Efficient Indexing of Heterogeneous Data Streams with Automatic Performance Tuning

We study the problem of indexing continuous data streams in which data are heterogeneous in structure. Such data streams arise naturally in many real-life scenarios such as sensor networks. Our index structure uses bitmap based techniques to efficiently sketch the structures to allow space-efficient lossless archiving of the data stream. It also allows very fast query processing on the archived data stream. Furthermore, our index structure adapts to structural evolutions of the stream to ensure good indexing and querying performance both in space and time. We developed a cost-based optimization framework so the indexing engine adjusts its configuration at run-time to adapt to changes in the data stream. By means of linear feedback controllers, structural clustering and steepest gradient ascent optimization, our indexing engine can achieve excellent performance without any human intervention.