Exploiting compression and approximation paradigms for effective and efficient online analytical processing over sensor network readings in data grid environments

摘要

Aggregate queries are useful tools in the context of sensor network-based systems as they retrieve knowledge from huge amounts of summarized readings to be exploited for knowledge discovery purposes. Actually, data representation and query models are problematic issues for managing sensor network data, because streams produced by sensors are theoretically unbounded. In this paper, we present a Grid framework, called SensorGrid, on the basis of data compression and approximation paradigms, which allows us to provide approximate answers to aggregate queries on summarized sensor network data. These queries are the basis for achieving Online Analytical Processing (OLAP) over sensor network readings in Data Grid environments, with both effectiveness and efficiency. We also present our experience in the context of a real-life system focused on the management of environmental sensor network data. Another contribution of our research is represented by the extensive experimental evaluation and analysis of SensorGrid, which, in more details, focuses on two main classes of aggregate range queries over sensor readings, namely, (ⅰ) the window queries, which apply an SQL aggregation operator over a fixed window over the reading stream produced by the sensor network, and (ⅱ) the continuous queries, which instead consider a 'moving' window and produce as output a stream of answers. Both classes of queries are extremely useful to extract summarized knowledge to be exploited by OLAP-like analysis tools over sensor network data. The experimental results, conducted on both synthetic and real-life data sets, clearly confirm the benefits deriving from embedding data compression and approximation paradigms into Grid-based sensor network data-intensive management systems.