Similarity-Based Accuracy Measures for Approximate Query Results

We introduce a new approach to empirical evaluation of the accuracy of the select statement results produced by a relational approximate query engine. We emphasize the meaning of a similarity of approximate and exact outcomes of queries from the perspective of practical applicability of approximate query processing solutions. We propose how to design the similarity-based procedure that lets us compare approximate and exact versions of the results of complex queries. We not only offer a measure of the accuracy of query results, but also describe the results of research on users intuition regarding the properties of such a measure, as well as perception query results as similar. The study is supported by theoretical and empirical analyses of different similarity functions and the case study of the investigative analytics over data sets related to network intrusion detection.