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- 2019 European Parliament Election (1)
- Association Rules (1)
- Belief change, concept contraction, EL (1)
- Business Process Management Recommender Systems Survey (1)
- Challenges (1)
- Effectiveness (1)
- Handsfree editing (1)
- Human resources management (1)
- International organization (1)
- Internet of Things (1)
Thesis is devoted to the topic of challenges and solutions for human resources management (HRM) in international organizations. The aim is to investigate methodological approaches to assessment of HRM challenges and solutions, and to apply them on practice, to develop ways of improvement of HRM of a particular enterprise. The practical research question investigated is “Is the Ongoing Professional Development – Strategic HRM (OPD-SHRM) model a better solution for HRM system of PrJSC “Philip Morris Ukraine”?”
To achieve the aim of this work and to answer the research question, we have studied theoretical approaches to explaining and assessing HRM in section 1, analyzed HRM system of an international enterprise in section 2, and then synthesized theory and practice to find intersection points in section 3.
Research findings indicate that the main challenge of HRM is to balance between individual and organizational interests. Implementation of OPD-SHRM is one of the solutions. Switching focus from satisfaction towards success will bring both tangible and intangible benefits for individuals and organization. In case of PrJSC “Philip Morris Ukraine”, the maximum forecasted increase is 330% in net profit, 350% in labor productivity, and 26% in Employee Development and Engagement Index.
Current political issues are often reflected in social media discussions, gathering politicians and voters on common platforms. As these can affect the public perception of politics, the inner dynamics and backgrounds of such debates are of great scientific interest. This thesis takes user generated messages from an up-to-date dataset of considerable relevance as Time Series, and applies a topic-based analysis of inspiration and agenda setting to it. The Institute for Web Science and Technologies of the University Koblenz-Landau has collected Twitter data generated beforehand by candidates of the European Parliament Election 2019. This work processes and analyzes the dataset for various properties, while focusing on the influence of politicians and media on online debates. An algorithm to cluster tweets into topical threads is introduced. Subsequently, Sequential Association Rules are mined, yielding wide array of potential influence relations between both actors and topics. The elaborated methodology can be configured with different parameters and is extensible in functionality and scope of application.
Belief revision is the subarea of knowledge representation which studies the dynamics of epistemic states of an agent. In the classical AGM approach, contraction, as part of the belief revision, deals with the removal of beliefs in knowledge bases. This master's thesis presents the study and the implementation of concept contraction in the Description Logic EL. Concept contraction deals with the following situation. Given two concept C and D, assuming that C is subsumed by D, how can concept C be changed so that it is not subsumed by D anymore, but is as similar as possible to C? This approach of belief change is different from other related work because it deals with contraction in the level of concepts and not T-Boxes and A-Boxes in general. The main contribution of the thesis is the implementation of the concept contraction. The implementation provides insight into the complexity of contraction in EL, which is tractable since the main inference task in EL is also tractable. The implementation consists of the design of five algorithms that are necessary for concept contraction. The algorithms are described, illustrated with examples, and analyzed in terms of time complexity. Furthermore, we propose an new approach for a selection function, adapt for the concept contraction. The selection function uses metadata about the concepts in order to select the best from an input set. The metadata is modeled in a framework that we have designed, based on standard metadata frameworks. As an important part of the concept contraction, the selection function is responsible for selecting the best concepts that are as similar as possible to concept C. Lastly, we have successfully implemented the concept contraction in Python, and the results are promising.
To construct a business process model manually is a highly complex and error-prone task which takes a lot of time and deep insights into the organizational structure, its operations and business rules. To improve the output of business analysts dealing with this process, different techniques have been introduced by researchers to support them during construction with helpful recommendations. These supporting recommendation systems vary in their way of what to recommend in the first place as well as their calculations taking place under the hood to recommend the most fitting element to the user. After a broad introduction into the field of business process modeling and its basic recommendation structures, this work will take a closer look at diverse proposals and descriptions published in current literature regarding implementation strategies to effectively and efficiently assist modelers during their business process model creation. A critical analysis of presentations in the selected literature will point out strengths and weaknesses of their approaches, studies and descriptions of those. As a result, the final concept matrix in this work will give a precise and helpful overview about the key features and recommendation methods used and implemented in previous research studies to pinpoint an entry into future works without the downsides already spotted by fellow researchers.
