The Experts below are selected from a list of 106566 Experts worldwide ranked by ideXlab platform
Jun Zhou - One of the best experts on this subject based on the ideXlab platform.
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distributed cubature Information Filtering based on weighted average consensus
2017Co-Authors: Qian Chen, Wancheng Wang, Chao Yin, Xiaoxiang Jin, Jun ZhouAbstract:In this paper, the distributed state estimation (DSE) problem for a class of discrete-time nonlinear systems over sensor networks is investigated. First, based on weighted average consensus, a new DSE algorithm named distributed cubature Information Filtering (DCIF) algorithm is developed to address the high-dimensional nonlinear DSE problem. The proposed Filtering algorithm not only has such advantages as easy initialization and less computation burden, but also possesses the guaranteed stability regardless of consensus steps. Moreover, it is proved that the corresponding estimation is consistent, and its mean-squared estimation errors are exponentially bounded. Finally, numerical simulations are given to verify the effectiveness of DCIF.
Zhang Ren - One of the best experts on this subject based on the ideXlab platform.
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distributed event triggered cubature Information Filtering based on weighted average consensus
2018Co-Authors: Qingke Tan, Xiwang Dong, Zhang RenAbstract:To deal with the distributed estimation problem for mobile sensor networks with non-linear systems and a large amount of data transfer, the distributed event-triggered cubature Information Filtering based on weighted average consensus is proposed. The filter benefits from the non-linear Filtering algorithm with consensus technique and event-triggered mechanism which reduces the amount of data transfer. The triggering decision is based on the data transmission mechanism, which is that each sensor makes a request to exchange Information with its neighbours only if the difference between the most recent transmitted estimate and the current estimate exceeds a tolerable threshold. The estimation error of the proposed filter is proved to be bounded in mean square. Finally, numerical examples are provided to demonstrate the effectiveness of the theoretical results.
Gerhard Weikum - One of the best experts on this subject based on the ideXlab platform.
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approximate Information Filtering in peer to peer networks
2008Co-Authors: Christian Zimmer, Christos Tryfonopoulos, Klaus Berberich, Manolis Koubarakis, Gerhard WeikumAbstract:Most approaches to Information Filtering taken so far have the underlying hypothesis of potentially delivering notifications from every Information producer to subscribers. This exact publish/subscribe model creates an efficiency and scalability bottleneck, and might not even be desirable in certain applications. The work presented here puts forward MAPS, a novel approach to support approximate Information Filtering in a peer-to-peer environment. In MAPS a user subscribes to and monitors only carefully selected data sources, and receives notifications about interesting events from these sources only. This way scalability is enhanced by trading recall for lower message traffic. We define the protocols of a peer-to-peer architecture especially designed for approximate Information Filtering, and introduce new node selection strategies based on time series analysis techniques to improve data source selection. Our experimental evaluation shows that MAPS is scalable; it achieves high recall by monitoring only few data sources.
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exploiting correlated keywords to improve approximate Information Filtering
2008Co-Authors: Christian Zimmer, Christos Tryfonopoulos, Gerhard WeikumAbstract:Information Filtering, also referred to as publish/subscribe, complements one-time searching since users are able to subscribe to Information sources and be notified whenever new documents of interest are published. In approximate Information Filtering only selected Information sources, that are likely to publish documents relevant to the user interests in the future, are monitored. To achieve this functionality, a subscriber exploits statistical metadata to identify promising publishers and index its continuous query only in those publishers. The statistics are maintained in a directory, usually on a per-keyword basis, thus disregarding possible correlations among keywords. Using this coarse Information, poor publisher selection may lead to poor Filtering performance and thus loss of interesting documents.1 Based on the above observation, this work extends query routing techniques from the domain of distributed Information retrieval in peer-to-peer (P2P) networks, and provides new algorithms for exploiting the correlation among keywords in a Filtering setting. We develop and evaluate two algorithms based on single-key and multi-key statistics and utilize two different synopses (Hash Sketches and KMV synopses) to compactly represent publishers. Our experimental evaluation using two real-life corpora with web and blog data demonstrates the Filtering effectiveness of both approaches and highlights the different tradeoffs.
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architectural alternatives for Information Filtering in structured overlays
2007Co-Authors: Christos Tryfonopoulos, Christian Zimmer, Gerhard Weikum, Manolis KoubarakisAbstract:Content providers are naturally distributed and produce large amounts of new Information every day. Peer-to-peer Information Filtering is a promising approach that offers scalability, adaptivity to high dynamics, and failure resilience. The authors developed two approaches that utilize the chord distributed hash table as the routing substrate, but one stresses retrieval effectiveness, whereas the other relaxes recall guarantees to achieve lower message traffic and thus better scalability. This article highlights the two approaches' main characteristics, presents the issues and trade-offs involved in their design, and compares them in terms of scalability, efficiency, and Filtering effectiveness.
