Issues in Process Variants Mining

In today's dynamic business world economic success of an enterprise increasingly depends on its ability to react to internal and external changes in a quick and flexible way. In response to this need, process-aware information systems (PAIS) emerged, which support the modeling, orchestration and monitoring of business processes and services respectively. Recently, a new generation of flexible PAIS was introduced, which additionally allows for dynamic process and service changes. This, in turn, will lead to a large number of process variants, which are created from the same original process model, but might slightly differ from each other. This paper deals with issues related to the mining of such process variant collections. Our overall goal is to learn from process changes and to merge the resulting model variants into a generic process model in the best possible way. By adopting this generic process model in the PAIS, future cost of process change and need for process adaptations will decrease. Finally, we compare our approach with existing process mining techniques, and show that process variants mining is additionally needed to learn from process changes

Discovering Process Reference Models from Process Variants Using Clustering Techniques

In today's dynamic business world, success of an enterprise increasingly depends on its ability to react to changes in a quick and flexible way. In response to this need, process-aware information systems (PAIS) emerged, which support the modeling, orchestration and monitoring of business processes and services respectively. Recently, a new generation of flexible PAIS was introduced, which additionally allows for dynamic process and service changes. This, in turn, has led to large number of process and service variants derived from the same model, but differs in structures due to the applied changes. This paper provides a sophisticated approach which fosters learning from past process changes and allows for determining such process variants. As a result we obtain a generic process model for which the average distances between this model and the process variants becomes minimal. By adopting this generic process model in the PAIS, need for future process configuration and adaptation will decrease. The mining method proposed has been implemented in a powerful proof-of-concept prototype and further validated by a comparison between other process mining algorithms

Агентно-орієнтований підхід до реалізації технології процес-майнінгу (Process Mininig)

Успіх діяльності будь-якого підприємства чи організації в ринкових умовах залежить від того, настільки адекватними та ефективними є обрані методи та підходи до управління. До числа таких підходів слід віднести процесно-орієнтований підхід. Серед задач, які повинні вирішуватися в рамках цього підходу, повинні бути не тільки задачі побудови еталонних (референтних) бізнес-процесів, але й задачі отримання інформації про реальне виконання процесів з побудовою відповідних моделей. Здійснити це покликана технологія Process Mining (процес-майнінг).The success of any business or organization in a market environment depends on how adequate and effective are the chosen methods and approaches to management. These approaches include process-oriented approach. Among the tasks that must be addressed within the framework of this approach should not only be the task of building standard (reference) business processes, but also the problem of obtaining information about the actual implementation process with the construction of appropriate models. Implement this technology is designed to Process Mining. The article suggests the agent-oriented approach to building systems of Process Mining

Warenkorbanalysen mit Data Mining

With increasing price-competition in retail business, marketing becomes the critical success factor. All activities have to be oriented towards customer needs. Within marketing research, the individual act of purchase plays a decisive role and necessitates a holistic analysis of the market basket. Moreover, progress in information technology has enabled us to store huge amounts of data, including a valuable pool of business experiences. It is not possible to make this potential knowledge completely available with conventional statistical approaches. This is where Data Mining provides an ideal approach. It is an analysis process for extracting information from large databases. The aim of the study, submitted as diploma thesis at the ETH Zurich, is to show a possible Data Mining Process for analyzing sales data in retail business. Thereby the analysis is focused on the efficiency of the process model and the identification of professional needs. According to Hippner, the Data Mining Process is applied systematically on the basis of a Market Basket Analysis. The Association Analysis, which detects relevant correlations between different products of an assortment, is the core of the process. Problems of interpreting identified association rules make a transformation into marketing recommendations very difficult. Nevertheless, the Data Mining Process turned out to be very efficient.Research Methods/ Statistical Methods, Agribusiness,

Continuous Quality Improvement of IT Processes based on Reference Models and Process Mining

The inherent quality of business processes increasingly plays a significant role in the economic success of an organization. More and more business processes are supported through IT processes. In this contribution, we present a new approach which allows the continuous quality improvement of IT processes by the interconnection of IT Infrastructure Library (ITIL) reference model and process mining. On the basis of the reference model, to-be processes are set and key indicators are determined. As-is processes and their key indicators derived by process mining are subsequently compared to the to-be processes. This new approach enables the design and control of ITIL based customer support processes which will be trialed in a practice case of a customer relationship management (CRM) system. The procedural models, as well as its results, are introduced in this publication

Business Intelligence Analysis in Small and Medium Enterprises

In order to share knowledge, through discussion and exchange of information, about the technological challenges and management in the digital age, this article discusses in the following sections: First, the mining process - prerequisites and their application to “Small and Medium Enterprises” (SMEs) are discussed. Section two discusses "Using Customer Analytics for Success: The Case of Mexican SMEs." In next Section reviews data management software solutions for business sustainability. Finally, a "marketing analysis" is provided by analysis of SMEs

