A Resource Recommendation Approach based on Co-Working History (original) (raw)

Overview of Recommendation Techniques in Business Process Modeling⋆

Modeling business processes is an important issue in Business Process Management. As model repositories often contain similar or related models, they should be used when modeling new processes. The goal of this paper is to provide an overview of recommendation possibilities for business process models. We introduce a categorization and give examples of recommendation approaches. For these approaches, we present several machine learning methods which can be used for recommending features of business process models.

Mining Resource Assignments and Teamwork Compositions from Process Logs

Softwaretechnik-Trends, 2016

Process mining aims at discovering processes by extracting knowledge from event logs. Such knowledge may refer to different business process perspectives. The organisational perspective deals, among other things, with the assignment of human resources to process activities. Information about the resources that are involved in process activities can be mined from event logs in order to discover resource assignment conditions. This is valuable for process analysis and redesign. Prior process mining approaches in this context present one of the following issues: (i) they are limited to discovering a restricted set of resource assignment conditions; (ii) they are not fully efficient; (iii) the discovered process models are difficult to read due to the high number of assignment conditions included; or (iv) they are limited by the assumption that only one resource is responsible for each process activity and hence, collaborative activities are disregarded. To overcome these issues, we pre...

ResRec: A Multi-criteria Tool for Resource Recommendation

2016

Dynamic resource allocation is considered a key aspect within business process management. Selecting the most suitable resources is a challenge for those in charge of making the allocation, because the efficiency with which this task is executed, can contribute to the quality of the results, and improve the process performance. Different mechanisms have been proposed to improve resource allocation. However, there is a need for more flexible allocation methods that integrate a set of conditions and requirements defined at run-time, and also, allow the combination of different criteria to evaluate resources. In this paper, we present ResRec, a novel Multi-factor Criteria tool that can be used to recommend and allocate resources dynamically. The tool provides the feature of solving individual requests (On-demand), or requests made in blocks (Batch) through a recommender system developed in ProM.

A Role-based Adjacent Workflow Recommendation Technique

Proceedings of the 2015 International Conference on Computer Science and Intelligent Communication, 2015

Recently recommendation techniques and systems have gained more and more attention and are researched in a variety of fields. In this paper we put the recommendation technique into the problem of workflow or Web browsing so that can facilitate the workflow participants to find their next most desired activities or the people in the Business Internet to find their next wanted clicks. The distinguished feature of our automated workflow recommendation technique is that the concept of role-based adjacent workflow instance is incorporated. Our proposed algorithm first calculates the multi-set of rolebased adjacent workflow instances of an incomplete workflow instance, and then utilizes this multi-set to predict the next desired activity for that incomplete workflow instance. The experimental results confirm the promising effectiveness of our role-based adjacent workflow recommendation algorithm.

Supporting the optimized execution of business processes through recommendations

2012

In order to be able to flexibly adjust a company's business processes (BPs) there is an increasing interest in flexible Process-Aware Information Systems (PAISs). This increasing flexibility, however, typically implies decreased user guidance by the PAIS and thus poses additional challenges to its users. This work proposes a recommendation system which assists users during process execution to optimize performance goals of the processes. The recommendation system is based on a constraint-based approach for planning and scheduling the BP activities and considers both the control-flow and the resource perspective.

Supporting Flexible Processes Through Recommendations Based on History

In today's fast changing business environment flexible Process Aware Information Systems (PAISs) are required to allow companies to rapidly adjust their business processes to changes in the environment. However, increasing flexibility in large PAISs usually leads to less guidance for its users and consequently requires more experienced users. In order to allow for flexible systems with a high degree of support, intelligent user assistance is required. In this paper we propose a recommendation service, which, when used in combination with flexible PAISs, can support end users during process execution by giving recommendations on possible next steps. Recommendations are generated based on similar past process executions by considering the specific optimization goals. In this paper we also evaluate the proposed recommendation service, which is implemented in ProM, by means of experiments.

Real-Time Business Process Recommendations

2018

Business processes entail a large number of decisions during its execution. The decision logic in these decision points is often not explicit or optimized and might leave the process actors in an indecision situation, potentially leading to errors and inefficiencies. Solutions in Process Mining and Decision Mining have tackled this issue, focusing on discovering and explicitly representing the decision logic, in an offline setting, for management and analysis purposes. However, Process Mining and Decision Mining can also be used in an online setting, offering Operational Support (e.g. decision support), in order to support and manage ongoing process executions. This dissertation presents a semi-automatic solution aimed at providing a real-time recommendation system. This solution uses the event logs created by a deployed business process to discover its decision points and provide real-time ”Next Best Action” recommendations to the business process actors who find themselves in an i...

User recommendations for the optimized execution of business processes

Data & Knowledge Engineering, 2013

Inordertobeabletoflexiblyadjustacompany'sbusinessprocesses(BPs)thereisanincreasing interest in flexible process-aware information systems (PAISs). This increasing flexibility, however, typically implies decreased user guidance by the PAIS and thus poses significant challenges to its users. As a major contribution of this work, we propose a recommendation system which assists users during process execution to optimize performance goals of the processes. The recommendation system is based on a constraint-based approach for planning and scheduling the BP activities and considers both the control-flow and the resource perspective. To evaluate the proposed constraint-based approach different algorithms are applied to a range of test models of varyingcomplexity. The results indicate that, although the optimization of process execution is a highly constrained problem, the proposed approach produces a satisfactory number of suitable solutions.

User Recommendations for the Optimized Execution of Business Processes (Extended Abstract)

Emisa Forum, 2014

In order to be able to flexibly adjust a company's business processes (BPs) there is an increasing interest in flexible process-aware information systems (PAISs). This increasing flexibility, however, typically implies decreased user guidance by the PAIS and thus poses significant challenges to its users. As a major contribution of this work, we propose a recommendation system which assists users during process execution to optimize performance goals of the processes. The recommendation system is based on a constraint-based approach for planning and scheduling the BP activities and considers both the control-flow and the resource perspective. To evaluate the proposed constraint-based approach different algorithms are applied to a range of test models of varying complexity. The results indicate that, although the optimization of process execution is a highly constrained problem, the proposed approach produces a satisfactory number of suitable solutions.

A Recommender System for Process Discovery

Lecture Notes in Computer Science, 2014

Over the last decade, several algorithms for process discovery and process conformance have been proposed. Still, it is well-accepted that there is no dominant algorithm in any of these two disciplines, and then it is often difficult to apply them successfully. Most of these algorithms need a close-to expert knowledge in order to be applied satisfactorily. In this paper, we present a recommender system that uses portfolio-based algorithm selection strategies to face the following problems: to find the best discovery algorithm for the data at hand, and to allow bridging the gap between general users and process mining algorithms. Experiments performed with the developed tool witness the usefulness of the approach for a variety of instances.