Repository logo

Infoscience

  • English
  • French
Log In
Logo EPFL, École polytechnique fédérale de Lausanne

Infoscience

  • English
  • French
Log In
  1. Home
  2. Academic and Research Output
  3. Conferences, Workshops, Symposiums, and Seminars
  4. Online indexing and distributed querying model-view sensor data in the cloud
 
conference paper

Online indexing and distributed querying model-view sensor data in the cloud

Guo, Tian  
•
G. Papaioannou, Thanasis
•
Zhuang, Hao  
Show more
2014
Database Systems For Advanced Applications, Dasfaa 2014, Pt I
The 19th International Conference on Database Systems for Advanced Applications

As various kinds of sensors penetrate our daily life (e.g., sensor networks for environmental monitoring, GPS for localization and navigation), the efficient management of massive amount of sensor data becomes increasingly important at present. Many sensor data management systems are implemented based on key-value stores in the cloud; the traditional solutions based on relational database lack scalability to accommodate the large-scale sensor data efficiently. Meanwhile, model-view sensor data management, which stores the sensor data in the form of modelled segments, largely reduces the amount of raw data. However, currently there is no index and query optimizations on these modelled segments in the cloud, which results in full table scan for query processing in the worst case. In this paper, we propose an innovative model index for sensor data segments in key-value stores (KVM-index). KVM-index consists of two interval indices on the time and sensor value dimensions respectively, each of which has an in-memory search tree and a secondary list materialized in the key-value store. This in-memory and key-value composite structure enables to update new incoming sensor data segments with constant network I/O. Second, for time (or value)-range and point queries a MapReduce-based approach is designed to process the discrete predicate-related ranges of the table of KVM-index, thereby eliminating computation and communication overheads incurred by accessing irrelevant parts of the index table in conventional MapReduce programs. Finally, we propose a cost based adaptive strategy for the KVM-index-MapReduce framework to process composite queries on both time and value dimensions. As proved by extensive experiments in a private cloud, our approach outperforms in query response time both MapReduce-based processing of the raw sensor data and multiple alternative approaches of querying model-view sensor data.

  • Details
  • Metrics
Type
conference paper
DOI
10.1007/978-3-319-05810-8_3
Web of Science ID

WOS:000342909200003

Author(s)
Guo, Tian  
G. Papaioannou, Thanasis
Zhuang, Hao  
Aberer, Karl  
Date Issued

2014

Publisher

Springer-Verlag Berlin

Publisher place

Berlin

Published in
Database Systems For Advanced Applications, Dasfaa 2014, Pt I
ISBN of the book

978-3-319-05810-8

978-3-319-05809-2

Total of pages

19

Series title/Series vol.

Lecture Notes in Computer Science; 8421

Start page

28

End page

46

Subjects

Sensor data management

•

Index

•

Key-value stores

•

MapReduce

•

Query optimization

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LSIR  
Event nameEvent placeEvent date
The 19th International Conference on Database Systems for Advanced Applications

Bali, Indonesia

21-24 April 2014

Available on Infoscience
November 14, 2013
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/97014
Logo EPFL, École polytechnique fédérale de Lausanne
  • Contact
  • infoscience@epfl.ch

  • Follow us on Facebook
  • Follow us on Instagram
  • Follow us on LinkedIn
  • Follow us on X
  • Follow us on Youtube
AccessibilityLegal noticePrivacy policyCookie settingsEnd User AgreementGet helpFeedback

Infoscience is a service managed and provided by the Library and IT Services of EPFL. © EPFL, tous droits réservés