13,959 research outputs found
Mining Event Logs to Support Workflow Resource Allocation
Workflow technology is widely used to facilitate the business process in
enterprise information systems (EIS), and it has the potential to reduce design
time, enhance product quality and decrease product cost. However, significant
limitations still exist: as an important task in the context of workflow, many
present resource allocation operations are still performed manually, which are
time-consuming. This paper presents a data mining approach to address the
resource allocation problem (RAP) and improve the productivity of workflow
resource management. Specifically, an Apriori-like algorithm is used to find
the frequent patterns from the event log, and association rules are generated
according to predefined resource allocation constraints. Subsequently, a
correlation measure named lift is utilized to annotate the negatively
correlated resource allocation rules for resource reservation. Finally, the
rules are ranked using the confidence measures as resource allocation rules.
Comparative experiments are performed using C4.5, SVM, ID3, Na\"ive Bayes and
the presented approach, and the results show that the presented approach is
effective in both accuracy and candidate resource recommendations.Comment: T. Liu et al., Mining event logs to support workflow resource
allocation, Knowl. Based Syst. (2012), http://dx.doi.org/
10.1016/j.knosys.2012.05.01
Mining Sequences of Developer Interactions in Visual Studio for Usage Smells
In this paper, we present a semi-automatic approach for mining a large-scale dataset of IDE interactions to extract usage smells, i.e., inefficient IDE usage patterns exhibited by developers in the field. The approach outlined in this paper first mines frequent IDE usage patterns, filtered via a set of thresholds and by the authors, that are subsequently supported (or disputed) using a developer survey, in order to form usage smells. In contrast with conventional mining of IDE usage data, our approach identifies time-ordered sequences of developer actions that are exhibited by many developers in the field. This pattern mining workflow is resilient to the ample noise present in IDE datasets due to the mix of actions and events that these datasets typically contain. We identify usage patterns and smells that contribute to the understanding of the usability of Visual Studio for debugging, code search, and active file navigation, and, more broadly, to the understanding of developer behavior during these software development activities. Among our findings is the discovery that developers are reluctant to use conditional breakpoints when debugging, due to perceived IDE performance problems as well as due to the lack of error checking in specifying the conditional
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 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
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