Maximizing Throughput for Queries over Streaming Sensor Data

Sensors are becoming ubiquitous, and increasingly integrated with our lives. Sensors usually send sampled data periodically using wireless connections to server machines. The servers perform various operations (e.g. filter, aggregate, join etc) on this data in real-time according to predefined queries or rules. In this paper, we address the problem of finding an optimal join tree that maximizes throughput for sliding window based multi-join queries over continuous sensor data streams. We develop a dynamic programming algorithm OptDP, that produces an optimal tree but runs in an exponential time in the number of input streams. We then present a polynomial time greedy algorithm XGreedyJoin. Our experiments in ARES show that for almost all instances, trees from XGreedyJoin perform close to the optimal trees from OptDP, and significantly better than existing XJoin based heuristic algorithms