From Nicolas Hanusse

Main: BigDataSeminar

Le jeudi de 11 à 12h au LaBRI (salle 178)

Le séminaire Données massives et hétérogènes est ouvert à tous. Il traite de:

L'objectif est de faire une programmation régulière et des présentations assez vulgarisées afin que tous puissent suivre.

Pour vous inscrire:

PROGRAMME (prévisionnel):

par André C. P. L. F. de Carvalho (Sao Paulo University)

As the number of successful applications of Machine Learning algorithms grows, there is also an increase in the need to make these algorithms easily accessible by users without Machine Learning expertise. There have been several efforts in this direction, involving not only the recommendation of the most suitable algorithm, but also their most appropriate hyper-parameter values. These several efforts started a new research area, named Automated Machine Learning, AutoML, which has attracted the attention of researchers and practitioners not only from the academia, but also from several companies working with data science. This talk will present the main approaches and recent advances in this area, covering also works carried out in the Analytics Laboratory, at USP São Carlos.

RESUME: In the landscape of database management systems, data analysis systems (OLAP) and transaction processing systems (OLTP) are separately managed. The reasons for this dichotomy are that both systems have very different functionalities, characteristics and requirements. My talk will focus on the first class of OLAP systems. The purpose of this talk is twofold: (i) to provide a synthetic state of the art concerning (large-scale) data management systems, and (ii) how can the evolution of these systems help for big data applications. In this perspective, data management based on parallel and cloud systems are overviewed and compared by relying on fundamental criterion such as Software Requirements (Data Independence, Software Reuse), High Performance, Data Availability, Scalability and Elasticity . With respect to the state of the art, proposed systems, and qualitative and quantitative comparative studies between Parallel DBMS (PDBMS) and Big Data Management Systems (BDMS), we try to learn some lessons and point out some open issues that should be tackled to ensure the viability of the next generation of large-scale data management systems for big data applications.

Key Words: Big Data Management, Data Partitioning, Data Integration, Parallel Database Systems, Cloud Data Management Systems, Query Processing and Optimization, High Performance, Scalability, Elasticity, Hadoop MapReduce, Spark, Multistore Systems.

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.

Un exposé qui promet des solutions au problème posé par la forte densité des produits de réseaux.

Regular Expressions (REs) are ubiquitous in database and programming languages. While many applications make use of REs extended with interleaving (shuffle) and unordered concatenation operators, this extension badly affects the complexity of basic operations, and, especially, makes membership checking NP-hard, which is unacceptable in most practical scenarios.

In this talk I will present results about recent work where we study the problem of membership checking for a restricted class of these extended REs, called conflict-free REs, which are expressive enough to cover a wide class of real-world applications. Several polynomial algorithms for membership checking over conflict-free REs will be presented. The algorithms are all polynomial and differ in terms of adopted optimization techniques, ensuring linearity, and in the kind of supported operators. As a particular application, the approach can be generalized in order to efficiently check membership of XML trees into a class of Extended DTDs which models the crucial aspects of DTDs and XSD schemas

La démocratisation du calcul distribué au travers de système tels que Hadoop ou Spark permet d'envisager de traiter de grands jeux de données en des temps raisonnables. Nous montrerons comment il est possible d'utiliser l'architecture Big Data pour effectuer un précalcul permettant de visualiser de grands jeux de données en adaptant des algorithmes existants. Nous utiliserons deux exemples : les cartes de chaleur et la visualisation de graphe

Data has been quoted as the new oil, to reflect that big data can be turned into high-value information and new knowledge. Although data analysis has been around for a while, starting with statistics and evolving lately into exploratory data analysis, data mining and business intelligence, the new dimensions of big data (volume, variety, velocity, etc.) make it very hard to process and analyze data, and derive good conclusions. To address this grand challenge, data science is emerging as a new science that combines computer science, statistics and machine learning, visualization and human-computer interactions to collect, clean, integrate, analyze and visualize big data. The ultimate goal is to create new data products and services, as well as training legions of data scientists. In this talk, I will introduce data science, including big data and cloud technologies. I will illustrate the main opportunities and risks, in particular by telling my favorite stories about the good, the bad and the ugly. Finally, I will discuss the new challenges in combining HPC and big data analytics.

En relation avec le séminaire

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