Err

Err

Related papers

• Do We Need to Verify Step by Step? Rethinking Process Supervision from a Theoretical Perspective [59.61868506896214]
  We show that under standard data coverage assumptions, reinforcement learning is no more statistically difficult than through process supervision. We prove that any policy's advantage function can serve as an optimal process reward model.
  arXiv  Detail & Related papers  (2025-02-14T22:21:56Z)
• Three Decades of Formal Methods in Business Process Compliance: A Systematic Literature Review [0.0]
  Digitalization efforts often face a key challenge: business processes must adhere to legal regulations. This study focuses on rigorous frameworks using formal methods to verify or ensure compliance.
  arXiv  Detail & Related papers  (2024-10-13T21:19:57Z)
• RIRAG: Regulatory Information Retrieval and Answer Generation [51.998738311700095]
  We introduce a task of generating question-passages pairs, where questions are automatically created and paired with relevant regulatory passages. We create the ObliQA dataset, containing 27,869 questions derived from the collection of Abu Dhabi Global Markets (ADGM) financial regulation documents. We design a baseline Regulatory Information Retrieval and Answer Generation (RIRAG) system and evaluate it with RePASs, a novel evaluation metric.
  arXiv  Detail & Related papers  (2024-09-09T14:44:19Z)
• Towards an Enforceable GDPR Specification [49.1574468325115]
  Privacy by Design (PbD) is prescribed by modern privacy regulations such as the EU's. One emerging technique to realize PbD is enforcement (RE) We present a set of requirements and an iterative methodology for creating formal specifications of legal provisions.
  arXiv  Detail & Related papers  (2024-02-27T09:38:51Z)
• Identification of Regulatory Requirements Relevant to Business Processes: A Comparative Study on Generative AI, Embedding-based Ranking, Crowd and Expert-driven Methods [10.899912290518648]
  This work examines how legal and domain experts can be assisted in the assessment of relevant requirements. We compare an embedding-based NLP ranking method, a generative AI method using GPT-4, and a crowdsourced method with the purely manual method of creating labels by experts. A gold standard is created for both BPMN2.0 processes and matched to real-world requirements from multiple regulatory documents.
  arXiv  Detail & Related papers  (2024-01-02T12:08:31Z)
• Legal Requirements Analysis [2.3349787245442966]
  We explore a variety of methods for analyzing legal requirements and exemplify them on representations. We describe possible alternatives for creating machine-analyzable representations from regulations.
  arXiv  Detail & Related papers  (2023-11-23T09:31:57Z)
• Towards Target Sequential Rules [52.4562332499155]
  We propose an efficient algorithm, called targeted sequential rule mining (TaSRM) It is shown that the novel algorithm TaSRM and its variants can achieve better experimental performance compared to the existing baseline algorithm.
  arXiv  Detail & Related papers  (2022-06-09T18:59:54Z)
• Predictive Compliance Monitoring in Process-Aware Information Systems: State of the Art, Functionalities, Research Directions [0.0]
  Business process compliance is a key area of business process management. Process compliance can be checked during process design time based on verification of process models. For existing compliance monitoring approaches it remains unclear whether and how compliance violations can be predicted.
  arXiv  Detail & Related papers  (2022-05-10T13:38:56Z)
• 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)
• 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)

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.