How to Normalize Co-Occurrence Data? An Analysis of Some Well-Known Similarity Measures

In scientometric research, the use of co-occurrence data is very common. In many cases, a similarity measure is employed to normalize the data. However, there is no consensus among researchers on which similarity measure is most appropriate for normalization purposes. In this paper, we theoretically analyze the properties of similarity measures for co-occurrence data, focusing in particular on four well-known measures: the association strength, the cosine, the inclusion index, and the Jaccard index. We also study the behavior of these measures empirically. Our analysis reveals that there exist two fundamentally different types of similarity measures, namely set-theoretic measures and probabilistic measures. The association strength is a probabilistic measure, while the cosine, the inclusion index, and the Jaccard index are set-theoretic measures. Both our theoretical and our empirical results indicate that co-occurrence data can best be normalized using a probabilistic measure. This provides strong support for the use of the association strength in scientometric research

The Spatial Distribution of Economic Activities in the European Union

Abstract This paper considers the spatial distribution of economic activities in the European Union. It has three main aims. (i) To describe the data that is available in the EU and give some idea of the rich spatial data sets that are fast becoming available at the national level. (ii) To present descriptive evidence on the location of aggregate activity and particular industries and to consider how these location patterns are changing over time. (iii) To consider the nature of the agglomeration and dispersion forces that determine these patterns and to contrast them to forces acting elsewhere, in particular the US. Our survey suggests that much has been achieved in the wave of empirical work that has occurred in the past decade, but that much work remains to be done.Location, European Union, descriptive statistics, empirical studies

A framework for the analysis and evaluation of enterprise models

Bibliography: leaves 264-288.The purpose of this study is the development and validation of a comprehensive framework for the analysis and evaluation of enterprise models. The study starts with an extensive literature review of modelling concepts and an overview of the various reference disciplines concerned with enterprise modelling. This overview is more extensive than usual in order to accommodate readers from different backgrounds. The proposed framework is based on the distinction between the syntactic, semantic and pragmatic model aspects and populated with evaluation criteria drawn from an extensive literature survey. In order to operationalize and empirically validate the framework, an exhaustive survey of enterprise models was conducted. From this survey, an XML database of more than twenty relatively large, publicly available enterprise models was constructed. A strong emphasis was placed on the interdisciplinary nature of this database and models were drawn from ontology research, linguistics, analysis patterns as well as the traditional fields of data modelling, data warehousing and enterprise systems. The resultant database forms the test bed for the detailed framework-based analysis and its public availability should constitute a useful contribution to the modelling research community. The bulk of the research is dedicated to implementing and validating specific analysis techniques to quantify the various model evaluation criteria of the framework. The aim for each of the analysis techniques is that it can, where possible, be automated and generalised to other modelling domains. The syntactic measures and analysis techniques originate largely from the disciplines of systems engineering, graph theory and computer science. Various metrics to measure model hierarchy, architecture and complexity are tested and discussed. It is found that many are not particularly useful or valid for enterprise models. Hence some new measures are proposed to assist with model visualization and an original "model signature" consisting of three key metrics is proposed.Perhaps the most significant contribution ofthe research lies in the development and validation of a significant number of semantic analysis techniques, drawing heavily on current developments in lexicography, linguistics and ontology research. Some novel and interesting techniques are proposed to measure, inter alia, domain coverage, model genericity, quality of documentation, perspicuity and model similarity. Especially model similarity is explored in depth by means of various similarity and clustering algorithms as well as ways to visualize the similarity between models. Finally, a number of pragmatic analyses techniques are applied to the models. These include face validity, degree of use, authority of model author, availability, cost, flexibility, adaptability, model currency, maturity and degree of support. This analysis relies mostly on the searching for and ranking of certain specific information details, often involving a degree of subjective interpretation, although more specific quantitative procedures are suggested for some of the criteria. To aid future researchers, a separate chapter lists some promising analysis techniques that were investigated but found to be problematic from methodological perspective. More interestingly, this chapter also presents a very strong conceptual case on how the proposed framework and the analysis techniques associated vrith its various criteria can be applied to many other information systems research areas. The case is presented on the grounds of the underlying isomorphism between the various research areas and illustrated by suggesting the application of the framework to evaluate web sites, algorithms, software applications, programming languages, system development methodologies and user interfaces

