Séminaire du pôle Données Connaissances

Séminaire Pole Données et Connaissances
1/7/2013 à 11h, salle 127 Galera

A peer-to-peer-based virtual environment system
Eliya Buyukkaya – ENSSAT (École Nationale Supérieure des Sciences Appliquées et de Technologie)

Abstract: 
Virtual environments (VEs) are 3-D virtual worlds in which a huge number of participants play roles and interact with their surroundings through virtual representations called avatars. VEs are traditionally supported by a client/server architecture. However, centralized architectures can lead to bottleneck on the server due to high communication and computation overhead during peak loads. Thus, P2P overlay networks are emerging as a promising architecture for VEs. However, exploiting P2P schemes in VEs is not straightforward, and several challenging issues related to data distribution and state consistency should be considered.

One of the key aspects of P2P-based VEs is the logical platform consisting of connectivity, communication and data architectures, on which the VE is based. The connectivity architecture is the overlay topology structure, which defines how peers are connected to each other. The communication architecture is the routing protocol defining how peers can exchange messages, while the data architecture defines how data are distributed over the logical overlay. The design of these architectures has significant influence on the performance and scalability of VEs.

First, we propose a scalable connectivity architecture based on a new triangulation algorithm reducing maintenance cost of the system. Second, we construct a communication architecture built on top of the connectivity architecture ensuring that each message reaches its intended destination. Finally, we propose a data architecture ensuring the management of data with different characteristics in terms of mobility in the VE, while providing a fair data distribution and low data transfer between peers in the VE.

Séminaire IBC

Lundi 25 mars, 14h

Salle 127, Batiment la Galera

Organisé par l’équipe Zenith

Big Data Workflows – how provenance can help
Marta Mattoso
UFRJ, Rio de Janeiro
Brazil

Big data analyses are critical for decision support in business data processing. These analyses involve the execution of many activities such as: programs to explore data from the web, databases, data warehouses and files; data cleaning procedures; programs to aggregate data; core programs that perform analyses; and tools to visualize and interpret the results. Each step (activity) of the analysis is performed isolated from the other and the analysts need to manually manage the larger life cycle of big data analysis. Big data analysis started to be represented as pipelines or dataflows. However, current approaches lack features to provide a consistent view of many different explorations and activities as part of a broader analysis, like a computational experiment. Scientific workflows have long provided such features for scientific experiments, and although originally designed for science, they may be useful to support the life cycle of big data analysis. Scientific analyses typically involve
experimenting with several steps using different datasets and computer programs. Scientists need to manage the composition, execution and analysis of their experiments carefully, so the results can be trusted and the experiments reproducible. To help managing experiments, scientific workflow management systems (SWfMS) have been proposed to let scientists design workflows of different complexities and manage their execution, including high performance computing (HPC) in cloud environments. Most SWfMS also have provenance data support. Provenance tracks how the results of the experiments were produced, which is essential to make an experiment (big data analysis) reproducible and trustworthy. Business Process Workflows are focused on modeling the process rather than managing big data flows with provenance and HPC. In this talk we discuss on provenance support along the big data analysis workflow as an alternative to improve results of big data
analysis, especially in a long-term view

Ce 6ième séminaire, dans le cadre de l’axe 5 “Données” de l’institut, aura lieu le Vendredi 22 Mars, de 14h à 15h30, à l’IBC, salle 127 (pour venir: http://g.co/maps/ygsrk):

Patrick Valduriez,
Zenith team, INRIA and LIRMM
http://www-sop.inria.fr/members/Patrick.Valduriez/

Parallel Techniques for Big Data

Big data has become a buzzword, referring to massive amounts of data that are very hard to deal with traditional data management tools. In particular, the ability to produce high-value information and knowledge from big data makes it critical for many applications such as decision support, forecasting, business intelligence, research, and (data-intensive) science. Processing and analyzing massive, possibly complex data is a major challenge since solutions must combine new data management techniques (to deal with new kinds of data) with large-scale parallelism in cluster, grid or cloud environments. Parallel data processing has long been exploited in the context of distributed and parallel database systems for highly structured data. But big data encompasses different data formats (documents, sequences, graphs, arrays, …) that require significant extensions to traditional parallel techniques. In this talk, I will discuss such extensions, from the basic techniques and architectures to NoSQL systems and MapReduce.