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162 results on '"Lukyanenko, Roman"'

1. The MAGIC of Data Management: Understanding the Value and Activities of Data Management

Author

Lukyanenko, Roman

Subjects
Computer Science - Databases, Computer Science - Information Theory
Abstract

In an era dominated by information technology, the critical discipline of data management remains undervalued compared to the innovations it enables, such as artificial intelligence and social media. The ambiguity surrounding what constitutes data management and its associated activities complicates efforts to explain its importance and ensure data are collected, stored and used in a way that maximizes value and avoids failures. This paper aims to address these shortcomings by presenting a simple framework for understanding data management, referred to as MAGIC. MAGIC encompasses five key activities: Modeling, Acquisition, Governance, Infrastructuring, and Consumption support tasks. By delineating these components, the MAGIC framework provides a clear, accessible approach to data management that can be used for teaching, research and practice.

Published
2024

4. Principles of Universal Conceptual Modeling

Author

Lukyanenko, Roman, Parsons, Jeffrey, Storey, Veda C., Samuel, Binny M., Pastor, Oscar, van der Aalst, Wil, Series Editor, Ram, Sudha, Series Editor, Rosemann, Michael, Series Editor, Szyperski, Clemens, Series Editor, Guizzardi, Giancarlo, Series Editor, van der Aa, Han, editor, Bork, Dominik, editor, Proper, Henderik A., editor, and Schmidt, Rainer, editor

Published
2023
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7. Citizen Science: An Information Quality Research Frontier

Author

Lukyanenko, Roman, Wiggins, Andrea, and Rosser, Holly K.

Subjects
Computer Science - Computers and Society
Abstract

The rapid proliferation of online content producing and sharing technologies resulted in an explosion of user-generated content (UGC), which now extends to scientific data. Citizen science, in which ordinary people contribute information for scientific research, epitomizes UGC. Citizen science projects are typically open to everyone, engage diverse audiences, and challenge ordinary people to produce data of highest quality to be usable in science. This also makes citizen science a very exciting area to study both traditional and innovative approaches to information quality management. With this paper we position citizen science as a leading information quality research frontier. We also show how citizen science opens a unique opportunity for the information systems community to contribute to a broad range of disciplines in natural and social sciences and humanities.

Published
2019

10. The Notion of 'System' as a Core Conceptual Modeling Construct for Life Sciences

Author

Lukyanenko, Roman, Storey, Veda C., Pastor, Oscar, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Reinhartz-Berger, Iris, editor, and Sadiq, Shazia, editor

Published
2021
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11. From Mental Models to Machine Learning Models via Conceptual Models

Author

Maass, Wolfgang, Storey, Veda C., Lukyanenko, Roman, van der Aalst, Wil, Series Editor, Mylopoulos, John, Series Editor, Rosemann, Michael, Series Editor, Shaw, Michael J., Series Editor, Szyperski, Clemens, Series Editor, Augusto, Adriano, editor, Gill, Asif, editor, Nurcan, Selmin, editor, Reinhartz-Berger, Iris, editor, Schmidt, Rainer, editor, and Zdravkovic, Jelena, editor

Published
2021
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14. Superimposition: Augmenting Machine Learning Outputs with Conceptual Models for Explainable AI

Author

Lukyanenko, Roman, Castellanos, Arturo, Storey, Veda C., Castillo, Alfred, Tremblay, Monica Chiarini, Parsons, Jeffrey, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Grossmann, Georg, editor, and Ram, Sudha, editor

Published
2020
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15. Validity in Design Science Research

Author

Larsen, Kai R., Lukyanenko, Roman, Mueller, Roland M., Storey, Veda C., VanderMeer, Debra, Parsons, Jeffrey, Hovorka, Dirk S., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Hofmann, Sara, editor, Müller, Oliver, editor, and Rossi, Matti, editor

Published
2020
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17. Falsification or Confirmation: From Logic to Psychology

Author

Lukyanenko, Roman

Subjects
Physics - History and Philosophy of Physics
Abstract

Corroboration or confirmation is a prominent philosophical debate of the 20th century. Many philosophers have been involved in this debate most notably the proponents of confirmation led by Hempel and its most powerful criticism by the falsificationists led by Popper. In both cases however the debates were primarily based on the arguments from logic. In this paper we review these debates and suggest that a different perspective on falsification versus confirmation can be taken by grounding arguments in cognitive psychology.