Commonsense reasoning can be seen as a process of identifying dependencies amongst events and actions. Understanding the circumstances surrounding these events requires background knowledge with sufficient breadth to cover a wide variety of domains. In the recent decades, there has been a lot of work in extracting commonsense knowledge, a number of these projects provide their collected data as semantic networks such as ConceptNet and CausalNet. In this thesis, we attempt to undertake the Choice Of Plausible Alternatives (COPA) challenge, a problem set with 1000 questions written in multiple-choice format with a premise and two alternative choices for each question. Our approach differs from previous work by using shortest paths between concepts in a causal graph with the edge weight as causality metric. We use CausalNet as primary network and implement a few design choices to explore the strengths and drawbacks of this approach, and propose an extension using ConceptNet by leveraging its commonsense knowledge base.
Implementation of Agile Software Development Methodology in a Company – Why? Challenges? Benefits?
(2019)
The software development industry is enhancing day by day. The introduction of agile software development methodologies was a tremendous structural change in companies. Agile transformation provides unlimited opportunities and benefits to the existing and new developing companies. Along with benefits, agile conversion also brings many unseen challenges. New entrants have the advantage of being flexible and cope with the environmental, consumer, and cultural changes, but existing companies are bound to rigid structure.
The goal of this research is to have deep insight into agile software development methodology, agile manifesto, and principles behind the agile manifesto. The prerequisites company must know for agile software development implementation. The benefits a company can achieve by implementing agile software development. Significant challenges that a company can face during agile implementation in a company.
The research objectives of this study help to generate strong motivational research questions. These research questions cover the cultural aspects of company agility, values and principles of agile, benefits, and challenges of agile implementation. The project management triangle will show how benefits of cost, benefits of time, and benefits of quality can be achieved by implementing agile methodologies. Six significant areas have been explored, which shows different challenges a company can face during implementation agile software development methodology. In the end, after the in depth systematic literature review, conclusion is made following some open topics for future work and recommendations on the topic of implementation of agile software development methodology in a company.
Business Process Querying (BPQ) is a discipline in the field of Business Process Man- agement which helps experts to understand existing process models and accelerates the development of new ones. Its queries can fetch and merge these models, answer questions regarding the underlying process, and conduct compliance checking in return. Many languages have been deployed in this discipline but two language types are dominant: Logic-based languages use temporal logic to verify models as finite state machines whereas graph-based languages use pattern matching to retrieve subgraphs of model graphs directly. This thesis aims to map the features of both language types to features of the other to identify strengths and weaknesses. Exemplarily, the features of Computational Tree Logic (CTL) and The Diagramed Modeling Language (DMQL) are mapped to one another. CTL explores the valid state space and thus is better for behavioral querying. Lacking certain structural features and counting mechanisms it is not appropriate to query structural properties. In contrast, DMQL issues structural queries and its patterns can reconstruct any CTL formula. However, they do not always achieve exactly the same semantic: Patterns treat conditional flow as sequential flow by ignoring its conditions. As a result, retrieved mappings are invalid process execution sequences, i.e. false positives, in certain scenarios. DMQL can be used for behavioral querying if these are absent or acceptable. In conclusion, both language types have strengths and are specialized for different BPQ use cases but in certain scenarios graph-based languages can be applied to both. Integrating the evaluation of conditions would remove the need for logic-based languages in BPQ completely.
