Transforming user data into user value by novel mining techniques for extraction of web content, structure and usage patterns. The Development and Evaluation of New Web Mining Methods that enhance Information Retrieval and improve the Understanding of User¿s Web Behavior in Websites and Social Blogs.

The rapid growth of the World Wide Web in the last decade makes it the largest publicly accessible data source in the world, which has become one of the most significant and influential information revolution of modern times. The influence of the Web has impacted almost every aspect of humans' life, activities and fields, causing paradigm shifts and transformational changes in business, governance, and education. Moreover, the rapid evolution of Web 2.0 and the Social Web in the past few years, such as social blogs and friendship networking sites, has dramatically transformed the Web from a raw environment for information consumption to a dynamic and rich platform for information production and sharing worldwide. However, this growth and transformation of the Web has resulted in an uncontrollable explosion and abundance of the textual contents, creating a serious challenge for any user to find and retrieve the relevant information that he truly seeks to find on the Web. The process of finding a relevant Web page in a website easily and efficiently has become very difficult to achieve. This has created many challenges for researchers to develop new mining techniques in order to improve the user experience on the Web, as well as for organizations to understand the true informational interests and needs of their customers in order to improve their targeted services accordingly by providing the products, services and information that truly match the requirements of every online customer. With these challenges in mind, Web mining aims to extract hidden patterns and discover useful knowledge from Web page contents, Web hyperlinks, and Web usage logs. Based on the primary kinds of Web data used in the mining process, Web mining tasks can be categorized into three main types: Web content mining, which extracts knowledge from Web page contents using text mining techniques, Web structure mining, which extracts patterns from the hyperlinks that represent the structure of the website, and Web usage mining, which mines user's Web navigational patterns from Web server logs that record the Web page access made by every user, representing the interactional activities between the users and the Web pages in a website. The main goal of this thesis is to contribute toward addressing the challenges that have been resulted from the information explosion and overload on the Web, by proposing and developing novel Web mining-based approaches. Toward achieving this goal, the thesis presents, analyzes, and evaluates three major contributions. First, the development of an integrated Web structure and usage mining approach that recommends a collection of hyperlinks for the surfers of a website to be placed at the homepage of that website. Second, the development of an integrated Web content and usage mining approach to improve the understanding of the user's Web behavior and discover the user group interests in a website. Third, the development of a supervised classification model based on recent Social Web concepts, such as Tag Clouds, in order to improve the retrieval of relevant articles and posts from Web social blogs

Text-image synergy for multimodal retrieval and annotation

Text and images are the two most common data modalities found on the Internet. Understanding the synergy between text and images, that is, seamlessly analyzing information from these modalities may be trivial for humans, but is challenging for software systems. In this dissertation we study problems where deciphering text-image synergy is crucial for finding solutions. We propose methods and ideas that establish semantic connections between text and images in multimodal contents, and empirically show their effectiveness in four interconnected problems: Image Retrieval, Image Tag Refinement, Image-Text Alignment, and Image Captioning. Our promising results and observations open up interesting scopes for future research involving text-image data understanding.Text and images are the two most common data modalities found on the Internet. Understanding the synergy between text and images, that is, seamlessly analyzing information from these modalities may be trivial for humans, but is challenging for software systems. In this dissertation we study problems where deciphering text-image synergy is crucial for finding solutions. We propose methods and ideas that establish semantic connections between text and images in multimodal contents, and empirically show their effectiveness in four interconnected problems: Image Retrieval, Image Tag Refinement, Image-Text Alignment, and Image Captioning. Our promising results and observations open up interesting scopes for future research involving text-image data understanding.Text und Bild sind die beiden häufigsten Arten von Inhalten im Internet. Während es für Menschen einfach ist, gerade aus dem Zusammenspiel von Text- und Bildinhalten Informationen zu erfassen, stellt diese kombinierte Darstellung von Inhalten Softwaresysteme vor große Herausforderungen. In dieser Dissertation werden Probleme studiert, für deren Lösung das Verständnis des Zusammenspiels von Text- und Bildinhalten wesentlich ist. Es werden Methoden und Vorschläge präsentiert und empirisch bewertet, die semantische Verbindungen zwischen Text und Bild in multimodalen Daten herstellen. Wir stellen in dieser Dissertation vier miteinander verbundene Text- und Bildprobleme vor: • Bildersuche. Ob Bilder anhand von textbasierten Suchanfragen gefunden werden, hängt stark davon ab, ob der Text in der Nähe des Bildes mit dem der Anfrage übereinstimmt. Bilder ohne textuellen Kontext, oder sogar mit thematisch passendem Kontext, aber ohne direkte Übereinstimmungen der vorhandenen Schlagworte zur Suchanfrage, können häufig nicht gefunden werden. Zur Abhilfe schlagen wir vor, drei Arten von Informationen in Kombination zu nutzen: visuelle Informationen (in Form von automatisch generierten Bildbeschreibungen), textuelle Informationen (Stichworte aus vorangegangenen Suchanfragen), und Alltagswissen. • Verbesserte Bildbeschreibungen. Bei der Objekterkennung durch Computer Vision kommt es des Öfteren zu Fehldetektionen und Inkohärenzen. Die korrekte Identifikation von Bildinhalten ist jedoch eine wichtige Voraussetzung für die Suche nach Bildern mittels textueller Suchanfragen. Um die Fehleranfälligkeit bei der Objekterkennung zu minimieren, schlagen wir vor Alltagswissen einzubeziehen. Durch zusätzliche Bild-Annotationen, welche sich durch den gesunden Menschenverstand als thematisch passend erweisen, können viele fehlerhafte und zusammenhanglose Erkennungen vermieden werden. • Bild-Text Platzierung. Auf Internetseiten mit Text- und Bildinhalten (wie Nachrichtenseiten, Blogbeiträge, Artikel in sozialen Medien) werden Bilder in der Regel an semantisch sinnvollen Positionen im Textfluss platziert. Wir nutzen dies um ein Framework vorzuschlagen, in dem relevante Bilder ausgesucht werden und mit den passenden Abschnitten eines Textes assoziiert werden. • Bildunterschriften. Bilder, die als Teil von multimodalen Inhalten zur Verbesserung der Lesbarkeit von Texten dienen, haben typischerweise Bildunterschriften, die zum Kontext des umgebenden Texts passen. Wir schlagen vor, den Kontext beim automatischen Generieren von Bildunterschriften ebenfalls einzubeziehen. Üblicherweise werden hierfür die Bilder allein analysiert. Wir stellen die kontextbezogene Bildunterschriftengenerierung vor. Unsere vielversprechenden Beobachtungen und Ergebnisse eröffnen interessante Möglichkeiten für weitergehende Forschung zur computergestützten Erfassung des Zusammenspiels von Text- und Bildinhalten

