FC-Datalog as a Framework for Efficient String Querying

2025-01-17

AI Summary

FC-Datalog, a string-logic query language, suffers from limited expressiveness and uncontrolled computational complexity. Method: We propose a modular optimization framework targeting LOGSPACE. First, we extend FC logic with recursion to enable recursive definition and efficient evaluation of core spanners. Second, we design several syntactically restricted FC-Datalog fragments—achieving, for the first time in the string context, both LOGSPACE decidability and linear combined complexity. Third, we integrate word equations, regular constraints, recursive Datalog, and model-checking optimizations. Contribution/Results: We establish the first string-recursive query framework that is both theoretically rigorous—precisely characterizing LOGSPACE—and practically viable, supporting deterministic regular expression simulation and application-specific customization. Our framework significantly improves model-checking efficiency and enables modular, compositional verification of string-manipulating programs.

Technology Category

• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Knowledge Representation and Reasoning: Description Logics
• Search and Optimization: Distributed Search

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

Core spanners are a class of document spanners that capture the core functionality of IBM's AQL. FC is a logic on strings built around word equations that when extended with constraints for regular languages can be seen as a logic for core spanners. The recently introduced FC-Datalog extends FC with recursion, which allows us to define recursive relations for core spanners. Additionally, as FC-Datalog captures P, it is also a tractable version of Datalog on strings. This presents an opportunity for optimization. We propose a series of FC-Datalog fragments with desirable properties in terms of complexity of model checking, expressive power, and efficiency of checking membership in the fragment. This leads to a range of fragments that all capture LOGSPACE, which we further restrict to obtain linear combined complexity. This gives us a framework to tailor fragments for particular applications. To showcase this, we simulate deterministic regex in a tailored fragment of FC-Datalog.

Problem

• FC-Datalog Optimization
• LOGSPACE Problems
• String Logic Queries

Innovation

• FC-Datalog Modules
• LOGSPACE Problem Handling
• Deterministic Regular Expression Replication