Model-View Sensor Data Management in the Cloud

Infinite nature of sensor data poses a serious challenge for query processing even in a cloud infrastructure. Model-based sensor data approximation reduces the amount of data for query processing, but all modeled segments need to be scanned, in the worst case. In this paper, we propose an innovative index for modeled segments in key-value stores, namely KVI-index. KVI-index has an in-memory tree component and a secondary structure materialized in the key-value store that maps the tree nodes to the modeled data segments. Then, we introduce a KVI-index-Scan-MapReduce hybrid approach to perform efficient query processing. As proved by a series of experiments in a real private cloud infrastructure, our approach outperforms in query response time and index updating efficiency both Hadoop-based parallel processing of the raw sensor data and multiple alternative indexing approaches of model-view data.


Editor(s):
Hu, X.
Lin, Ty
Fan, W.
Lempel, R.
Nambiar, R.
Raghavan, V.
Wah, B.
Baezayates, R.
Fox, G.
Shahabi, C.
Smith, M.
Yang, Q.
Ghani, R.
Published in:
2013 IEEE International Conference On Big Data, 282 - 290
Presented at:
IEEE International Conference on Big Data (BigData), IEEE International Conference on Big Data (BigData), Santa Clara, CA, USA, October 6-9, 2013
Year:
2013
Publisher:
New York, IEEE
ISBN:
978-1-4799-1292-6
978-1-4799-1293-3
Keywords:
Laboratories:




 Record created 2014-06-02, last modified 2018-09-13

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