Direct Encoding of Declare Constraints in ASP
This work addresses a specific bottleneck in Process Mining for researchers and practitioners by providing a more efficient method to handle declarative process constraints, though it is incremental as it builds on existing ASP-based solutions.
The paper tackles the problem of encoding Declare constraints in Answer Set Programming (ASP) for Process Mining by introducing a direct semantic encoding that avoids intermediate representations like LTLf formulas or automata, resulting in improved performance compared to alternative ASP encodings and a Python library.
Answer Set Programming (ASP), a well-known declarative logic programming paradigm, has recently found practical application in Process Mining. In particular, ASP has been used to model tasks involving declarative specifications of business processes. In this area, Declare stands out as the most widely adopted declarative process modeling language, offering a means to model processes through sets of constraints valid traces must satisfy, that can be expressed in Linear Temporal Logic over Finite Traces (LTLf). Existing ASP-based solutions encode Declare constraints by modeling the corresponding LTLf formula or its equivalent automaton which can be obtained using established techniques. In this paper, we introduce a novel encoding for Declare constraints that directly models their semantics as ASP rules, eliminating the need for intermediate representations. We assess the effectiveness of this novel approach on two Process Mining tasks by comparing it with alternative ASP encodings and a Python library for Declare. Under consideration in Theory and Practice of Logic Programming (TPLP).