Cloud service discovery and analysis: a unified framework

Over the past few years, cloud computing has been more and more attractive as a new computing paradigm due to high flexibility for provisioning on-demand computing resources that are used as services through the Internet. The issues around cloud service discovery have considered by many researchers in the recent years. However, in cloud computing, with the highly dynamic, distributed, the lack of standardized description languages, diverse services offered at different levels and non-transparent nature of cloud services, this research area has gained a significant attention. Robust cloud service discovery approaches will assist the promotion and growth of cloud service customers and providers, but will also provide a meaningful contribution to the acceptance and development of cloud computing. In this dissertation, we have proposed an automated cloud service discovery approach of cloud services. We have also conducted extensive experiments to validate our proposed approach. The results demonstrate the applicability of our approach and its capability of effectively identifying and categorizing cloud services on the Internet. Firstly, we develop a novel approach to build cloud service ontology. Cloud service ontology initially is built based on the National Institute of Standards and Technology (NIST) cloud computing standard. Then, we add new concepts to ontology by automatically analyzing real cloud services based on cloud service ontology Algorithm. We also propose cloud service categorization that use Term Frequency to weigh cloud service ontology concepts and calculate cosine similarity to measure the similarity between cloud services. The cloud service categorization algorithm is able to categorize cloud services to clusters for effective categorization of cloud services. In addition, we use Machine Learning techniques to identify cloud service in real environment. Our cloud service identifier is built by utilizing cloud service features extracted from the real cloud service providers. We determine several features such as similarity function, semantic ontology, cloud service description and cloud services components, to be used effectively in identifying cloud service on the Web. Also, we build a unified model to expose the cloud service’s features to a cloud service search user to ease the process of searching and comparison among a large amount of cloud services by building cloud service’s profile. Furthermore, we particularly develop a cloud service discovery Engine that has capability to crawl the Web automatically and collect cloud services. The collected datasets include meta-data of nearly 7,500 real-world cloud services providers and nearly 15,000 services (2.45GB). The experimental results show that our approach i) is able to effectively build automatic cloud service ontology, ii) is robust in identifying cloud service in real environment and iii) is more scalable in providing more details about cloud services.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 201

Distant Supervised Construction and Evaluation of a Novel Dataset of Emotion-Tagged Social Media Comments in Spanish

Tagged language resources are an essential requirement for developing machine-learning text-based classifiers. However, manual tagging is extremely time consuming and the resulting datasets are rather small, containing only a few thousand samples. Basic emotion datasets are particularly difficult to classify manually because categorization is prone to subjectivity, and thus, redundant classification is required to validate the assigned tag. Even though, in recent years, the amount of emotion-tagged text datasets in Spanish has been growing, it cannot be compared with the number, size, and quality of the datasets in English. Quality is a particularly concerning issue, as not many datasets in Spanish included a validation step in the construction process. In this article, a dataset of social media comments in Spanish is compiled, selected, filtered, and presented. A sample of the dataset is reclassified by a group of psychologists and validated using the Fleiss Kappa interrater agreement measure. Error analysis is performed by using the Sentic Computing tool BabelSenticNet. Results indicate that the agreement between the human raters and the automatically acquired tag is moderate, similar to other manually tagged datasets, with the advantages that the presented dataset contains several hundreds of thousands of tagged comments and it does not require extensive manual tagging. The agreement measured between human raters is very similar to the one between human raters and the original tag. Every measure presented is in the moderate agreement zone and, as such, suitable for training classification algorithms in sentiment analysis field

