How to apply process mining techniques In SCAMPI appraisals

"Process Mining Extension to SCAMPI" is an extension to SCAMPI version 1.3b: Method Definition Document for SCAMPI A, B, and C. It is an extended method that adds Process Mining techniques, such as process discovery and conformance checking, into SCAMPI to provide an explicit and focused basis for appraising an organization using such techniques aiming to lead to more reliable and effective appraisals. Applying Process Mining techniques in a SCAMPI appraisal enhances how the appraisal team collects and analyzes data as well as judges the extent to which an organizational unit has implemented the reference model practices being appraised

Using Process Mining - a special type of data mining - to improve process performance

This presentation addresses a process that was designed and implemented aiming to apply Process Mining tools and techniques in projects aimed to improve process performance. The intent is to help you: - Understand Process Mining (PM); - Understand how organizations could apply Process Mining tools and techniques in order to conduct project aimed to improve process performance

Evidencia Empírica de la Minería de Procesos en la Implantación de CMMI-DEV

Resumen: La minería de procesos tiene como objetivo descubrir, monitorear y mejorar procesos a través del análisis de los diversos registros de eventos generados por los procesos de la organización. El objetivo de este trabajo es presentar la evidencia empírica de la inclusión estratégica de la disciplina de minería de procesos en proyectos de mejora de procesos de software implementados con CMMI. En el proceso de mapeo sistemático de la revisión de la literatura, se establecieron cuatro categorías para clasificar los hallazgos encontrados (Fundamentos teóricos, propuestas, herramientas y sistemas de información y algoritmos) para presentar los estudios que cumplen con el objetivo. Se concluye que la interdisciplinariedad de la minería de procesos con un modelo de referencia de procesos como CMMI-DEV apoya la implementación y evaluación de las áreas de procesos, al aplicar técnicas y algoritmos de minería de procesos que faciliten la exploración y explotación de los registros de eventos relacionados a la ejecución de las actividades almacenados en un repositorio. Palabras clave: Minería de Procesos, Mejora de Procesos de Software, Registro de Eventos

Research in Action: Emerging technologies and trends in IT

Research in Action event - showcasing the innovative research that we are doing at Wintec in our Centre for Business, Information Technology and Enterprise, and in the wider Waikato

Process Mining Opportunities for CMMI Assessments

Software process improvement has become essential for striving at satisfying the needs of customers and requeriments of markets reagrding the quality of software products. To improve software processes in a systematic and structures way, also called process maturity some models have been developed, e.g. CMMI. CMMI specifies key process areas (KPA5) on different levels of maturity. To grow in maturity, software companies should implement theses KPAs. For assess KPAs in a quantitative way, data should be collected from that process, preferably on the basis of already existing and used software engineering tools. Previous research showed that particular configuration management can collect data from software processes, the called software process mining. The objective of this project is to investigate literature on tool support in particular CMMI KPAs to find out whether there are tools that could be supported by process mining.Outgoin

Computational Analysis and Prediction of Intrinsic Disorder and Intrinsic Disorder Functions in Proteins

COMPUTATIONAL ANALYSIS AND PREDICTION OF INTRINSIC DISORDER AND INTRINSIC DISORDER FUNCTIONS IN PROTEINS By Akila Imesha Katuwawala A dissertation submitted in partial fulfillment of the requirements for the degree of Engineering, Doctor of Philosophy with a concentration in Computer Science at Virginia Commonwealth University. Virginia Commonwealth University, 2021 Director: Lukasz Kurgan, Professor, Department of Computer Science Proteins, as a fundamental class of biomolecules, have been studied from various perspectives over the past two centuries. The traditional notion is that proteins require fixed and stable three-dimensional structures to carry out biological functions. However, there is mounting evidence regarding a “special” class of proteins, named intrinsically disordered proteins, which do not have fixed three-dimensional structures though they perform a number of important biological functions. Computational approaches have been a vital component to study these intrinsically disordered proteins over the past few decades. Prediction of the intrinsic disorder and functions of intrinsic disorder from protein sequences is one such important computational approach that has recently gained attention, particularly in the advent of the development of modern machine learning techniques. This dissertation runs along two basic themes, namely, prediction of the intrinsic disorder and prediction of the intrinsic disorder functions. The work related to the prediction of intrinsic disorder covers a novel approach to evaluate the predictive performance of the current computational disorder predictors. This approach evaluates the intrinsic disorder predictors at the individual protein level compared to the traditional studies that evaluate them over large protein datasets. We address several interesting aspects concerning the differences in the protein-level vs. dataset-level predictive quality, complementarity and predictive performance of the current predictors. Based on the findings from this assessment we have conceptualized, developed, tested and deployed an innovative platform called DISOselect that recommends the most suitable computational disorder predictors for a given protein, with an underlying goal to maximize the predictive performance. DISOselect provides advice on whether a given disorder predictor would provide an accurate prediction for a given protein of user’s interest, and recommends the most suitable disorder predictor together with an estimate of its expected predictive quality. The second theme, prediction of the intrinsic disorder functions, includes first-of-its-kind evaluation of the current computational disorder predictors on two functional sub-classes of the intrinsically disordered proteins. This study introduces several novel evaluation strategies to assess predictive performance of disorder prediction methods and focuses on the evaluation for disorder functions associated with interactions with partner molecules. Results of this analysis motivated us to conceptualize, design, test and deploy a new and accurate machine learning-based predictor of the disordered lipid-binding residues, DisoLipPred. We empirically show that the strong predictive performance of DisoLipPred stems from several innovative design features and that its predictions complements results produced by current disorder predictors, disorder function predictors and predictors of transmembrane regions. We deploy DisoLipPred as a convenient webserver and discuss its predictions on the yeast proteome

