Artifact Lifecycle Discovery

Artifact-centric modeling is a promising approach for modeling business processes based on the so-called business artifacts - key entities driving the company's operations and whose lifecycles define the overall business process. While artifact-centric modeling shows significant advantages, the overwhelming majority of existing process mining methods cannot be applied (directly) as they are tailored to discover monolithic process models. This paper addresses the problem by proposing a chain of methods that can be applied to discover artifact lifecycle models in Guard-Stage-Milestone notation. We decompose the problem in such a way that a wide range of existing (non-artifact-centric) process discovery and analysis methods can be reused in a flexible manner. The methods presented in this paper are implemented as software plug-ins for ProM, a generic open-source framework and architecture for implementing process mining tools

Fuzzy c-Means Clustering untuk Pengenalan Pola Studi kasus Data Saham

Fuzzy Clustering is one of the five roles used by data mining experts to transform large amounts of data into useful information, and one method that is often and widely used is Fuzzy c-Means (FCM) Clustering. FCM is a data clustering technique where the existence of each data point in the cluster is based on the degree of membership. This study aims to see the pattern of data samples or data categories using FCM clustering. The analyzed data is stock data on Jakarta Stock Exchange (BEJ) in the Property and Real Estate sector (issuer group). The data mining processes comply Cross Industry Standard Process Model for Data mining Process (Crisp-DM), with several stages, starting with the stage of getting to know the business process (Business Understanding) then studying the data (Data Understanding), continuing with the Data Preparation stage, Modeling stage, Evaluation stage and finally the Deployment stage. In the modeling stage, the FCM model is used. FCM clustering model data mining can analyze data in large databases with many variables and complicated, especially to get patterns from the data. Then a Fuzzy Inference System (FIS) was built based on a known pattern for simulating input data into output data based on fuzzy logic. Keywords: Fuzzy c-Means Clustering, Pattern Recognitio

Agile mining : a novel data mining process for industry practice based on Agile Methods and visualization

University of Technology Sydney. Faculty of Engineering and Information Technology.Current standard data mining processes like CRoss-Industry Standard Process for Data mining (CRISP-DM) are vulnerable to frequent change of customer requirement. Meanwhile, Stakeholders might not acquire sufficient understanding to generate business value from analytic results due to a lack of intelligible explanatory stage. These two cases repeatedly happen on those companies which are inexperienced in data mining practice. Towards this issue, Agile Mining, a refined CRISP-DM based data mining (DM) process, is proposed to address these two friction points between current data mining processes and inexperienced industry practitioners. By merging agile methods into CRISP-DM, Agile Mining process achieves a requirement changing friendly data mining environment for inexperienced companies. Moreover, this Agile Mining transforms traditional analytic-oriented evaluation to business-oriented visualization-based evaluation. In the case study, two industrial data mining projects are used to illustrate the application of this new data mining process and its advantages

A study of the application of computational intelligence and machine learning techniques in business process mining

International audienceProcess mining is a emerging research area that combines data mining and machine learning, on one hand, and business process modeling and analysis, on the other hand. This work aims to assess the application of computational intelligence and machine learning techniques in process mining context. The main focus of the study was to identify why the computational intelligence and machine learning techniques are not being widely used in process mining field and identify the main reasons for this phenomenon. The stage of experiments in this study was carried out based on an unstructured process related to a distance learning supported by a Learning Management System (LMS). ABSTRACT BACKGROUN

Business integration models in the context of web services.

E-commerce development and applications have been bringing the Internet to business and marketing and reforming our current business styles and processes. The rapid development of the Web, in particular, the introduction of the semantic web and web service technologies, enables business processes, modeling and management to enter an entirely new stage. Traditional web based business data and transactions can now be analyzed, extracted and modeled to discover new business rules and to form new business strategies, let alone mining the business data in order to classify customers or products. In this paper, we investigate and analyze the business integration models in the context of web services using a micro-payment system because a micro-payment system is considered to be a service intensive activity, where many payment tasks involve different forms of services, such as payment method selection for buyers, security support software, product price comparison, etc. We will use the micro-payment case to discuss and illustrate how the web services approaches support and transform the business process and integration model.

