From Social Data Mining to Forecasting Socio-Economic Crisis

Socio-economic data mining has a great potential in terms of gaining a better understanding of problems that our economy and society are facing, such as financial instability, shortages of resources, or conflicts. Without large-scale data mining, progress in these areas seems hard or impossible. Therefore, a suitable, distributed data mining infrastructure and research centers should be built in Europe. It also appears appropriate to build a network of Crisis Observatories. They can be imagined as laboratories devoted to the gathering and processing of enormous volumes of data on both natural systems such as the Earth and its ecosystem, as well as on human techno-socio-economic systems, so as to gain early warnings of impending events. Reality mining provides the chance to adapt more quickly and more accurately to changing situations. Further opportunities arise by individually customized services, which however should be provided in a privacy-respecting way. This requires the development of novel ICT (such as a self- organizing Web), but most likely new legal regulations and suitable institutions as well. As long as such regulations are lacking on a world-wide scale, it is in the public interest that scientists explore what can be done with the huge data available. Big data do have the potential to change or even threaten democratic societies. The same applies to sudden and large-scale failures of ICT systems. Therefore, dealing with data must be done with a large degree of responsibility and care. Self-interests of individuals, companies or institutions have limits, where the public interest is affected, and public interest is not a sufficient justification to violate human rights of individuals. Privacy is a high good, as confidentiality is, and damaging it would have serious side effects for society.Comment: 65 pages, 1 figure, Visioneer White Paper, see http://www.visioneer.ethz.c

Association Rule Discovery from Collaborative Mobile Data

Sophisticated mobile devices have rapidly become essential tools for various daily activities of billions of people worldwide. Subsequently, the demand for longer battery lives is constantly increasing. The Carat project is advancing the understanding of mobile energy consumption by using collaborative mobile data to estimate and model energy consumption of mobile devices. This thesis presents a method for estimating mobile application energy consumption from mobile device system settings and context factors using association rules. These settings and factors include CPU usage, device travel distance, battery temperature, battery voltage, screen brightness, used mobile networking technology, network type, WiFi signal strength, and WiFi connection speed. The association rules are mined using Apache Spark cluster-computing framework from collaborative mobile data collected by the Carat project. Additionally, this thesis presents a prototype of a web based API for discovering these association rules. The web service integrates Apache Spark based analysis engine with a user friendly front-end allowing an aggregated view of the dataset to be accessible without revealing data of individual participants of the Carat project. This thesis shows that association rules can be used effectively in modelling mobile device energy consumption. Example rules are presented and the performance of the implementation is evaluated experimentally

A comparison of statistical machine learning methods in heartbeat detection and classification

In health care, patients with heart problems require quick responsiveness in a clinical setting or in the operating theatre. Towards that end, automated classification of heartbeats is vital as some heartbeat irregularities are time consuming to detect. Therefore, analysis of electro-cardiogram (ECG) signals is an active area of research. The methods proposed in the literature depend on the structure of a heartbeat cycle. In this paper, we use interval and amplitude based features together with a few samples from the ECG signal as a feature vector. We studied a variety of classification algorithms focused especially on a type of arrhythmia known as the ventricular ectopic fibrillation (VEB). We compare the performance of the classifiers against algorithms proposed in the literature and make recommendations regarding features, sampling rate, and choice of the classifier to apply in a real-time clinical setting. The extensive study is based on the MIT-BIH arrhythmia database. Our main contribution is the evaluation of existing classifiers over a range sampling rates, recommendation of a detection methodology to employ in a practical setting, and extend the notion of a mixture of experts to a larger class of algorithms

Découverte d'évènements par contenu visuel dans les médias sociaux

The ease of publishing content on social media sites brings to the Web an ever increasing amount of user generated content captured during, and associated with, real life events. Social media documents shared by users often reflect their personal experience of the event. Hence, an event can be seen as a set of personal and local views, recorded by different users. These event records are likely to exhibit similar facets of the event but also specific aspects. By linking different records of the same event occurrence we can enable rich search and browsing of social media events content. Specifically, linking all the occurrences of the same event would provide a general overview of the event. In this dissertation we present a content-based approach for leveraging the wealth of social media documents available on the Web for event identification and characterization. To match event occurrences in social media, we develop a new visual-based method for retrieving events in huge photocollections, typically in the context of User Generated Content. The main contributions of the thesis are the following : (1) a new visual-based method for retrieving events in photo collections, (2) a scalable and distributed framework for Nearest Neighbors Graph construction for high dimensional data, (3) a collaborative content-based filtering technique for selecting relevant social media documents for a given event.L’évolution du web, de ce qui était typiquement connu comme un moyen de communication à sens unique en mode conversationnel, a radicalement changé notre manière de traiter l’information. Des sites de médias sociaux tels que Flickr et Facebook, offrent des espaces d’échange et de diffusion de l’information. Une information de plus en plus riche, mais aussi personnelle, et qui s’organise, le plus souvent, autour d’événements de la vie réelle. Ainsi, un événement peut être perçu comme un ensemble de vues personnelles et locales, capturées par différents utilisateurs. Identifier ces différentes instances permettrait, dès lors, de reconstituer une vue globale de l’événement. Plus particulièrement, lier différentes instances d’un même événement profiterait à bon nombre d’applications tel que la recherche, la navigation ou encore le filtrage et la suggestion de contenus. L’objectif principal de cette thèse est l’identification du contenu multimédia, associé à un événement dans de grandes collections d’images. Une première contribution est une méthode de recherche d’événements basée sur le contenu visuel. La deuxième contribution est une approche scalable et distribuée pour la construction de graphes des K plus proches voisins. La troisième contribution est une méthode collaborative pour la sélection de contenu pertinent. Plus particulièrement, nous nous intéresserons aux problèmes de génération automatique de résumés d’événements et suggestion de contenus dans les médias sociaux