Semantic OLAP Patterns: Elements of Reusable Business Analytics

Schuetz, Christoph G.; Schausberger, Simon; Kovacic, Ilko; Schrefl, Michael

doi:10.1007/978-3-319-69459-7_22

Cited by 5 publications

(32 citation statements)

References 9 publications

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“…The idea of OLAP patterns was first developed in the course of the agriProKnow project while also drawing from experience in previous projects [5], [6] -in order to deal with uncertainties regarding the stakeholders' requirements with respect to the OLAP queries that would have to be composed [2]. The agriProKnow project was a joint research and development effort between industry and academia to design and implement a data warehouse for precision dairy farming, integrating sensor data as well as available operational databases [4].…”

Section: Approachmentioning

confidence: 99%

“…In this thesis, we carefully revise and significantly extend our previous work on OLAP patterns [1], [2], which established the basic notion of OLAP patterns and was later applied in the context of linked open data on the semantic web [16]. The original pattern-based approach to data analysis was developed in an ad hoc manner, out of necessity, tailored to the specific needs of the agriProKnow project -a byproduct to cope with the uncertainties related to the project's requirements.…”

Section: Contributionsmentioning

confidence: 99%

“…We conclude this chapter with an overview of visualization-and model-driven techniques for data analysis (Section 3.4). This chapter is based in parts on related work sections from our previous publications [1], [2]. In summary, no related work covers in its entirety the functionality of the pattern-based approach to multidimensional data analysis.…”

Section: Related Workmentioning

confidence: 99%

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OLAP Patterns: A pattern-based approach to multidimensional data analysis

Kovacic

Schuetz

Neumayr

et al. 2022

Data & Knowledge Engineering

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I would like to thank the many dedicated people who have actively supported me in the preparation of this thesis. First of all, I would like to thank my supervisor Michael Schrefl, without whom this thesis would not have been possible. I would like to thank him especially for the creative and exhaustive discussions in which we thought through envisaged ideas.I owe special thanks to Christoph Schütz, who contributed significantly to this thesis and showed me how challenging and emotional scientific work can be. I also owe thanks to my colleague Bernd Neumayr, who provided valuable input and cheered me up by reminding me that there are far worse things in the world than an unfinished thesis. My thanks also go to Margit Brandl, who saved me from administrative mischief during the thesis. In addition, I thank the master students Michael Moritz, who implemented the pattern-based approach presented in this thesis, and Simon Schausberger, who implemented the prototype in agriProKnow.In addition, I would like to acknowledge all external circumstances, fortunate opportunities, and coincidences beyond my control that contributed significantly to making this thesis possible. First and foremost, the fact that I live in Europe and that it is possible to pursue an academic path in Austria despite my parents' lack of academic background.Besides my superiors, colleagues, and fate, I would like to thank my mother Ora Kovačić, who taught me the value of education and achievement from an early age. I also thank my siblings Mara Rührlinger and Ivo Kovačić and their partners Thomas Rührlinger and Bettina Buchendorfer, who have always stood by me. I also thank myself for being so pain-free and disciplined and not following tempting offers from the private sector. Finally, I would like to express my deepest gratitude to my partner, Marlene Bachleitner, for her patience, everlasting support, and companionship throughout this bumpy journey. PrefaceProblem solving, in the absence of experience, requires a great deal of cognitive effort, both to understand the problem and to design and elicit possible solutions. Even if the chosen solution proves to be sufficient for the problem, it may not be the best solution because possible consequences of following it have not been considered. However, in order to apply the best possible solution, the experiences of others must be externalized in order to draw on them. For this purpose, a pattern-based approach has proven successful in a wide variety of domains, as patterns can be used to document and communicate best-practice solutions for recurring problems.In the agriProKnow research and development project, funded by the Austrian Research Promotion Agency under grant number FFG-848610 to develop a business intelligence (BI) system for precision dairy, we applied a pattern-based approach to document, communicate, and reuse best-practice solutions for composing ad hoc OLAP queries to meet specific types of information needs. The lessons learned during the agriProKnow project with respect to the use ...

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Section: Approachmentioning

confidence: 99%

Section: Contributionsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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OLAP Patterns: A pattern-based approach to multidimensional data analysis

Kovacic

Schuetz

Neumayr

et al. 2022

Data & Knowledge Engineering

Self Cite

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“…That is not to say that both methodologies have no overlapping characteristics. Indeed, some of the actions presented in this paper, for example, semantic OLAP patterns, are design artifacts formally described in separate works (Kovacic et al 2018;Schuetz et al 2017). In this paper, however, we present the case of the construction of an active semantic data warehouse and the taken actions to develop the system from a more general point of view, rather than in a formal manner, and we derive implications to theory and practice drawn from the experience in the agriProKnow project.…”

Section: Research Approachmentioning

confidence: 99%

“…While the agriProKnow project has also produced design artifacts in the area of BI and data warehousing-as described in separate design science research papers (Kovacic et al 2018;Schuetz et al 2017) -this action research study had a different focus: analyze the actions taken in the course of the agriProKnow project with respect to their implications for information systems practice and research. Hence, the contributions of this paper lie in the general description of a wide array of actions that led to the success of the agriProKnow project, as well as the lessons learned while taking these actions, rather than in the formal definition of the produced design artifacts.…”

Section: Contribution Summarymentioning

confidence: 99%

Building an active semantic data warehouse for precision dairy farming

Schuetz

Schausberger

Schrefl

2018

Journal of Organizational Computing and Electronic Commerce

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Digitalization of agricultural technology has led to the emergence of precision dairy farming, which strives for the simultaneous improvement of productivity as well as animal well-being in dairy farming through advanced use of technology such as movement sensors and milking parlors to monitor, control, and improve dairy production processes. The data warehouse serves as the appropriate technology for effective and efficient data management, which is paramount to the success of precision dairy farming. This paper presents a joint effort between industry and academia on the experimental development of an active semantic data warehouse to support business intelligence and business analytics in precision dairy farming. The research follows an action research approach, deriving lessons for theory and practice from a set of actions taken in the course of the project. Among these actions are the development of a loading stage to facilitate data integration, the definition of an analysis view as well as the introduction of semantic OLAP patterns to facilitate analysis, and analysis rules to automate periodic analyses. The large volumes of generated sensor data in precision dairy farming required careful decision-making concerning the appropriate level of detail of the data stored in the data warehouse. Semantic technologies played a key role in rendering analysis accessible to end users.

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Conceptualizing Analytics: An Overview of Business Intelligence and Analytics from a Conceptual-Modeling Perspective

Schuetz¹,

Schrefl²

2023

Digital Transformation

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Semantic OLAP Patterns: Elements of Reusable Business Analytics

Cited by 5 publications

References 9 publications

OLAP Patterns: A pattern-based approach to multidimensional data analysis

OLAP Patterns: A pattern-based approach to multidimensional data analysis

Building an active semantic data warehouse for precision dairy farming

Conceptualizing Analytics: An Overview of Business Intelligence and Analytics from a Conceptual-Modeling Perspective

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