Automated simulation and verification of process models discovered by process mining

This paper presents a novel approach for automated analysis of process models discovered using process mining techniques. Process mining explores underlying processes hidden in the event data generated by various devices. Our proposed Inductive machine learning method was used to build business process models based on actual event log data obtained from a hotel\u27s Property Management System (PMS). The PMS can be considered as a Multi Agent System (MAS) because it is integrated with a variety of external systems and IoT devices. Collected event log combines data on guests stay recorded by hotel staff, as well as data streams captured from telephone exchange and other external IoT devices. Next, we performed automated analysis of the discovered process models using formal methods. Spin model checker was used to simulate process model executions and automatically verify the process model. We proposed an algorithm for the automatic transformation of the discovered process model into a verification model. Additionally, we developed a generator of positive and negative examples. In the verification stage, we have also used Linear temporal logic (LTL) to define requested system specifications. We find that the analysis results will be well suited for process model repair

Integration of GIS modeling with Fuzzy Logic method for land optimization of post mining on coal mine in South Kalimantan province: A case study of PT Wahana Baratama Mining

Currently coal companies, especially in South Kalimantan, have not been yet or only slightly entered the post-mining stage, although part of the mining blocks have been totally exploited, so that the company should have been preparing for the development of other sectors (non-mining). It shows that optimization of coal resources from exploration, mining to post-mining land use is necessary to ensure sustainable mining and sustainable development in terms of meeting the conservation aspect. To meet all aspects of conservation, the achievement of optimization in a series of mining business activities is started from the potential optimization of the potential of the coal remain resources until the optimization of post-mining land use is absolutely required. This research has analyzed several alternative sectors outside mining, which will be selected for optimization of utilization or post-mining land use, including plantation, recreation, industry and conservation sectors. The analyzing process used several parameters to assess the selected sector including rainfall, slope and land use. Therefore, this study uses an approach of GIS-based methods (knowledge-driven), mainly fuzzy logic for post-mining land use planning. The selected mining area for this study belongs to PT. Wahana Baratama Mining company that has a Work Agreement for Coal Mining Exploitation. The result shows the suitability of plantation for the optimization of land use in all mining sites and also for conservation areas or protected forests.Keywords: Optimization, Land use, Post-mining, Fuzzy logic

Computational Aesthetics and Identification of Working Style

Tänapäeval kasutab meeletu hulk ettevõtteid protsessimudelitel põhinevate äriprotsesside haldamiseks, teostamiseks, monitoorimiseks ja analüüsimiseks protsessiteadlikke infosüsteeme. Lisaks genereerivad need tarkvarasüsteemid monitoorimisetapi osana ka sündmuste logisid, mis kujutavad endast tegelikku faktidest tuletatud (aposteriori) töövoogu ning neid analüüsitakse protsessiandmete hankimise tehnikate abil. Selles töös, osana protsessiandmete hankimisest, tutvustame tööstiili kontseptsiooni töö olemuse kõikehõlmava analüüsi tööriistana. Äriprotsesse ja komponentidevahelist vastastikust sõltuvust saab hinnata tööstiili perspektiivist, mis väljendub meetmetes ja mustrites. Defineerime uuendusliku sündmuste logi esitlemise lähenemise, kus logifaili käsitletakse kujutisena. Lisaks pakume välja meetmete arvutamise ja mustrite identifitseerimise algoritmid, mis põhinevad kujutiste analüüsitehnika ja arvutusesteetika kombinatsioonil. Selle tulemusena on loodud tööstiili hindamise veebipõhise rakenduse prototüüp.Nowadays, an enormous amount of companies use Process-Aware Information Systems to manage, perform, monitor and analyze business processes based on process models. Moreover, as a part of the monitoring stage, these software systems generate event logs, which represent actual a-posteriori workflow and are analyzed by process mining techniques. In this work, as a part of process mining, we introduce the concept of working style as the tool for comprehensive analysis of the nature of work. Business processes and interdependencies between its constituents can be evaluated from the perspective of working style which is represented by measures and patterns. We define the novel event log representation approach, where the log file is treated as an image. Additionally, we propose measure computation and pattern identification algorithms based on image analysis technique in combination with computational aesthetics. As a result, the web-based prototype application for working style evaluation has been built