Even though that business plans are created every day, and that there is many literature on them, there is very little known on the process of the business plan development itself. Using a qualitative research method called autoethnography, this thesis explored the business plan development process. The main findings of this thesis were that there are four main themes or problems that can be associated with this process: problems regarding the data search and data collection; emotions; time constraints and the attractiveness of the idea.

Linked Data (LD) follows the web in providing low barriers to publication, and in deploying web-scale keyword search as a central way of identifying relevant data. As in the web, searchesinitially identify results in broadly the form in which they were published, and the published form may be provided to the user as the result of a search. This will be satisfactory in some cases, but the diversity of publishers means that the results of the search may be obtained from many different sources, and described in many different ways. As such, there seems to bean opportunity to add value to search results by providing userswith an integrated representation that brings together features from different sources. This involves an on-the-fly and automated data integration process being applied to search results, which raises the question as to what technologies might bemost suitable for supporting the integration of LD searchresults. In this thesis we take the view that the problem of integrating LD search results is best approached by assimilating different forms ofevidence that support the integration process. In particular, thisdissertation shows how Statistical Relational Learning (SRL) formalisms (viz., Markov Logic Networks (MLN) and Probabilistic Soft Logic (PSL)) can beexploited to assimilate different sources of evidence in a principledway and to beneficial effect for users. Specifically, in this dissertation weconsider syntactic evidence derived from LD search results and from matching algorithms, semantic evidence derived from LD vocabularies, and user evidence,in the form of feedback. This dissertation makes the following key contributions: (i) a characterisation of key features of LD search results that are relevant to their integration, and a description of some initial experiences in the use of MLN for interpreting search results; (ii)a PSL rule-base that models the uniform assimilation of diverse kinds of evidence;(iii) an empirical evaluation of how the contributed MLN and PSL approaches perform in terms of their ability to infer a structure for integrating LD search results;and (iv) concrete examples of how populating such inferred structures for presentation to the end user is beneficial, as well as guiding the collection of feedbackwhose assimilation further improves search results presentation.

In this work, we focus on automatically classifying music by mood. For this purpose, we propose computational models using information extracted from the audio signal. The foundations of such algorithms are based on techniques from signal processing, machine learning and information retrieval. First, by studying the tagging behavior of a music social network, we find a model to represent mood. Then, we propose a method for automatic music mood classification. We analyze the contributions of audio descriptors and how their values are related to the observed mood. We also propose a multimodal version using lyrics, contributing to the field of text retrieval. Moreover, after showing the relation between mood and genre, we present a new approach using automatic music genre classification. We demonstrate that genre-based mood classifiers give higher accuracies than standard audio models. Finally, we propose a rule extraction technique to explicit our models. / En esta tesis, nos centramos en la clasificación automática de música a partir de la detección de la emoción que comunica. Primero, estudiamos cómo los miembros de una red social utilizan etiquetas y palabras clave para describir la música y las emociones que evoca, y encontramos un modelo para representar los estados de ánimo. Luego, proponemos un método de clasificación automática de emociones. Analizamos las contribuciones de descriptores de audio y cómo sus valores están relacionados con los estados de ánimo. Proponemos también una versión multimodal de nuestro algoritmo, usando las letras de canciones. Finalmente, después de estudiar la relación entre el estado de ánimo y el género musical, presentamos un método usando la clasificación automática por género. A modo de recapitulación conceptual y algorítmica, proponemos una técnica de extracción de reglas para entender como los algoritmos de aprendizaje automático predicen la emoción evocada por la música

Die Informationsverhaltensforschung liefert zahlreiche Erkenntnisse darüber, wie Menschen Informationen suchen, abrufen und nutzen. Wir verfügen über Forschungsergebnisse zu Informationsverhaltensmustern in einem breiten Spektrum von Kontexten und Situationen, aber wir wissen nicht genug über die Informationsbedürfnisse und Ziele von Forschenden hinsichtlich der Nutzung von Forschungsdaten. Die Informationsverhaltensforschung gibt insbesondere Aufschluss über das literaturbezogene Informationsverhalten. Die vorliegende Studie basiert auf der Annahme, dass diese Erkenntnisse nicht ohne weiteres auf datenbezogenes Informationsverhalten übertragen werden können. Um diese Annahme zu untersuchen, wurde eine Studie zum Informationssuchverhalten von Datennutzenden durchgeführt. Übergeordnetes Ziel der Studie war es, Erkenntnisse über das Informationsverhalten der Nutzenden eines bestimmten Retrievalsystems für sozialwissenschaftliche Daten zu erlangen, um die Entwicklung von Forschungsdateninfrastrukturen zu unterstützen, die das Data Sharing erleichtern sollen. Das empirische Design dieser Studie folgt einem Mixed-Methods-Ansatz. Dieser umfasst eine qualitative Studie in Form von Experteninterviews und – darauf aufbauend – eine quantitative Studie in Form einer Online-Befragung von Sekundärnutzenden von Daten aus Bevölkerungs- und Meinungsumfragen (Umfragedaten). Im Kern hat die Untersuchung ergeben, dass die Einbindung in die Forschungscommunity bei der Datensuche eine zentrale Rolle spielt. Die Analysen zeigen, dass Communities eine wichtige Determinante für das Informationssuchverhalten sind. Die Einbindung in die Community hat das Potential, Probleme oder Barrieren bei der Datensuche zu reduzieren. Diese Studie trägt zur Theorieentwicklung in der Informationsverhaltensforschung durch die Modellierung des Datensuchverhaltens bei. In praktischer Hinsicht gibt die Studie Empfehlungen für das Design von Dateninfrastrukturen, basierend auf empirischen Anforderungsanalysen. / From information behaviour research we have a rich knowledge of how people are looking for, retrieving, and using information. We have scientific evidence for information behaviour patterns in a wide scope of contexts and situations, but we don’t know enough about researchers’ information needs and goals regarding the usage of research data. Having emerged from library user studies, information behaviour research especially provides insight into literature-related information behaviour. This thesis is based on the assumption that these insights cannot be easily transferred to data-related information behaviour. In order to explore this assumption, a study of secondary data users’ information-seeking behaviour was conducted. The study was designed and evaluated in comparison to existing theories and models of information-seeking behaviour. The overall goal of the study was to create evidence of actual information practices of users of one particular retrieval system for social science data in order to inform the development of research data infrastructures that facilitate data sharing. The empirical design of this study follows a mixed methods approach. This includes a qualitative study in the form of expert interviews and – building on the results found therein – a quantitative web survey of secondary survey data users. The core result of this study is that community involvement plays a pivotal role in survey data seeking. The analyses show that survey data communities are an important determinant in survey data users' information seeking behaviour and that community involvement facilitates data seeking and has the capacity of reducing problems or barriers. Community involvement increases with growing experience, seniority, and data literacy. This study advances information behaviour research by modelling the specifics of data seeking behaviour. In practical respect, the study specifies data-user oriented requirements for systems design.