Collaborative Knowledge Framework for Mediation Information System Engineering

With the worldwide interenterprise collaboration and interoperability background, automatic collaborative business process deduction is crucial and imperative researching subject. A methodology of deducing collaborative process is designed by collecting collaborative knowledge. Due to the complexity of deduction methodology, a collaborative knowledge framework is defined to organize abstract and concrete collaborative information. The collaborative knowledge framework contains three dimensions: elements, levels, and life cycle. To better define the framework, the relations in each dimension are explained in detail. They are (i) relations among elements, which organize the gathering orders and methods of different collaborative elements, (ii) relations among life cycle, which present modeling processes and agility management, and (iii) relations among levels, which define relationships among different levels of collaborative processes: strategy, operation, and support. This paper aims to explain the collaborative knowledge framework and the relations inside

CIRAS News, October 2001, Vol. 35, no. 4

CIRAS is to enhance the performance of Iowa industry, and associated entities, through education and technology-based services. This newsletter holds information regarding these services

CIRAS News, October 2001, Vol.35, no.4

Center for Industrial Research and Service: CIRAS partners with Iowa manufacturing companies to enhance the performance of Iowa industries with education and technology

Teaching Software Process Modeling

Most university curricula consider software pro- cesses to be on the fringes of software engineering (SE). Students are told there exists a plethora of software processes ranging from RUP over V-shaped processes to agile methods. Furthermore, the usual students’ programming tasks are of a size that either one student or a small group of students can manage the work. Comprehensive processes being essential for large companies in terms of reflecting the organization structure, coordinating teams, or interfaces to business processes such as contracting or sales, are complex and hard to teach in a lecture, and, therefore, often out of scope. We experienced tutorials on using Java or C#, or on developing applications for the iPhone to gather more attention by students, simply speaking, as these are more fun for them. So, why should students spend their time in software processes? From our experiences and the discussions with a variety of industrial partners, we learned that students often face trouble when taking their first “real” jobs, even if the company is organized in a lean or agile shape. Therefore, we propose to include software processes more explicitly into the SE curricula. We designed and implemented a course at Master’s level in which students learn why software processes are necessary, and how they can be analyzed, designed, implemented, and continuously improved. In this paper, we present our course’s structure, its goals, and corresponding teaching methods. We evaluate the course and further discuss our experiences so that lecturers and researchers can directly use our lessons learned in their own curricula.Peer reviewe

MATURE: A Model Driven bAsed Tool to Automatically Generate a langUage That suppoRts CMMI Process Areas spEcification

Many companies have achieved a higher quality in their processes by using CMMI. Process definition may be efficiently supported by software tools. A higher automation level will make process improvement and assessment activities easier to be adapted to customer needs. At present, automation of CMMI is based on tools that support practice definition in a textual way. These tools are often enhanced spreadsheets. In this paper, following the Model Driven Development paradigm (MDD), a tool that supports automatic generation of a language that can be used to specify process areas practices is presented. The generation is performed from a metamodel that represents CMMI. This tool, differently from others available, can be customized according to user needs. Guidelines to specify the CMMI metamodel are also provided. The paper also shows how this approach can support other assessment method

Business Process Simulation: A Systematic Literature Review

Business process simulation marks an essential Business Process Management technique for analysing business processes and for reasoning about process improvement. Despite its importance, literature is lacking a comprehensive, updated overview of research contributions to the field of business process simulation. In this systematic literature review, we assess the present state of research on business process simulation including prior work between 1990 and 2016. Results reported in the present study assist in advancing the discussion on future research on business process simulation by compiling and analysing prior work. The present literature review focuses on prior research involving conceptual business process models, e.g., BPMN models, with a graphical model representation as a starting point for business process simulation and excludes other foundations to build simulation models

Extending the L* Process Mining Model with Quality Management and Business Improvement Tools and Techniques

