7,848 research outputs found
Workflow Patterns for Business Process Modeling
For its reuse advantages, workflow patterns (e.g., control flow patterns, data patterns, resource patterns) are increasingly attracting the interest of both researchers and vendors. Frequently, business process or workflow models can be assembeled out of a set of recurrent process fragments (or recurrent business functions), each of them having generic semantics that can be described as a pattern. To our best knowledge, so far, there has been no (empirical) work evidencing the existence of such recurrent patterns in real workflow applications. Thus, in this paper we elaborate the frequency with which certain patterns occur in practice. Furthermore, we investigate completeness of workflow patterns (based on recurrent functions) with respect to their ability to capture a large variety of business processes
Towards an Intelligent Workflow Designer based on the Reuse of Workflow Patterns
In order to perform process-aware information systems we need sophisticated methods and concepts for designing and modeling processes. Recently, research on workflow patterns has emerged in order to increase the reuse of recurring workflow structures. However, current workflow modeling tools do not provide functionalities that enable users to define, query, and reuse workflow patterns properly. In this paper we gather a suite for both process modeling and normalization based on workflow patterns reuse. This suite must be used in the extension of some workflow design tool. The suite comprises components for the design of processes
from both legacy systems and process modeling
Collaborative Virtual Enterprise Environment and Decision Mining
This paper will present some meaningful insights into the analysis and modeling phases of an Enterprise Virtual Environment (EVE) prototype. The main goal of EVE is to provide an environment for collaborative decisions using a DSS-like approach. In the second part, the proposed architecture of the system will be introduced. This system is developed primarily to simulate decision situations in the academic training of students. The second goal of the system is to provide us with user activity logs that will be the starting point of decision pattern mining process. In the third part of the paper, we will provide evidence regarding the possibility of: mining decision models from user activity logs; comparing different decision making strategies of users; and building decision reference models.Enterprise Virtual Environment, Decision Simulation, DSS Analysis and Modeling, Decision Mining, Decision Analysis, Decision Models
PrOnto: an Ontology Driven Business Process Mining Tool
Abstract The main aim of data mining techniques and tools is that of identify and extract, from a set of (big) data, implicit patterns which can describe static or dynamic phenomena. Among these latter business processes are gaining more and more attention due to their crucial role in modern organizations and enterprises. Being able to identify and model processes inside organizations is for sure a key asset to discover their weak and strong points thus helping them in the improvement of their competitiveness. In this paper we describe a prototype system able to discover business processes from an event log and classify them with a suitable level of abstraction with reference to a related business ontology. The identified process, and its corresponding level of abstraction, depends on the knowledge encoded in the reference ontology which is dynamically exploited at runtime. The tool has been validated by considering examples and case studies from the literature on process mining
Recommended from our members
Hybrid process modelling within business process management projects
Business Process Management (BPM) is still an important research topic amongst both academics
and businesses. The recent recession has forced businesses to focus on cost control and efficiency
in order to better cope with the economic downturn. Many companies in this situation turn to BPM
software as a means of improving their efficiency and costs by reducing aspects of the business
such as process lead-times and material costs. In order to identify areas of the business and its
processes which require changing the business will most likely adopt a method of modelling their
business processes. Because of the large number of available techniques decision makers usually
struggle to decide the best approach. Recent literature has also pointed out that prevalent
modelling techniques are designed to serve one specific purpose and may not be capable of
modelling the whole picture. The key relationship between the information systems and the human
behaviour is one example of where existing techniques are biased towards opposite ends of the
scale. This paper proposes the use of a hybrid modelling notation composed of multiple existing
notations in order to bridge this. The hybrid notation was applied to a BPM project at a company
in the construction industry and a case study conducted with its users
Evaluation and extracting factual software architecture of distributed system by process mining techniques
The factual software architectures that are actually implemented of distributed systems do not conform the planned software architectures (Beck 2010). It happens due to the complexity of distributed systems. This problem begets two main challenges; First, how to extract the factual software architectures with the proper techniques and second, how to compare the planned software architecture with the extracted factual architecture. This study aims to use process mining to discover factual software architecture from codes and represents software architecture model in Petri Net to evaluate model by the linear temporal logic and process mining. In this paper, the applicability of process mining techniques, implemented in the ProM6.7 framework is shown to extract and evaluate factual software architectures. Furthermore, capabilities of Hierarchical Colored Petri Net implemented in CPN4.0 are exploited to model and simulate software architectures. The proposed approach has been conducted on a case study to indicate applicability of the approach in the distributed data base system. The final result of the case study indicates process mining is able to extract factual software architectures and also to check its conformance
Analysis reuse exploiting taxonomical information and belief assignment in industrial problem solving
To take into account the experience feedback on solving complex problems in business is deemed as a way to improve the quality of products and processes. Only a few academic works, however, are concerned with the representation and the instrumentation of experience feedback systems. We propose, in this paper, a model of experiences and mechanisms to use these experiences. More specifically, we wish to encourage the reuse of already performed expert analysis to propose a priori analysis in the solving of a new problem. The proposal is based on a representation in the context of the experience of using a conceptual marker and an explicit representation of the analysis incorporating expert opinions and the fusion of these opinions. The experience feedback models and inference mechanisms are integrated in a commercial support tool for problem solving methodologies. The results obtained to this point have already led to the definition of the role of ââRex Managerââ with principles of sustainable management for continuous improvement of industrial processes in companies
Learning Hybrid Process Models From Events: Process Discovery Without Faking Confidence
Process discovery techniques return process models that are either formal
(precisely describing the possible behaviors) or informal (merely a "picture"
not allowing for any form of formal reasoning). Formal models are able to
classify traces (i.e., sequences of events) as fitting or non-fitting. Most
process mining approaches described in the literature produce such models. This
is in stark contrast with the over 25 available commercial process mining tools
that only discover informal process models that remain deliberately vague on
the precise set of possible traces. There are two main reasons why vendors
resort to such models: scalability and simplicity. In this paper, we propose to
combine the best of both worlds: discovering hybrid process models that have
formal and informal elements. As a proof of concept we present a discovery
technique based on hybrid Petri nets. These models allow for formal reasoning,
but also reveal information that cannot be captured in mainstream formal
models. A novel discovery algorithm returning hybrid Petri nets has been
implemented in ProM and has been applied to several real-life event logs. The
results clearly demonstrate the advantages of remaining "vague" when there is
not enough "evidence" in the data or standard modeling constructs do not "fit".
Moreover, the approach is scalable enough to be incorporated in
industrial-strength process mining tools.Comment: 25 pages, 12 figure
Semantic business process management: a vision towards using semantic web services for business process management
Business process management (BPM) is the approach to manage the execution of IT-supported business operations from a business expert's view rather than from a technical perspective. However, the degree of mechanization in BPM is still very limited, creating inertia in the necessary evolution and dynamics of business processes, and BPM does not provide a truly unified view on the process space of an organization. We trace back the problem of mechanization of BPM to an ontological one, i.e. the lack of machine-accessible semantics, and argue that the modeling constructs of semantic Web services frameworks, especially WSMO, are a natural fit to creating such a representation. As a consequence, we propose to combine SWS and BPM and create one consolidated technology, which we call semantic business process management (SBPM
- âŠ