Site web<\/a><\/p>\n Abstract : Big Data and the Internet of Things (IoT) have the potential to fundamentally shift the way we interact with our surroundings. The challenge of deriving insights from the Internet of Things (IoT) has been recognized as one of the most exciting and key opportunities for both academia and industry. Advanced analysis of big data streams from sensors and devices is bound to become a key area of data mining research as the number of applications requiring such processing increases. Dealing with the evolution over time of such data streams, i.e., with concepts that drift or change completely, is one of the core issues in stream mining. In this talk, I will present an overview of data stream mining, and I will introduce some popular open source tools for data stream mining.<\/span><\/p>\n Site web<\/a><\/p>\n Abstract : Owing to the constant evolution of technologies many scientific studies lead nowadays to the collection of large amounts of data that are usually modelled as functional data. Functional data are characterized by their complexity, their underlying structure and their high dimensionality. After brief recall on functional data analysis, we will focus on the modelling of inter-individual curves deviations through functional mixed models and deformation models.<\/span><\/p>\n Site web<\/a><\/p>\n Abstract : Les Interfaces Cerveau-Ordinateur ou BCI (de l\u2019anglais \u00ab\u00a0Brain-Computer Interfaces\u00a0\u00bb) sont des syst\u00e8mes de communication permettant \u00e0 leurs utilisateurs d\u2019envoyer des commandes \u00e0 un ordinateur en utilisant uniquement leurs signaux c\u00e9r\u00e9braux, gr\u00e2ce \u00e0 la mesure et au traitement de ces signaux. Puisque les BCI permettent de contr\u00f4ler un ordinateur sans aucune activit\u00e9 physique, elles sont tr\u00e8s prometteuses pour de nombreux domaines d\u2019application, notamment les technologies d\u2019assistance, par exemple pour contr\u00f4ler des fauteuils roulants, et l\u2019interaction homme-machine. Dans cette pr\u00e9sentation, je pr\u00e9senterai un panorama des m\u00e9thodes d\u2019analyses et de classification des signaux \u00e9lectroenc\u00e9phalographiques (EEG) utilis\u00e9s pour mesurer l\u2019activit\u00e9 c\u00e9r\u00e9brale dans les BCI. J\u2019illustrerai notamment les informations qu\u2019on peut extraire de ces signaux, et comment les extraire et les classifier malgr\u00e9 la nature bruit\u00e9e et non-stationnaire de ceux-ci.<\/span><\/p>\n Abstract : Time series have gained much interest in the last decade. They appear naturally in industrial, medical or\u00a0economical environments to name a few. Time series mining refers to the activities related with the\u00a0extraction of knowledge from time series databases. Particularly, typical machine learning activities such\u00a0as supervised classification or clustering are carried out from this kind of data. Furthermore, the timely\u00a0nature of the data allows considering new problems. An example is the early classification of time series,\u00a0where the objective is to classify the series as early as possible a before its end. In this talk we will\u00a0review time series mining algorithms and pointed out to new avenues to do research in the area.<\/span><\/p>\n Site web<\/a><\/p>\n Abstract: In the light of regularized dynamic time warping kernels, we re-considers the concept of time elastic centroid for a set of time series. More precisely, we will present an algorithm based on a probabilistic interpretation of kernel alignment matrices. This algorithm expresses the averaging process in terms of a stochastic alignment automata. It uses an iterative agglomerative heuristic method for averaging the aligned samples, while also averaging the times of occurrence of the aligned samples. We will demonstrate its use in some applications, namely dataset simplification, bootstrapping and time series denoising.<\/span><\/p>\n Site web<\/a><\/p>\n Abstract: There is an increasingly pressing need, by several applications in diverse domains, for developing techniques able to index and mine very large collections of sequences, or data series. Examples of such applications come from social media analytics and internet service providers, as well as from a multitude of scientific domains. It is not unusual for these applications to involve numbers of data series in the order of hundreds of millions to billions, which are often times not analyzed in their full detail due to their sheer size. However, no existing data management solution (such as relational databases, column stores, array databases, and time series management systems) can offer native support for sequences and the corresponding operators necessary for complex analytics.Title :\u00a0Machine Learning for Data Streams<\/h5>\n
\nJoyce-Madison Giacofci (Universit\u00e9 Rennes 2)<\/h4>\n
Title :\u00a0Functional data analysis : modelling curves variations<\/h5>\n
\nFabien Lotte (Inria Bordeaux)<\/h4>\n
Title : Traitement et classification de signaux Electroenc\u00e9phalographiques pour les interfaces cerveau-ordinateur<\/h5>\n
\nJos\u00e9 Lozano (University of the Basque Country, Spain)<\/h4>\n
Site web<\/a><\/h6>\n
Title : Time series data mining challenges<\/h5>\n
\nPierre-Fran\u00e7ois Marteau (EXPRESSION\/IRISA\/UBS)<\/h4>\n
Title : Kernelized time elastic averaging of time series<\/h5>\n
\nThemis Palpanas (Lipade)<\/h4>\n
Title :\u00a0From Data Series Indexing to Big Data Series Analytics<\/h5>\n
\nIn this talk, we argue for the need to study the theory and foundations for sequence management of big data sequences, and to build corresponding systems that will enable scalable management and analysis of very large sequence collections. We describe recent efforts in designing techniques for indexing and mining truly massive collections of data series that will enable scientists to easily analyze their data. Finally, we present our vision for the future in big sequence management research, including the promising directions in terms of storage, distributed processing, and query benchmarks.<\/span><\/p>\n
\nRomain Tavenard (Universit\u00e9 Rennes 2)<\/h4>\n