A Knowledge-driven Business Process Analysis Canvas

• URL: http://arxiv.org/abs/2201.06860v1
• Date: Tue, 18 Jan 2022 10:34:00 GMT
• Title: A Knowledge-driven Business Process Analysis Canvas
• Abstract summary: Business process (BP) analysis represents a first key phase of information system development. One promising strategy is an early and more important involvement of business experts in the BP analysis. This paper presents a methodology that aims at an early involvement of business experts while providing a formal grounding that guarantees the quality of the produced specifications.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Business process (BP) analysis represents a first key phase of information system development. It consists in the gathering of domain knowledge and its organization to be later used in the software development, and beyond (e.g., for Business Process Reengineering). The quality of the developed information system largely depends on how the BP analysis has been carried out and the quality of the produced requirement specification documents. Despite the fact that the issue is on the table for decades, business process analysis is still a critical phase of information systems development. One promising strategy is an early and more important involvement of business experts in the BP analysis. This paper presents a methodology that aims at an early involvement of business experts while providing a formal grounding that guarantees the quality of the produced specifications. To this end, we propose the Business Process Analysis Canvas, a knowledge framework organized in eight knowledge sections aimed at supporting the business expert in carrying out the analysis, eventually yielding a BP analysis Ontology.

Related papers

• Advances and Frontiers of LLM-based Issue Resolution in Software Engineering: A Comprehensive Survey [59.3507264893654]
  Issue resolution is a complex Software Engineering task integral to real-world development.<n> benchmarks like SWE-bench revealed this task as profoundly difficult for large language models.<n>This paper presents a systematic survey of this emerging domain.
  arXiv  Detail & Related papers  (2026-01-15T18:55:03Z)
• Process Analytics -- Data-driven Business Process Management [0.688204255655161]
  This paper conceptualises the term process analytics and its various dimensions by following both an inductive and deductive approach.<n>The results are discussed by contrasting them to a real-life case study from a large company implementing data-driven process analysis and automation.
  arXiv  Detail & Related papers  (2025-12-23T19:05:18Z)
• OIDA-QA: A Multimodal Benchmark for Analyzing the Opioid Industry Documents Archive [50.468138755368805]
  Opioid crisis represents a significant moment in public health.<n>Data and documents disclosed in the UCSF-JHU Opioid Industry Documents Archive (OIDA)<n>In this paper, we tackle this challenge by organizing the original dataset according to document attributes.
  arXiv  Detail & Related papers  (2025-11-13T03:27:32Z)
• KnowCoder-V2: Deep Knowledge Analysis [64.63893361811968]
  We propose a textbfKnowledgeable textbfDeep textbfResearch (textbfKDR) framework that empowers deep research with deep knowledge analysis capability.<n>It introduces an independent knowledge organization phase to preprocess large-scale, domain-relevant data into systematic knowledge offline.<n>It then extends deep research with an additional kind of reasoning steps that perform complex knowledge computation in an online manner.
  arXiv  Detail & Related papers  (2025-06-07T18:01:25Z)
• The AI Co-Ethnographer: How Far Can Automation Take Qualitative Research? [51.40252017262535]
  The AI Co-Ethnographer (AICoE) is a novel end-to-end pipeline developed for qualitative research.<n>AICoE organizes the entire process, encompassing open coding, code consolidation, code application, and even pattern discovery.
  arXiv  Detail & Related papers  (2025-04-21T21:31:28Z)
• Automated Business Process Analysis: An LLM-Based Approach to Value Assessment [8.24265045995148]
  Our paper harnesses Large Language Models (LLMs) to automate value-added analysis. Our method operates in two phases: first, decomposing high-level activities into detailed steps to enable granular analysis, and second, performing a value-added analysis to classify each step according to Lean principles.
  arXiv  Detail & Related papers  (2025-04-09T05:52:50Z)
• WISE: Unraveling Business Process Metrics with Domain Knowledge [0.0]
  Anomalies in complex industrial processes are often obscured by high variability and complexity of event data. We introduce WISE, a novel method for analyzing business process metrics through the integration of domain knowledge, process mining, and machine learning. We show that WISE enhances automation in business process analysis and effectively detects deviations from desired process flows.
  arXiv  Detail & Related papers  (2024-10-06T07:57:08Z)
• Data Analysis in the Era of Generative AI [56.44807642944589]
  This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow. We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
