Data sharing in DHT based P2P systems

International audienceThe evolution of peer-to-peer (P2P) systems triggered the building of large scale distributed applications. The main application domain is data sharing across a very large number of highly autonomous participants. Building such data sharing systems is particularly challenging because of the "extreme" characteristics of P2P infrastructures: massive distribution, high churn rate, no global control, potentially untrusted participants... This article focuses on declarative querying support, query optimization and data privacy on a major class of P2P systems, that based on Distributed Hash Table (P2P DHT). The usual approaches and the algorithms used by classic distributed systems and databases forproviding data privacy and querying services are not well suited to P2P DHT systems. A considerable amount of work was required to adapt them for the new challenges such systems present. This paper describes the most important solutions found. It also identies important future research trends in data management in P2P DHT systems

Distributed top-k aggregation queries at large

Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper introduces novel optimization methods for top-k aggregation queries in such distributed environments. The optimizations can be applied to all algorithms that fall into the frameworks of the prior TPUT and KLEE methods. The optimizations address three degrees of freedom: 1) hierarchically grouping input lists into top-k operator trees and optimizing the tree structure, 2) computing data-adaptive scan depths for different input sources, and 3) data-adaptive sampling of a small subset of input sources in scenarios with hundreds or thousands of query-relevant network nodes. All optimizations are based on a statistical cost model that utilizes local synopses, e.g., in the form of histograms, efficiently computed convolutions, and estimators based on order statistics. The paper presents comprehensive experiments, with three different real-life datasets and using the ns-2 network simulator for a packet-level simulation of a large Internet-style network

Deep neural network model of haptic saliency

Haptic exploration usually involves stereotypical systematic movements that are adapted to the task. Here we tested whether exploration movements are also driven by physical stimulus features. We designed haptic stimuli, whose surface relief varied locally in spatial frequency, height, orientation, and anisotropy. In Experiment 1, participants subsequently explored two stimuli in order to decide whether they were same or different. We trained a variational autoencoder to predict the spatial distribution of touch duration from the surface relief of the haptic stimuli. The model successfully predicted where participants touched the stimuli. It could also predict participants’ touch distribution from the stimulus’ surface relief when tested with two new groups of participants, who performed a different task (Exp. 2) or explored different stimuli (Exp. 3). We further generated a large number of virtual surface reliefs (uniformly expressing a certain combination of features) and correlated the model’s responses with stimulus properties to understand the model’s preferences in order to infer which stimulus features were preferentially touched by participants. Our results indicate that haptic exploratory behavior is to some extent driven by the physical features of the stimuli, with e.g. edge-like structures, vertical and horizontal patterns, and rough regions being explored in more detail

As-Soon-As-Possible Top-k Query Processing in P2P Systems

International audienceTop-k query processing techniques provide two main advantages for unstructured peer-to-peer (P2P) systems. First they avoid overwhelming users with too many results. Second they reduce significantly network resources consumption. However, existing approaches suffer from long waiting times. This is because top-k results are returned only when all queried peers have finished processing the query. As a result, query response time is dominated by the slowest queried peer. In this paper, we address this users' waiting time problem. For this, we revisit top-k query processing in P2P systems by introducing two novel notions in addition to response time: the stabilization time and the cumulative quality gap. Using these notions, we formally define the as-soonas-possible (ASAP) top-k processing problem. Then, we propose a family of algorithms called ASAP to deal with this problem. We validate our solution through implementation and extensive experimentation. The results show that ASAP significantly outperforms baseline algorithms by returning final top-k result to users in much better times

SARAVÁ: data sharing for online communities in P2P

International audienceThis paper describes SARAVÁ, a research project that aims at investigating new challenges in P2P data sharing for online communities. The major advantage of P2P is a completely decentralized approach to data sharing which does not require centralized administration. Users may be in high numbers and interested in different kinds of collaboration and sharing their knowledge, ideas, experiences, etc. Data sources can be in high numbers, fairly autonomous, i.e. locally owned and controlled, and highly heterogeneous with different semantics and structures. Our project deals with new, decentralized data management techniques that scale up while addressing the autonomy, dynamic behavior and heterogeneity of both users and data sources. In this context, we focus on two major problems: query processing with uncertain data and management of scientific workflows

Heterogeneous nanofluids: natural convection heat transfer enhancement

Convective heat transfer using different nanofluid types is investigated. The domain is differentially heated and nanofluids are treated as heterogeneous mixtures with weak solutal diffusivity and possible Soret separation. Owing to the pronounced Soret effect of these materials in combination with a considerable solutal expansion, the resulting solutal buoyancy forces could be significant and interact with the initial thermal convection. A modified formulation taking into account the thermal conductivity, viscosity versus nanofluids type and concentration and the spatial heterogeneous concentration induced by the Soret effect is presented. The obtained results, by solving numerically the full governing equations, are found to be in good agreement with the developed solution based on the scale analysis approach. The resulting convective flows are found to be dependent on the local particle concentration φ and the corresponding solutal to thermal buoyancy ratio N. The induced nanofluid heterogeneity showed a significant heat transfer modification. The heat transfer in natural convection increases with nanoparticle concentration but remains less than the enhancement previously underlined in forced convection case

NICE : A Computational solution to close the gap from colour perception to colour categorization

The segmentation of visible electromagnetic radiation into chromatic categories by the human visual system has been extensively studied from a perceptual point of view, resulting in several colour appearance models. However, there is currently a void when it comes to relate these results to the physiological mechanisms that are known to shape the pre-cortical and cortical visual pathway. This work intends to begin to fill this void by proposing a new physiologically plausible model of colour categorization based on Neural Isoresponsive Colour Ellipsoids (NICE) in the cone-contrast space defined by the main directions of the visual signals entering the visual cortex. The model was adjusted to fit psychophysical measures that concentrate on the categorical boundaries and are consistent with the ellipsoidal isoresponse surfaces of visual cortical neurons. By revealing the shape of such categorical colour regions, our measures allow for a more precise and parsimonious description, connecting well-known early visual processing mechanisms to the less understood phenomenon of colour categorization. To test the feasibility of our method we applied it to exemplary images and a popular ground-truth chart obtaining labelling results that are better than those of current state-of-the-art algorithms