Discrete Elastic Inner Vector Spaces with Application in Time Series and Sequence Mining

This paper proposes a framework dedicated to the construction of what we call discrete elastic inner product allowing one to embed sets of non-uniformly sampled multivariate time series or sequences of varying lengths into inner product space structures. This framework is based on a recursive definition that covers the case of multiple embedded time elastic dimensions. We prove that such inner products exist in our general framework and show how a simple instance of this inner product class operates on some prospective applications, while generalizing the Euclidean inner product. Classification experimentations on time series and symbolic sequences datasets demonstrate the benefits that we can expect by embedding time series or sequences into elastic inner spaces rather than into classical Euclidean spaces. These experiments show good accuracy when compared to the euclidean distance or even dynamic programming algorithms while maintaining a linear algorithmic complexity at exploitation stage, although a quadratic indexing phase beforehand is required.Comment: arXiv admin note: substantial text overlap with arXiv:1101.431

Interpr\'etation vague des contraintes structurelles pour la RI dans des corpus de documents XML - \'Evaluation d'une m\'ethode approch\'ee de RI structur\'ee

We propose specific data structures designed to the indexing and retrieval of information elements in heterogeneous XML data bases. The indexing scheme is well suited to the management of various contextual searches, expressed either at a structural level or at an information content level. The approximate search mechanisms are based on a modified Levenshtein editing distance and information fusion heuristics. The implementation described highlights the mixing of structured information presented as field/value instances and free text elements. The retrieval performances of the proposed approach are evaluated within the INEX 2005 evaluation campaign. The evaluation results rank the proposed approach among the best evaluated XML IR systems for the VVCAS task.Comment: 26 pages, ISBN 978-2-7462-1969-

Spiral Walk on Triangular Meshes : Adaptive Replication in Data P2P Networks

We introduce a decentralized replication strategy for peer-to-peer file exchange based on exhaustive exploration of the neighborhood of any node in the network. The replication scheme lets the replicas evenly populate the network mesh, while regulating the total number of replicas at the same time. This is achieved by self adaptation to entering or leaving of nodes. Exhaustive exploration is achieved by a spiral walk algorithm that generates a number of messages linearly proportional to the number of visited nodes. It requires a dedicated topology (a triangular mesh on a closed surface). We introduce protocols for node connection and departure that maintain the triangular mesh at low computational and bandwidth cost. Search efficiency is increased using a mechanism based on dynamically allocated super peers. We conclude with a discussion on experimental validation results