KayLiang Ong

TL;DR: A solution that combines generality with efficiency is presented, as demonstrated by its implementation in the new LDL++ system, and a novel and general treatment of set aggregates, allowing for user-defined aggregates.

TL;DR: This chapter presents a framework that uses metaqueries to integrate inductive learning methods with deductive database technologies in the context of knowledge discovery from databases, and describes in detail a system that uses this idea to unify a Bayesian Data Cluster with the Logical Data Language (LDL++).

TL;DR: The LDL++ system as mentioned in this paper is an open architecture designed to support non-monotonic and non-deterministic constructs that extend the functionality of the LDL++ language, while preserving its model-theoretic and fixpoint semantics.

TL;DR: An approach that uses metaqueries to integrate inductive learning with deductive database technology in the context of knowledge discovery from databases, and a system that uses this idea to unify a Bayesian Data Cluster with the Logical Data Language (LDL++).

TL;DR: The LDL++ system as discussed by the authors is an open architecture designed to support non-monotonic and non-deterministic constructs that extend the functionality of the LDL++ language, while preserving its model-theoretic and fixpoint semantics.