters Spark Data analytics Performance benchmarking
Content Type	Text
Resource Type	Article
Subject	Information Systems

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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