Towards solving ontological dissonance using network graphs

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Stäbler, Maximilian; Köster, Frank; Schlueter-Langdon, Christoph

Abstract: Data Spaces are an emerging concept for the trusted implementation of data-base ... Expand

Abstract: Data Spaces are an emerging concept for the trusted implementation of data-based applications and business models, offering a high degree of flexibility and sovereignty to all stakeholders. As Data Spaces are currently emerging in different domains such as mobility, health or food, semantic interfaces need to be identified and implemented to ensure the technical interoperability of these Data Spaces. This paper consolidates data models from 13 different domains and analyzes the ontological dissonance of these domains. Using a network graph, central data models and ontology attributes are identified, while the semantic heterogeneity of these domains is described qualitatively. The research outlook describes how these results help to connect different Data Spaces across domains. Collapse

Topics: Python database system MongoDB knowledge representation

Methods: ontological modelling data modeling graph analysis

(Re)Designing IT Support: How Embedded and Conversational AI Can Augment Technical Support Work

2021 | International Conference on Information Systems | Citations: 0

Authors: Poser, Mathis; Bittner, Eva

Abstract: Occupations, like many other social systems, are hierarchical. They evolve with ... Expand

Abstract: Occupations, like many other social systems, are hierarchical. They evolve with other elements within the work ecosystem including technology and skills. This paper investigates the relationships among these elements using an approach that combines network theory and modular systems theory. A new method of using work related data to build occupation networks and theorize occupation evolution is proposed. Using this technique, structural properties of occupations are discovered by way of community detection on a knowledge network built from labor statistics, based on more than 900 occupations and 18,000 tasks. The occupation networks are compared across the work ecosystem as well as over time to understand the interdependencies between task components and the coevolution of occupation, tasks, technology, and skills. In addition, a set of conjectures are articulated based on the observations made from occupation structure comparison and change over time. Collapse

Topics: crowdsourcing marketing management blockchain innovation management IT skill

Methods: theory development optimization model experiment qualitativ observational study computational algorithm

Theories: general systems theory social network theory socio technical theory

Follow the money: Revealing risky nodes in a Ransomware-Bitcoin network

2021 | HICSS | Citations: 0

Authors: Turner, Adam; Mccombie, Stephen; Uhlmann, Allon

Abstract: This paper demonstrates the use of network analysis to identify core nodes assoc ... Expand

Abstract: This paper demonstrates the use of network analysis to identify core nodes associated with ransomware attacks in cryptocurrency transaction networks. The method helps trace the cyber entities involved in cryptocurrency attacks and supports intelligence efforts to identify and disrupt cryptocurrency networks. Collapse

Topics: ransomware Bitcoin open source application programming interface cryptocurrency

Methods: computational algorithm machine learning k-means clustering data transformation principal component analysis

Occupation Modularity and the Work Ecosystem

2021 | International Conference on Information Systems | Citations: 0

Authors: Zhang, Shiyan; Nickerson, Jeffrey

Abstract: Occupations, like many other social systems, are hierarchical. They evolve with ... Expand

Abstract: Occupations, like many other social systems, are hierarchical. They evolve with other elements within the work ecosystem including technology and skills. This paper investigates the relationships among these elements using an approach that combines network theory and modular systems theory. A new method of using work related data to build occupation networks and theorize occupation evolution is proposed. Using this technique, structural properties of occupations are discovered by way of community detection on a knowledge network built from labor statistics, based on more than 900 occupations and 18,000 tasks. The occupation networks are compared across the work ecosystem as well as over time to understand the interdependencies between task components and the coevolution of occupation, tasks, technology, and skills. In addition, a set of conjectures are articulated based on the observations made from occupation structure comparison and change over time. Collapse

Topics: crowdsourcing marketing management blockchain innovation management IT skill

Methods: theory development optimization model experiment computational algorithm qualitativ observational study

Theories: general systems theory social network theory socio technical theory

Extracting Maritime Traffic Networks from AIS Data Using Evolutionary Algorithm

2020 | Business & Information Systems Engineering | Citations: 0

Authors: Filipiak, Dominik; Węcel, Krzysztof; Stróżyna, Milena; Michalak, Michał; Abramowicz, Witold

Abstract: The presented method reconstructs a network (a graph) from AIS data, which refle ... Expand

Abstract: The presented method reconstructs a network (a graph) from AIS data, which reflects vessel traffic and can be used for route planning. The approach consists of three main steps: maneuvering points detection, waypoints discovery, and edge construction. The maneuvering points detection uses the CUSUM method and reduces the amount of data for further processing. The genetic algorithm with spatial partitioning is used for waypoints discovery. Finally, edges connecting these waypoints form the final maritime traffic network. The approach aims at advancing the practice of maritime voyage planning, which is typically done manually by a ship’s navigation officer. The authors demonstrate the results of the implementation using Apache Spark, a popular distributed and parallel computing framework. The method is evaluated by comparing the results with an on-line voyage planning application. The evaluation shows that the approach has the capacity to generate a graph which resembles the real-world maritime traffic network. Collapse

Topics: Apache Spark parallel computing computer hardware

Methods: computational algorithm cumulative sum control chart experiment machine learning optimization model

Theories: graph theory

A Label-based Edge Partitioning for Multi-Layer Graphs

2019 | HICSS | Citations: 0

Authors: Constantin, Camelia; Li, Yifan

Abstract: Social network systems rely on very large underlying graphs. Consequently, to ac ... Expand