Data visualization is an effective way to explore data. It helps people to get a valuable insight of the data by placing it in a visual context. However, choosing a good chart without prior knowledge in the area is not a trivial job. Users have to manually explore all possible visualizations and decide upon ones that reflect relevant and desired trend in the data, are insightful and easy to decode, have a clear focus and appealing appearance. To address these challenges we developed a Tool for Automatic Generation of Good viSualizations using Scoring (TAG²S²). The approach tackles the problem of identifying an appropriate metric for judging visualizations as good or bad. It consists of two modules: visualization detection: given a data-set it creates a list of combination of data attributes for scoring and visualization ranking: scores each chart and decides which ones are good or bad. For the later, an utility metric of ten criteria was developed and each visualization detected in the first module is evaluated on these criteria. Only those visualizations that received enough scores are then presented to the user. Additionally to these data parameters, the tool considers user perception regarding the choice of visual encoding when selecting a visualization. To evaluate the utility of the metric and the importance of each criteria, test cases were developed, executed and the results presented.
The erosion of the closed innovation paradigm in conjunction with increasing competitive pressure has boosted the interest of both researchers and organizations in open innovation. Despite such rising interest, several companies remain reluctant to open their organizational boundaries to practice open innovation. Among the many reasons for such reservation are the pertinent complexity of transitioning toward open innovation and a lack of understanding of the procedures required for such endeavors. Hence, this thesis sets out to investigate how organizations can open their boundaries to successfully transition from closed to open innovation by analyzing the current literature on open innovation. In doing so, the transitional procedures are structured and classified into a model comprising three phases, namely unfreezing, moving, and institutionalizing of changes. Procedures of the unfreezing phase lay the foundation for a successful transition to open innovation, while procedures of the moving phase depict how the change occurs. Finally, procedures of the institutionalizing phase contribute to the sustainability of the transition by employing governance mechanisms and performance measures. Additionally, the individual procedures are characterized along with their corresponding barriers and critical success factors. As a result of this structured depiction of the transition process, a guideline is derived. This guideline includes the commonly employed actions of successful practitioners of open innovation, which may serve as a baseline for interested parties of the paradigm. With the derivation of the guideline and concise depiction of the individual transitional phases, this thesis consequently reduces the overall complexity and increases the comprehensibility of the transition and its implications for organizations.
Mit dem Erscheinen moderner Virtual Reality (VR) Headsets auf dem Verbrauchermarkt, gab es den bisher größten Aufschwung in der Geschichte der VR Technologie. Damit einhergehend rücken aber auch die Problematiken aktueller VR Hardware immer mehr in den Vordergrund. Insbesondere die Steuerung in VR war schon immer ein komplexes Thema.
Eine mögliche Lösung bietet die Leap Motion: Ein Hand-Tracking Gerät, welches ursprünglich für den Desktop-Einsatz entwickelt wurde, aber mit dem letzten größeren Softwareupdate an üblichen VR Headsets angebracht werden kann. Dieses Gerät ermöglicht ein sehr genaues Tracking beider Hände und aller Finger. Damit ist es möglich, diese vollständig in der VR Welt zu replizieren und zur Steuerung zu verwenden.
Ziel dieser Arbeit ist es, virtuelle Benutzeroberflächen zu entwerfen, die mit der Leap Motion bedient werden können. Dies soll eine natürliche Interaktion zwischen dem Benutzer und der VR-Umgebung ermöglichen. Danach werden mit Hilfe einer Demoanwendung Probanden-Tests durchgeführt, um ihre Leistung zu bewerten und mit herkömmlichen VR-Reglern zu vergleichen.
Despite the inception of new technologies at a breakneck pace, many analytics projects fail mainly due to the use of incompatible development methodologies. As big data analytics projects are different from software development projects, the methodologies used in software development projects could not be applied in the same fashion to analytics projects. The traditional agile project management approaches to the projects do not consider the complexities involved in the analytics. In this thesis, the challenges involved in generalizing the application of agile methodologies will be evaluated, and some suitable agile frameworks which are more compatible with the analytics project will be explored and recommended. The standard practices and approaches which are currently applied in the industry for analytics projects will be discussed concerning enablers and success factors for agile adaption. In the end, after the comprehensive discussion and analysis of the problem and complexities, a framework will be recommended that copes best with the discussed challenges and complexities and is generally well suited for the most data-intensive analytics projects.