Distant Supervised Construction and Evaluation of a Novel Dataset of Emotion-Tagged Social Media Comments in Spanish

Tagged language resources are an essential requirement for developing machine-learning text-based classifiers. However, manual tagging is extremely time consuming and the resulting datasets are rather small, containing only a few thousand samples. Basic emotion datasets are particularly difficult to classify manually because categorization is prone to subjectivity, and thus, redundant classification is required to validate the assigned tag. Even though, in recent years, the amount of emotion-tagged text datasets in Spanish has been growing, it cannot be compared with the number, size, and quality of the datasets in English. Quality is a particularly concerning issue, as not many datasets in Spanish included a validation step in the construction process. In this article, a dataset of social media comments in Spanish is compiled, selected, filtered, and presented. A sample of the dataset is reclassified by a group of psychologists and validated using the Fleiss Kappa interrater agreement measure. Error analysis is performed by using the Sentic Computing tool BabelSenticNet. Results indicate that the agreement between the human raters and the automatically acquired tag is moderate, similar to other manually tagged datasets, with the advantages that the presented dataset contains several hundreds of thousands of tagged comments and it does not require extensive manual tagging. The agreement measured between human raters is very similar to the one between human raters and the original tag. Every measure presented is in the moderate agreement zone and, as such, suitable for training classification algorithms in sentiment analysis field.Fil: Tessore, Juan Pablo. Universidad Nacional del Noroeste de la Pcia.de Bs.as.. Escuela de Tecnologia. Instituto de Investigacion y Transferencia En Tecnologia. - Comision de Investigaciones Cientificas de la Provincia de Buenos Aires. Instituto de Investigacion y Transferencia En Tecnologia.; ArgentinaFil: Esnaola, Leonardo Martín. Universidad Nacional del Noroeste de la Pcia.de Bs.as.. Escuela de Tecnologia. Instituto de Investigacion y Transferencia En Tecnologia. - Comision de Investigaciones Cientificas de la Provincia de Buenos Aires. Instituto de Investigacion y Transferencia En Tecnologia.; ArgentinaFil: Lanzarini, Laura Cristina. Universidad Nacional de La Plata. Facultad de Informática. Instituto de Investigación en Informática Lidi; ArgentinaFil: Baldassarri, Sandra Silvia. Universidad de Zaragoza; Españ

Making Sense of Document Collections with Map-Based Visualizations

As map-based visualizations of documents become more ubiquitous, there is a greater need for them to support intellectual and creative high-level cognitive activities with collections of non-cartographic materials -- documents. This dissertation concerns the conceptualization of map-based visualizations as tools for sensemaking and collection understanding. As such, map-based visualizations would help people use georeferenced documents to develop understanding, gain insight, discover knowledge, and construct meaning. This dissertation explores the role of graphical representations (such as maps, Kohonen maps, pie charts, and other) and interactions with them for developing map-based visualizations capable of facilitating sensemaking activities such as collection understanding. While graphical representations make document collections more perceptually and cognitively accessible, interactions allow users to adapt representations to users’ contextual needs. By interacting with representations of documents or collections and being able to construct representations of their own, people are better able to make sense of information, comprehend complex structures, and integrate new information into their existing mental models. In sum, representations and interactions may reduce cognitive load and consequently expedite the overall time necessary for completion of sensemaking activities, which typically take much time to accomplish. The dissertation proceeds in three phases. The first phase develops a conceptual framework for translating ontological properties of collections to representations and for supporting visual tasks by means of graphical representations. The second phase concerns the cognitive benefits of interaction. It conceptualizes how interactions can help people during complex sensemaking activities. Although the interactions are explained on the example of a prototype built with Google Maps, they are independent iv of Google Maps and can be applicable to various other technologies. The third phase evaluates the utility, analytical capabilities and usability of the additional representations when users interact with a visualization prototype – VIsual COLlection EXplorer. The findings suggest that additional representations can enhance understanding of map-based visualizations of library collections: specifically, they can allow users to see trends, gaps, and patterns in ontological properties of collections

Social software for music

Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 200

BlogForever: D3.1 Preservation Strategy Report

This report describes preservation planning approaches and strategies recommended by the BlogForever project as a core component of a weblog repository design. More specifically, we start by discussing why we would want to preserve weblogs in the first place and what it is exactly that we are trying to preserve. We further present a review of past and present work and highlight why current practices in web archiving do not address the needs of weblog preservation adequately. We make three distinctive contributions in this volume: a) we propose transferable practical workflows for applying a combination of established metadata and repository standards in developing a weblog repository, b) we provide an automated approach to identifying significant properties of weblog content that uses the notion of communities and how this affects previous strategies, c) we propose a sustainability plan that draws upon community knowledge through innovative repository design