- Title
- A technique for parallel share-frequent sensor pattern mining from wireless sensor networks
- Creator
- Rashid, Md. Mamunur; Gondal, Iqbal; Kamruzzaman, Joarder
- Date
- 2014
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/156764
- Identifier
- vital:11470
- Identifier
-
https://doi.org/10.1016/j.procs.2014.05.012
- Identifier
- ISBN:1877-0509
- Abstract
- WSNs generate huge amount of data in the form of streams and mining useful knowledge from these streams is a challenging task. Existing works generate sensor association rules using occurrence frequency of patterns with binary frequency (either absent or present) or support of a pattern as a criterion. However, considering the binary frequency or support of a pattern may not be a sufficient indicator for finding meaningful patterns from WSN data because it only reflects the number of epochs in the sensor data which contain that pattern. The share measure of sensorsets could discover useful knowledge about numerical values associated with sensor in a sensor database. Therefore, in this paper, we propose a new type of behavioral pattern called share-frequent sensor patterns by considering the non-binary frequency values of sensors in epochs. To discover share-frequent sensor patterns from sensor dataset, we propose a novel parallel technique. In this technique, we develop a novel tree structure, called parallel share-frequent sensor pattern tree (PShrFSP-tree) that is constructed at each local node independently, by capturing the database contents to generate the candidate patterns using a pattern growth technique with a single scan and then merges the locally generated candidate patterns at the final stage to generate global share-frequent sensor patterns. Comprehensive experimental results show that our proposed model is very efficient for mining share-frequent patterns from WSN data in terms of time and scalability.
- Publisher
- Elsevier Ltd
- Relation
- 14th Annual International Conference on Computational Science, ICCS 2014; Cairns, Australia; 10th-12th June 2014; published in Procedia Computer Science p. 124-133
- Rights
- Copyright © The Authors. Published by Elsevier B.V. Open access under CC BY-NC-ND License http://creativecommons.org/licenses/by-nc-nd/3.0/.
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
- Subject
- 08 Information and Computing Sciences; 10 Technology; Data mining; Distributed system; Knowledge; Parallel processing; Share-frequent sensor patterns; Wireless sensor networks (WSNs)
- Full Text
- Reviewed
- Hits: 2953
- Visitors: 3018
- Downloads: 231
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | Published version | 1 MB | Adobe Acrobat PDF | View Details Download |