Parsing consumption preferences of music streaming audiences

As demands for insights on music streaming listeners continue to grow, scientists and industry analysts face the challenge to comprehend a mutated consumption behavior, which demands a renewed approach to listener typologies. This study aims to determine how audience segmentation can be performed in a time-relevant and replicable manner. Thus, it interrogates which parameters best serve as indicators of preferences to ultimately assist in delimiting listener segments. Accordingly, the primary objective of this research is to develop a revised typology that classifies music streaming listeners in the light of the progressive phenomenology of music listening. The hypothesis assumes that this could be solved by positioning listeners – rather than products – at the center of streaming analysis and supplementing sales- with user-centered metrics. The empirical research of this paper was based on grounded theories, enriched by analytical case studies. For this purpose, behavioral and psychological research results were interconnected with market analysis and streaming platform usage data. Analysis of the results demonstrates that a concatenation of multi-dimensional data streams facilitates the derivation of a typology that is applicable to varying audience pools. The findings indicate that for the delimitation of listener types, the motivation, and listening context are essential key constituents. Since these variables demand insights that reach beyond existing metrics, descriptive data points relating to the listening process are subjoined. Ultimately, parameter indexation results in listener profiles that offer novel access points for investigations, which make imperceptible, interdisciplinary correlations tangible. The framework of the typology can be consulted in analytical and creational processes. In this respect, the results of the derived analytical approach contribute to better determine and ultimately satisfy listener preferences.Während die Nachfrage nach Erkenntnissen über Musik-Streaming-Hörer kontinuierlich steigt, stehen Wissenschaftler sowie Industrieanalysten einem geänderten Konsumptions- verhalten gegenüber, das eine überarbeitete Hörertypologie fordert. Die vorliegende Studie erörtert, wie eine Hörersegmentierung auf zeitgemäße und replizierbare Weise umgesetzt werden kann. Demnach beschäftigt sie sich mit der Frage, welche Parameter am besten als Indikatoren für Hörerpräferenzen dienen und wie diese zur Abgrenzung der Publikumsseg- mente beitragen können. Dementsprechend ist es das primäre Ziel dieser Forschung, eine überarbeitete Typologie aufzustellen, die Musik-Streaming-Hörer in Anbetracht der progressiven Erscheinungsform des Musikhörens klassifiziert. Die Hypothese nimmt an, dass dies realisierbar ist, wenn der Hörer – anstelle von Produkten – im Zentrum der Streaming-Analyse steht und absatzzen- trierte durch hörerzentrierte Messungen ergänzt werden. Die empirische Forschung basiert auf systematischen Theorien, untermauert durch analytische Fallbeispiele. Hierfür werden psychologische und verhaltenswissenschaftliche Forschungserkenntnisse mit Marktanalysen und Nutzerdaten von Musikstreaming-Portalen fusioniert. Die Analyse der Ergebnisse verdeutlicht, dass eine Verkettung von multidimensionalen Rohdaten die Erhebung einer Typologie ermöglicht, die auf mehrere Hörergruppen anwend- bar ist. Die Befunde signalisieren, dass die Hörmotivation und der Hörkontext bei der Abgrenzung der Publikumstypen Schlüsselelemente darstellen. Da diese Variablen spezifis- che Kenntnisse fordern, die über vorliegende Kennzahlen hinausgehen, werden deskriptive Datenpunkte über den Hörvorgang ergänzt. Letztlich, resultiert die Indexierung der Pa- rameter in Hörerprofilen, die neue Zugangspunkte für Untersuchungen bieten, die nicht ersichtliche, interdisziplinäre Korrelationen greifbar machen. Das Gerüst der Hörertypologie kann sowohl in Erstellungs- als auch in Analyseprozessen herangezogen werden. Somit tragen die Ergebnisse der entwickelten Analysemethode zum Verständnis und letztlich zur Erfüllung von Hörerpräferenzen bei

