An in-network reduction algorithm for real-time wireless sensor network applications
Proceedings of the 4th ACM workshop on Wireless multimedia networking and performance modeling
Approximating sliding windows by cyclic tree-like histograms for efficient range queries
Data & Knowledge Engineering
When Is the Right Time to Refresh Knowledge Discovered from Data?
Operations Research
Integer partitioning based encryption for privacy preservation in data mining
Proceedings of the First International Conference on Security of Internet of Things
Information Sciences: an International Journal
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We propose a novel predictive quantization (PQ) based approach for online summarization of multiple time varying data streams. A synopsis over a sliding window of most recent entries is computed in one pass and dynamically updated in constant time. The correlation between consecutive data elements is effectively taken into account without the need for preprocessing. We extend PQ to multiple streams and propose structures for real-time summarization and querying of a massive number of streams. Queries on any subsequence of a sliding window over multiple streams are processed in real-time. We examine each component of the proposed approach, prediction and quantization, separately and investigate the space-accuracy trade off for synopsis generation. Complementing the theoretical optimality of PQ based approaches, we show that the proposed technique, even for very short prediction windows, significantly outperforms the current techniques for a wide variety of query types on both synthetic and real data sets.