A QUERY LEARNING ROUTING APPROACH BASED ON SEMANTIC CLUSTERS

ABSTRACT Peer-to-peer systems have recently a remarkable success in the social, academic, and commercial communities. A fundamental problem in Peer-to-Peer systems is how to efficiently locate appropriate peers to answer a specific query (Query Routing Proble

Frequent Pattern-growth Algorithm on Multi-core CPU and GPU Processors

Discovering association rules that identify relationships among sets of items is an important problem in data mining. It’s a two steps process, the first step finds all frequent itemsets and the second one constructs association rules from these frequent sets. Finding frequent itemsets is computationally the most expensive step in association rules discovery algorithms. Utilizing parallel architectures has been a viable means for improving FIM algorithms performance. We present two FP-growth implementations that take advantage of multi-core processors and utilize new generation Graphic Processing Units (GPU).</p

A query learning routing approach based on semantic clusters

International audiencePeer-to-peer systems have recently a remarkable success in the social, academic, and commercial communities. A fundamental problem in Peer-to-Peer systems is how to efficiently locate appropriate peers to answer a specific query (Query Routing Problem). A lot of approaches have been carried out to enhance search result quality as well as to reduce network overhead. Recently, researches focus on methods based on query-oriented routing indices. These methods utilize the historical information of past queries and query hits to build a local knowledge base per peer, which represents the user's interests or profile. When a peer forwards a given query, it evaluates the query against its local knowledge base in order to select a set of relevant peers to whom the query will be routed. Usually, an insufficient number of relevant peers is selected from the current peer's local knowledge base thus a broadcast search is investigated which badly affects the approach efficiency. To tackle this problem, we introduce a novel method that clusters peers having similar interests. It exploits not only the current peer's knowledge base but also that of the others in the cluster to extract relevant peers. We implemented the proposed approach, and tested (i) its retrieval effectiveness in terms of recall and precision, (ii) its search cost in terms of messages traffic and visited peers number. Experimental results show that our approach improves the recall and precision metrics while reducing dramatically messages traffic

Using ontologies to build testbed for peer-to-peer information retrieval systems

International audienceA branch of Distributed Information Retrieval(DIR) is Peer-to-Peer Information Retrieval (P2PIR). Peer-to-Peer (P2P) networks are a recently evolved paradigm for distributed computing and many researchers are developing new algorithms for such systems. Evaluate algorithms performance, in a P2P network, is already a challenging task caused by the lack of realistic test beds. The construction of standard test beds to evaluate search approaches and to compare them is a major open problem. Indeed, the poverty of documents and queries representation, in test bed, limits retrieval opportunities. This study addresses the lack of an adequate test bed that can be used to evaluate peer-to-peer information retrieval approaches. In particular, we propose, in this paper, a test bed based on an already existing test bedand on a semantic resource enrichment

Personalization mobile P2P network using FCA based multidimensional aggregation

International audienceMobile applications, that cover context-aware user modeling, are becoming increasingly prevalent. In this vein, information about the users are needed for the systems in order to provide them relevant services. This information enables the systems to figure out the users and their interests. For these reasons, different applications, in several areas, organize the user's properties, preferences and interests based on a structure, called a user model, which hold all relevant user-related information. In this respect, we propose a context-based user model in mobile Peer-to-Peer (P2P) environment. This model is based on aggregating different user information like past interests and the associated context. These past interests represent information about peers from which results were obtained and which were achieved from similar queries as well as from the user context. The basic idea of our proposal is to guess correlations between past requests, past peers from which results were obtained, associated user location and user interests. The generated correlations are based upon Formal Concept Analysis. We study, the exploitation of the proposed user model in results merging task in Peer-to-Peer Information Retrieval (P2PIR)

An efficient peer-to-peer semantic overlay network for learning query routing

International audienceIn unstructured P2P systems, peers organize themselves into a random overlay. A challenging problem in these systems is to efficiently locate appropriate peers to answer a specific query. Recently, research works have focused on methods based on query history, which use the historical information of past queries and query hits to build a local knowledge base per peer. When a peer forwards a given query, it runs a learning algorithm that evaluates the query against the local knowledge base in order to select a set of relevant peers to whom the query will be routed. If the current peer fails to select a sufficient number of relevant peers it floods the query through the random overlay network, which badly affects the routing efficiency and effectiveness. To address the unsuccessful relevant peers search problem, we propose to organize the P2P network into semantic clusters of peers sharing similar knowledge bases. We implemented the proposed approach, and tested (i) its retrieval effectiveness in term of recall and precision, (ii) its routing efficiency in term of messages traffic. Experimental results show that our approach improves the recall and precision metrics while it dramatically reduce network traffic

Test-bed building process for context-aware peer-to-peer information retrieval evaluation

International audienceWith the rapid growth of information rate in the Web, it is of paramount importance to develop scalable information retrieval approaches. These approaches are based on Distributed Information Retrieval Systems. Likewise, the emergence of contextual Information Retrieval has introduced the research personalization which attempts to reduce the research ambiguity and to return most appropriate information to the user. Therefore, it seems a compelling issue, to implement techniques related to contextual Peer-to-Peer Information Retrieval (P2P-IR) to better exploit the personalization of information in order to achieve a more efficient access to data. In this respect, evaluating these approaches in contextual P2P-IR is a crucial step. Indeed, to the best of our knowledge, there is no available test-beds dedicated to the evaluation of contextual P2P-IR. In particular, we propose, two methods. The first is based on existing collections that are enriched with external semantic resources. The second offers the possibility to build test-beds from multiple sources data

DCR: a new distributed model for human activity recognition in smart homes

International audienceHuman Activity Recognition (HAR) is an important research issue for pervasive computing that aims to identify human activities in smart homes. In the literature, most reasoning approaches for HAR are based on centralized approach where a central system is responsible for processing and reasoning about sensor data in order to recognize activities. Since sensor data are distributed, heterogeneous, and dynamic (i.e., whose characteristics are varying over time) in the smart home, reasoning process on these data for HAR needs to be distributed over a group of heterogeneous, autonomous and interacting entities in order to be more efficient. This paper proposes a main contribution, the DCR approach, a fully Distributed Collaborative Reasoning multi-agent approach where agents, with diverse classifiers, observe sensor data, make local predictions, communicate and collaborate to identify current activities. Then, an improved version of the DCR approach is proposed, the DCR-OL approach, a distributed Online Learning approach where learning agents learns from their collaborations to improve their own performance in activity recognition. Finally, we test our approaches by performing an evaluation study on Aruba dataset, that indicates an enhancement in terms of accuracy, F-measure and G-mean metrics compared to the centralized approach and also compared to a distributed approach existing in the literature

A context features selecting and weighting methods for context-aware recommendation

International audienceThe notion of "Context" plays a key role in recommender systems. In this respect, many researches have been dedicated for Context-Aware Recommender Systems (CARS). Rating prediction in CARS is being tackled by researchers attempting to recommend appropriate items to users. However, in rating prediction, three thriving challenges still to tackle:(i) context feature's selection; (ii) context feature's weighting; and (iii) users context matching. Context-aware algorithms made a strong assumption that context features are selected in advance and their weights are the same or initialized with random values. After context features weighting, users context matching is required. In current approaches, syntactic measures are used which require an exact matching between features. To address these issues, we propose a novel approach for Selecting and Weighting Context Features (SWCF). The evaluation experiments show that the proposed approach is helpful to improve the recommendation qualit