tailieunhanh - Query Recommendations for Interactive Database Exploration

Early relational systems used indices as table replicas that allowed vertical partitioning, allowed associative search, and allowed con- venient data ordering. Database optimizers and executors use semi-join on these structures to run common queries on covering indices. These query strategies give huge speedups. These early ideas evolved to materialized views (often maintained by triggers) that went far beyond simple covering indices and provided fast access to star and snowflake schema. In. | Query Recommendations for Interactive Database Exploration Gloria Chatzopoulou 1 Magdalini Eirinaki2 and Neoklis Polyzotis3 1 Computer Science Dept. University of California Riverside USA chatzopd@ 2 Computer Engineering Dept. San Jose State University USA 3 Computer Science Dept. University of California Santa Cruz USA alkis@ Abstract. Relational database systems are becoming increasingly popular in the scientific community to support the interactive exploration of large volumes of data. In this scenario users employ a query interface typically a web-based client to issue a series of SQL queries that aim to analyze the data and mine it for interesting information. First-time users however may not have the necessary knowledge to know where to start their exploration. Other times users may simply overlook queries that retrieve imp ortant information. To assist users in this context we draw inspiration from Web recommender systems and propose the use of personalized query recommendations. The idea is to track the querying behavior of each user identify which parts of the database may be of interest for the corresponding data analysis task and recommend queries that retrieve relevant data. We discuss the main challenges in this novel application of recommendation systems and outline a possible solution based on collaborative filtering. Preliminary experimental results on real user traces demonstrate that our framework can generate effective query recommendations. 1 Introduction Relational database systems are becoming increasingly popular in the scientific community to manage large volumes of experimental data. Examples include the Genome browser1 that provides access to a genomic database and Sky Server2 that stores large volumes of astronomical measurements. The main advantage of a relational database system is that it supports the efficient execution of complex queries thus enabling users to interactively explore the

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