Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
194 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Measuring Rule-based LTLf Process Specifications: A Probabilistic Data-driven Approach (2305.05418v2)

Published 9 May 2023 in cs.AI and cs.LO

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.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (60)
  1. Enacting declarative languages using LTL: avoiding errors and improving performance, in: SPIN, 2010, pp. 146–161.
  2. HyperLDLf: a logic for checking properties of finite traces process logs, in: IJCAI, 2021, pp. 1859–1865.
  3. F. Bacchus, F. Kabanza, Planning for temporally extended goals, in: AAAI/IAAI, Vol. 2, 1996, pp. 1215–1222.
  4. Non-deterministic planning with temporally extended goals: LTL over finite and infinite traces, in: AAAI, 2017, pp. 3716–3724.
  5. General LTL specification mining (T), in: ASE, 2015, pp. 81–92.
  6. Rule-based specification mining leveraging learning to rank, Autom. Softw. Eng. 25 (2018) 501–530.
  7. C. Di Ciccio, M. Montali, Declarative process specifications: Reasoning, discovery, monitoring, in: W. M. P. van der Aalst, J. Carmona (Eds.), Process Mining Handbook, Springer, 2022, pp. 108–152.
  8. User-guided discovery of declarative process models, in: Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2011, part of the IEEE Symposium Series on Computational Intelligence 2011, April 11-15, 2011, Paris, France, IEEE, 2011, pp. 192–199. doi:10.1109/CIDM.2011.5949297.
  9. Knowledge-intensive Processes: Characteristics, requirements and analysis of contemporary approaches, J. Data Semantics 4 (2015) 29–57.
  10. Declarative workflows: Balancing between flexibility and support, Computer Science - R&D 23 (2009) 99–113.
  11. Declarative process mining in healthcare, Expert Syst. Appl. 42 (2015) 9236–9251.
  12. Process mining for healthcare: Characteristics and challenges, J. Biomed. Informatics 127 (2022) 103994.
  13. Process mining applications in the healthcare domain: A comprehensive review, WIREs Data Mining Knowl. Discov. 12 (2022).
  14. On local anomaly detection and analysis for clinical pathways, Artif. Intell. Medicine 65 (2015) 167–177.
  15. Measuring the interestingness of temporal logic behavioral specifications in process mining, Information Systems (2021) 101920.
  16. Interestingness of traces in declarative process mining: The Janus LTLp_f approach, in: BPM, 2018, pp. 121–138. doi:10.1007/978-3-319-98648-7_8.
  17. L. Geng, H. Hamilton, Interestingness measures for data mining: A survey, ACM Comput. Surv. 38 (2006) 9.
  18. Measurement of rule-based ltlf declarative process specifications, in: ICPM, IEEE, 2022, pp. 96–103. doi:10.1109/ICPM57379.2022.9980690.
  19. G. De Giacomo, M. Vardi, Linear temporal logic and linear dynamic logic on finite traces, in: IJCAI, 2013, pp. 854–860.
  20. A. Pnueli, The temporal logic of programs, in: FOCS, 1977, pp. 46–57. doi:10.1109/SFCS.1977.32.
  21. The glory of the past, in: Logic of Programs, 1985, pp. 196–218.
  22. I. Hodkinson, M. Reynolds, Separation - past, present, and future, in: We Will Show Them! (2), 2005, pp. 117–142.
  23. Reasoning on LTL on finite traces: Insensitivity to infiniteness, in: AAAI, 2014, pp. 1027–1033.
  24. V. Fionda, G. Greco, The complexity of LTL on finite traces: Hard and easy fragments, in: AAAI, 2016, pp. 971–977.
  25. O. Kupferman, M. Vardi, Vacuity detection in temporal model checking, Int. J. Softw. Tools Technol. Transf. 4 (2003) 224–233.
  26. User-guided discovery of declarative process models, in: CIDM, 2011, pp. 192–199. doi:10.1109/CIDM.2011.5949297.
  27. Computing trace alignment against declarative process models through planning, in: ICAPS, 2016, pp. 367–375.
  28. R. Agrawal, R. Srikant, Fast algorithms for mining association rules in large databases, in: J. B. Bocca, M. Jarke, C. Zaniolo (Eds.), VLDB’94, Proceedings of 20th International Conference on Very Large Data Bases, September 12-15, 1994, Santiago de Chile, Chile, Morgan Kaufmann, 1994, pp. 487–499. URL: http://www.vldb.org/conf/1994/P487.PDF.
  29. On selecting interestingness measures for association rules: User oriented description and multiple criteria decision aid, Eur. J. Oper. Res. 184 (2008) 610–626.
  30. Selecting the right objective measure for association analysis, Information Systems 29 (2004) 293 – 313. Knowledge Discovery and Data Mining (KDD 2002).
  31. Multivariate bernoulli distribution, Bernoulli 19 (2013) 1465–1483.
  32. Generating event logs through the simulation of Declare models, in: EOMAS@CAiSE, 2015, pp. 20–36. doi:10.1007/978-3-319-24626-0_2.
