Querying Process Models Based on the Temporal Relations between Tasks

Abstract

As business process management technology matures, organisations accumulate hundreds, even thousands of models whose management poses real challenges. One of these challenges concerns model retrieval where support should be provided for the formulation and efficient evaluation of business process model queries. Recent years, queries based on behavioral information attracted more and more attentions both in the academic and industrial fields. However, there is not a widely accepted formal language to express the behavioral requirements. And the efficiency of the behavioral queries is another challenge when the process models contains arbitrary loop and/or extra-large parallel structures. The complete finite prefix (CFP) has been widely used to express the partial-order semantics of a Petri net. In this paper, we first extend CFP by connecting the cut-off events to continuation events, we call it temporal-order preserving complete finite prefix (TPCFP), where all the temporal relations between tasks can be extracted. Then we employ linear temporal logic formulae as the behavioral query language, whose formal semantics are defined over the TPCFP. The related algorithms are implemented in BeehiveZ 1 . Experimental results are investigated with real-life processes and artificial processes in detail.

Multi-Dimensional Event Data in Graph Databases

“…Of the 95 works surveyed [24, pp.133], several approaches exist to retrieve cases from event logs for temporal properties [5,7], for most frequent behavior [4], for sequences of activities [3] or algebraic expressions of sequence, choice, and parallelism over activities [8], or to check whether a temporal-logic property holds [6]. Several techniques support graph-based queries [5,44,45].…”

“…The following query returns the event attribute "timestamp" and the case attribute "LoanGoal" of Case "Application_681547497". 7 Note that all (event and entity) attributes are encoded as properties of event nodes. The query has been processed in 0.079 s. After modifying the query to consider all cases, i.e., remove the condition for a specific case in line 2, the query completed in 0.117 s. Q2.…”

“…It can be stored in an event log as one sequence of events per case according to the data model of the XES-Standard [2]. Such sequences can be easily queried for behavioral properties such as event (sub-)sequences or temporal relations such as "directly/eventually-follows" in combination with other data attributes [3][4][5][6][7][8]. Aggregating directly/eventuallyfollows relations between events is fundamental for discovering process models from event logs [1,9,10].…”

Efficient Semantics-Based Compliance Checking Using LTL Formulae and Unfolding

“…In earlier work [25], we provided an initial attempt at defining a query language based on execution semantics of process models. The language was written in linear temporal logic (LTL) and only supported precedence/succession relations among individual tasks (not sets of tasks).…”

“…The language was written in linear temporal logic (LTL) and only supported precedence/succession relations among individual tasks (not sets of tasks). Queries in this language are evaluated based directly on annotated CFPs (i.e., TPCFPs in [25]), rather than on the directed bigraphs which are built from decomposing the annotated CFPs (a directed bigraphs represents an execution of a process). As a result, this language only returns the process models which satisfy the requirements just in some process executions, rather than in every execution.…”