Die vorliegende Dissertation widmet sich dem Thema Synonymie im besonderen Fall der phraseologischen Einheiten. Es handelt sich um eine korpusbasierte, empirische Untersuchung, die sich insbesondere mit der Frage beschäftigt, ob und inwiefern es möglich ist, sich typisch semantischen Kategorien wie Bedeutung, Idiomatizität, Bildlichkeit etc. über die Untersuchung typischer Verwendungsmuster zu nähern. Diese Themenstellung motiviert sich aus bisher in der linguistischen Literatur strittigen grundsätzlichen Aspekten der Diskussion um Bedeutung und Synonymie: Sie ist zum einen als Beitrag zum besseren Verständnis des Verhältnisses zwischen Verwendungsdaten und Bedeutung sowie zum Status traditioneller wörterbuchähnlicher Bedeutungsangaben innerhalb einer gebrauchsbasierten Semantik gedacht: Zum anderen geht es darum, detaillierte Erkenntnisse über die Übertragbarkeit des Konzepts Synonymie von Einzellexemen auf phraseologische Einheiten zu gewinnen. Unter der Annahme, dass menschliches Lernen bzw. Erschließen von Bedeutung primär empirisch funktioniert, ist die Analyse der Breite der Varianz des tatsächlichen kontextuellen Verhaltens phraseologischer Einheiten bei gleicher oder ähnlicher Bedeutung dazu geeignet, detaillierte Erkenntnisse über die Korrelation zwischen Bedeutungs- und Verwendungsaspekten sowie über den Einfluss phraseologiespezifischer Eigenschaften zu gewinnen. Ausgangspunkt der Untersuchung ist eine Gruppe phraseologischer Einheiten, die in Wörterbüchern als bedeutungsähnlich bzw. synonym klassifiziert werden. Unter diesen phraseologischen Einheiten finden sich Ausdrücke unterschiedlicher Bildlichkeit, Idiomatizität und morphosyntaktischer Struktur, von denen einige aus mehreren Inhaltswörtern bestehen, wie etwa jmd. schüttelt etw. aus dem Ärmel, jmd. hat etw. mit der Muttermilch eingesogen und jmd. weiß Bescheid, während andere lediglich Verbindungen eines Inhaltswortes mit einem oder mehreren Funktionswörtern und dem Verb haben oder sein darstellen. Zur letzteren Gruppe gehören unter anderem Ausdrücke wie jmd. hat das Zeug zu etw., jmd. ist auf Zack und jmd. ist vom Fach. Diese Heterogenität der zu untersuchenden Ausdrücke bezüglich ihres phraseologischen Status macht sie zu einem geeigneten Gegenstand der differenzierten Betrachtung der Rolle phraseologiespezifischer Eigenschaften für die Beschreibung von Bedeutung und Synonymie. Die Untersuchung besteht im ersten Schritt in einer detaillierten Annotation aller Vorkommenskontexte der phraseologischen Einheiten im Korpus des DWDS (Berlin-Brandeburgische Akademie der Wissenschaften, www.dwds.de) auf Basis einer Reihe vordefinierter Analysekriterien. Aus dieser Annotation wird in einem zweiten Schritt eine Beschreibung der typischen Verwendungsmerkmale jeder einzelnen lexikalischen Einheit gewonnen. Gleichzeitig enstehen nach einer festgelegten Methode Bedeutungsbeschreibungen in Form von Paraphrasen, die auf elementare Bedeutungsbestandteile reduziert werden. Diese Bedeutungs- und Verwendungsbeschreibungen der untersuchten phraseologschen Einheiten bilden die Basis für den dritten Teil der Arbeit, in der die beiden Beschreibungsebenen aufeinander bezogen werden. Im Ergebnis zeigt die Arbeit, dass a) Zwischen Verwendung und Bedeutung lexikalischer (phraseologischer) Einheiten identifizierbare systematische Korrelationen bestehen, die einen datenzentrierten Zugang zur Untersuchung und Beschreibung lexikalischer Semantik ermöglichen. b) Innerhalb einer Gruppe von Synonymen so wie auch innerhalb einer erweiterten Menge quasisynonymer Ausdrücke jedem einzelnen Element ein eigener Platz zukommt, der dieses Element von allen anderen Elementen unterscheidet. c) Die Verwendungsdaten einer phraseologischen Einheit positive Evidenz für den individuellen Grad der Relevanz der Merkmale Festigkeit, Idiomatizität und Motiviertheit einer phraseologischen Einheit liefern. / The present Ph.D.