Top-k queries on uncertain data: on score distribution and typical answers

Uncertain data arises in a number of domains, including data integration and sensor networks. Top-k queries that rank results according to some user-defined score are an important tool for exploring large uncertain data sets. As several recent papers have observed, the semantics of top-k queries on uncertain data can be ambiguous due to tradeoffs between reporting high-scoring tuples and tuples with a high probability of being in the resulting data set. In this paper, we demonstrate the need to present the score distribution of top-k vectors to allow the user to choose between results along this score-probability dimensions. One option would be to display the complete distribution of all potential top-k tuple vectors, but this set is too large to compute. Instead, we propose to provide a number of typical vectors that effectively sample this distribution. We propose efficient algorithms to compute these vectors. We also extend the semantics and algorithms to the scenario of score ties...

• Type: Conference
• Year: 2009
• Where: SIGMOD
• Authors: Tingjian Ge, Stanley B. Zdonik, Samuel Madden
• Keywords: Database, Large Uncertain Data, SIGMOD 2009, Uncertain Data, Uncertain Data Arises