On the Need for Explicit Confidence Assessments of Flexible Query Answers
Flexible query answering systems aim to exploit data collections in a richer way than traditional systems can do. In approaches where flexible criteria are used to reflect user preferences, expressing query satisfaction becomes a matter of degree. Nowadays, it becomes more and more common that data originating from different sources and different data providers are involved in the processing of a single query. Also, data sets can be very large such that not all data within a database or data store can be trusted to the same extent and consequently the results in a query answer can neither be trusted to the same extent. For this reason, data quality assessment becomes an important aspect of query processing. In this paper we discuss the need for explicit data quality assessments of query results. Indeed, To correctly inform users, it is in our opinion essential to communicate not only the satisfaction degrees in a query answer, but also the confidence about these satisfaction degrees as can be derived from data quality assessment. As illustration, we propose a hierarchical approach for query processing and data quality assessment, supporting the computation of as well a satisfaction degree, as its associated confidence degree for each element of the query result. Providing confidence information adds an extra dimension to query processing and leads to more soundly query answers.
KeywordsFuzzy criterion evaluation Big data Data quality handling
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