  arXiv  Detail & Related papers  (2024-09-27T06:31:03Z)
• The Imperative of Conversation Analysis in the Era of LLMs: A Survey of Tasks, Techniques, and Trends [64.99423243200296]
  Conversation Analysis (CA) strives to uncover and analyze critical information from conversation data. In this paper, we perform a thorough review and systematize CA task to summarize the existing related work. We derive four key steps of CA from conversation scene reconstruction, to in-depth attribution analysis, and then to performing targeted training, finally generating conversations.
  arXiv  Detail & Related papers  (2024-09-21T16:52:43Z)
• A Systematic Review of Business Process Improvement: Achievements and Potentials in Combining Concepts from Operations Research and Business Process Management [0.0]
  Business Process Management and Operations Research aim to enhance value creation in organizations. This systematic literature review identifies and analyzes work that uses combined concepts from both disciplines. Results indicate a strong focus on resource allocation and scheduling problems.
  arXiv  Detail & Related papers  (2024-09-02T14:13:14Z)
• Unraveling the Never-Ending Story of Lifecycles and Vitalizing Processes [2.474551220017185]
  We show the existence of lifecycle processes in many industries and that their appropriate conceptualizations pave the way for the definition of suitable modeling and analysis techniques. This paper provides a set of requirements for their analysis, and a conceptualization of lifecycle and vitalizing processes.
  arXiv  Detail & Related papers  (2024-07-25T08:52:23Z)
• Deep Learning based Key Information Extraction from Business Documents: Systematic Literature Review [51.61531917413708]
  Deep learning-based approaches for Key Information Extraction have been proposed under the umbrella term Document Understanding. The goal of this systematic literature review is an in-depth analysis of existing approaches in this domain and the identification of opportunities for further research.
  arXiv  Detail & Related papers  (2024-07-23T08:15:55Z)
• An Automated Startup Evaluation Pipeline: Startup Success Forecasting Framework (SSFF) [0.18416014644193066]
  This paper introduces the Startup Success Forecasting Framework (SSFF), a new automated system that combines traditional machine learning with advanced language models. The SSFF is designed to reason, act, synthesize, and decide like a venture capitalist to perform the analysis end-to-end. This framework requires minimal input data about the founder and startup description, enhances it with additional data from external resources, and performs a detailed analysis with high accuracy.
  arXiv  Detail & Related papers  (2024-05-29T19:07:42Z)
• A Survey on Personalized Content Synthesis with Diffusion Models [57.01364199734464]
  PCS aims to customize the subject of interest to specific user-defined prompts. Over the past two years, more than 150 methods have been proposed. This paper offers a comprehensive survey of PCS, with a particular focus on the diffusion models.
  arXiv  Detail & Related papers  (2024-05-09T04:36:04Z)
• Extracting Process-Aware Decision Models from Object-Centric Process Data [54.04724730771216]
  This paper proposes the first object-centric decision-mining algorithm called Integrated Object-centric Decision Discovery Algorithm (IODDA) IODDA is able to discover how a decision is structured as well as how a decision is made.
  arXiv  Detail & Related papers  (2024-01-26T13:27:35Z)
• Causal Fairness Analysis [68.12191782657437]
  We introduce a framework for understanding, modeling, and possibly solving issues of fairness in decision-making settings. The main insight of our approach will be to link the quantification of the disparities present on the observed data with the underlying, and often unobserved, collection of causal mechanisms. Our effort culminates in the Fairness Map, which is the first systematic attempt to organize and explain the relationship between different criteria found in the literature.
  arXiv  Detail & Related papers  (2022-07-23T01:06:34Z)
• Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review [62.997667081978825]
  This review aims at providing a comprehensive vision of the main state-of-the-art libraries and frameworks for machine learning and data analytics available today. The main simulation, emulation, deployment systems, and testbeds for experimental research on the Edge-to-Cloud Continuum available today are also surveyed.
  arXiv  Detail & Related papers  (2022-04-29T08:06:05Z)
• A Taxonomy and Archetypes of Business Analytics in Smart Manufacturing [0.0]
  Business analytics is a key driver for smart manufacturing. However, researchers and practitioners struggle to keep track of the progress and acquire new knowledge within the field. We develop a quadripartite taxonomy as well as to derive archetypes of business analytics in smart manufacturing.
  arXiv  Detail & Related papers  (2021-10-12T16:13:45Z)

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.