Das Internet of Things (IoT) ist ein schnell wachsendes, technologisches Konzept, das darauf abzielt, verschiedenste physikalische und virtuelle Objekte in einem globalen Netzwerk zu vereinen um Interaktion und Kommunikation zwischen diesen Objekten zu ermöglichen (Atzori, Iera and Morabito, 2010). Die Einsatzmöglichkeiten dieser Technologie sind vielfältig und könnten Gesellschaft und Wirtschaft in ähnlicher Weise verändern wie die Nutzung des Internets (Chase, 2013). Darüber hinaus nimmt das Internet of Things eine zentrale Rolle in der Realisation von visionären Zukunftskonzepten ein, beispielsweise Smart City oder Smart Healthcare. Zudem verspricht die Anwendung dieser Technologie Möglichkeiten, verschiedene Aspekte der Nachhaltigkeit zu verbessern und zu einem bewussteren, effizienteren und schonenderen Umgang mit natürlichen Ressourcen beizutragen (Maksimovic, 2017). Das Handlungsprinzip der Nachhaltigkeit gewinnt im gesellschaftlichen und akademischen Diskurs zunehmend an Bedeutung und trägt den teils schädlichen Produktions- und Konsummustern des vergangenen Jahrhunderts Rechnung (Mcwilliams et al., 2016). Im Zusammenhang mit Nachhaltigkeit ist die fortschreitende Verbreitung von IoT Technologie allerdings auch mit Risiken verknüpft, die im Rahmen des Vorsorgeprinzips rechtzeitig bedacht werden müssen (Harremoës et al., 2001). Dazu zählen der massive Energie- und Rohstoffbedarf der Produktion und des Betriebs von IoT Objekten, sowie deren Entsorgung (Birkel et al., 2019). Die genauen Zusammenhänge und Auswirkungen von IoT im Bezug auf Nachhaltigkeit sind bisher nur unzureichend erforscht und nehmen keine zentrale Rolle in der Diskussion dieser Technologie ein (Behrendt, 2019). Diese Arbeit hat daher das Ziel, einen umfassenden Überblick der Zusammenhänge zwischen IoT Technologie und Nachhaltigkeitsaspekten zu erarbeiten.
Um dieses Ziel zu verwirklichen, verwendet diese Arbeit die Grounded Theory Methodik in Verbindung mit einer umfassenden Literaturanalyse. Die analysierte Literatur besteht dabei aus Forschungsbeiträgen, die besonders dem Gebiet der Informationstechnik (IT) entstammen. Auf Grundlage dieser Literaturanalyse wurden Aspekte, Lösungsansätze, Effekte und Barrieren im Kontext von IoT und Nachhaltigkeit erarbeitet. Im Laufe der Analyse kristallisierten sich zwei zentrale Sichtweisen auf IoT im Zusammenhang mit Nachhaltigkeit heraus. IoT für Nachhaltigkeit (IoT4Sus) beschreibt dabei den Einsatz und die Nutzung von IoT generierten Informationen, um eine Verbesserung im Hinblick auf verschiedene Nachhaltigkeitsaspekte zu erzielen. Nachhaltigkeit für IoT (Sus4IoT) hingegen fokussiert Nachhaltigkeitsaspekte der eingesetzten Technologie und zeigt Lösungen auf um, mit der Produktion und dem Betrieb verknüpfte, negative Auswirkungen auf Nachhaltigkeit zu verringern. Die erarbeiteten Aspekte und Beziehungen wurden in einem umfangreichen Rahmenwerk, dem CCIS Framework, festgehalten und dargestellt. Dieses Rahmenwerk stellt ein Werkzeug zur Erfassung relevanter Aspekte und Beziehungen in diesem Bereich dar und trägt damit zur Bewusstseinsbildung in diesem Kontext bei. Darüber hinaus empfiehlt das Rahmenwerk ein Handlungsprinzip um die Performance von IoT Systemen im Rahmen der Nachhaltigkeit zu optimieren.
