SURVEY ON CUSTOMIZED POLICIES FOR HANDLING PARTIAL INFORMATION IN RELATIONAL DATABASES

Most real-world databases have at least some missing data. Today, users of such databases are “on their own” in terms of how they manage this incompleteness. In this paper, we propose the general concept of partial information policy (PIP) operator to handle incompleteness in relational databases. PIP operators build upon preference frameworks for incomplete information, but accommodate dif erent types of incomplete data (e.g., a value exists but is not known; a value does not exist; a value may or may not exist). Dif erent users in the real world have dif erent ways in which they want to handle incompleteness—PIP operators allow them to specify a policy that matches their attitude to risk and their knowledge of the application and how the data was collected. We propose index structures for efficiently evaluating PIP operators and experimentally assess their effectiveness on a real-world airline data set. We also study how relational algebra operators and PIP operators