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Section: New Results
Distributed Indexing and Searching
Query Reformulation in P2P Data Management Systems
Participant : Esther Pacitti.
We consider peer-to-peer data management systems (PDMS), where each peer maintains mappings between its schema and some acquaintances, along with social links with peer friends. In this context, we deal with reformulating conjunctive queries from a peer’s schema into other peer’s schemas. Precisely, queries against a peer node are rewritten into queries against other nodes using schema mappings thus obtaining query rewritings. Unfortunately, not all the obtained rewritings are relevant to a given query, as the information gain may be negligible or the peer is not worth exploring. On the other hand, the existence of social links with peer friends might be useful to get relevant rewritings.
In [19] , we propose a new notion of “relevance” of a query with respect to a mapping that encompasses both a local relevance (the relevance of the query wrt. the mapping) and a global relevance (the relevance of the query wrt. the entire network). Based on this notion, we design a new query reformulation approach for social PDMS which achieves great accuracy and flexibility. We combine several techniques: (i) social links are expressed as FOAF (Friend of a Friend) links to characterize peer’s friendship; (ii) concise mapping summaries are used to obtain mapping descriptions; (iii) local semantic views are special views that contain information about mappings captured from the network by using gossiping techniques. Our experimental evaluation, based on a prototype on top of PeerSim and a simulated network demonstrate that our solution yields greater recall, compared to traditional query translation approaches proposed in the literature.
Diversified and Distributed Recommendation for Scientific Data
Participants : Esther Pacitti, Maximilien Servajean.
Recommendation is becoming a popular mechanism to help users find relevant information in large-scale data (scientific data, web). Different diversification techniques have been proposed to avoid redundancy in the process of recommendation. Intuitively, the goal of recommendation diversification is to identify a list of items that are dissimilar, but nonetheless relevant to the user's interests.
The main goal of this work [39] , [17] is to define a new diversified search and recommendation solution suited for scientific data (i.e., plant phenotyping, botanical data). We first propose an original profile diversification scoring function that enables to address the problem of returning redundant items, and enhances the quality of diversification compared to the state-of-the-art solutions. We believe our work is the first to investigate profile diversity to address the problem of returning highly popular but too-focused items.Through experimental evaluation using two benchmarks we showed that our scoring function presents the best compromise between diversity and relevancy. Next, to implement our new scoring function, we propose a Top-k threshold-based algorithm that exploits a candidate list to achieve diversification. However this algorithm is greedy and does not scale up well.To overcome this limitation, we propose several techniques to improve performance. First, we simplify the scoring model to reduce its computational complexity. Second, we propose two techniques to reduce the number of items in the candidate list, and therefore the number of diversified scores to compute. Third, we propose different indexing scores (i.e., the score used to sort the items in the inverted lists) that take into account the diversification of items, and using them, we developed an adaptive indexing approach to reduce the number of accesses in the index dynamically based on the queries workload. We evaluated the performance of our techniques through experimentation. The results show that they enable to reduce the response time up to 12 times compared to a baseline greedy diversification algorithm.
We also address the problem of distributed and diversified recommendation (P2P and multi-site) that fits very well in different application scenarios. We propose a new scoring function (usefulness) to cluster relevant users over a distributed overlay. We analyzed the new clustering algorithm in details, and we studied its behavior with an experimental evaluation using different datasets. Compared with state-of-the-art solutions, we obtain major gains in recall (order of 3 times).