We propose a new pattern matching algorithm for composite context-aware services. The new algorithm, RETE-ADH, extends RETE to enhance systems that are based on the composite context-aware service architecture. RETE-ADH increases the speed of matching by searching only a subset of the rules that can be matched. In addition, RETE-ADH is scalable and suitable for parallelization. We describe the design of the proposed algorithm and present experimental results from a simulated smart office environment to compare the proposed algorithm with other pattern matching algorithms, showing that the proposed algorithm outperforms original RETE by 85%.
We propose novel algorithms for the timing correlation of streaming sensor data. The sensor data are assumed to have interval timestamps so that they can represent temporal uncertainties. The proposed algorithms can support efficient timing correlation for various timing predicates such as deadline, delay, and within. In addition to the classical techniques, lazy evaluation and result cache are utilized to improve the algorithm performance. The proposed algorithms are implemented and compared under various workloads.
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