For noncollaborative distributed data sources, quality-driven query processing is difficult to achieve because the sources generally do not export data quality indicators. This chapter deals with the extension and adaptation of query processing for taking into account constraints on quality of distributed data. This chapter presents a novel framework for adaptive query processing on quality-extended query declarations. It proposes an expressive query language extension combining SQL and QML, the quality of service modeling language proposed by Frølund and Koistinen (1998) for defining, in a flexible way, dimensions, and metrics on data, source, and service quality. The originality of the approach is to include the negotiation of quality contracts between the distributed data sources competing for answering the query. The principle is to find dynamically the best trade-off between the local query cost and the result quality. The author is convinced that quality of data (QoD) and quality of service (QoS) can be advantageously conciliated for tackling the problems of quality-aware query processing in distributed environments and, more generally, open innovative research perspectives for quality-aware adaptive query processing.
ASJC Scopus subject areas
- Business, Management and Accounting(all)