RDBPM 2015 Abstracts

Full Papers Short Papers
Full Papers
Paper Nr: 1
Title:

A Cost-centric Model for Context-aware Simulations of Business Processes
Authors:

Vincenzo Cartelli, Giuseppe Di Modica and Orazio Tomarchio
Abstract: Business process modeling is recognized as being one of the most crucial step in the management of the Business Process life cycle. Models which either are incorrect or do not accurately represent the desired process dynamics may seriously impair the achievement of an enterprise’s business goals. There is a growing interest towards simulative tools that take in input a process model and provide an estimate of the costs incurred by an hypothetical process execution. It is then of paramount importance that the input model be as accurate as possible, otherwise actual process execution costs may dramatically diverge from the estimates. In this paper we propose the definition of a comprehensive Business Process Model which inspires to the basic principles of the Activity Based Costing (ABC) analysis to provide a cost-centric perspective of the Business Processes to be executed. Emphasis is put on the need to represent the process Context, i.e., the resources processes need to consume and the environment where processes will execute. The introduced process context model is proposed as an extension to the BPMN standard. Further, we implemented a Business Process Simulator capable of simulating business processes defined according to the newly proposed model. A case study example was simulated and results are presented in the paper.
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Paper Nr: 2
Title:

From a Cloudy View Towards a More Structured Approach for Business Process Related Concepts
Authors:

Necmettin Ozkan
Abstract: In today’s information era, one of the greatest areas of confusion is the terminology used to name abstract business process concepts which are mostly unclear, blurred and ambiguous among people. This study attempts to remedy the problem of the often-occurring issue of terminology confusion in business process domain. Following a nested approach, from mission to event, the work firstly defines essential terms used in the area of research in order to create a common understanding. The paper then formalizes the relations between the terms represented within a consolidated class diagram. Therefore, the study aims to contribute the body of knowledge in this area especially for people from practice by consolidating all relevant terms and providing a meta-model from a consistent point of view.
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Paper Nr: 3
Title:

A Survey on Modelling Knowledge-intensive Business Processes from the Perspective of Knowledge Management
Authors:

Christoph Sigmanek and Birger Lantow
Abstract: Existing modelling approaches for knowledge-intensive business processes try to match the character of these processes by specific modelling concepts and methods. The approaches differ significantly depending on the focus of modelling. DeCo and KIPN for example recommend to be less strict on control flow orientation. KMDL allows for modelling down to the level of individuals. SBPM and KPR as well emphasize a detailed model and additionally underline the importance of distributed modelling. GPO-WM in contrast suggests avoiding too much details. However, which approach or what level of abstraction is now suitable for which modelling task from the perspective of knowledge management? Can the models be reused for other tasks? The search for the "right" way for modelling knowledge-intensive processes and issues derived therefrom are in the focus of discussion.
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Short Papers
Paper Nr: 4
Title:

Exploring the Role of Named Entities for Uncertainty Recognition in Event Detection
Authors:

Masnizah Mohd and Kiyoaki Shirai
Abstract: Ambiguous information contributes to the uncertainty issue. Type of information such as using named entities has been proved to provide significant information to the user compared to the ‘bag-of-words’ in identifying an event. So what else could contribute to the uncertainty in an event detection? We propose to answer this question by analysing the distribution of named entities across topics, and explore the potential of named entities in a user experiment. We construct an event detection task with 20 users and use news dataset from Topic Detection and Tracking (TDT) corpus, under the Sports and Politics categories. We analyse the results from five uncertainty dimensions: too little information, too much information, complex information, ambiguous information and conflicting information. These dimensions are categorise as two factors; amount and type of information. There was no statistical significance difference in the amount of information given with the number of successful event detected. However, with little information and high named entities has contributes in reducing uncertainty. In addition, the amount of information and information quality are mutually independent. Our results suggest that uncertainty vary substantially between the amount of information and type of information in event detection.
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Paper Nr: 5
Title:

Detecting Topics Popular in the Recent Past from a Closed Caption TV Corpus as a Categorized Chronicle Data
Authors:

Hajime Mochizuki and Kohji Shibano
Abstract: In this paper, we propose a method for extracting topics we were interested in over the course of the past 28 months from a closed-caption TV corpus. Each TV program is assigned one of the following genres: drama, informational or tabloid-style program, music, movie, culture, news, variety, welfare, or sport. We focus on informational/tabloid-style programs, dramas and news in this paper. Using our method, we extracted bigrams that formed part of the signature phrase of a heroine and the name of a hero in a popular drama, as well as recent world, domestic, showbiz, and so on news. Experimental evaluations show that our simple method is as useful as the LDA model for topic detection, and our closed-caption TV corpus has the potential value to act as a rich, categorized chronicle for our culture and social life.
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