Published
2015

18. Using Conceptual Modeling to Support Machine Learning

Author

Lukyanenko, Roman, Castellanos, Arturo, Parsons, Jeffrey, Chiarini Tremblay, Monica, Storey, Veda C., van der Aalst, Wil, Series Editor, Mylopoulos, John, Series Editor, Rosemann, Michael, Series Editor, Shaw, Michael J., Series Editor, Szyperski, Clemens, Series Editor, Cappiello, Cinzia, editor, and Ruiz, Marcela, editor

Published
2019
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24. Validity in Design Science Research

Author

Larsen, Kai R., primary, Lukyanenko, Roman, additional, Mueller, Roland M., additional, Storey, Veda C., additional, VanderMeer, Debra, additional, Parsons, Jeffrey, additional, and Hovorka, Dirk S., additional

Published
2020
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27. Designing Information as a By-Product

Author

Yaramreddy, Sruthi, Knight, Kyle, Kona, Nitin Ram, Jain, Nitin, Salgado, Adriana, Lukyanenko, Roman, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Parsons, Jeffrey, editor, Tuunanen, Tuure, editor, Venable, John, editor, Donnellan, Brian, editor, Helfert, Markus, editor, and Kenneally, Jim, editor

Published
2016
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30. Principles for Modeling User-Generated Content

Author

Lukyanenko, Roman, Parsons, Jeffrey, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Johannesson, Paul, editor, Lee, Mong Li, editor, Liddle, Stephen W., editor, Opdahl, Andreas L., editor, and Pastor López, Óscar, editor

Published
2015
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31. Guidelines for Establishing Instantiation Validity in IT Artifacts: A Survey of IS Research

Author

Lukyanenko, Roman, Evermann, Joerg, Parsons, Jeffrey, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Donnellan, Brian, editor, Helfert, Markus, editor, Kenneally, Jim, editor, VanderMeer, Debra, editor, Rothenberger, Marcus, editor, and Winter, Robert, editor

Published
2015
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32. Transparency in Design Science Research

Author

Hevner, Alan, primary, Parsons, Jeffrey, additional, Brendel, Alfred Benedikt, additional, Lukyanenko, Roman, additional, Tiefenbeck, Verena, additional, Tremblay, Monica, additional, and vom Brocke, Jan, additional

Published
2023
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34. Instantiation Validity in IS Design Research

Author

Lukyanenko, Roman, Evermann, Joerg, Parsons, Jeffrey, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Tremblay, Monica Chiarini, editor, VanderMeer, Debra, editor, Rothenberger, Marcus, editor, Gupta, Ashish, editor, and Yoon, Victoria, editor

Published
2014
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35. Empowering Users with Narratives: Examining the Efficacy of Narratives for Understanding Data-Oriented Conceptual Models.

Author

Hvalshagen, Merete, Lukyanenko, Roman, and Samuel, Binny M.

Subjects
CONCEPTUAL models, SELF-efficacy, INFORMATION technology, OFFICES, INFORMATION skills
Abstract

