Social, Structured and Semantic Search

International audienceSocial content such as blogs, tweets, news etc. is a rich source of interconnected information. We identify a set of requirements for the meaningful exploitation of such rich content, and present a new data model, called S3, which is the first to satisfy them. S3 captures social relationships between users, and between users and content, but also the structure present in rich social content, as well as its semantics. We provide the first top-k keyword search algorithm taking into account the social, structured, and semantic dimensions and formally establish its termination and correctness. Experiments on real social networks demonstrate the efficiency and qualitative advantage of our algorithm through the joint exploitation of the social, structured, and semantic dimensions of S3

Nonadiabatic generation of spin currents in a quantum ring with Rashba and Dresselhaus spin-orbit interactions

When subjected to a linearly polarized terahertz pulse, a mesoscopic ring endowed with spin-orbit interaction (SOI) of the Rashba-Dresselhaus type exhibits nonuniform azimuthal charge and spin distributions. Both types of SOI couplings are considered linear in the electron momentum. Our results are obtained within a formalism based on the equation of motion satisfied by the density operator which is solved numerically for different values of the angle $\phi$, the angle determining the polarization direction of the laser pulse. Solutions thus obtained are later employed in determining the time-dependent charge and spin currents, whose values are calculated in the stationary limit. Both these currents exhibit an oscillatory behavior complicated in the case of the spin current by a beating pattern. We explain this occurrence on account of the two spin-orbit interactions which force the electron spin to oscillate between the two spin quantization axes corresponding to Rashba and Dresselhaus interactions. The oscillation frequencies are explained using the single particle spectrum.Comment: 9 pages, 5 figures, Conference "Advanced many-body and statistical methods in mesoscopic systems", June 27 -July 2, 2011, Constanta, Romani

HADAD: A Lightweight Approach for Optimizing Hybrid Complex Analytics Queries

International audienceHybrid complex analytics workloads typically include (i) data management tasks (joins, filters, etc.), easily expressed using relational algebra (RA)-based languages, and (ii) complex analytics tasks (regressions, matrix decompositions, etc.), mostly expressed in linear algebra (LA) expressions. Such workloads are common in a number of areas, including scientific computing, web analytics, business recommendation, natural language processing, speech recognition. Existing solutions for evaluating hybrid complex analytics queriesranging from LA-oriented systems, to relational systems (extended to handle LA operations), to hybrid systems-fail to provide a unified optimization framework for such a hybrid setting. These systems either optimize data management and complex analytics tasks separately, or exploit RA properties only while leaving LA-specific optimization opportunities unexplored. Finally, they are not able to exploit precomputed (materialized) results to avoid computing again (part of) a given mixed (LA and RA) computation. We describe HADAD, an extensible lightweight approach for optimizing hybrid complex analytics queries, based on a common abstraction that facilitates unified reasoning: a relational model endowed with integrity constraints, which can be used to express the properties of the two computation formalisms. Our approach enables full exploration of LA properties and rewrites, as well as semantic query optimization. Importantly, our approach does not require modifying the internals of the existing systems. Our experimental evaluation shows significant performance gains on diverse workloads, from LA-centered ones to hybrid ones

Persistent charge and spin currents in a 1D ring with Rashba and Dresselhaus spin-orbit interactions by excitation with a terahertz pulse

Persistent, oscillatory charge and spin currents are shown to be driven by a two-component terahertz laser pulse in a one-dimensional mesoscopic ring with Rashba-Dresselhaus spin orbit interactions (SOI) linear in the electron momentum. The characteristic interference effects result from the opposite precession directions imposed on the electron spin by the two SOI couplings. The time dependence of the currents is obtained by solving numerically the equation of motion for the density operator, which is later employed in calculating statistical averages of quantum operators on few electron eigenstates. The parameterization of the problem is done in terms of the SOI coupling constants and of the phase difference between the two laser components. Our results indicate that the amplitude of the oscillations is controlled by the relative strength of the two SOI's, while their frequency is determined by the difference between the excitation energies of the electron states. Furthermore, the oscillations of the spin current acquire a beating pattern of higher frequency that we associate with the nutation of the electron spin between the quantization axes of the two SOI couplings. This phenomenon disappears at equal SOI strengths, whereby the opposite precessions occur with the same probability.Comment: 10 pages 9 figure

Mixed-instance querying: a lightweight integration architecture for data journalism

International audienceAs the world's affairs get increasingly more digital, timely production and consumption of news require to efficiently and quickly exploit heterogeneous data sources. Discussions with journalists revealed that content management tools currently at their disposal fall very short of expectations. We demonstrate TATOOINE, a lightweight data integration prototype, which allows to quickly set up integration queries across (very) heterogeneous data sources, capitalizing on the many data links (joins) available in this application domain. Our demonstration is based on scenarios we study in collaboration with Le Monde, France's major newspaper

Recherche sur du contenu structuré, social et sémantique

Social content such as blogs, tweets, news etc. is a rich source of interconnected information. We identify a set of requirements for the meaningful exploitation of such rich content, and present a new data model, called S3, which is the first to satisfy them. S3 captures social relationships between users, and between users and content, but also the structure present in rich social content, as well as its semantics. We provide the first top-k keyword search algorithm taking into account the social, structured, and semantic dimensions and formally establish its termination and correctness. Experiments on real social networks demonstrate the efficiency and qualitative advantage of our algorithm through the joint exploitation of the social, structured, and semantic dimensions of S3.Les contenus sociaux comme les blogs, les tweets, les journaux en ligne etc. sont une source riche d’informations liées. Nous identifions dans ce rapport un ensemble de conditions nécessaires à une exploration pertinente de ce contenu riche et introduisons un nouvel modèle de données, S3, qui est le premier à les satisfaire. S3 capte les relations sociales entre les utilisateurs et les contenus mais aussi la structure et la sémantique de ces derniers. Nous proposons aussi le premier algorithme de recherche top k qui prend en compte les dimensions structurelles, sociales et sémantiques et donnons une preuve formelle de sa correction et de sa terminaison. Une évaluation expérimentale sur des vrais réseaux sociaux valide l’efficacité et la qualité de notre approche sur l’exploration conjointe des dimensions structurelles, sociales et sémantiques de S3

HADAD: A Lightweight Approach for Optimizing Hybrid Complex Analytics Queries

International audienceHybrid complex analytics workloads typically include (i) data management tasks (joins, filters, etc.), easily expressed using relational algebra (RA)-based languages, and (ii) complex analytics tasks (regressions, matrix decompositions, etc.), mostly expressed in linear algebra (LA) expressions. Such workloads are common in a number of areas, including scientific computing, web analytics, business recommendation, natural language processing, speech recognition. Existing solutions for evaluating hybrid complex analytics queriesranging from LA-oriented systems, to relational systems (extended to handle LA operations), to hybrid systems-fail to provide a unified optimization framework for such a hybrid setting. These systems either optimize data management and complex analytics tasks separately, or exploit RA properties only while leaving LA-specific optimization opportunities unexplored. Finally, they are not able to exploit precomputed (materialized) results to avoid computing again (part of) a given mixed (LA and RA) computation. We describe HADAD, an extensible lightweight approach for optimizing hybrid complex analytics queries, based on a common abstraction that facilitates unified reasoning: a relational model endowed with integrity constraints, which can be used to express the properties of the two computation formalisms. Our approach enables full exploration of LA properties and rewrites, as well as semantic query optimization. Importantly, our approach does not require modifying the internals of the existing systems. Our experimental evaluation shows significant performance gains on diverse workloads, from LA-centered ones to hybrid ones