language: en

User-Defined Aggregates for Logical Data Languages

Haixun Wang and Carlo Zaniolo
Computer Science Department University of California at Los Angeles
Los Angeles, CA 90095
hxwang@cs.ucla.edu zaniolo@cs.ucla.edu

Abstract. A new wave of data-intensive and knowledge-based applications|such as data mining and decision support|require the introduction of complex application-specific aggregate functions. In this paper, we propose extensions for deductive database systems to support these new applications. We develop constructs, formal semantics, and implementation techniques for user-defined aggregates, and describe their realization in an extended LDL++ system recently built at UCLA. With these extensions, the system can support online aggregation, roll-ups for data cubing, temporal aggregates for time-series, iceberg queries, and other recently proposed operators used in decision support and data mining procedures. We then discuss the application of this technology to other DBMSs, and in particular to the SQL3 specifications that support the notion of user-defined aggregates. We show that SQL3 suffers from limitations that severely restrict its use in new applications; thus we propose simple extensions similar to those used for LDL++ to overcome such limitations.

1 Introduction