Measuring Rule-based LTLf Process Specifications: A Probabilistic Data-driven Approach

• URL: http://arxiv.org/abs/2305.05418v2
• Date: Wed, 20 Dec 2023 11:14:20 GMT
• Title: Measuring Rule-based LTLf Process Specifications: A Probabilistic Data-driven Approach
• Authors: Alessio Cecconi, Luca Barbaro, Claudio Di Ciccio, Arik Senderovich
• Abstract summary: Declarative process specifications define the behavior of processes by means of rules based on Linear Temporal Logic on Finite Traces. In a mining context, these specifications are inferred from, and checked on, multi-sets of runs recorded by information systems. We propose a technique that measures the degree of satisfaction of specifications over event logs.
• Score: 2.5407767658470726
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Declarative process specifications define the behavior of processes by means of rules based on Linear Temporal Logic on Finite Traces (LTLf). In a mining context, these specifications are inferred from, and checked on, multi-sets of runs recorded by information systems (namely, event logs). To this end, being able to gauge the degree to which process data comply with a specification is key. However, existing mining and verification techniques analyze the rules in isolation, thereby disregarding their interplay. In this paper, we introduce a framework to devise probabilistic measures for declarative process specifications. Thereupon, we propose a technique that measures the degree of satisfaction of specifications over event logs. To assess our approach, we conduct an evaluation with real-world data, evidencing its applicability in discovery, checking, and drift detection contexts.

Related papers

• Alignment-based conformance checking over probabilistic events [4.060731229044571]
  We introduce a weighted trace model and weighted alignment cost function, and a custom threshold parameter that controls the level of confidence on the event data. The resulting algorithm considers activities of lower but sufficiently high probability that better align with the process model.
  arXiv  Detail & Related papers  (2022-09-09T14:07:37Z)
• Relational Action Bases: Formalization, Effective Safety Verification, and Invariants (Extended Version) [67.99023219822564]
  We introduce the general framework of relational action bases (RABs) RABs generalize existing models by lifting both restrictions. We demonstrate the effectiveness of this approach on a benchmark of data-aware business processes.
  arXiv  Detail & Related papers  (2022-08-12T17:03:50Z)
• Accessing and Interpreting OPC UA Event Traces based on Semantic Process Descriptions [69.9674326582747]
  This paper proposes an approach to access a production systems' event data based on the event data's context. The approach extracts filtered event logs from a database system by combining: 1) a semantic model of a production system's hierarchical structure, 2) a formalized process description and 3) an OPC UA information model.
  arXiv  Detail & Related papers  (2022-07-25T15:13:44Z)
• Conformance Checking with Uncertainty via SMT (Extended Version) [66.58864135810981]
  We show how to solve the problem of checking conformance of uncertain logs against data-aware reference processes. Our approach is modular, in that it homogeneously accommodates for different types of uncertainty. We show the correctness of our approach and witness feasibility through a proof-of-concept implementation.
  arXiv  Detail & Related papers  (2022-06-15T11:39:45Z)
• Prescriptive Process Monitoring: Quo Vadis? [64.39761523935613]
  The paper studies existing methods in this field via a Systematic Literature Review ( SLR) The SLR provides insights into challenges and areas for future research that could enhance the usefulness and applicability of prescriptive process monitoring methods.
  arXiv  Detail & Related papers  (2021-12-03T08:06:24Z)
• A2Log: Attentive Augmented Log Anomaly Detection [53.06341151551106]
  Anomaly detection becomes increasingly important for the dependability and serviceability of IT services. Existing unsupervised methods need anomaly examples to obtain a suitable decision boundary. We develop A2Log, which is an unsupervised anomaly detection method consisting of two steps: Anomaly scoring and anomaly decision.
  arXiv  Detail & Related papers  (2021-09-20T13:40:21Z)
• SaCoFa: Semantics-aware Control-flow Anonymization for Process Mining [4.806322013167162]
  We argue for privacy preservation that incorporates a process semantics. We show how, based on the exponential mechanism, semantic constraints are incorporated to ensure differential privacy of the query result.
  arXiv  Detail & Related papers  (2021-09-17T12:26:49Z)
• CoCoMoT: Conformance Checking of Multi-Perspective Processes via SMT (Extended Version) [62.96267257163426]
  We introduce the CoCoMoT (Computing Conformance Modulo Theories) framework. First, we show how SAT-based encodings studied in the pure control-flow setting can be lifted to our data-aware case. Second, we introduce a novel preprocessing technique based on a notion of property-preserving clustering.
  arXiv  Detail & Related papers  (2021-03-18T20:22:50Z)
• PROVED: A Tool for Graph Representation and Analysis of Uncertain Event Data [0.966840768820136]
  The discipline of process mining aims to study processes in a data-driven manner by analyzing historical process executions. Recent novel types of event data have gathered interest among the process mining community, including uncertain event data. The PROVED tool helps to explore, navigate and analyze such uncertain event data.
  arXiv  Detail & Related papers  (2021-03-09T17:11:54Z)
• Conformance Checking over Uncertain Event Data [0.45119235878273]
  We analyze the previously unexplored setting of uncertain event logs. In such event logs uncertainty is recorded explicitly, i.e., the time, activity and case of an event may be unclear or imprecise. We show how upper and lower bounds for conformance can be obtained by aligning an uncertain trace onto a regular process model.
  arXiv  Detail & Related papers  (2020-09-29T14:27:30Z)
• Partial Order Resolution of Event Logs for Process Conformance Checking [10.58705988536919]
  A key assumption of existing conformance checking techniques is that all events are associated with timestamps that allow to infer a total order of events per process instance. We present several estimators for this task, incorporating different notions of behavioral abstraction. Our experiments with real-world and synthetic data reveal that our approach improves accuracy over the state-of-the-art considerably.
  arXiv  Detail & Related papers  (2020-07-05T18:43:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.