Process Selection in RPA Projects – Towards a Quantifiable Method of Decision Making

The digital age requires companies to invest in value-creating rather than routine activities to drive innovation as a future source of competitiveness and business success. Thus, many companies are reluctant to invest in large-scale, costly backend integration projects and seek adaptable solutions to automate their front-office activities. Bridging artificial intelligence and business process management, robotic process automation (RPA) provides the promise of robots as a virtual workforce that performs these tasks in a self-determined manner. Many studies have highlighted potential benefits of RPA. However, little data is available on operationalizing and automating RPA to maximize its benefits. In this paper, we shed light on the automation potential of processes with RPA and operationalize it. Based on process mining techniques, we propose an automatable indicator system as well as present and evaluate decision support for companies that seek to better prioritize their RPA activities and to maximize their return on investment

The main features that influence the academic success of bachelors’ students at Nova School of Business and Economics

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe prediction of academic success is a major topic in higher education, especially among the academic community. In this dissertation, we are going to present a data mining approach taking into consideration the features that are the most relevant in terms of successful academic achievement of the Bachelors’ programs at Nova School of Business and Economics (Nova SBE). Initially, we are going to perform a literature review in order to understand the framework of academic success and also to make a summary of previous research on the field of educational data mining when used to assess student success. Subsequently, the empirical approach will start being developed with the extraction of socio-economic, socio-demographic, and academic data of students, which will result in our main dataset. Later, and after the data discovery, data cleansing, and transformation activities, a set of features are going to be taken into consideration according to their relevance for the subject. Based on the dataset containing these features, several predictive data-driven techniques are going to be applied, resulting in models which are going to be assessed in order to understand if the selected features are relevant enough to answer our problem or if there is a need to substitute them by other attributes. This process will result in several iterations that will confer credibility and robustness to the model that demonstrates the best performance in classifying students’ academic success. In the end, it is intended that the insights extracted from the model will provide the school key stakeholders with enough knowledge to capacitate them to take actions that will result in the maximization of the students learning success

The Effect of Business Intelligence on Management Accounting Information System

In today's business world, we are faced with high volumes of data. New developments in IT provide organizations with effective and efficient access and storage of information. In any case, there is a long distance between the mass of data and its use. Management accounting information system has changed as a key to success in today's business environment. In the field of management accounting, if the accounting information system is not capable of providing information to business managers timely and quickly, organizations' success will be threatened in the competitive environment. To cope with competitors and growth of long-term strategies, the accounting information system should benefit from business intelligence techniques to provide timely and effective financial information. The important competitive advantage against opponents and business competitors in the market is the most important reason to create intelligent systems. The purpose of business intelligence is to help control the flow and resources of business information within and around the organization. In this study, based on the research objectives, using a meta-analysis, some of the applied criteria and parameters of accounting information systems were examined based on business intelligence features. In addition, a model was proposed based on four categories of relationships and inferences, warning and reporting systems, and tools for effective analysis and decision-making. Among the criteria in the literature review are group decision-making, optimization, integration, simulation, traffic reports, prototyping based on the original version, two-way argument process, awareness technology, informing on the content, fuzzificatio, data mining, data storage, real-time analysis process, establishing communication  channels, creating intelligent factors etc. Therefore, the necessity to use a business intelligence-based model in management accounting information system is proposed

A fuzzy logic-based approach for assessing the quality of business process models

Copyright © 2017 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved. Similar to software products, the quality of a Business Process model is vital to the success of all the phases of its lifecycle. Indeed, a high quality BP model paves the way to the successful implementation, execution and performance of the business process. In the literature, the quality of a BP model has been assessed through either the application of formal verification, or most often the evaluation of quality metrics calculated in the static and/or simulated model. Each of these assessment means addresses different quality characteristics and meets particular analysis needs. In this paper, we adopt metrics-based assessment to evaluate the quality of business process models, modeled with Business Process Modeling and Notation (BPMN), in terms of their comprehensibility and modifiability. We propose a fuzzy logic-based approach that uses existing quality metrics for assessing the attainment level of these two quality characteristics. By analyzing the static model, the proposed approach is easy and fast to apply. In addition, it overcomes the threshold determination problem by mining a repository of BPMN models. Furthermore, by relying on fuzzy logic, it resembles human reasoning during the evaluation of the quality of business process models. We illustrate the approach through a case study and its tool support system developed under the eclipse framework. The preliminary experimental evaluation of the proposed system shows encouraging results