8,526 research outputs found
Big data analytics:Computational intelligence techniques and application areas
Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment
Organizational Improvement Readiness Assessment (OIRA) Tool Evaluation
Background: Research shows that despite an increase in the number of organizational improvement initiatives there is a lack of consistent, sustained outcomes. Organizations struggle with how to reliably and accurately measure their readiness to drive and sustain outcomes. A search of the literature failed to identify a comprehensive, evidence-based tool that has been developed or evaluated to assess organizational improvement readiness. The objective of this project was to evaluate a newly developed Organizational Improvement Readiness Assessment (OIRA) Tool.
Project Design: Guided by two theoretical models, Delphi-Based Systems Architecting Framework (DB-SAF) and the Rogers Diffusion of Innovation Model, a 3-round, modified Delphi nominal group method was utilized. An evaluation panel of 13 organizational improvement subject matter experts (SMEs) was recruited, with 11 SMEs completing all 3 evaluation rounds. The relevancy and clarity of the OIRA Tool competencies was evaluated using an item-level content validity index (I-CVI) and a scale-level content validity index (S-CVI). Additionally, the tool was evaluated from a usability perspective using Google Analytics.
Results: The OIRA Tool was found to be clear, understandable, and relevant for organizations evaluating their readiness to drive and sustain outcomes improvements (S-CVI index of 0.92 and I-CVI indices ranging from 0.82 to 1.0). The final version of the tool included 22 competencies, modified based on expert consensus from the original 25. Usability test results confirmed the OIRA Tool, a web-based tool, is easy to use and well designed as measured by exit rates (15.44%), bounce rates (51.81%), and conversion rates (14%), all of which were significantly better than industry benchmarks.
Recommendations and Conclusions: Results of this project provide evidence of the content validity and usability of the OIRA Tool. The tool has the potential to help healthcare organizations assess their readiness to sustain organizational improvements and to identify gaps in leadership and culture, processes, technologies, and standards. The OIRA Tool provides the foundation for future analytics modeling and additional studies to test the theory and the advancement of outcomes improvement science
A Framework for Artificial Intelligence Applications in the Healthcare Revenue Management Cycle
There is a lack of understanding of specific risks and benefits associated with AI/RPA implementations in healthcare revenue cycle settings. Healthcare companies are confronted with stricter regulations and billing requirements, underpayments, and more significant delays in receiving payments. Despite the continued interest of practitioners, revenue cycle management has not received much attention in research. Revenue cycle management is defined as the process of identifying, collecting, and managing the practice’s revenue from payers based on the services provided.This dissertation provided contributions to both areas, as mentioned above. To accomplish this, a semi-structured interview was distributed to healthcare executives. The semi-structured interview data obtained from each participant underwent a triangulation process to determine the validity of responses aligned with the extant literature. Data triangulation ensured further that significant themes found in the interview data answered the central research questions. The study focused on how the broader issues related to AI/RPA integration into revenue cycle management will affect individual organizations. These findings also presented multiple views of the technology’s potential benefits, limitations, and risk management strategies to address its associative threats. The triangulation of the responses and current literature helped develop a theoretical framework that may be applied to a healthcare organization in an effort to migrate from their current revenue management technique to one that includes the use of AI/ML/RPA as a means of future cost control and revenue boost
Data Science and Analytics in Industrial Maintenance: Selection, Evaluation, and Application of Data-Driven Methods
Data-driven maintenance bears the potential to realize various benefits based on multifaceted data assets generated in increasingly digitized industrial environments. By taking advantage of modern methods and technologies from the field of data science and analytics (DSA), it is possible, for example, to gain a better understanding of complex technical processes and to anticipate impending machine faults and failures at an early stage. However, successful implementation of DSA projects requires multidisciplinary expertise, which can rarely be covered by individual employees or single units within an organization. This expertise covers, for example, a solid understanding of the domain, analytical method and modeling skills, experience in dealing with different source systems and data structures, and the ability to transfer suitable solution approaches into information systems. Against this background, various approaches have emerged in recent years to make the implementation of DSA projects more accessible to broader user groups. These include structured procedure models, systematization and modeling frameworks, domain-specific benchmark studies to illustrate best practices, standardized DSA software solutions, and intelligent assistance systems.
The present thesis ties in with previous efforts and provides further contributions for their continuation. More specifically, it aims to create supportive artifacts for the selection, evaluation, and application of data-driven methods in the field of industrial maintenance. For this purpose, the thesis covers four artifacts, which were developed in several publications. These artifacts include (i) a comprehensive systematization framework for the description of central properties of recurring data analysis problems in the field of industrial maintenance, (ii) a text-based assistance system that offers advice regarding the most suitable class of analysis methods based on natural language and domain-specific problem descriptions, (iii) a taxonomic evaluation framework for the systematic assessment of data-driven methods under varying conditions, and (iv) a novel solution approach for the development of prognostic decision models in cases of missing label information.