-dissertation addresses the subject of synonymy in the special case on Multi-Word Expressions. As a corpus-based, empirical study, it focusses around the question if and how it is possible to approach semantic categories such as meaning, idiomaticity and metaphoricity by analysing the characteristic usage patterns of a lexical unit. The study is driven by basic unresolved issues in the linguistic discussion on meaning and synonymy: On the one hand, it contributes to a better understanding of the relationship between usage data and meaning as well as on the status of traditional dictionary-like definitions in empirical semantics. On the other hand, it provides detailed insights on the applicability of the linguistc concept of synonymy - as it has been discussed for single lexemes – to lexicalized Multi-Word Expressions. On the basis of the assumption that human learning and grasping of meaning is a basically empirical process, the analysis of the variability of contextual behaviour of Multi-Word Expressions with identical or similar meaning constitutes a suitable way of gaining insights on the correlation between meaning and use as well as on the influence of specific properties of Multi-Word Expressions. The study starts out from a group of phraseological units that have been classified as synonymous in German phraseological dictionaries. These expressions differ their degree of idiomaticity, metaphoricity and morphosyntactic structure. Some of them – e.g. 'jmd. schüttelt etw. aus dem Ärmel' or 'jmd. hat etw. mit der Muttermilch eingesogen' (sb. does sth. with great ease) - comprise several content words whereas others are composed of only one content word together with one or more function words and the verb 'haven' (have) or 'sein' (be) – as it is the case for 'jmd. ist auf Zack', jmd. hat das Zeug zu etw.' and 'jmd. ist vom Fach' (sb. is able/competent in sth.). Their heterogeneity makes them a very good starting point for the research on the role played by phraseology-specific properties for meaning and synonymy. As a first step, the author realizes a detailed annotation of all occurrences of the these Multi-Word Expressions in the corpus of the DWDS (Berlin-Brandenburg Academy of Sciences and the Humanities, www.dwds.de), using a series of predefined analysis criteria. From this analysis, she gains a description of their typical usage traits. In parallel, a neatly defined methodology leads to a set of meaning descriptions on the basis of dictionary paraphrases that are reduced to their elementary meaning components. In the last part of the study., these meaning and usage descriptions are then correlated yielding the following conclusions: a) Between meaning descriptions and usage patterns, systematic correlations can be devised that allow a data-driven approach to the semantics of lexical units. b)Within a group of synonyms, each element has its unique place, determined by its characteristic usage patterns. c) The usage data of a multi-word unit provide positive evidence forrr the degree of idiomaticity, morphosyntactic stability and metaphoricity of the expression.

