Contributions to Time Series Classification: Meta-Learning and Explainability

This thesis includes 3 contributions of different types to the area of supervised time series classification, a growing field of research due to the amount of time series collected daily in a wide variety of domains. In this context, the number of methods available for classifying time series is increasing, and the classifiers are becoming more and more competitive and varied. Thus, the first contribution of the thesis consists of proposing a taxonomy of distance-based time series classifiers, where an exhaustive review of the existing methods and their computational costs is made. Moreover, from the point of view of a non-expert user (even from that of an expert), choosing a suitable classifier for a given problem is a difficult task. The second contribution, therefore, deals with the recommendation of time series classifiers, for which we will use a meta-learning approach. Finally, the third contribution consists of proposing a method to explain the prediction of time series classifiers, in which we calculate the relevance of each region of a series in the prediction. This method of explanation is based on perturbations, for which we will consider specific and realistic transformations for the time series.BES-2016-07689

Contributions to Time Series Classification: Meta-Learning and Explainability

141 p.La presente tesis incluye 3 contribuciones de diferentes tipos al área de la clasificación supervisada de series temporales, un campo en auge por la cantidad de series temporales recolectadas día a día en una gran variedad en ámbitos. En este contexto, la cantidad de métodos disponibles para clasificar series temporales es cada vez más grande, siendo los clasificadores cada vez más competitivos y variados. De esta manera, la primera contribución de la tesis consiste en proponer una taxonomía de los clasificadores de series temporales basados en distancias, donde se hace una revisión exhaustiva de los métodos existentes y sus costes computacionales. Además, desde el punto de vista de un/a usuario/a no experto/a (incluso desde la de un/a experto/a), elegir un clasificador adecuado para un problema concreto es una tarea difícil. En la segunda contribución, por tanto, se aborda la recomendación de clasificadores de series temporales, para lo que usaremos un enfoque basado en el meta-aprendizaje. Por último, la tercera contribución consiste en proponer un método para explicar la predicción de los clasificadores de series temporales, en el que calculamos la relevancia de cada región de una serie en la predicción. Este método de explicación está basado en perturbaciones, para lo que consideraremos transformaciones específicas y realistas para las series temporales

Unterstützung des Editierens von Graphen in Visuellen Repräsentationen

The goal of this thesis is to provide solutions for supporting the direct editing of graphs in visual representations for analyzing graphs. For that, a conceptual view on the user's tasks is established first. On this basis, several novel approaches to "visually edit" the different data aspects of graphs - the graph's structure and associated attribute values - are introduced. Thereby, different visual graph representations suitable for communicating the data are considered.Das Ziel der vorliegenden Dissertation ist, Lösungen zur Unterstützung des direkten Editierens von Graphen in visuellen Repräsentationen zur Analyse von Graphen bereitzustellen. Dafür wird zunächst eine konzeptuelle Sicht auf die Aufgaben des Nutzers entwickelt. Auf dieser Basis werden anschließend mehrere neue Verfahren eingeführt, welche das "visuelle Editieren" der verschiedenen Datenaspekte von Graphen - der Struktur sowie dazu assoziierte Attributwerte - ermöglichen. Dabei werden verschiedene visuelle Graphrepräsentationen berücksichtigt, welche die Daten in geeigneter Form kommunizieren

Developing sustainability pathways for social simulation tools and services

The use of cloud technologies to teach agent-based modelling and simulation (ABMS) is an interesting application of a nascent technological paradigm that has received very little attention in the literature. This report fills that gap and aims to help instructors, teachers and demonstrators to understand why and how cloud services are appropriate solutions to common problems they face delivering their study programmes, as well as outlining the many cloud options available. The report first introduces social simulation and considers how social simulation is taught. Following this factors affecting the implementation of agent-based models are explored, with attention focused primarily on the modelling and execution platforms currently available, the challenges associated with implementing agent-based models, and the technical architectures that can be used to support the modelling, simulation and teaching process. This sets the context for an extended discussion on cloud computing including service and deployment models, accessing cloud resources, the financial implications of adopting the cloud, and an introduction to the evaluation of cloud services within the context of developing, executing and teaching agent-based models

Cyber–Physical–Social Frameworks for Urban Big Data Systems: A Survey

The integration of things’ data on the Web and Web linking for things’ description and discovery is leading the way towards smart Cyber–Physical Systems (CPS). The data generated in CPS represents observations gathered by sensor devices about the ambient environment that can be manipulated by computational processes of the cyber world. Alongside this, the growing use of social networks offers near real-time citizen sensing capabilities as a complementary information source. The resulting Cyber–Physical–Social System (CPSS) can help to understand the real world and provide proactive services to users. The nature of CPSS data brings new requirements and challenges to different stages of data manipulation, including identification of data sources, processing and fusion of different types and scales of data. To gain an understanding of the existing methods and techniques which can be useful for a data-oriented CPSS implementation, this paper presents a survey of the existing research and commercial solutions. We define a conceptual framework for a data-oriented CPSS and detail the various solutions for building human–machine intelligence