Introduction to GenAI: A Hands-on Teaching Workshop

Generative AI (GenAI) experienced a boom in 2022 with highlights such as the releases of Stable Diffusion for image generation and ChatGPT for conversational text generation. Since then, GenAI has continued to evolve and expand at a rapid pace, resulting in additional models capable of producing impressive highquality outputs, such as Large Language Models (LLMs) including Llama 2, Claude, Bard, and Falcon, and Diffusion Models including ControlNet, and Stable Diffusion XL. GenAI technologies and systems have revolutionized various business sectors, leading to the need for education to adapt and prepare the future workforce. In this workshop, you will learn the basics of LLMs and Diffusion Models using ChatGPT and Stable Diffusion

Relation-Centric Task Identification for Policy-Based Process Mining

Many organizations use business policies to govern their business processes. For complex business processes, this results in huge amount of policy documents. Given the large volume of policies, manually analyzing policy documents to discover process information imposes excessive cognitive load. In order to provide a solution to this problem, we have proposed previously a novel approach named Policy-based Process Mining (PBPM) to automatically extracting process models from policy documents using information extraction techniques. In this paper, we report our recent findings in an important PBPM step called task identification. Our investigation indicates that task identification from policy documents is quite challenging because it is not a typical information extraction problem. The novelty of our approach is to formalize task identification as a problem of extracting relations among three process components, i.e., resource, action, and data while using sequence kernel techniques. Our initial experiment produced very promising results

An Efficient Recommender System Using Locality Sensitive Hashing

Recommender systems are widely used for personalized recommendation in many business applications such as online shopping websites and social network platforms. However, with the tremendous growth of recommendation space (e.g., number of users, products, etc.), traditional systems suffer from time and space complexity issues and cannot make real-time recommendations when dealing with large-scale data. In this paper, we propose an efficient recommender system by incorporating the locality sensitive hashing (LSH) strategy. We show that LSH can approximately preserve similarities of data while significantly reducing data dimensions. We conduct experiments on synthetic and real-world datasets of various sizes and data types. The experiment results show that the proposed LSH-based system generally outperforms traditional item-based collaborative filtering in most cases in terms of statistical accuracy, decision support accuracy, and efficiency. This paper contributes to the fields of recommender systems and big data analytics by proposing a novel recommendation approach that can handle large-scale data efficiently

Guest Editors’ Introduction-PAJAIS special issue on Business Intelligence and Analytics Research

Business intelligence and analytics (BI&A) have been studied increasingly in the past 10 years by almost every business discipline. BI&A provide historical, current, and predictive views of business operations based on advanced data collection, extraction, integration, and analysis of large data sets to improve decision making. Web 2.0 has created an abundance of user-generated content (UGC) from online social media such as forums, online groups, web blogs, social networking sites, social multimedia sites, and even virtual worlds. “Big Data” and “Big Data Analytics” have been used to describe the data sets and analytical techniques in applications that are so large (from terabytes to exabytes) and complex (from sensor to unstructured social media data) that they require advanced and unique data storage, management, analysis, and visualization technologies

Research Directions and Issues of Service Research: A Perspective of Business Information System

Recent advances in service computing such as service-oriented architectures, software as a service, web services, data center outsourcing, and cloud computing have induced more widespread attention to concepts and issues in service engineering and management

Editorial: Business process intelligence : connecting data and processes

This introduction to the special issue on Business Process Intelligence (BPI) discusses the relation between data and processes. The recent attention for Big Data illustrates that organizations are aware of the potential of the torrents of data generated by today's information systems. However, at the same time, organizations are struggling to extract value from this overload of data. Clearly, there is a need for data scientists able to transform event data into actionable information. To do this, it is crucial to take a process perspective. The ultimate goal of BPI is not to improve information systems or the recording of data; instead the focus should be in improving the process. For example, we may want to aim at reducing costs, minimizing response times, and ensuring compliance. This requires a confrontation between process models and event data. Recent advances in process mining allow us to automatically learn process models showing the bottlenecks from raw event data. Moreover, given a normative model, we can use conformance checking to quantify and understand deviations. Automatically learned models may also be used for prediction and recommendation. BPI is rapidly developing as a field linking data science to business process management. This article aims to provide an overview thereby paving the way for the other contributions in this special issue

Constructing Workflow Models from Agent Profiles

Collaboration support technologies, including workflow systems and groupware, have been widely adopted by organizations to facilitate collaborative work. How to choose suitable team members for a project is an open research issue in project-related workflow management. Although capabilities and experiences of human agents have been used to form teams and determine their roles in business processes, more research is needed on how to do that formally. In this paper, we show how to profile the capabilities and experiences of human agents based on the analysis of the data dependency structures of tasks, workflow models, and project requirements. A composition approach is also proposed for constructing workflow models based on tasks and the associated team members