Background: As the requirements for the modern-day comforts are raising from day to day, the great evolution in the field of lawn-mowers is recorded. This evolution made companies produce a fleet of lawn-mowers(commercial, house-hold) for different kinds of usages. Despite the great evolution and market in this field, to the best of our knowledge, no effort was made to understand customer usage by analysis of real-time usage of lawn-mowers. This research made an attempt to analyse the real-time usage of lawn-mowers using techniques like machine learning. Objectives: The main objective of the thesis work is to understand customer usage of lawn-mowers by analysing the real-time usage data using machine learning algorithms. To achieve this, we first review several studies to identify what are the different ways(scenarios) and how to understand customer usage from those scenarios. After discussing these scenarios with the stakeholders at the company, we evaluated a suitable scenario in the case of lawn-mowers. Finally, we achieved the primary objective by clustering the usage of lawn-mowers by analysing the real-world time-series data from the Controller Area Network(CAN) bus based on the driving patterns. Methods: A Systematic literature review(SLR) is performed to identify the different ways to understand customer usage by analysing the usage data using machine learning algorithms and SLR is also performed to gain detailed knowledge about different machine learning algorithms to apply to the real-world data. Finally, an experiment is performed to apply the machine learning algorithms on the CAN bus time-series data to evaluate the usage of lawn-mowers into various clusters and the experiment also involves the comparison and selection of different machine learning algorithms applied to the data. Results: As a result of SLR, we achieved different scenarios to understand customer behaviours by analysing the usage data. After formulating the best suitable scenario for lawn-mowers, SLR also suggested the best suitable machine learning algorithms to be applied to the data for the scenario. Upon applying the machine learning algorithms after making necessary pre-processing steps, we achieved the clusters of usage of lawn-mowers for every driving pattern selected. We also achieved the clusters for different features of driving patterns that indicate the various characteristics like a change of intensity in the usage, rate of change in the usage, etc. Conclusions: This study identified customer behaviours based on their usage data by clustering the usage data. Moreover, clustering the CAN bus time-series data from lawn-mowers gave fresh insights to study human behaviours and interaction with the lawn-mowers. The formulated clusters have a great scope to classify and develop the individual strategy for each cluster formulated. Further, clusters can also be useful for identifying the outlying behaviour of users and/or individual components.

Software as a Service (SaaS) platforms paired with cloud-based storage is a common schema used among software providers across the globe. Such solutions usually accumulate vast amounts of usage and usage behaviour data. Utilizing this data in the form of monitoring solutions can potentially generate great value for both software users and software providers. Swedish authorities have taken a restrictive standpoint against incorporating cloud-based storage solutions into software used in the public sector. The project aims to identify a practical and reusable way of utilizing cloud data and to demonstrate to Swedish authorities how cloud-based storage models can be beneficial. The case of this project is the SaaS solution Gemini Portal+, a water and sewage management solution. The end result was a monitoring module to Gemini Portal+ where users can view the digital maturity of their Gemini Portal+ usage. The digital maturity is conveyed in an easily digested manner, with concrete and actionable information on how to increase digital maturity. The result has passed the requirements and stakeholders are satisfied with the result.

A successful mobile app monetization strategy is the foundation of any sustainable future business. App developers, in this regard, face the demanding challenge of building, maintaining and monetizing this strategy respectively. Factors, such as users' increasing unwillingness to pay for an app, impacts monetization methods negatively which makes current monetization strategies ever more challenging. Particularly for temporary apps, this phenomenon is ever influential. This research therefore addresses how companies can maximize the monetization of users if the usage of the app is limited by time. The researchers examined existing literature on app monetization and discovered that no research has been conducted on temporary apps yet, which highlights a specific research gap in a changing business environment. By conducting expert interviews on app monetization in combination with a multiple case study, investigating four temporary apps, this research found out that temporary apps do not monetize differently than non-temporary apps. This paper uncovered that there is a trend happening within the mobile app monetization industry that shifts from user-based monetization, where the user pays for the app, towards a partner-based monetization strategy. In this regard, external companies provide the revenue for the app. Particularly interesting is the potential of mobile data monetization, which is invisible for the user, thus providing a valuable strategy for the company. Comprising all executed research and insights gathered, the paper built the Mobile App Monetization Model. It examines the challenges and opportunities companies face during their monetization and which success and goal metrics are influential in their decision-making. It summarizes the current most important topics in the mobile app monetization field.