  33. C. R. Rao, Maximum likelihood estimation for the multinomial distribution, Sankhyā: The Indian Journal of Statistics (1933-1960) 18 (1957) 139–148.
  34. Ltlf satisfiability checking, in: ECAI, volume 263 of Frontiers in Artificial Intelligence and Applications, IOS Press, 2014, pp. 513–518. doi:10.3233/978-1-61499-419-0-513.
  35. Patterns in property specifications for finite-state verification, in: ICSE, 1999, pp. 411–420.
  36. C. Di Ciccio, M. Mecella, On the discovery of declarative control flows for artful processes, ACM Trans. Manag. Inf. Syst. 5 (2015) 24:1–24:37.
  37. Perracotta: mining temporal API rules from imperfect traces, in: ICSE, 2006, pp. 282–291.
  38. W. Hämäläinen, G. Webb, A tutorial on statistically sound pattern discovery, Data Min. Knowl. Discov. 33 (2019) 325–377.
  39. Detecting sudden and gradual drifts in business processes from execution traces, IEEE Trans. Knowl. Data Eng. 29 (2017) 2140–2154.
  40. VDD: A visual drift detection system for process mining, in: ICPM Doctoral Consortium / Tools, 2020, pp. 31–34. URL: https://ceur-ws.org/Vol-2703/paperTD4.pdf.
  41. Comprehensive process drift detection with visual analytics, in: ER, 2019, pp. 119–135. doi:10.1007/978-3-030-33223-5_11.
  42. Visual drift detection for event sequence data of business processes, IEEE Trans. Vis. Comput. Graph. 28 (2022) 3050–3068.
  43. T. B. Le, D. Lo, Beyond support and confidence: Exploring interestingness measures for rule-based specification mining, in: SANER, 2015, pp. 331–340. doi:10.1109/SANER.2015.7081843.
  44. Rum: Declarative process mining, distilled, in: BPM, 2021, pp. 23–29. doi:10.1007/978-3-030-85469-0_3.
  45. DisCoveR: accurate and efficient discovery of declarative process models, Int. J. Softw. Tools Technol. Transf. 24 (2022) 563–587.
  46. Efficient computation of behavioral changes in declarative process models, in: BPMDS/EMMSAD@CAiSE, volume 479 of Lecture Notes in Business Information Processing, Springer, 2023, pp. 136–151. doi:10.1007/978-3-031-34241-7_10.
  47. Formally reasoning about quality, J. ACM 63 (2016) 24:1–24:56.
  48. This time the robot settles for a cost: A quantitative approach to temporal logic planning with partial satisfaction, in: AAAI, 2015, pp. 3664–3671.
  49. Quantified linear temporal logic over probabilistic systems with an application to vacuity checking, in: CONCUR, 2021, pp. 7:1–7:18. doi:10.4230/LIPIcs.CONCUR.2021.7.
  50. Statistical model checking, in: Computing and Software Science - State of the Art and Perspectives, 2019, pp. 478–504. doi:10.1007/978-3-319-91908-9_23.
  51. Temporal logics over finite traces with uncertainty, in: AAAI, 2020, pp. 10218–10225.
  52. Quality dimensions in process discovery: The importance of fitness, precision, generalization and simplicity, Int. J. Cooperative Inf. Syst. 23 (2014) 1440001.
  53. An alignment-based framework to check the conformance of declarative process models and to preprocess event-log data, Inf. Syst. 47 (2015) 258–277.
  54. Monotone precision and recall measures for comparing executions and specifications of dynamic systems, ACM Trans. Softw. Eng. Methodol. 29 (2020) 17:1–17:41.
  55. Fifty shades of green: How informative is a compliant process trace?, in: CAiSE, 2019, pp. 611–626.
  56. Discovery of multi-perspective declarative process models, in: ICSOC, 2016, pp. 87–103. doi:10.1007/978-3-319-46295-0_6.
  57. Evaluating conformance measures in process mining using conformance propositions, Trans. Petri Nets Other Model. Concurr. 14 (2019) 192–221.
  58. J. De Weerdt, Trace clustering, in: Encyclopedia of Big Data Technologies, 2019. doi:10.1007/978-3-319-63962-8_91-1.
  59. Getting a grasp on clinical pathway data: An approach based on process mining, in: Emerging Trends in Knowledge Discovery and Data Mining - PAKDD 2012 International Workshops: DMHM, GeoDoc, 3Clust, and DSDM, Kuala Lumpur, Malaysia, May 29 - June 1, 2012, Revised Selected Papers, volume 7769 of Lecture Notes in Computer Science, Springer, 2012, pp. 22–35. doi:10.1007/978-3-642-36778-6_3.
  60. Business process modelling in healthcare and compliance management: A logical framework, FLAP 9 (2022) 1131–1154.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now