Selle lõputöö ülesandeks on leida, kas L* elutsükli mudelit on võimalik laiendada Six Sigma DMAIC mudeli, ISO 9001:2008 kvaliteedijuhtimissüsteemi ja äriparandusraamistikega nagu Baldrige Criteria for Performance ExcellenceTM äri ja mittetulundusühingutele ning European Foundation for Quality Management Excellence ModelTM. Protsessikaevandamisprojektiga, mille L*elutsükli mudel laiendati Six Sigma DMAIC metoodikaga, seotud töö viidi läbi Itaalia IT firmas, kasutades andmeid firma abilauast ning tarkvara kvaliteedikontrolli tegevustest. Firmas läbi viidud töö näitab, et DMAIC tsükkel saab pakkuda laiendatud raamistikku L* elutsükli mudelile selle kõikides staadiumites, kasutades tänapäevaseid protsessikaevandamistehnikaid ning -tarkvara.The purpose of this thesis is to determine whether is possible to expand the L*life-cycle model with Six Sigma’s DMAIC model, the ISO 9001:2008 Quality Management System, and business improvement frameworks like the Baldrige Criteria for Performance Excellence for Business and NonprofitTM, and the European Foundation for Quality Management Excellence ModelTM. The work related to the Process Mining project where the L* life-cycle model was expanded with Six Sigma’s DMAIC model has been conducted in an Italian IT Company with data from company’s Help Desk and Software Quality Assurance operations. The work conducted in the company pursues in proving that the DMAIC cycle can provide an expanded framework for the L* life-cycle model in all of its stages while employing state of the art Process Mining techniques and Process Mining software