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Cette thèse propose une nouvelle méthode, via les outils de dosimétrie et de statistiques, pour l'évaluation de l'exposition globale d'une population aux champs électromagnétiques (EMFs) radiofréquences en prenant en compte les différentes technologies, usages et environnements... Nous avons analysé pour la première fois l'exposition moyenne d'une population induite par un réseau 3G, tout en considérant à la fois les émissions EMFs montantes et descendantes dans des différents pays, dans des différentes zones géographiques et pour les différents usages des mobiles. Les résultats montrent une forte hétérogénéité de l'exposition dans le temps et dans l'espace. Contrairement à la croyance populaire, l'exposition aux ondes EMFs 3G est dominée par les émissions montantes, résultant de l'usage voix et data. En outre, l'exposition moyenne de la population diffère d'une zone géographique à une autre, ainsi que d'un pays à un autre, en raison des différentes architectures de réseau cellulaire et de la variabilité de l'usage des mobiles. Ensuite, la variabilité et les incertitudes liées à ces facteurs ont été caractérisées. Une analyse de sensibilité basée sur la variance de l'exposition globale a été effectuée dans le but de simplifier son évaluation. Enfin, une méthodologie simplifiée basée sur des outils statistiques avancés a été proposée pour évaluer l'exposition réelle de la population en tenant compte de la variabilité liée à l'environnement de propagation, à l'usage, ainsi qu'aux émissions EMFs provenant des mobiles et des stations de base (BTS). Les résultats ont souligné l'importance de la densité de puissance reçue depuis les BTS pour l'exposition globale induite par un réseau macro LTE. / Wireless communication technologies, since their introduction, have evolved very quickly and people have been brought in 30 years into a much closer world. In parallel radiofrequency (RF) electromagnetic fields (EMF) are more and more used. As a consequence, people's attentions around health risks of exposure to RF EMFs have grown just as much as their usages of wireless communication technologies. Exposure to RF EMFs can be characterized using different exposure metrics (e.g., incident field metrics, absorption metrics...). However, the existing methodologies are well suited to the maximum exposure assessment for the individual under the worst-case condition. Moreover in most cases, when dealing with exposure issues, exposures linked to RF EMF emitted from base stations (BTS) and by wireless devices (e.g, mobile phones and tablets) are generally treated separately. This thesis has been dedicated to construct and validate a new method for assessing the real day-to-day RF EMF exposure to a wireless network as a whole, exploring the people's daily life, including both downlink and uplink exposures and taking into account different technologies, usages, environments, etc. Towards these objectives, we analyzed for the first time the average population exposure linked to third generation network (3G) induced EMFs, from both uplink and downlink radio emissions in different countries, geographical areas, and for different wireless device usages. Results, derived from device usage statistics, show a strong heterogeneity of exposure, both in time and space. We show that, contrary to popular belief, exposure to 3G EMFs is dominated by uplink radio emissions, resulting from voice and data traffic, and average population EMF exposure differs from one geographical area to another, as well as from one country to another, due to the different cellular network architectures and variability of mobile usage. Thus the variability and uncertainties linked to these influencing factors were characterized. And a variance-based sensitivity analysis of the global exposure was performed for the purpose of simplifying its evaluation. Finally, a substitution model was built to evaluate the day-to-day global LTE induced EMFs exposure of a population taking into account the variability linked to propagation environment, usage, as well as EMFs from personal wireless devices and BTS. Results have highlighted the importance of received power density from BTS to the issue of global exposure induced by a macro LTE network. This substitution model can be further used to analyze the evolution of the wireless network in terms of EMF exposure.

