109,819 research outputs found
Run-time prediction of business process indicators using evolutionary decision rules
Predictive monitoring of business processes is a challenging topic of process mining which is concerned with the prediction of process indicators of running process instances. The main value of predictive monitoring is to provide information in order to take proactive and corrective actions to improve process performance and mitigate risks in real time. In this paper, we present an approach for predictive monitoring based on the use of evolutionary algorithms. Our method provides a novel event window-based encoding and generates a set of decision rules for the run-time prediction of process indicators according to event log properties. These rules can be interpreted by users to extract further insight of the business processes while keeping a high level of accuracy. Furthermore, a full software stack consisting of a tool to support the training phase and a framework that enables the integration of run-time predictions with business process management systems, has been developed. Obtained results show the validity of our proposal for two large real-life datasets: BPI Challenge 2013 and IT Department of Andalusian Health Service (SAS).Ministerio de Economía y Competitividad TIN2015-70560-RJunta de Andalucía P12TIC-186
Towards an ontology for process monitoring and mining
Business Process Analysis (BPA) aims at monitoring, diagnosing, simulating and mining enacted processes in order to support the analysis and enhancement of process models. An effective BPA solution must provide the means for analysing existing e-businesses at three levels of abstraction: the Business Level, the Process Level and the IT Level. BPA requires semantic information that spans these layers of abstraction and which should be easily retrieved from audit trails. To cater for this, we describe the Process Mining Ontology and the Events Ontology which aim to support the analysis of enacted processes at different levels of abstraction spanning from fine grain technical details to coarse grain aspects at the Business Level
Integration of decision support systems to improve decision support performance
Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes
Change Mining in Adaptive Process Management Systems
The wide-spread adoption of process-aware information systems has resulted in a bulk of computerized information about real-world processes. This data can be utilized for process performance analysis as well as for process improvement. In this context process mining offers promising perspectives. So far, existing mining techniques have been applied to operational processes, i.e., knowledge is extracted from execution logs (process discovery), or execution logs are compared with some a-priori process model (conformance checking). However, execution logs only constitute one kind of data gathered during process enactment. In particular, adaptive processes provide additional information about process changes (e.g., ad-hoc changes of single process instances) which can be used to enable organizational learning. In this paper we present an approach for mining change logs in adaptive process management systems. The change process discovered through process mining provides an aggregated overview of all changes that happened so far. This, in turn, can serve as basis for all kinds of process improvement actions, e.g., it may trigger process redesign or better control mechanisms
Recursion Aware Modeling and Discovery For Hierarchical Software Event Log Analysis (Extended)
This extended paper presents 1) a novel hierarchy and recursion extension to
the process tree model; and 2) the first, recursion aware process model
discovery technique that leverages hierarchical information in event logs,
typically available for software systems. This technique allows us to analyze
the operational processes of software systems under real-life conditions at
multiple levels of granularity. The work can be positioned in-between reverse
engineering and process mining. An implementation of the proposed approach is
available as a ProM plugin. Experimental results based on real-life (software)
event logs demonstrate the feasibility and usefulness of the approach and show
the huge potential to speed up discovery by exploiting the available hierarchy.Comment: Extended version (14 pages total) of the paper Recursion Aware
Modeling and Discovery For Hierarchical Software Event Log Analysis. This
Technical Report version includes the guarantee proofs for the proposed
discovery algorithm
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
Mining for Culture: Reaching Out of Range
The goal of this paper is to present a tool that will sustain the
development of culturally relevant computing artifacts by
providing an effective means of detecting culture identities and
cultures of participation. Culturally relevant designs rely heavily
on how culture impacts design and though the guidelines for
producing culturally relevant objects provide a mechanism for
incorporating culture in the design, there still requires an effective
method for garnering and identifying said cultures that reflects a
holistic view of the target audience. This tool presents culturally
relevant designs as a process of communicating with key
audiences and thus bridging people and technology in a way that
once seemed out of range
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