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- ZeitschriftenartikelAn Expert-Validated Bridging Model for IoT Process Mining(Business & Information Systems Engineering: Vol. 66, No. 6, 2024) Bertrand, Yannis; De Weerdt, Jochen; Serral, EstefaníaContextualization is an important challenge in process mining. While Internet of Things (IoT) devices are collecting increasing amounts of data on the physical context in which business processes are executed, the IoT and process mining fields are still considerably disintegrated. Important concepts such as event or context are not understood in the same way, which causes confusion and hinders cooperation between the two domains. Accordingly, in the paper, a consolidated model to bridge the conceptualization gap between the IoT and process mining fields, based on IoT ontologies and business process context models, is presented. This consolidation based on an initial model was obtained after an extensive validation both with an expert panel and with case studies. The results of the expert survey show that the model properly describes the links between the IoT and process mining and that it has added value for IoT process mining. Furthermore, the model was refined according to the experts’ feedback. Accordingly, the paper’s key contribution consists of a common reference model that can instigate true interdisciplinary research connecting IoT and process mining.
- ZeitschriftenartikelEvaluating BPMN Extensions for Continuous Processes Based on Use Cases and Expert Interviews(Business & Information Systems Engineering: Vol. 66, No. 6, 2024) Strutzenberger, Diana; Mangler, Juergen; Rinderle-Ma, StefanieThe majority of (business) processes described in literature are discrete, i.e., they result in an identifiable and distinct outcome such as a settled customer claim or a produced part. However, there also exists a plethora of processes in process and control engineering that are continuous, i.e., processes that require real-time control systems with constant inlet and outlet flows as well as temporally stable conditions. Examples comprise chemical synthesis and combustion processes. Despite their prevalence and relevance a standard method for modeling continuous processes with BPMN is missing. Hence, the paper provides BPMN modeling extensions for continuous processes enabling an exact definition of the parameters and loop conditions as well as a mapping to executable processes. The BPMN modeling extensions are evaluated based on selected use cases from process and control engineering and interviews with experts from three groups, i.e., process engineers and two groups of process modelers, one with experience in industrial processes and one without. The results from the expert interviews are intended to identify (i) the key characteristics for the representation of continuous processes, (ii) how experts evaluate the current usability and comprehensibility of BPMN for continuous processes, and (iii) potential improvements can be identified regarding the introduced BPMN modeling extensions.
- ZeitschriftenartikelA Data Quality Multidimensional Model for Social Media Analysis(Business & Information Systems Engineering: Vol. 66, No. 6, 2024) Aramburu, María José; Berlanga, Rafael; Lanza-Cruz, IndiraSocial media platforms have become a new source of useful information for companies. Ensuring the business value of social media first requires an analysis of the quality of the relevant data and then the development of practical business intelligence solutions. This paper aims at building high-quality datasets for social business intelligence (SoBI). The proposed method offers an integrated and dynamic approach to identify the relevant quality metrics for each analysis domain. This method employs a novel multidimensional data model for the construction of cubes with impact measures for various quality metrics. In this model, quality metrics and indicators are organized in two main axes. The first one concerns the kind of facts to be extracted, namely: posts, users, and topics. The second axis refers to the quality perspectives to be assessed, namely: credibility, reputation, usefulness, and completeness. Additionally, quality cubes include a user-role dimension so that quality metrics can be evaluated in terms of the user business roles. To demonstrate the usefulness of this approach, the authors have applied their method to two separate domains: automotive business and natural disasters management. Results show that the trade-off between quantity and quality for social media data is focused on a small percentage of relevant users. Thus, data filtering can be easily performed by simply ranking the posts according to the quality metrics identified with the proposed method. As far as the authors know, this is the first approach that integrates both the extraction of analytical facts and the assessment of social media data quality in the same framework.
- ZeitschriftenartikelSJORS: A Semantic Recommender System for Journalists(Business & Information Systems Engineering: Vol. 66, No. 6, 2024) Garrido, Ángel Luis; Pera, Maria Soledad; Bobed, CarlosRecommender Systems support a broad range of domains, each with peculiarities that recommendation algorithms must consider to produce appropriate suggestions. In the paper, we bring attention to a little-studied scenario related to the news domain: recommendations catering to media journalists. Based on the particular needs inherent to a newsroom, the authors introduce SJORS, a wire news Recommender System that takes into account the activities of each journalist as well as other critical factors that arise in this particular domain, such as wire news recency. Given the nature of the items recommended, SJORS deals with the inherent ambiguity of natural language by exploiting different semantic techniques and technologies. The authors have conducted several experiments in a media company, which validated the performance and applicability of the system. Outcomes emerging from this work could be extended to other domains of interest, such as online stores, streaming platforms, or digital libraries, to name a few.