Parsing consumption preferences of music streaming audiences

As demands for insights on music streaming listeners continue to grow, scientists and industry analysts face the challenge to comprehend a mutated consumption behavior, which demands a renewed approach to listener typologies. This study aims to determine how audience segmentation can be performed in a time-relevant and replicable manner. Thus, it interrogates which parameters best serve as indicators of preferences to ultimately assist in delimiting listener segments. Accordingly, the primary objective of this research is to develop a revised typology that classifies music streaming listeners in the light of the progressive phenomenology of music listening. The hypothesis assumes that this could be solved by positioning listeners – rather than products – at the center of streaming analysis and supplementing sales- with user-centered metrics. The empirical research of this paper was based on grounded theories, enriched by analytical case studies. For this purpose, behavioral and psychological research results were interconnected with market analysis and streaming platform usage data. Analysis of the results demonstrates that a concatenation of multi-dimensional data streams facilitates the derivation of a typology that is applicable to varying audience pools. The findings indicate that for the delimitation of listener types, the motivation, and listening context are essential key constituents. Since these variables demand insights that reach beyond existing metrics, descriptive data points relating to the listening process are subjoined. Ultimately, parameter indexation results in listener profiles that offer novel access points for investigations, which make imperceptible, interdisciplinary correlations tangible. The framework of the typology can be consulted in analytical and creational processes. In this respect, the results of the derived analytical approach contribute to better determine and ultimately satisfy listener preferences.Während die Nachfrage nach Erkenntnissen über Musik-Streaming-Hörer kontinuierlich steigt, stehen Wissenschaftler sowie Industrieanalysten einem geänderten Konsumptions- verhalten gegenüber, das eine überarbeitete Hörertypologie fordert. Die vorliegende Studie erörtert, wie eine Hörersegmentierung auf zeitgemäße und replizierbare Weise umgesetzt werden kann. Demnach beschäftigt sie sich mit der Frage, welche Parameter am besten als Indikatoren für Hörerpräferenzen dienen und wie diese zur Abgrenzung der Publikumsseg- mente beitragen können. Dementsprechend ist es das primäre Ziel dieser Forschung, eine überarbeitete Typologie aufzustellen, die Musik-Streaming-Hörer in Anbetracht der progressiven Erscheinungsform des Musikhörens klassifiziert. Die Hypothese nimmt an, dass dies realisierbar ist, wenn der Hörer – anstelle von Produkten – im Zentrum der Streaming-Analyse steht und absatzzen- trierte durch hörerzentrierte Messungen ergänzt werden. Die empirische Forschung basiert auf systematischen Theorien, untermauert durch analytische Fallbeispiele. Hierfür werden psychologische und verhaltenswissenschaftliche Forschungserkenntnisse mit Marktanalysen und Nutzerdaten von Musikstreaming-Portalen fusioniert. Die Analyse der Ergebnisse verdeutlicht, dass eine Verkettung von multidimensionalen Rohdaten die Erhebung einer Typologie ermöglicht, die auf mehrere Hörergruppen anwend- bar ist. Die Befunde signalisieren, dass die Hörmotivation und der Hörkontext bei der Abgrenzung der Publikumstypen Schlüsselelemente darstellen. Da diese Variablen spezifis- che Kenntnisse fordern, die über vorliegende Kennzahlen hinausgehen, werden deskriptive Datenpunkte über den Hörvorgang ergänzt. Letztlich, resultiert die Indexierung der Pa- rameter in Hörerprofilen, die neue Zugangspunkte für Untersuchungen bieten, die nicht ersichtliche, interdisziplinäre Korrelationen greifbar machen. Das Gerüst der Hörertypologie kann sowohl in Erstellungs- als auch in Analyseprozessen herangezogen werden. Somit tragen die Ergebnisse der entwickelten Analysemethode zum Verständnis und letztlich zur Erfüllung von Hörerpräferenzen bei

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

Investigating and Supporting Sensemaking within Online Health Communities

This dissertation focuses on understanding and supporting individual and collective sensemaking within online health communities (OHCs). This major goal was achieved in three aims. In Aim 1, this dissertation contributes a rich descriptive account of collective sensemaking in OHCs forums by describing how it occurs and develops, what triggers it, what elements constitute collective construction of meaning, and what conversational moves positively contribute to this process. Further, it describes how collective sensemaking in OHCs is impacted by the interplay between informational and socio-emotional needs of OHCs members. Moreover, it examines how design of different social computing platforms influences OHCs members’ ability to meet their informational and socio-emotional needs and engage in collective sensemaking. In Aim 2, this dissertation explores the design space of tools for supporting individual sensemaking through optimized information access. Through the design and evaluation of a prototype DisVis it examines the impact of such tools on OHCs members’ ability to understand information within discussion threads. In the final Aim 3, this dissertation proposes a novel approach for meeting the three main needs identified in Aims 1 and 2: promoting individual sensemaking, while at the same time encouraging collective sensemaking, and facilitating development of social awareness and ties among community members. The design and evaluation of the novel solution for visualizing discussion threads that synergistically addresses these three needs—dSense—provides insights for future research and design of interactive solutions for supporting individual and collective sensemaking within OHCs