TuG Synopses for Approximate Query Answering
This article introduces the Tuple Graph (TuG) synopses, a new class of data summaries that enable accurate approximate answers for complex relational queries. The proposed summarization framework adopts a "semi-structured" view of the relational database, modeling a relational data set as a graph of tuples and join queries as graph traversals, respectively. The key idea is to approximate the structure of the induced data graph in a concise synopsis, and to approximate the answer to a query by performing the corresponding traversal over the summarized graph. We detail the (TuG) synopsis model that is based on this novel approach, and we describe an efficient and scalable construction algorithm for building accurate (TuG) within a specific storage budget. We validate the performance of (TuG) with an extensive experimental study on real-life and synthetic datasets. Our results verify the effectiveness of (TuG) in generating accurate approximate answers for complex join queries, and demonstrate their benefits over existing summarization techniques.
performance algorithms theory data synopses approximate query processing selectivity estimation