Accepted papers

A data-centric approach for networking applications

DATA 2012, Roma.

Abstract:

The paper introduces our vision for rapid prototyping of heterogeneous and distributed applications. It abstracts a network as a large distributed database providing a unified view of "objects" handled in networks and applications. The applications interact through declarative queries including declarative networking programs (e.g. routing) and/or specific data-oriented distributed algorithms (e.g. distributed join). Case-Based Reasoning is used for optimization of distributed queries by learning when there is no prior knowledge on queried data sources and no related metadata such as data statistics.

Submitted papers

UBIQUEST, A data-centric approach for networking applications

BDA 2012, IDEAS 2012

Abstract :

The UBIQUEST project proposes a high level programming abstraction for rapid prototyping of heterogeneous and distributed applications in a dynamic environment. Such an environment is perceived as a distributed database and the applications interact through declarative queries including declarative networking programs (e.g. routing) and/or specific data-oriented distributed algorithms (e.g. distributed join). Rule programs are specified using Datalog-based languages, while data oriented manipulations are specified using an SQL-Like language. Case-Based Reasoning is used for optimization of distributed queries when as there is no prior knowledge on data (sources) in networking applications, and certainly no related metadata such as data statistics. An UBIQUEST system is therefore well adapted to social systems (e.g. games, social networks, sharing), where data are pushed or pulled without knowledge or with incomplete knowledge.

The QOL approach for optimizing distributed queries without complete knowledge on data

BDA 2012, IDEAS 2012

Abstract :

This paper describes the QOL approach to optimize distributed queries by learning. It is well-adapted to social systems (e.g. games, social networks, sharing), where data are pushed or pulled with incomplete knowledge in a dynamic environment. The contribution of this work is twofold. It first concerns the integration of the Case Based Reasoning (CBR) paradigm in query processing, providing a way to optimize queries when there is no prior knowledge on queried data sources and certainly no related metadata such as data statistics. Our approach optimizes queries using cases generated from the evaluation of similar past queries. A query case comprises: (i) the query, (ii) the query plan and (iii) the measures (computational resources consumed) of the query plan. The second aspect of the work concerns the way the CBR process interacts with the query plan generation process. This process uses classical heuristics and makes decisions randomly (e.g. when there is no statistics for join ordering and selection of algorithms, routing protocols); It also (re)uses cases (existing query plans) for similar queries parts, improving the query optimization and evaluation efficiency.