Semantics of Ranking Queries for Probabilistic Data and Expected Ranks

Abstract-- When dealing with massive quantities of data, topk queries are a powerful technique for returning only the k most relevant tuples for inspection, based on a scoring function. The problem of efficiently answering such ranking queries has been studied and analyzed extensively within traditional database settings. The importance of the top-k is perhaps even greater in probabilistic databases, where a relation can encode exponentially many possible worlds. There have been several recent attempts to propose definitions and algorithms for ranking queries over probabilistic data. However, these all lack many of the intuitive properties of a top-k over deterministic data. Specifically, we define a number of fundamental properties, including exact-k, containment, unique-rank, value-invariance, and stability, which are all satisfied by ranking queries on certain data. We argue that all these conditions should also be fulfilled by any reasonable definition for ranking uncertain data. U...

• Type: Conference
• Year: 2009
• Where: ICDE
• Authors: Graham Cormode, Feifei Li, Ke Yi
• Keywords: Database, ICDE 2009, Probabilistic Tail Bounds, Ranking Queries, Ranking Query, Uncertain Data, Uncertain Relation