Elevator Pitch A quiet revolution is happening in the offices, cubicles, and boardrooms of the world. Non-IT professionals are becoming empowered by leveraging organizational data for analytics. We support this movement by offering a powerful way to make data more usable via a combination of graphical conceptual models with narratives. Longer Version We are witnessing a quiet revolution—the rise of empowered users. These non-IT professionals increasingly seek to leverage the ever-expanding amount of organizational data for analytics to support their initiatives, decisions, and actions. All too often, however, the enthusiasm of these users collides against the harsh reality—many of them lack sophisticated IT skills, and they struggle to find/access relevant data, understand their meaning, and extract and adapt them to meet their needs. We propose a powerful way to support empowered users with a combination of conceptual models and narratives. Conceptual models are diagrams that accurately and succinctly represent rules and patterns captured in data. Although somewhat intuitive, these models alone do not suffice, as interpreting them still requires some specialized IT knowledge. Hence, we add narratives—stories written in natural language. The narratives intuitively explain some of the challenging aspects of the conceptual models. We conducted a series of experiments and interviews with empowered users to assess our idea. These studies show the value of the conceptual models with narratives for understanding organizational data. Our work unlocks an important missing puzzle for further user empowerment—a way for non-IT professionals to get the most out of the data. With the ongoing digitalization of human society, regular employees (e.g., noninformation technology experts) become increasingly autonomous and proactive in using organizational data and information technologies to facilitate data-driven actions and insights. This growing autonomy of regular employees (dubbed here, empowered users) in using information technologies creates new challenges. As most empowered users lack sophisticated information technology skills, they struggle to find and access relevant data, understand their meaning, and extract and adapt them to meet their needs. We propose a powerful way to support empowered users with a combination of conceptual models and narratives. A data narrative is a descriptive and textual representation of one or more aspects of data in a domain that is organized and presented as a connected sequence of sentences in a natural language. We assess the efficacy of narrative representations for conceptual modeling cardinality constraints, which are an essential part of understanding data. We conducted two laboratory experiments and found a positive and strong effect for narratives: cardinality constraints that were described in the narratives were better understood than constraints only represented in the model (i.e., script). This effect was robust across cardinalities, measures, script complexities, and familiarity levels with the conceptual modeling grammar. History: Manju Ahuja, Senior Editor; Choon-Ling Sia, Associate Editor. Supplemental Material: The online appendices are available at https://doi.org/10.1287/isre.2022.1141. [ABSTRACT FROM AUTHOR]

Published
2023
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36. FROM REPRESENTATION TO MEDIATION: A NEW AGENDA FOR CONCEPTUAL MODELING RESEARCH IN A DIGITAL WORLD.

Author

Recker, Jan, Lukyanenko, Roman, Jabbari, Mohammad, Samuel, Binny M., and Castellanos, Arturo

Abstract

The role of information systems (IS) as representations of real-world systems is changing in an increasingly digitalized world, suggesting that conceptual modeling is losing its relevance to the IS field. We argue the opposite: Conceptual modeling research is more relevant to the IS field than ever, but it requires an update with new theory. We develop a new theoretical framework of conceptual modeling that delivers a fundamental shift in the assumptions that govern research in this area. This move can make traditional knowledge about conceptual modeling consistent with the emerging requirements of a digital world. Our framework draws attention to the role of conceptual modeling scripts as mediators between physical and digital realities. We identify new research questions about grammars, methods, scripts, agents, and contexts that are situated in intertwined physical and digital realities. We discuss several implications for conceptual modeling scholarship that relate to the necessity of developing new methods and grammars for conceptual modeling, broadening the methodological array of conceptual modeling scholarship, and considering new dependent variables. [ABSTRACT FROM AUTHOR]

Published
2021
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37. Is Traditional Conceptual Modeling Becoming Obsolete?

Author

Lukyanenko, Roman, Parsons, Jeffrey, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Ng, Wilfred, editor, Storey, Veda C., editor, and Trujillo, Juan C., editor

Published
2013
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38. Lightweight Conceptual Modeling for Crowdsourcing

Author

Lukyanenko, Roman, Parsons, Jeffrey, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Ng, Wilfred, editor, Storey, Veda C., editor, and Trujillo, Juan C., editor

Published
2013
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39. Reconciling Theories with Design Choices in Design Science Research

Author

Lukyanenko, Roman, Parsons, Jeffrey, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, vom Brocke, Jan, editor, Hekkala, Riitta, editor, Ram, Sudha, editor, and Rossi, Matti, editor

Published
2013
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41. Unintended Consequences of Class-Based Ontological Commitment

Author

Lukyanenko, Roman, Parsons, Jeffrey, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, De Troyer, Olga, editor, Bauzer Medeiros, Claudia, editor, Billen, Roland, editor, Hallot, Pierre, editor, Simitsis, Alkis, editor, and Van Mingroot, Hans, editor

Published
2011
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42. Citizen Science 2.0: Data Management Principles to Harness the Power of the Crowd

Author

Lukyanenko, Roman, Parsons, Jeffrey, Wiersma, Yolanda, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Jain, Hemant, editor, Sinha, Atish P., editor, and Vitharana, Padmal, editor

Published
2011
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43. Conceptual Alignment Method.