Cette étude exploratoire s’intéresse à l’usage des revues en sciences sociales et humaines diffusées en libre accès complet et en libre accès différé par la plateforme Érudit. Basée sur les données de téléchargements d’Érudit, elle vise à 1) fournir un portrait détaillé de l’usage des articles, 2) décrire les habitudes de téléchargement des usagers au Canada et à l’international, et 3) analyser l’effet des politiques de libre accès des revues sur les téléchargements qu’elles reçoivent. Pour ce faire, 39 437 659 téléchargements, extraits de 999 367 190 requêtes HTTP enregistrées dans les logs du serveur d’Érudit de 2010 à 2015, ont été analysés. Les résultats montrent que la majorité des usagers provient du Québec, de la France et d’autres pays francophones, et que, la plupart du temps, ceux-ci accèdent aux articles par l’intermédiaire de Google. Les habitudes de téléchargement varient d’un pays à l’autre : alors que les usagers canadiens et français utilisent Érudit principalement en journée et en semaine, leurs homologues américains sont davantage actifs en soirée, la nuit, ainsi que les week-ends. Enfin, un avantage important lié au libre accès a été observé : les articles des revues en libre accès sont davantage téléchargés que ceux des revues en libre accès différé et, pour ces dernières, la fin de l’embargo est associée à une croissance importante des téléchargements – croissance moins marquée au Canada où bon nombre d’institutions sont abonnées aux revues de la plateforme. Ces résultats démontrent l’importance des revues nationales pour les sciences sociales et humaines, ainsi que l’effet positif du libre accès sur la diffusion des connaissances, tant au Canada qu’à l’étranger. / This study explores the usage of open access (OA) and delayed OA journals in the social sciences and humanities hosted by the journal platform Érudit. Relying on Érudit’s download data, the goals of the study are: 1) to describe the usage of scholarly articles, 2) to examine download patterns of national and international users, and 3) to analyze the effect of OA policies on journal download rates. The study is based on an analysis of 39,437,659 downloads, which were extracted from 999,367,190 HTTP requests stored in Érudit’s log files between 2010 and 2015. The results show that the majority of users came from Quebec, France and other French-speaking countries, and that most users access articles through Google. Download patterns varied between countries: although articles were most frequently accessed during working hours, US users were more active in the evening, at night and during weekends than Canadian and French users. The study also demonstrates a clear OA advantage, as freely available articles were downloaded more frequently than delayed OA articles affected by an embargo, and downloads per article increased substantially after embargos ended. This effect was less pronounced for Canadian users, who often have access to Érudit journals via institutional subscriptions and are thus not affected by the embargo periods. The results show the positive effect of OA on knowledge dissemination in Canada as well as internationally, and emphasize the importance of national journals in the social sciences and humanities.

The large potential of big data and how it has brought value into various industries have been established in research. Since big data has such large potential if handled and analyzed in the right way, revealing information to support decision making in an organization, this thesis is conducted as a case study at an automotive manufacturer with access to large amounts of customer usage data of their vehicles. The reason for performing an analysis of this kind of data is based on the cornerstones of Total Quality Management with the end objective of increasing customer satisfaction of the concerned products or services. The case study includes a data analysis exploring how and if patterns about what affects fuel consumption can be revealed from aggregated customer usage data of trucks linked to truck applications. Based on the case study, conclusions are drawn about how a company can use this type of analysis as well as how to handle the data in order to turn it into business value. The data analysis reveals properties describing truck usage using Factor Analysis and Principal Component Analysis. Especially one property is concluded to be important as it appears in the result of both techniques. Based on these properties the trucks are clustered using k-means and Hierarchical Clustering which shows groups of trucks where the importance of the properties varies. Due to the homogeneity and complexity of the chosen data, the clusters of trucks cannot be linked to truck applications. This would require data that is more easily interpretable. Finally, the importance for fuel consumption in the clusters is explored using model estimation. A comparison of Principal Component Regression (PCR) and the two regularization techniques Lasso and Elastic Net is made. PCR results in poor models difficult to evaluate. The two regularization techniques however outperform PCR, both giving a higher and very similar explained variance. The three techniques do not show obvious similarities in the models and no conclusions can therefore be drawn concerning what is important for fuel consumption. During the data analysis many problems with the data are discovered, which are linked to managerial and technical issues of big data. This leads to for example that some of the parameters interesting for the analysis cannot be used and this is likely to have an impact on the inability to get unanimous results in the model estimations. It is also concluded that the data was not originally intended for this type of analysis of large populations, but rather for testing and engineering purposes. Nevertheless, this type of data still contains valuable information and can be used if managed in the right way. From the case study it can be concluded that in order to use the data for more advanced analysis a big-data plan is needed at a strategic level in the organization. The plan summarizes the suggested solution for the managerial issues of the big data for the organization. This plan describes how to handle the data, how the analytic models revealing the information should be designed and the tools and organizational capabilities needed to support the people using the information.