Individual research objectives guide the construction of the artifacts as part of a systematic research design. The findings are presented in a structured manner by summarizing the results of the corresponding publications. Moreover, the connections between the developed artifacts as well as related work are discussed. Subsequently, a critical reflection is offered concerning the generalization and transferability of the achieved results. Thus, the thesis not only provides a contribution based on the proposed artifacts; it also paves the way for future opportunities, for which a detailed research agenda is outlined.:List of Figures
List of Tables
List of Abbreviations
1 Introduction
1.1 Motivation
1.2 Conceptual Background
1.3 Related Work
1.4 Research Design
1.5 Structure of the Thesis
2 Systematization of the Field
2.1 The Current State of Research
2.2 Systematization Framework
2.3 Exemplary Framework Application
3 Intelligent Assistance System for Automated Method Selection
3.1 Elicitation of Requirements
3.2 Design Principles and Design Features
3.3 Prototypical Instantiation and Evaluation
4 Taxonomic Framework for Method Evaluation
4.1 Survey of Prognostic Solutions
4.2 Taxonomic Evaluation Framework
4.3 Exemplary Framework Application
5 Method Application Under Industrial Conditions
5.1 Conceptualization of a Solution Approach
5.2 Prototypical Implementation and Evaluation
6 Discussion of the Results
6.1 Connections Between Developed Artifacts and Related Work
6.2 Generalization and Transferability of the Results
7 Concluding Remarks
Bibliography
Appendix I: Implementation Details
Appendix II: List of Publications
A Publication P1: Focus Area Systematization
B Publication P2: Focus Area Method Selection
C Publication P3: Focus Area Method Selection
D Publication P4: Focus Area Method Evaluation
E Publication P5: Focus Area Method ApplicationDatengetriebene Instandhaltung birgt das Potential, aus den in Industrieumgebungen vielfältig anfallenden Datensammlungen unterschiedliche Nutzeneffekte zu erzielen. Unter Verwendung von modernen Methoden und Technologien aus dem Bereich Data Science und Analytics (DSA) ist es beispielsweise möglich, das Verhalten komplexer technischer Prozesse besser nachzuvollziehen oder bevorstehende Maschinenausfälle und Fehler frühzeitig zu erkennen. Eine erfolgreiche Umsetzung von DSA-Projekten erfordert jedoch multidisziplinäres Expertenwissen, welches sich nur selten von einzelnen Personen bzw. Einheiten innerhalb einer Organisation abdecken lässt. Dies umfasst beispielsweise ein fundiertes Domänenverständnis, Kenntnisse über zahlreiche Analysemethoden, Erfahrungen im Umgang mit verschiedenen Quellsystemen und Datenstrukturen sowie die Fähigkeit, geeignete Lösungsansätze in Informationssysteme zu überführen. Vor diesem Hintergrund haben sich in den letzten Jahren verschiedene Ansätze herausgebildet, um die Durchführung von DSA-Projekten für breitere Anwendergruppen zugänglich zu machen. Dazu gehören strukturierte Vorgehensmodelle, Systematisierungs- und Modellierungsframeworks, domänenspezifische Benchmark-Studien zur Veranschaulichung von Best Practices, Standardlösungen für DSA-Software und intelligente Assistenzsysteme.
An diese Arbeiten knüpft die vorliegende Dissertation an und liefert weitere Artefakte, um insbesondere die Selektion, Evaluation und Anwendung datengetriebener Methoden im Bereich der industriellen Instandhaltung zu unterstützen. Insgesamt erstreckt sich die Abhandlung auf vier Artefakte, die in einzelnen Publikationen erarbeitet wurden. Dies umfasst (i) ein umfangreiches Systematisierungsframework zur Beschreibung zentraler Ausprägungen wiederkehrender Datenanalyseprobleme im Bereich der industriellen Instandhaltung, (ii) ein textbasiertes Assistenzsystem, welches ausgehend von natürlichsprachlichen und domänenspezifischen Problembeschreibungen eine geeignete Klasse von Analysemethoden vorschlägt, (iii) ein taxonomisches Evaluationsframework zur systematischen Bewertung von datengetriebenen Methoden unter verschiedenen Rahmenbedingungen sowie (iv) einen neuartigen Lösungsansatz zur Entwicklung von prognostischen Entscheidungsmodellen im Fall von eingeschränkter Informationslage.
