|
Title:
|
MASS DATA PROCESSING USING IN-MEMORY AND LARGE SCALE DISTRIBUTED STORAGE FOR ENERGY MANAGEMENT SCENARIOS |
|
Author(s):
|
Cyrille Waguet, Olga Mordvinova, Severin Schols |
|
ISBN:
|
978-989-8533-14-2 |
|
Editors:
|
Hans Weghorn and Pedro Isaías |
|
Year:
|
2012 |
|
Edition:
|
Single |
|
Keywords:
|
Real-time Processing, In-memory Database, Hadoop, HBase, Energy Management Applications, Smart Meters |
|
Type:
|
Full Paper |
|
First Page:
|
27 |
|
Last Page:
|
33 |
|
Language:
|
English |
|
Cover:
|
|
|
Full Contents:
|
click to dowload 
|
|
Paper Abstract:
|
Todays data mining applications have to serve more and more users looking for timely, personalized information, which requires instant processing of huge sets of data. At the same time, those applications need to deal with the capture, archiving and provisioning of extreme data volumes. In the area of energy management, smart meters generate a huge quantity of data used to serve a rapidly growing number of energy consumers requesting complex analytical information. In this paper, we propose an approach how to connect big data processing (Hadoop) and in-memory technologies for storing and processing in real-time the huge amount of data generated by smart meters. By providing this connecting infrastructure, applications are able to scale simultaneously with the number of users who require real-time access to information and the amount of data from which this information is derived. |
|
|
|
|
|
|