Nutzungsdaten von elektronischen wissenschaftlichen Publikationen und insbesondere die Anzahl ihrer Downloads rücken mit der Verbreitung des Internets zunehmend in den Blickpunkt des Interesses der Autoren, der Herausgeber, der technischen Anbieter und der Nutzer solcher Publikationen. Downloadzahlen von Publikationen, welche durch Auswertung der Protokolle der IT-Systeme der Anbieter ermittelt werden, sind solche Nutzungsdaten. Die Erhebung erfolgt durch Filterung aller stattgefundenen Zugriffe und Summierung über eine definierte Zeiteinheit. Downloadzahlen sind Gegenstand wissenschaftlicher Untersuchungen, in welchen das Konzept des Citation Impact auf die Nutzungshäufigkeit einer Publikation übertragen und der sogenannte Download Impact gebil-det wird. Besonderes Augenmerk wird dem Zusammenhang von Citation Impact und Download Impact gewidmet. Handelt es sich um Open-Access-Publikationen, muss davon ausgegangen werden, dass in den Downloadzahlen nicht nur menschliche, sondern auch maschinelle Zugriffe erfasst wurden, da eine sichere Unterscheidung unmöglich ist. Das hat zur Folge, dass die gewonnenen Daten für die einzelnen Publikationen unzuverlässig sind und starken Schwankungen unterliegen. Trotzdem enthalten sie wertvolle Informationen, welche mit Hilfe der Mathematischen Statistik nutzbar gemacht werden können. Mit nichtparametrischen Methoden ausgewertet, geben Downloadzahlen Auskunft über die Sichtbarkeit von elektronischen Publikationen im Internet. Diese Methoden bilden den Kern von NoRA (Non-parametric Repository Analysis), mit deren Hilfe die Betreiber von Open Access Repositories die Downloadzahlen ihrer elektronischen Publikationen auswerten können, um Sichtbarkeitsdefizite zu ermitteln und zu beheben und so die Qualität ihres Online-Angebotes zu erhöhen. Die Analysemethode NoRA wurde auf die Daten von vier universitären Institutional Repositories erfolgreich angewendet. Es konnten jeweils Gruppen von Publikationen identifiziert werden, die sich hinsichtlich ihrer Nutzung signifikant unterscheiden. Die Parallelen in den Ergebnissen weisen auf Einflussfaktoren für die Nutzungsdaten hin, welche in der gegenwärtigen Diskussion bisher keine Berücksichtigung finden. Hier erschließen sich weitere Anwendungsfelder für NoRA. Gleichzeitig geben die Ergebnisse Anlass, den Informationsgehalt von Downloadzahlen für die einzelne Publikation kritisch zu hinterfragen. / With the spread of internet usage over the past decades, access characteristics of electronic scientific publica-tions, especially the number of document downloads, are of increasing interest to the authors, publishers, technical providers and users of such publications. These download data of publications are usually obtained from the protocols of the IT systems of the provider. A data set is then created by filtering all accesses and subsequent summarizing over a certain time unit. Download data are the subject of scientific investigations, in which the concept of the Citation Impact is applied to the rate of use of a publication and the so-called Download Impact is formed. Special attention is paid to the relation between Citation Impact and Download Impact. In the case of Open Access publications, two types of access need to be distinguished. Human access and machine access are both captured and a reliable distinction is not possible yet. As a result, the data obtained for single publications are unreliable and subject to strong fluctuations. Nevertheless, they contain valuable information that can be made useful with the help of mathematical statistics. Analyzed with nonparametric methods, download data give information about the visibility of electronic publications on the Internet. These methods form the core of NoRA (Non-parametric Repository Analysis). With the help of NoRA, the operators of Open Access Repositories are able to analyze the download data of their electronic publications, to identify and correct deficiencies of visibility and to increase the quality of their online platform. The analytical method NoRA was successfully applied to data from Institutional Repositories of four universities. In each case, groups of publications were identified that differed significantly in their usage. Similarities in the results reveal factors that influence the usage data, which have not been taken into account previously. The presented results imply further applications of NoRA but also raise doubts about the value of download data of single publications.