Die Konstruktion der Artefakte wird durch einzelne Forschungsziele im Rahmen eines systematischen Forschungsdesigns angeleitet. Neben der Darstellung der einzelnen Forschungsbeiträge unter Bezugnahme auf die erzielten Ergebnisse der dazugehörigen Publikationen werden auch die Verbindungen zwischen den entwickelten Artefakten beleuchtet und Zusammenhänge zu angrenzenden Arbeiten hergestellt. Zudem erfolgt eine kritische Reflektion der Ergebnisse hinsichtlich ihrer Verallgemeinerung und Übertragung auf andere Rahmenbedingungen. Dadurch liefert die vorliegende Abhandlung nicht nur einen Beitrag anhand der erzeugten Artefakte, sondern ebnet auch den Weg für fortführende Forschungsarbeiten, wofür eine detaillierte Forschungsagenda erarbeitet wird.:List of Figures
List of Tables
List of Abbreviations
1 Introduction
1.1 Motivation
1.2 Conceptual Background
1.3 Related Work
1.4 Research Design
1.5 Structure of the Thesis
2 Systematization of the Field
2.1 The Current State of Research
2.2 Systematization Framework
2.3 Exemplary Framework Application
3 Intelligent Assistance System for Automated Method Selection
3.1 Elicitation of Requirements
3.2 Design Principles and Design Features
3.3 Prototypical Instantiation and Evaluation
4 Taxonomic Framework for Method Evaluation
4.1 Survey of Prognostic Solutions
4.2 Taxonomic Evaluation Framework
4.3 Exemplary Framework Application
5 Method Application Under Industrial Conditions
5.1 Conceptualization of a Solution Approach
5.2 Prototypical Implementation and Evaluation
6 Discussion of the Results
6.1 Connections Between Developed Artifacts and Related Work
6.2 Generalization and Transferability of the Results
7 Concluding Remarks
Bibliography
Appendix I: Implementation Details
Appendix II: List of Publications
A Publication P1: Focus Area Systematization
B Publication P2: Focus Area Method Selection
C Publication P3: Focus Area Method Selection
D Publication P4: Focus Area Method Evaluation
E Publication P5: Focus Area Method Applicatio
Determinants for successful deployment of clinical prediction models : a design science research in the Dutch healthcare sector
Whereas the promises of (predictive) analytics in healthcare are clear and extensively reported, the executive practicalities are not. Mapping the factors that have a hand in the implementation and continuation (i.e. deployment) of such projects improves the execution of prediction models and hence improves diagnostic and prognostic healthcare for patients. This research takes a design science approach to create an artifact aimed at successful deployment of clinical prediction models (CPMs). Through a literature review, various factors that play a role in the deployment of CPMs are categorized. Interviews with an extensive expert panel lead to the development of the CRISP-DM Deployment Extension for CPMs. Next to opinions on the importance of each factor, new in-sights are collected on related topics. A case study at a Dutch hospital allows for the testing of the artifact. A gap analysis is conducted, leading to a practical advice in terms of successful deployment. The research concludes with a proposed deployment strategy and a list of eight recommendations that can be considered the determinants for successful deployment of clinical prediction models
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Big data academic and learning analytics: connecting the dots for academic excellence in higher education
Purpose
Although big data analytics have great benefits for higher education institutions, due to lack of sufficient evidence on how big data analytics investment can pay off, it is tough for HEIs practitioners to realize value from such adoption. The current study proposes a big data academic and learning analytics enabled business value model to explain big data analytics potential benefits and business value which can be obtained by developing such analytics capabilities in HEIs.
Design/methodology/approach
The study examined 47 case descriptions from 26 HEIs to investigate the causal association between the big data analytics current and potential benefits and business value creation path for big data academic and learning analytics success in higher education institutions.
Findings
The pressure of compliance with all legal & regulatory requirements and competition had pushed higher education institutions hard to adopt BDA tools. However, the study found out that application of risk & security and predictive analytics to higher education fields is still in its infancy. Using this theoretical model, our results provide new insights to higher education administrators on ways to create big data analytics capabilities for higher education institutions transformation and suggest an empirical foundation that can lead to more thorough analysis of big data analytics implementation.
Originality/value
A distinctive theoretical contribution of this study is its conceptualization of understanding business value from big data analytics in the typical setting of higher education. The study provides HEIs with an all-inclusive understanding of big data analytics and gives insights on how it helps to transform HEIs. The new perspectives associated with the big data academic and learning analytics enabled business value model will contribute to future research in this area
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