Approximate Query Answering on Sensor Network Data Streams

Sensor networks represent a non traditional source of information, as readings generated by sensors flow continuously, leading to an infinite stream of data. Traditional DBMSs, which are based on an exact and detailed representation of information, are not suitable in this context, as all the information carried by a data stream cannot be stored within a bounded storage space. Thus, compressing data (by possibly loosing less relevant information) and storing their compressed representation, rather than the original one, becomes mandatory. This approach aims to store as much information carried by the stream as possible, but makes it unfeasible to provide exact answers to queries on the stream content. However, exact answers to queries are often not necessary, as approximate ones usually suffice to get useful reports on the world monitored by the sensors. In this paper we propose a technique for providing fast approximate answers to aggregate queries on sensor data streams. Our proposal is based on a hierarchical summarization of the data stream embedded into a flexible indexing structure, which permits us to both access and update compressed data efficiently. The compressed representation of data is updated continuously, as new sensor readings arrive. When the available storage space is not enough to store new data, some space is released by compressing the “oldest” stored data progressively, so that recent information (which is usually the most relevant to retrieve) is represented with more detail than old one.