A Data-Driven Prediction Framework for Analyzing and Monitoring Business Process Performances

2014 ◽  
pp. 100-117 ◽  
Author(s):  
Antonio Bevacqua ◽  
Marco Carnuccio ◽  
Francesco Folino ◽  
Massimo Guarascio ◽  
Luigi Pontieri
Keyword(s):  
Business Process ◽  
Data Driven

Data-driven business process management-based development of Industry 4.0 solutions

2022 ◽  
Vol 36 ◽  
pp. 117-132
Author(s):  
Tímea Czvetkó ◽  
Alex Kummer ◽  
Tamás Ruppert ◽  
János Abonyi
Keyword(s):  
Business Process ◽  
Industry 4.0 ◽  
Business Process Management ◽  
Process Management ◽  
Data Driven

Building an Analytics Culture to Boost a Data-Driven Entrepreneur's Business Model

2019 ◽  
pp. 260-291
Author(s):  
Soraya Sedkaoui ◽  
Mounia Khelfaoui
Keyword(s):  
Big Data ◽  
Small Business ◽  
Business Model ◽  
Business Process ◽  
Data Analytics ◽  
Data Driven ◽  
Business And Society ◽  
Crucial Importance

This chapter treats the movement that marks, affects, and transforms any part of business and society. It is about big data that is creating, and the value generating that companies, startups, and entrepreneurs have to derive through sophisticated methods and advanced tools. This chapter suggests that analytics can be of crucial importance for business and entrepreneurial practices if correctly aligned with business process needs and can also lead to significant improvement of their performance and quality of the decisions they make. So, the main purpose of this chapter are exploring why small business, entrepreneur, and startups have to use data analytics and how they can integrate, operationally, analytics methods to extract value and create new opportunities.

scholarly journals TASC²S: designing a data driven business process

2016 ◽  
Vol 25 (sup1) ◽  
pp. 639-646 ◽  
Author(s):  
David Sammon ◽  
John McNulty ◽  
Aonghus Sugrue
Keyword(s):  
Business Process ◽  
Data Driven

scholarly journals Optimizing Player Management Processes in Sports: Translating Lessons from Healthcare Process Improvements to Sports

2021 ◽  
Vol 20 (2) ◽  
pp. 119-146
Author(s):  
J. Ranaweera ◽  
M. Zanin ◽  
D. Weaving ◽  
C. Withanage ◽  
G. Roe
Keyword(s):  
Business Process ◽  
Business Process Management ◽  
Process Management ◽  
Service Industry ◽  
Professional Sports ◽  
Data Driven ◽  
Main Mode ◽  
Process Improvements ◽  
Management Processes ◽  
Healthcare Process

Abstract Typical player management processes focus on managing an athlete’s physical, physiological, psychological, technical and tactical preparation and performance. Current literature illustrates limited attempts to optimize such processes in sports. Therefore, this study aimed to analyze the application of Business Process Management (BPM) in healthcare (a service industry resembling sports) and formulate a model to optimize data driven player management processes in professional sports. A systematic review, adhering to PRISMA framework was conducted on articles extracted from seven databases, focused on using BPM to digitally optimize patient related healthcare processes. Literature reviews by authors was the main mode of healthcare process identification for BPM interventions. Interviews with process owners followed by process modelling were common modes of process discovery. Stakeholder and value-based analysis highlighted potential optimization areas. In most articles, details on process redesign strategies were not explicitly provided. New digital system developments and implementation of Business Process Management Systems were common. Optimized processes were evaluated using usability assessments and pre-post statistical analysis of key process performance indicators. However, the scientific rigor of most experiments designed for such latter evaluations were suboptimal. From the findings, a stepwise approach to optimize data driven player management processes in professional sports has been proposed.

scholarly journals Discovering generative models from event logs: data-driven simulation vs deep learning

2021 ◽  
Vol 7 ◽  
pp. e577
Author(s):  
Manuel Camargo ◽  
Marlon Dumas ◽  
Oscar González-Rojas
Keyword(s):  
Deep Learning ◽  
Business Process ◽  
Business Processes ◽  
Simulation Models ◽  
Generative Models ◽  
Generative Model ◽  
Process Models ◽  
Data Driven ◽  
Learning Approaches ◽  
Business Process Models

A generative model is a statistical model capable of generating new data instances from previously observed ones. In the context of business processes, a generative model creates new execution traces from a set of historical traces, also known as an event log. Two types of generative business process models have been developed in previous work: data-driven simulation models and deep learning models. Until now, these two approaches have evolved independently, and their relative performance has not been studied. This paper fills this gap by empirically comparing a data-driven simulation approach with multiple deep learning approaches for building generative business process models. The study sheds light on the relative strengths of these two approaches and raises the prospect of developing hybrid approaches that combine these strengths.

Distributed and Adaptive Business Process Execution

2014 ◽  
pp. 44-75
Author(s):  
Michael Pantazoglou ◽  
George Athanasopoulos ◽  
Aphrodite Tsalgatidou ◽  
Pigi Kouki
Keyword(s):  
Business Process ◽  
Environmental Science ◽  
Data Driven ◽  
Context Aware Computing ◽  
Science Models ◽  
Service Oriented ◽  
Process Execution ◽  
Business Process Execution ◽  
Runtime Infrastructure ◽  
Deployment Time

Centralized business process execution engines are not adequate to guarantee smooth process execution in the presence of multiple, concurrent, long-running process instances exchanging voluminous data. In the centralized architecture of most BPEL engine solutions, the execution of BPEL processes is performed in a closed runtime environment where process instances are isolated from each other, as well as from any other potential sources of information. This prevents processes from finding relative data at runtime to adapt their behavior in a dynamic manner. The goal of this chapter is to present a solution for the performance improvement of BPEL engines by using a distributed architecture that enables the scalable execution of service-oriented processes, while also supporting their data-driven adaptation. The authors propose a decentralized BPEL engine architecture using a hypercube peer-to-peer topology with data-driven adaptation capabilities that incorporates Artificial Intelligence (AI) planning and context-aware computing techniques to support the discovery of process execution paths at deployment time and improve the overall throughput of the execution infrastructure. The proposed solution is part of the runtime infrastructure that was developed for the environmental science industry to support the efficient execution and monitoring of service-oriented environmental science models.

A data-driven approach for extracting and analyzing collaboration patterns at the interagent and intergroup levels in business process

2018 ◽  
Vol 19 (2) ◽  
pp. 451-470
Author(s):  
Shanshan Wang ◽  
Kun Chen ◽  
Zhiyong Liu ◽  
Ren-Yong Guo ◽  
Jianshan Sun ◽  
...  
Keyword(s):  
Business Process ◽  
Data Driven ◽  
Data Driven Approach ◽  
Collaboration Patterns
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