Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

 
Session Overview
Session
Student Track 1
Time:
Monday, 09/Mar/2020:
1:30pm - 3:00pm

Session Chair: Jan vom Brocke
Session Chair: Martin Matzner
Session Chair: Bernd Schenk
Session Chair: Thomas Grisold
Location: S12

Presentations

Literature Review Linking Blockchain and Business Process Management

Corey Lauster, Philipp Klinger, Nicolas Schwab, Freimut Bodendorf

Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany

Blockchain Technology emerged recently in the area of Business Process Management and is still in its infancy. This paper analyses and evaluates the current scientific literature on the subject matter and synthesizes common topics, in order to create an understanding of the status quo. In a structured literature review, more than 300 publications were identified, of which 24 were finally selected as relevant to the cross-sectional topic of Blockchain and Business Process Management (BPM). A quantitative analysis affirms the recent upcoming of the relatively young research field and narrows the identified papers into three topic clusters, namely application areas and challenges, process architecture and design, and process-execution-related publications.



A Subscription Service for Automated Communication and Fair Cost Distribution in Collaborative Blockchain-based Business Processes

Moritz Schindelmann, Philipp Klinger, Freimut Bodendorf

Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany

Blockchain capabilities like Ethereum Smart Contracts offer great opportunities to manage cross-organizational business processes due to their trustless and tamperproof nature. However, communication in such processes poses a major issue since there is no direct option for one participating organization to inform other collaborators about their individual progress in their impersonation as a Smart Contract. As that knowledge is vital to execute a cross-organizational process, we design a Smart Contract architecture in which participants express their progress through Blockchain events. Further, we implement a prototype that subscribes to the relevant events of one or more participants and reacts to their occurrence by triggering the subsequent step(s) of the process. Evaluation of the prototype and architecture shows that this does not only avoid unnecessary latency in process communication but also results in a fair cost distribution as each participant is only charged for the expenses of its individual actions.



Robotic Process Automation: Hype or Hope?

Julia Hindel, Lena M. Cabrera, Matthias Stierle

Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany

Robotic Process Automation (RPA) is a fast-emerging process automation technology suited for high-volume, repetitive, and rule-based tasks. The promises of rising RPA vendors and the lack of documented track records leave researchers and practitioners with the challenge of positioning the term and assessing RPA’s true potential. To objectively discuss the strengths and weaknesses of this technology, we conduct a literature review, a practical implementation of an RPA solution, and an interview with an industry expert. We reveal that the current literature primarily focuses on economic factors. This paper, therefore, adds various social and technical aspects to the discussion. Most importantly, robustness and stability pose technical challenges for successfully implementing RPA. Further research directed at error handling and maintenance of software robots is required to support the successful implementation of RPA.



Using Artificial Neural Networks to Derive Process Model Activity Labels from Process Descriptions

Mirco Pyrtek1,2, Philip Hake1,2, Peter Loos1,2

1German Research Center for Artificial Intelligence (DFKI), Germany; 2Saarland University, Germany

Recently, Artificial Neural Networks (ANN) have shown high potential in the area of Natural Language Processing (NLP). In the area of sentence compression, the application of ANNs has proven to outperform existing rule-based approaches. Nevertheless, these approaches require a decent amount of training data to achieve high accuracy. In this work, we aim at employing ANNs to derive process model labels from process descriptions. Since the amount of publicly available pairs of text and process model is scarce, we employ a transfer learning approach. While training the compression model on a large corpus consisting of sentence-compression pairs, we transfer the model to the problem of deriving label descriptions. We implement our approach and conduct an experimental evaluation using pairs of process descriptions and models. We found that our transfer learning model keeps high recall while losing performance on precision and compression rate.