Der zentrale Beitrag dieser Arbeit besteht in der Bereitstellung des CCIS Framework, sowie der darin enthaltenen Informationen hinsichtlich der Aspekte und Beziehungen von IoT und Nachhaltigkeit.
Willingness to pay and willingness to accept on a two-sided platform - The use case of DoBeeDo
(2019)
It is widely known that especially for technology-based start-ups, entrepreneurs need to set up the boundaries of the business and define the product/service to offer in order to minimize the risk of failure. The goal of this thesis is to not only emphasize the importance of the business model development and evaluation but also show an example customer validation process for an emerging start-up named DoBeeDo, which is a mobile app operating on a two-sided market. During the process of customer validation a survey has been conducted to evaluate the interest of the target groups as well as the fit of their expectations using the Willingness to Pay and Willingness to Accept measures. The paper includes an analysis and evaluation of the gathered results and assesses whether the execution of the Customer Development Model can be continued.
The status of Business Process Management (BPM) recommender systems is not quite clear as research states. The use of recommenders familiarized itself with the world during the rise of technological evolution in the past decade.Ever since then, several BPM recommender systems came about. However, not a lot of research is conducted in this field. It is not well known to what broad are the technologies used and how are they used. Moreover, this master’s thesis aims at surveying the BPM recommender systems existing. Building on this, the recommendations come in different shapes. They can be positionbased where an element is to be placed at an element’s front, back or to autocomplete a missing link. On the other hand, Recommendations can be textual, to fill the labels of the elements. Furthermore, the literature review for BPM recommender systems took place under the guides of a literature review framework. The framework suggests 5stages of consecutive stages for this sake. The first stage is defining a scope for the research. Secondly, conceptualizing the topic by choosing key terms for literature research. After that in the third stage, comes the research stage.As for the fourth stage, it suggests choosing analysis features over which the literature is to be synthesized and compared. Finally, it recommends defining the research agenda to describe the reason for the literature review. By invoking the mentioned methodology, this master’s thesis surveyed 18 BPM recommender systems. It was found as a result of the survey that there
are not many different technologies for implementing the recommenders. It was also found that the majority of the recommenders suggest nodes that are yet to come in the model, which is called forward recommending. Also, one of the results of the survey indicated the scarce use of textual recommendations to BPM labels. Finally, 18 recommenders are considered less than excepted for a developing field therefore as a result, the survey found a shortage in the number of BPM recommender systems. The results indicate several shortages in several aspects in the field of BPM recommender systems. On this basis, this master’s thesis recommends the future work on it the results.
Tracking ist ein zentraler Bestandteil vieler moderner technischer Anwendungen, insbesondere in den Bereichen autonome Systeme und Augmented Reality. Für Tracking gibt es viele unterschiedliche Ansätze. Ein erst seit kurzem verfolgter ist die Verwendung von Neuronalen Netzen. Im Rahmen dieser Masterarbeit wird eine eine Anwendung erstellt, welche für das Tracking ein Neuronales Netz verwendet. Dazu gehört ebenfalls die Erstellung von Trainingsdaten, sowie die Erstellung des Neuronalen Netzes und dessen Training.
Anschließend wird die Verwendung von Neuronalen Netzen für Tracking analysiert und ausgewertet. Hierunter fallen verschiedene Aspekte. Es wird für eine unterschiedliche Anzahl an Freiheitsgraden geprüft wie gut das Tracking funktioniert und wie viel Performance dieser Ansatz kostet. Des Weiteren wird die Menge der benötigten Trainingsdaten untersucht, der Einfluss der Architektur des Netzwerks und wie wichtig das Vorhandensein von Tiefendaten für die Funktion des Trackings ist. Dies soll einen Einblick ermöglichen wie relevant dieser Ansatz für den Einsatz in zukünftigen Produkten sein könnte.
