A Scalable Smart Meter Data Generator Using Spark

Publikation: Bidrag til bog/antologi/rapportKonferenceartikel i proceeding

Abstract

Today, smart meters are being used worldwide. As a matter of fact smart meters produce large volumes of data. Thus, it is important for smart meter
data management and analytics systems to process petabytes of data. Benchmarking and testing of these systems require scalable data, however, it can be challenging to get large data sets due to privacy and/or data protection regulations. This paper presents a scalable smart meter data generator using Spark that can generate realistic data sets. The proposed data generator is based on a supervised machine learning method that can generate data of any size by using small data sets as seed. Moreover, the generator can preserve the characteristics of data with respect to consumption patterns and user groups. This paper evaluates the proposed data generator in a cluster based environment in order to validate its effectiveness and scalability.
OriginalsprogEngelsk
TitelOn the Move to Meaningful Internet Systems. OTM 2017 Conferences
RedaktørerHervé Panetto, Christophe Debruyne, Walid Gaaloul, Mike Papazoglou, Adrian Paschke, Claudio Agostino Ardagna, Robert Meersman
Antal sider16
ForlagSpringer
Publikationsdato2017
Sider21-36
ISBN (Trykt)978-3-319-69461-0
ISBN (Elektronisk)978-3-319-69462-7
DOI
StatusUdgivet - 2017
Begivenhed25th International Conference on COOPERATIVE INFORMATION SYSTEMS - Rhodes, Grækenland
Varighed: 25 okt. 201727 okt. 2017

Konference

Konference25th International Conference on COOPERATIVE INFORMATION SYSTEMS
LandGrækenland
ByRhodes
Periode25/10/1727/10/17
NavnLecture Notes in Computer Science
Nummer10.1007/978-3-319-69462-7_2
Vol/bind10573

Emneord

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Citationsformater

Iftikhar, N., Liu, X., Danalachi, S., Nordbjerg, F. E., & Vollesen, J. H. (2017). A Scalable Smart Meter Data Generator Using Spark. I H. Panetto, C. Debruyne, W. Gaaloul, M. Papazoglou, A. Paschke, C. A. Ardagna, & R. Meersman (red.), On the Move to Meaningful Internet Systems. OTM 2017 Conferences (s. 21-36). Springer. Lecture Notes in Computer Science, Nr. 10.1007/978-3-319-69462-7_2, Bind. 10573 https://doi.org/10.1007/978-3-319-69462-7_2