No dia 18 de agosto, às 10h, teremos a honra de receber os Professores Esther Pacitti e Patrick Valduriez, ambos do Inria, Montpellier, France para proferirem palestras na área de Banco de Dados. As palestras serão no auditório V.
Título: Profile Diversity for Query Processing using Users Recommendations (Prof. Esther Pacitti, Inria & University of Montpellier, France)
Abstract: Many scientific fields produce and consume a considerable amount of diverse data (e.g. biology , astronomy, physics) stored in different heterogeneous sites, and produced by different types of users profiles. We investigate two different use cases: a) In the domain of plant phenotyping, there has recently been increasing interests in finding diverse data coming from different research communities. b)In botany, the emergence of citizen sciences has fostered the creation of large and structured communities of nature observers. In this context, there is a need to retrieve diverse plant observations from a diverse spectrum of plant families, genus and species. In this talk I will present some new issues of profile diversity, a novel idea in searching and recommending scientific items (e.g. documents, images, datasets, etc), and how profile diversity can be deployed in different kinds of infrastructures (centralized and distributed).
Titulo: CloudMdsQL: Querying Heterogeneous Cloud Data Stores with a Common Language (Prof. Patrick Valduriez, Inria, Montpellier, France)
The blooming of different cloud data management infrastructures, specialized for different kinds of data and tasks, has led to a wide diversification of DBMS interfaces and the loss of a common programming paradigm. In this talk, we present the design of a Cloud Multidatastore Query Language (CloudMdsQL), and its query engine. CloudMdsQL is a functional SQL-like language, capable of querying multiple heterogeneous data stores (relational and NoSQL) within a single query that may contain embedded invocations to each data store’s native query interface. The query engine has a fully distributed architecture, which provides important opportunities for optimization. The major innovation is that a CloudMdsQL query can exploit the full power of local data stores, by simply allowing some local data store native queries (e.g. a breadth-first search query against a graph database) to be called as functions, and at the same time be optimized, e.g. by pushing down select predicates, using bind join, performing join ordering, or planning intermediate data shipping. Our experimental validation, with three data stores (graph, document and relational) and representative queries, shows that CloudMdsQL satisfies the five important requirements for a cloud multidatastore query language.
[1] Work partially funded by the European Commission under the Integrated Project CoherentPaaS [3].
Joint work with Boyan Kolev and Carlyna Bondiombouy (Inria, Montpellier), Ricardo Jiménez-Peris (UP Madrid), Raquel Pau (Sparcity, Barcelona), José Pereira (Inesc, Braga)
A Prof. Esther Pacitti co-lidera a equipe Zenith de Montpellier e tem como pesquisa principal a gerencia de dados distribuídas: replicação, processamento de consultas etc. Ela também está envolvida nas pesquisas de data-intensive scientific workflows sobre nuvens geograficamente distribuídas.
Patrick Valduriez é pesquisador senior do INRIA, liderando a equipe Zenith de Montpellier. O foco da pesquisa é em gerência de dados em larga escala em sistemas paralelos e distribuídos (P2P, cluster, grid, cloud),em particular, na gerência de dados científicos. Ele é autor/coautor de mais de 250 artigos científicos e diversos livros, dentro os quais o “Principles of Distributed Database Systems”.