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Authors: Mohammad Reza Harati Nik 1 ; Wil M. P. van der Aalst 2 and Mohammadreza Fani Sani 2

Affiliations: 1 Department of Industrial Management, Allameh Tabataba’i University, Tehran, Iran, PhD visitor at Process and Data Science (PADS) team at RWTH Aachen and Germany ; 2 Department of Computer Science, RWTH Aachen and Germany

Keyword(s): Process Mining, Business Intelligence, Microsoft Power Bi, Process Cubes, Business Analytics.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Business Analytics ; Business Intelligence ; Data Analytics ; Data Engineering ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Software Engineering ; Symbolic Systems

Abstract: In this paper, we introduce a custom visual for Microsoft Power BI that supports process mining and business intelligence analysis simultaneously using a single platform. This tool is called BIpm, and it brings the simple, agile, user-friendly, and affordable solution to study process models over multidimensional event logs. The Power BI environment provides many self-service BI and OLAP features that can be exploited through our custom visual aimed at the analysis of process data. The resulting toolset allows for accessing various input data sources and generating online reports and dashboards. Rather than designing and working with reports in the Power BI service on the web, it can be possible to view them in the Power BI mobile apps, and this means BIpm provides a solution to have process mining visualizations on mobiles. Therefore, BIpm can encourage many businesses and organizations to do process mining analysis with business intelligence analytics. Consequently, it yields manag ers and decision makers to translate discovered insights comprehensively to gain improved decisions and better performance more quickly. (More)

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Paper citation in several formats:
Harati Nik, M.; van der Aalst, W. and Fani Sani, M. (2019). BIpm: Combining BI and Process Mining. In Proceedings of the 8th International Conference on Data Science, Technology and Applications - DATA; ISBN 978-989-758-377-3; ISSN 2184-285X, SciTePress, pages 123-128. DOI: 10.5220/0007741901230128

@conference{data19,
author={Mohammad Reza {Harati Nik}. and Wil M. P. {van der Aalst}. and Mohammadreza {Fani Sani}.},
title={BIpm: Combining BI and Process Mining},
booktitle={Proceedings of the 8th International Conference on Data Science, Technology and Applications - DATA},
year={2019},
pages={123-128},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007741901230128},
isbn={978-989-758-377-3},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Data Science, Technology and Applications - DATA
TI - BIpm: Combining BI and Process Mining
SN - 978-989-758-377-3
IS - 2184-285X
AU - Harati Nik, M.
AU - van der Aalst, W.
AU - Fani Sani, M.
PY - 2019
SP - 123
EP - 128
DO - 10.5220/0007741901230128
PB - SciTePress