Towards a method and a guiding tool for conducting process mining projects

Due to the increased use of information systems by organizations, information on the execution of processes is recorded. This enables using process mining as a tool for improving process performance. Process mining allows gaining insights regarding actual processes by extracting and processing data from existing systems. Many projects have been conducted for process discovery, conformance checking, etc. Despite of the existence of general methods for data analysis, there’s a lack of specific methods to support process mining projects. Thus, completions of such projects are often dependent on expertise of the analysts. This paper presents a detailed method for conducting process mining projects and a tool for supporting its execution and retaining the outcomes of each step. A case is analysed for evaluating them. Organizations seeking process performance improvement can get benefit from a method that states how process mining techniques can be used in process mining projects

INDIGO: a generalized model and framework for performance prediction of data dissemination

According to recent studies, an enormous rise in location-based mobile services is expected in future. People are interested in getting and acting on the localized information retrieved from their vicinity like local events, shopping offers, local food, etc. These studies also suggested that local businesses intend to maximize the reach of their localized offers/advertisements by pushing them to the maxi- mum number of interested people. The scope of such localized services can be augmented by leveraging the capabilities of smartphones through the dissemination of such information to other interested people. To enable local businesses (or publishers) of localized services to take in- formed decision and assess the performance of their dissemination-based localized services in advance, we need to predict the performance of data dissemination in complex real-world scenarios. Some of the questions relevant to publishers could be the maximum time required to disseminate information, best relays to maximize information dissemination etc. This thesis addresses these questions and provides a solution called INDIGO that enables the prediction of data dissemination performance based on the availability of physical and social proximity information among people by collectively considering different real-world aspects of data dissemination process. INDIGO empowers publishers to assess the performance of their localized dissemination based services in advance both in physical as well as the online social world. It provides a solution called INDIGO–Physical for the cases where physical proximity plays the fundamental role and enables the tighter prediction of data dissemination time and prediction of best relays under real-world mobility, communication and data dissemination strategy aspects. Further, this thesis also contributes in providing the performance prediction of data dissemination in large-scale online social networks where the social proximity is prominent using INDIGO–OSN part of the INDIGO framework under different real-world dissemination aspects like heterogeneous activity of users, type of information that needs to be disseminated, friendship ties and the content of the published online activities. INDIGO is the first work that provides a set of solutions and enables publishers to predict the performance of their localized dissemination based services based on the availability of physical and social proximity information among people and different real-world aspects of data dissemination process in both physical and online social networks. INDIGO outperforms the existing works for physical proximity by providing 5 times tighter upper bound of data dissemination time under real-world data dissemination aspects. Further, for social proximity, INDIGO is able to predict the data dissemination with 90% accuracy and differently, from other works, it also provides the trade-off between high prediction accuracy and privacy by introducing the feature planes from an online social networks

Data driven decision support systems as a critical success factor for IT-Governance: an application in the financial sector

IT-Governance has a major impact not only on IT management but also and foremost in the Enterprises performance and control. Business uses IT agility, flexibility and innovation to pursue its objectives and to sustain its strategy. However being it more critical to the business, compliance forces IT on the opposite way of predictability, stability and regulations. Adding the current economical environment and the fact that most of the times IT departments are considered cost centres, IT-Governance decisions become more important and critical. Current IT-Governance research and practise is mainly based on management techniques and principles, leaving a gap for the contribution of information systems to IT-Governance enhancement. This research intends to provide an answer to IT-Governance requirements using Data Driven Decision Support Systems based on dimensional models. This seems a key factor to improve the IT-Governance decision making process. To address this research opportunity we have considered IT-Governance research (Peter Weill), best practises (ITIL), Body of Knowledge (PMBOK) and frameworks (COBIT). Key IT-Governance processes (Change Management, Incident Management, Project Development and Service Desk Management) were studied and key process stakeholders were interviewed. Based on the facts gathered, dimensional models (data marts) were modelled and developed to answer to key improvement requirements on each IT-Governance process. A Unified Dimensional Model (IT-Governance Data warehouse) was materialized. To assess the Unified Dimensional Model, the model was applied in a bank in real working conditions. The resulting model implementation was them assessed against Peter Weill‘s Governance IT Principles.Assessment results revealed that the model satisfies all the IT-Governance Principles. The research project enables to conclude that the success of IT-Governance implementation may be fostered by Data Driven Decision Support Systems implemented using Unified Dimensional Model concepts and based on best practises, frameworks and body of knowledge that enable process oriented, data driven decision support