Discovering Business Problems Using Problem Hypotheses: A Goal-Oriented and Machine Learning-Based Approach

Ahn, Robert; Gonçalves, Ronaldo A. L.; Hill, Tom; Chung, Lawrence; Supakkul, Sam; Zhao, Liping

doi:10.1109/bigcomp51126.2021.00034

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…From the perspective of effectively discovering business problems that hinder process execution, Ahn proposed a Metis+ framework that can identify potential business problems. This framework is based on big data to explore and verify problem assumptions [23]. Palm uses reinforcement learning algorithms to simulate the process of employees dealing with changes and making decisions and explores the implementation of adaptive systems [9].…”

Section: Related Literaturementioning

confidence: 99%

Dynamic Adaptation Method of Business Process Based on Hierarchical Feature Model

Gao

2021

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With the continuous complexity and frequent changes in business application scenarios, companies urgently need to establish a flexible business process management mechanism that includes dynamic rules, in which dynamic adaptation methods of business processes play a vital role. Aiming at the problem that the current methods only use the preset process template and the decision-making database, it cannot respond quickly to business changes and reconfigure the business process. This research proposes a dynamic adaptation method of business process based on the hierarchical feature model, builds a hierarchical feature model of complex processes, then establishes a hierarchical business policy set to achieve an agile response to business emergencies. By constructing a mapping model, the feature model is associated with the BPMN model to realize the rapid execution of the reconfiguration process model. The feasibility and effectiveness of the proposed method are verified by process examples and the developed business process dynamic adaptation tool.

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Section: Related Literaturementioning

confidence: 99%

Dynamic Adaptation Method of Business Process Based on Hierarchical Feature Model

Gao

2021

Information

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“…In addition to the efforts made by governments and social media platforms, cybersecurity researchers have also contributed to understanding and studying this phenomenon from various aspects, thereby aiding in the efforts of detecting and preventing it. They have utilized various approaches such as artificial intelligence (AI) and social networks analysis (SNA), which have previously shown effectiveness in ad- dressing issues in various fields, including detecting fake news [8], [9], examining public opinion changes towards COVID-19 vaccination in social networks [10]- [12], community detection [13], [14], business [15], [16], security, and intelligence [17], [18]. In this context, numerous studies have been conducted to minimize the impact of this process.…”

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confidence: 99%

A Comprehensive Survey of Detection and Prevention Approaches for Online Radicalization: Identifying Gaps and Future Directions

Berjawi,

Fenza,

Loia

2023

IEEE Access

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In the digital era, online radicalization has emerged as a significant concern for governments, social media platforms, and researchers. Detecting and preventing online radicalization have become key priorities, leading to extensive research efforts. This study presents a comprehensive survey of existing works in this field, covering various techniques and methodologies. An extensive assessment of 68 publications from databases such as IEEE, SCOPUS, and Web of Science (WoS) was conducted to analyze recent literature on detecting and preventing online radicalization. This research provides an overview of the definition of online radicalization and its relationship with social media. It explores different types and sources of datasets used in studying online radicalization. Additionally, it categorizes approaches and techniques, including Machine Learning (ML), Deep Learning (DL), and Graph algorithms, for detecting and preventing online radicalization. The survey identifies limitations and challenges in the field, highlighting existing gaps and suggesting potential directions for further study. To the best of the authors' knowledge, this work is the first of its kind to undertake such a holistic investigation that consolidates these methodologies presenting them in an accessible manner. The findings contribute as a valuable resource for academics, decision-makers, and professionals working in the field of counter-radicalization and provide insights into existing countermeasures against this expanding threat.

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Discovering Business Problems Using Problem Hypotheses: A Goal-Oriented and Machine Learning-Based Approach

Cited by 2 publications

References 8 publications

Dynamic Adaptation Method of Business Process Based on Hierarchical Feature Model

Dynamic Adaptation Method of Business Process Based on Hierarchical Feature Model

A Comprehensive Survey of Detection and Prevention Approaches for Online Radicalization: Identifying Gaps and Future Directions

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