- ZeitschriftenartikelOn the Pivotal Role of Data in Sustainability Transformations(Business & Information Systems Engineering: Vol. 66, No. 6, 2024) Püchel, Lea; Wang, Cancan; Buhmann, Karin; Brandt, Tobias; Schweinitz, Felizia; Edinger-Schons, Laura Marie; vom Brocke, Jan; Legner, Christine; Teracino, Elizabeth; Mardahl, Thomas Daniel
- ZeitschriftenartikelHedonic Signals in Crowdfunding(Business & Information Systems Engineering: Vol. 66, No. 6, 2024) Blohm, Ivo; Schulz, Moritz; Leimeister, Jan MarcoThis study draws on signaling theory to investigate the effect of hedonic signals in crowdfunding projects on funding performance. It compares the effect of hedonic signals across reward-, equity-, and donation-based crowdfunding platforms by combining archival data from 18 platforms and a large-scale panel of 64 experts that rate the strength of hedonic signals in 108 crowdfunding projects. Through the application of mixed linear modeling, the findings indicate a positive influence of stronger hedonic signals on funding performance. However, there are substantial differences across platform types. Increasing the strength of hedonic signals by one standard deviation increases funding performance by 28.9% on reward platforms, while there are no systematic effects on equity and donation platforms. This study contributes to existing crowdfunding research by clarifying the role of hedonic signals in crowdfunding and shedding light on the increasing need to better consider the characteristics of different crowdfunding platforms in crowdfunding research.
- ZeitschriftenartikelUser Behavior Mining(Business & Information Systems Engineering: Vol. 66, No. 6, 2024) Rehse, Jana-Rebecca; Abb, Luka; Berg, Gregor; Bormann, Carsten; Kampik, Timotheus; Warmuth, ChristianStudying the behavior of users in software systems has become an essential task for software vendors who want to mitigate usability problems and identify automation potentials, or for researchers who want to test behavioral theories. One approach to studying user behavior in a data-driven way is through the analysis of so-called user interaction (UI) logs, which record the low-level activities that a user performs while executing a task. In the paper, the authors refer to the analysis of UI logs as User Behavior Mining (UBM) and position it as a research topic. UBM is conceptualized by means of a four-component framework that elaborates how UBM data can be captured, which technologies can be applied to analyze it, which objectives UBM can accomplish, and how theories can guide the analytical process. The applicability of the framework is demonstrated by three exemplary applications from an ongoing research project with a partner company. Finally, the paper discusses practical challenges to UBM and derives an agenda for potential future research directions.
- ZeitschriftenartikelShadowbanning(Business & Information Systems Engineering: Vol. 66, No. 6, 2024) Risius, Marten; Blasiak, Kevin Marc
- ZeitschriftenartikelNew Laws and Regulation(Business & Information Systems Engineering: Vol. 66, No. 6, 2024) Pfeiffer, Jella; Lachenmaier, Jens F.; Hinz, Oliver; van der Aalst, Wil
- ZeitschriftenartikelA Survey on Association Rule Mining for Enterprise Architecture Model Discovery(Business & Information Systems Engineering: Vol. 66, No. 6, 2024) Pinheiro, Carlos; Guerreiro, Sergio; Mamede, Henrique S.Association Rule Mining (ARM) is a field of data mining (DM) that attempts to identify correlations among database items. It has been applied in various domains to discover patterns, provide insight into different topics, and build understandable, descriptive, and predictive models. On the one hand, Enterprise Architecture (EA) is a coherent set of principles, methods, and models suitable for designing organizational structures. It uses viewpoints derived from EA models to express different concerns about a company and its IT landscape, such as organizational hierarchies, processes, services, applications, and data. EA mining is the use of DM techniques to obtain EA models. This paper presents a literature review to identify the newest and most cited ARM algorithms and techniques suitable for EA mining that focus on automating the creation of EA models from existent data in application systems and services. It systematically identifies and maps fourteen candidate algorithms into four categories useful for EA mining: (i) General Frequent Pattern Mining, (ii) High Utility Pattern Mining, (iii) Parallel Pattern Mining, and (iv) Distribute Pattern Mining. Based on that, it discusses some possibilities and presents an exemplification with a prototype hypothesizing an ARM application for EA mining.