Author

Maass, Wolfgang, Castellanos, Arturo, Tremblay, Monica, Lukyanenko, Roman, Storey, Veda C., and Almeida, Jonas S.

Subjects
ARTIFICIAL intelligence, MACHINE learning, HOSPITAL admission & discharge, MEDICAL care costs, DATA analysis
Abstract

This paper proposes a Conceptual Alignment (CA) Method for conceptual modeling and machine learning. The model consists of a three-step cycle that selects an initial conceptual model, aligns it with machine learning models, and refines both models to reach predictive consistency. Alignment is based on composition methods that can be instantiated by methods that satisfy contribution properties. The Conceptual Alignment Method is applied to a healthcare use case on hospital inpatient discharges. The machine learning model trained for total cost predictions is aligned with a conceptual model. We show how this refined conceptual model is used for explaining the machine learning model for a very large healthcare dataset. [ABSTRACT FROM AUTHOR]

Published
2023

46. Explainable AI: Opening the black box or Pandora’s Box?.

Author

Storey, Veda C., Lukyanenko, Roman, Maass, Wolfgang, and Parsons, Jeffrey

Subjects
*ARTIFICIAL intelligence, *ALGORITHMS, *MACHINE learning, *BIG data, *HUMAN-artificial intelligence interaction
Abstract

The article discusses the alleged problem of "explainable" artificial intelligence using the extended metaphor of Pandora's box. The interplay between big data, machine learning, and algorithmic processing is scrutinized along with the potential flaws of machine learning models' training data and poorly defined organizational processes as well as socially or psychologically problematic human-artificial intelligence interactions.

Published
2022
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48. Conceptual modelling for life sciences based on systemist foundations.

Author

Lukyanenko, Roman, Storey, Veda C., and Pastor, Oscar

Subjects
*CONCEPTUAL models, *DIGITAL technology, *INDIVIDUALIZED medicine, *INFORMATION storage & retrieval systems, *WELL-being
Abstract

Background: All aspects of our society, including the life sciences, need a mechanism for people working within them to represent the concepts they employ to carry out their research. For the information systems being designed and developed to support researchers and scientists in conducting their work, conceptual models of the relevant domains are usually designed as both blueprints for a system being developed and as a means of communication between the designer and developer. Most conceptual modelling concepts are generic in the sense that they are applied with the same understanding across many applications. Problems in the life sciences, however, are especially complex and important, because they deal with humans, their well-being, and their interactions with the environment as well as other organisms. Results: This work proposes a "systemist" perspective for creating a conceptual model of a life scientist's problem. We introduce the notion of a system and then show how it can be applied to the development of an information system for handling genomic-related information. We extend our discussion to show how the proposed systemist perspective can support the modelling of precision medicine. Conclusion: This research recognizes challenges in life sciences research of how to model problems to better represent the connections between physical and digital worlds. We propose a new notation that explicitly incorporates systemist thinking, as well as the components of systems based on recent ontological foundations. The new notation captures important semantics in the domain of life sciences. It may be used to facilitate understanding, communication and problem-solving more broadly. We also provide a precise, sound, ontologically supported characterization of the term "system," as a basic construct for conceptual modelling in life sciences. [ABSTRACT FROM AUTHOR]

Published
2023
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49. Explainable AI

Author

Storey, Veda C., primary, Lukyanenko, Roman, additional, Maass, Wolfgang, additional, and Parsons, Jeffrey, additional

Published
2022
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