Incremental database rule processing in PARADISER

Abstract

Rule-based systems have been used extensively by the AI community in implementing knowledge-based expert systems. A current trend in the database community is to use rules for the purpose of providing inferential capabilities for large database applications. However, in the database context, performance of rule-program processing has proved to be a major stumbling block particularly in data-intensive and real-time applications. Similar work in the AI community has demonstrated the same problems mainly due to the predominantly sequential semantics of the underlying rule languages. In a previous paper, we presented an incremental evaluation algorithm forDatalog ⌝* programs. We show the general applicability of the incremental evaluation algorithm to a broad class ofDatalog-style rule languages with varying operational semantics, denoted byDatalog** to emphasize this generality. We present the proof of correctness of the algorithm within the Datalog** frame work, and its extension for the PARULEL rule language, a principle component of the PARADISER (PARAllel and DIStributed Environment for Rules) rule processing environment for databases. We discuss the meta-rule formalism of PARULEL and argue that it provides a means for programmable operational semantics which separates control from the base logic of a rule program. This allows the realization of a wide range of operational semantics including those of Datalog and OPS5.