Geschäftsregeln sind zu einem wichtigen Instrument geworden, um die Einhaltung der Vorschriften in ihren Geschäftsprozessen zu gewährleisten. Aber die Sammlung dieser Geschäftsregeln kann verschiedene widersprüchliche Elemente beinhalten. Dies kann zu einer Verletzung der zu erreichenden Compliance führen. Diese widersprüchlichen Elemente sind daher eine Art Inkonsistenzen oder Quasi-Inkonsistenzen in der Geschäftsregelbasis. Ziel dieser Arbeit ist es, zu untersuchen, wie diese Quasi-Inkonsistenzen in Geschäftsregeln erkannt und analysiert werden können. Zu diesem Zweck entwickeln wir eine umfassende Bibliothek, die es ermöglicht, Ergebnisse aus dem wissenschaftlichen Bereich der Inkonsistenzmessung auf Geschäftsregelformalismen anzuwenden, die tatsächlich in der Praxis verwendet werden.
Most social media platforms allow users to freely express their opinions, feelings, and beliefs. However, in recent years the growing propagation of hate speech, offensive language, racism and sexism on the social media outlets have drawn attention from individuals, companies, and researchers. Today, sexism both online and offline with different forms, including blatant, covert, and subtle lan- guage, is a common phenomenon in society. A notable amount of work has been done over identifying sexist content and computationally detecting sexism which exists online. Although previous efforts have mostly used peoples’ activities on social media platforms such as Twitter as a public and helpful source for collecting data, they neglect the fact that the method of gathering sexist tweets could be biased towards the initial search terms. Moreover, some forms of sexism could be missed since some tweets which contain offensive language could be misclassified as hate speech. Further, in existing hate speech corpora, sexist tweets mostly express hostile sexism, and to some degree, the other forms of sexism which also appear online was disregarded. Besides, the creation of labeled datasets with manual exertion, relying on users to report offensive comments with a tremendous effort by human annotators is not only a costly and time-consuming process, but it also raises the risk of involving discrimination under biased judgment.
This thesis generates a novel sexist and non-sexist dataset which is constructed via "UnSexistifyIt", an online web-based game that incentivizes the players to make minimal modifications to a sexist statement with the goal of turning it into a non-sexist statement and convincing other players that the modified statement is non-sexist. The game applies the methodology of "Game With A Purpose" to generate data as a side-effect of playing the game and also employs the gamification and crowdsourcing techniques to enhance non-game contexts. When voluntary participants play the game, they help to produce non-sexist statements which can reduce the cost of generating new corpus. This work explores how diverse individual beliefs concerning sexism are. Further, the result of this work highlights the impact of various linguistic features and content attributes regarding sexist language detection. Finally, this thesis could help to expand our understanding regarding the syntactic and semantic structure of sexist and non-sexist content and also provides insights to build a probabilistic classifier for single sentences into sexist or non-sexist classes and lastly find a potential ground truth for such a classifier.
Our work finds the fine grained edits in context of neighbouring tokens in Wikipedia articles. We cluster those edits according to similar neighbouring context. We encode neighbouring context into vector space using word vectors. We evaluate clusters returned by our algorithm on extrinsic and intrinsic metric and compare it with previous work. We analyse the relation between extrinsic and intrinsic measurements of fine grained edit tokens.
Das Ziel dieser Masterarbeit war es ein CRM System für das Assist Team der CompuGroup Medical zu entwickeln, welches Open Innovation in die Entwicklung der Minerva 2.0 Software integriert. Um dies zu erreichen wurden CRM Methoden mit Social Networ- king Systemen kombiniert, basierend auf der Forschung von Lin und Chen (2010, S. 11 – 30). Um die definierten Ziele zu erreichen wurde Literatur analysiert, wie ein CRM System und eine Online Community erfolgreich implementiert werden können und dies auf die Entwicklung der Minerva Community angewendet. Dabei wurde sich an den Design Science Richtlinien von Hevner u. a. (2004, S. 75 – 104) orientiert. Das fertige Produkt wurde basierend auf Kunden- und Managementanforderungen entworfen und wurde an- schließend aus Kunden- und Firmenperspektive evaluiert.