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

Evaluation of recommender systems in streaming environments

Evaluation of recommender systems is typically done with finite datasets. This means that conventional evaluation methodologies are only applicable in offline experiments, where data and models are stationary. However, in real world systems, user feedback is continuously generated, at unpredictable rates. Given this setting, one important issue is how to evaluate algorithms in such a streaming data environment. In this paper we propose a prequential evaluation protocol for recommender systems, suitable for streaming data environments, but also applicable in stationary settings. Using this protocol we are able to monitor the evolution of algorithms' accuracy over time. Furthermore, we are able to perform reliable comparative assessments of algorithms by computing significance tests over a sliding window. We argue that besides being suitable for streaming data, prequential evaluation allows the detection of phenomena that would otherwise remain unnoticed in the evaluation of both offline and online recommender systems.Comment: Workshop on 'Recommender Systems Evaluation: Dimensions and Design' (REDD 2014), held in conjunction with RecSys 2014. October 10, 2014, Silicon Valley, United State

Recommender System Based on Process Mining

Automation of repetitive tasks can be achieved with Robotic Process Automation (RPA) using scripts that encode fine-grained interactions with software applications on desktops and the web. Automating these processes can be achieved through several applications. It is possible for users to record desktop activity, including metadata, with these tools. The very fine-grained steps in the processes contain details about very small steps that the user takes. Several steps are involved in this process, including clicking on buttons, typing text, selecting the text, and changing the focus. Automating these processes requires connectors connecting them to the appropriate applications. Currently, users choose these connectors manually rather than automatically being linked to processes. In this thesis, we propose a method for recommending the top-k suitable connectors based on event logs for each process. This method indicates that we can use process discovery, create the process models of the train processes with identified connectors, and calculate the conformance checking between the process models and test event logs (unknown connectors). Then we select top-k maximum values of the conformance checking results and observe that we have the suitable connector with 80% accuracy among the top-3 recommended connectors. This solution can be configurable by changing the parameters and the methods of process discovery and conformance checking.Automation of repetitive tasks can be achieved with Robotic Process Automation (RPA) using scripts that encode fine-grained interactions with software applications on desktops and the web. Automating these processes can be achieved through several applications. It is possible for users to record desktop activity, including metadata, with these tools. The very fine-grained steps in the processes contain details about very small steps that the user takes. Several steps are involved in this process, including clicking on buttons, typing text, selecting the text, and changing the focus. Automating these processes requires connectors connecting them to the appropriate applications. Currently, users choose these connectors manually rather than automatically being linked to processes. In this thesis, we propose a method for recommending the top-k suitable connectors based on event logs for each process. This method indicates that we can use process discovery, create the process models of the train processes with identified connectors, and calculate the conformance checking between the process models and test event logs (unknown connectors). Then we select top-k maximum values of the conformance checking results and observe that we have the suitable connector with 80% accuracy among the top-3 recommended connectors. This solution can be configurable by changing the parameters and the methods of process discovery and conformance checking