Abstract: Social network systems rely on very large underlying graphs. Consequently, to achieve scalability, most data analytics and data mining algorithms are distributed and graphs are partitioned over a set of servers. In most real-world graphs, the edges and/or vertices have different semantics and queries largely consider this semantics. But while several works focus on efficient graph computations on these “multi-semantic” graphs, few ones are dedicated to their partitioning. In this work, we propose a novel approach to achieve edge partitioning for multi-layer graphs, which considers both structural and edge-types (labels) localities. Our experiments on real life datasets with benchmark graph applications confirm that the execution time and the inter-partition communication can be significantly reduced with our approach. Collapse

Topics: Twitter social network Google+ armed conflict

Methods: computational algorithm experiment graph analysis descriptive statistic literature study

A Graph-based Approach for Detecting Critical Infrastructure Disruptions on Social Media in Disasters

2019 | HICSS | Citations: 0

Authors: Fan, Chao; Mostafavi, Ali; Yao, Wenlin; Huang, Ruihong

Abstract: The objective of this paper is to propose and test a graph-based approach for de ... Expand

Abstract: The objective of this paper is to propose and test a graph-based approach for detection of critical infrastructure disruptions in social media data in disasters. Understanding the situation and disruptive events of critical infrastructure is essential to effective disaster response and recovery of communities. The potential of social media data for situation awareness during disasters has been highlighted in recent studies. However, the application of social sensing in detecting disruptions of critical infrastructure is limited because existing approaches cannot provide complete and nonambiguous situational information about critical infrastructure. Therefore, to address this methodological gap, we developed a graph-based approach including data filtering, burst time-frame detection, content similarity and graph analysis. A case study of Hurricane Harvey in 2017 in Houston was conducted to illustrate the application of the proposed approach. The findings highlighted the temporal patterns of critical infrastructure events that occurred in disasters including disruptive events and their adverse impacts on communities. The findings also provided insights for better understanding critical infrastructure interdependencies in disasters. From the practical perspective, the proposed methodology study can improve the ability of community members, first responders and decision makers to detect and respond to infrastructure disruptions in disasters. Collapse

Topics: Twitter social media hurricane Amazon Web Services

Methods: case study computational algorithm graph analysis literature study machine learning

Assets under Tokenization: Can Blockchain Technology Improve Post-Trade Processing?

2019 | International Conference on Information Systems | Citations: 0

Authors: Ross, Omri; Jensen, Johannes; Asheim, Truls

Abstract: Recent years have seen rapid growth in IS scholarship addressing the efficacy an ... Expand

Abstract: Recent years have seen rapid growth in IS scholarship addressing the efficacy and utility of blockchain technology in trade-processing and financial services. Numerous IS scholars encourage applied research into the ostensible synergies between the nascent technology and its various use cases. Yet the vast majority of published research approaches the issue from a purely contemplative or theoretical perspective. Addressing this gap in the IS literature, we apply the design science research methodology in the construction a software artefact for the abstract representation of physical assets in the form of blockchain tokens, a process colloquially referred to as tokenization. We present the final iteration of the artefact, evaluating our results against the requirements collected through the design search process. This informs a rigorous evaluation of the conceptual limitations of blockchain-based software artefacts. We conclude that, provided the aforementioned requirements are adequately observed within the design search process, blockchain technology can indeed improve post-trade processing. Collapse

Topics: blockchain user requirement Ethereum accounting cryptocurrency

Methods: qualitative interview design artifact design science design requirement graph analysis

Theories: technology adoption lifecycle

Behavioral data mining to produce novel and serendipitous friend recommendations in a social bookmarking system

2018 | Information Systems Frontiers | Citations: 2

Authors: Manca, Matteo; Boratto, Ludovico; Carta, Salvatore

Abstract: In the last few years, social media systems have experienced a fast growth. The ... Expand

Abstract: In the last few years, social media systems have experienced a fast growth. The amount of content shared in these systems increases fast, leading users to face the well known “interaction overload” problem, i.e., they are overwhelmed by content, so it becomes difficult to come across interesting items. To overcome this problem, social recommender systems have been recently designed and developed in order to filter content and recommend to users only interesting items. This type of filtering is usually affected by the “over-specialization” problem, which is related to recommendations that are too similar to the items already considered by the users. This paper proposes a friend recommender system that operates in the social bookmarking application domain and is based on behavioral data mining, i.e., on the exploitation of the users activity in a social bookmarking system. Experimental results show how this type of mining is able to produce accurate friend recommendations, allowing users to get to know bookmarked resources that are both novel and serendipitous. Using this approach, the impact of the “interaction overload” and the “over-specialization” problems is strongly reduced. Collapse

Topics: recommender system user behavior IT work systems design open source

Methods: experiment computational algorithm machine learning literature study descriptive statistic

State of Play: A Citation Network Analysis of Healthcare Gamification Studies

2018 | European Conference On Information Systems | Citations: 0

Authors: Schmidt-Kraepelin, Manuel; Thiebes, Scott; Baumsteiger, Devin; Sunyaev, Ali

Abstract: Researchers and practitioners alike increasingly recognize gamification as a pot ... Expand

Abstract: Researchers and practitioners alike increasingly recognize gamification as a potential tool to evoke desired behaviours in patients, healthcare professionals, and healthy end-users aiming to live a healthier lifestyle. Thus, the number of scientific publications in healthcare gamification is rapidly increasing and due to the interdisciplinary nature of the research field, knowledge about this topic is being scattered over many research communities. Building on a large number of articles on healthcare gamification and utilizing citation network analysis, this study sheds further light on the extant knowledge on healthcare gamification. Based on our approach, we were able to (1) evaluate essential articles and authors covering the topic, (2) analyse the recent development of research on healthcare gamification, and (3) identify past research foci and knowledge gaps in our knowledge on healthcare gamification. By doing so, we call for further research on healthcare gamification and provide researchers with potential avenues for future research projects. Collapse

Topics: gamification knowledge sharing social network outsourcing knowledge base

Methods: co-citation analysis literature study structured literature research network analysis path analysis