Process mining : conformance and extension

Today’s business processes are realized by a complex sequence of tasks that are performed throughout an organization, often involving people from different departments and multiple IT systems. For example, an insurance company has a process to handle insurance claims for their clients, and a hospital has processes to diagnose and treat patients. Because there are many activities performed by different people throughout the organization, there is a lack of transparency about how exactly these processes are executed. However, understanding the process reality (the "as is" process) is the first necessary step to save cost, increase quality, or ensure compliance. The field of process mining aims to assist in creating process transparency by automatically analyzing processes based on existing IT data. Most processes are supported by IT systems nowadays. For example, Enterprise Resource Planning (ERP) systems such as SAP log all transaction information, and Customer Relationship Management (CRM) systems are used to keep track of all interactions with customers. Process mining techniques use these low-level log data (so-called event logs) to automatically generate process maps that visualize the process reality from different perspectives. For example, it is possible to automatically create process models that describe the causal dependencies between activities in the process. So far, process mining research has mostly focused on the discovery aspect (i.e., the extraction of models from event logs). This dissertation broadens the field of process mining to include the aspect of conformance and extension. Conformance aims at the detection of deviations from documented procedures by comparing the real process (as recorded in the event log) with an existing model that describes the assumed or intended process. Conformance is relevant for two reasons: 1. Most organizations document their processes in some form. For example, process models are created manually to understand and improve the process, comply with regulations, or for certification purposes. In the presence of existing models, it is often more important to point out the deviations from these existing models than to discover completely new models. Discrepancies emerge because business processes change, or because the models did not accurately reflect the real process in the first place (due to the manual and subjective creation of these models). If the existing models do not correspond to the actual processes, then they have little value. 2. Automatically discovered process models typically do not completely "fit" the event logs from which they were created. These discrepancies are due to noise and/or limitations of the used discovery techniques. Furthermore, in the context of complex and diverse process environments the discovered models often need to be simplified to obtain useful insights. Therefore, it is crucial to be able to check how much a discovered process model actually represents the real process. Conformance techniques can be used to quantify the representativeness of a mined model before drawing further conclusions. They thus constitute an important quality measurement to effectively use process discovery techniques in a practical setting. Once one is confident in the quality of an existing or discovered model, extension aims at the enrichment of these models by the integration of additional characteristics such as time, cost, or resource utilization. By extracting aditional information from an event log and projecting it onto an existing model, bottlenecks can be highlighted and correlations with other process perspectives can be identified. Such an integrated view on the process is needed to understand root causes for potential problems and actually make process improvements. Furthermore, extension techniques can be used to create integrated simulation models from event logs that resemble the real process more closely than manually created simulation models. In Part II of this thesis, we provide a comprehensive framework for the conformance checking of process models. First, we identify the evaluation dimensions fitness, decision/generalization, and structure as the relevant conformance dimensions.We develop several Petri-net based approaches to measure conformance in these dimensions and describe five case studies in which we successfully applied these conformance checking techniques to real and artificial examples. Furthermore, we provide a detailed literature review of related conformance measurement approaches (Chapter 4). Then, we study existing model evaluation approaches from the field of data mining. We develop three data mining-inspired evaluation approaches for discovered process models, one based on Cross Validation (CV), one based on the Minimal Description Length (MDL) principle, and one using methods based on Hidden Markov Models (HMMs). We conclude that process model evaluation faces similar yet different challenges compared to traditional data mining. Additional challenges emerge from the sequential nature of the data and the higher-level process models, which include concurrent dynamic behavior (Chapter 5). Finally, we point out current shortcomings and identify general challenges for conformance checking techniques. These challenges relate to the applicability of the conformance metric, the metric quality, and the bridging of different process modeling languages. We develop a flexible, language-independent conformance checking approach that provides a starting point to effectively address these challenges (Chapter 6). In Part III, we develop a concrete extension approach, provide a general model for process extensions, and apply our approach for the creation of simulation models. First, we develop a Petri-net based decision mining approach that aims at the discovery of decision rules at process choice points based on data attributes in the event log. While we leverage classification techniques from the data mining domain to actually infer the rules, we identify the challenges that relate to the initial formulation of the learning problem from a process perspective. We develop a simple approach to partially overcome these challenges, and we apply it in a case study (Chapter 7). Then, we develop a general model for process extensions to create integrated models including process, data, time, and resource perspective.We develop a concrete representation based on Coloured Petri-nets (CPNs) to implement and deploy this model for simulation purposes (Chapter 8). Finally, we evaluate the quality of automatically discovered simulation models in two case studies and extend our approach to allow for operational decision making by incorporating the current process state as a non-empty starting point in the simulation (Chapter 9). Chapter 10 concludes this thesis with a detailed summary of the contributions and a list of limitations and future challenges. The work presented in this dissertation is supported and accompanied by concrete implementations, which have been integrated in the ProM and ProMimport frameworks. Appendix A provides a comprehensive overview about the functionality of the developed software. The results presented in this dissertation have been presented in more than twenty peer-reviewed scientific publications, including several high-quality journals

Characterizing and evaluating the quality of software process modeling language: Comparison of ten representative model-based languages

Software organizations are very conscious that deployments of well-defined software processes improve software product development and its quality. Over last decade, many Software Process Modeling Languages (SPMLs) have been proposed to describe and manage software processes. However, each one presents advantages and disadvantages. The main challenge for an organization is to choose the best and most suitable SPML to meet its requirements. This paper proposes a Quality Model (QM) which has been defined conforms to QuEF (Quality Evaluation Framework). This QM allows to compare model-based SPMLs and it could be used by organizations to choose the most useful model-based SPML for their particular requirements. This paper also instances our QM to evaluate and compare 10 representative SPMLs of the various alternative approaches (metamodel-level approaches; SPML based on UML and approaches based on standards). Finally, this paper concludes there are many model-based proposals for SPM, but it is very difficult to establish with could be the commitment to follow. Some non-considered aspects until now have been identified (e.g., validation within enterprise environments, friendly support tools, mechanisms to carry out continuous improvement, mechanisms to establish business rules and elements for software process orchestrating).Ministerio de Economía y Competitividad TIN2016-76956-C3-2-R (POLOLAS