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efficient search and approximate Information Filtering in a distributed peer to peer environment of digital libraries
2007Co-Authors: Christian Zimmer, Christos Tryfonopoulos, Gerhard WeikumAbstract:We present a new architecture for efficient search and approximate Information Filtering in a distributed Peer-to-Peer (P2P) environment of Digital Libraries. The MinervaLight search system uses P2P techniques over a structured overlay network to distribute and maintain a directory of peer statistics. Based on the same directory, the MAPS Information Filtering system provides an approximate publish/subscribe functionality by monitoring the most promising digital libraries for publishing appropriate documents regarding a continuous query. In this paper, we discuss our system architecture that combines searching and Information Filtering abilities. We show the system components of MinervaLight and explain the different facets of an approximate pub/sub system for subscriptions that is high scalable, efficient, and notifies the subscribers about the most interesting publications in the P2P network of digital libraries. We also compare both approaches in terms of common properties and differences to show an overview of search and pub/sub using the same infrastructure.
Kristina Lerman - One of the best experts on this subject based on the ideXlab platform.
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social networks and social Information Filtering on digg
2006Co-Authors: Kristina LermanAbstract:The new social media sites — blogs, wikis, Flickr and Digg, among others — underscore the transformation of the Web to a participatory medium in which users are actively creating, evaluating and distributing Information. Digg is a social news aggregator which allows users to submit links to, vote on and discuss news stories. Each day Digg selects a handful of stories to feature on its front page. Rather than rely on the opinion of a few editors, Digg aggregates opinions of thousands of its users to decide which stories to promote to the front page. Digg users can designate other users as “friends” and easily track friends’ activities: what new stories they submitted, commented on or read. The friends interface acts as a social Filtering system, recommending to user stories his or her friends liked or found interesting. By tracking the votes received by newly submitted stories over time, we showed that social Filtering is an effective Information Filtering approach. Specifically, we showed that (a) users tend to like stories submitted by friends and (b) users tend to like stories their friends read and liked. Social Filtering is a promising new technology that can be used to personalize and tailor Information to individual users: for example, through personal front pages.
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social networks and social Information Filtering on digg
2006Co-Authors: Kristina LermanAbstract:The new social media sites -- blogs, wikis, Flickr and Digg, among others -- underscore the transformation of the Web to a participatory medium in which users are actively creating, evaluating and distributing Information. Digg is a social news aggregator which allows users to submit links to, vote on and discuss news stories. Each day Digg selects a handful of stories to feature on its front page. Rather than rely on the opinion of a few editors, Digg aggregates opinions of thousands of its users to decide which stories to promote to the front page. Digg users can designate other users as ``friends'' and easily track friends' activities: what new stories they submitted, commented on or read. The friends interface acts as a \emph{social Filtering} system, recommending to user stories his or her friends liked or found interesting. By tracking the votes received by newly submitted stories over time, we showed that social Filtering is an effective Information Filtering approach. Specifically, we showed that (a) users tend to like stories submitted by friends and (b) users tend to like stories their friends read and liked. As a byproduct of social Filtering, social networks also play a role in promoting stories to Digg's front page, potentially leading to ``tyranny of the minority'' situation where a disproportionate number of front page stories comes from the same small group of interconnected users. Despite this, social Filtering is a promising new technology that can be used to personalize and tailor Information to individual users: for example, through personal front pages.
Tao Zhou - One of the best experts on this subject based on the ideXlab platform.
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Information Filtering via biased heat conduction
2011Co-Authors: Jianguo Liu, Tao Zhou, Qiang GuoAbstract:The process of heat conduction has recently found application in personalized recommendation [Zhou et al., Proc. Natl. Acad. Sci. USA 107, 4511 (2010)], which is of high diversity but low accuracy. By decreasing the temperatures of small-degree objects, we present an improved algorithm, called biased heat conduction, which could simultaneously enhance the accuracy and diversity. Extensive experimental analyses demonstrate that the accuracy on MovieLens, Netflix, and Delicious datasets could be improved by 43.5%, 55.4% and 19.2%, respectively, compared with the standard heat conduction algorithm and also the diversity is increased or approximately unchanged. Further statistical analyses suggest that the present algorithm could simultaneously identify users' mainstream and special tastes, resulting in better performance than the standard heat conduction algorithm. This work provides a creditable way for highly efficient Information Filtering.
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relevance is more significant than correlation Information Filtering on sparse data
2009Co-Authors: Mingsheng Shang, Tao Zhou, Wei Zeng, Yicheng ZhangAbstract:In some recommender systems where users can vote objects by ratings, the similarity between users can be quantified by a benchmark index, namely the Pearson correlation coefficient, which reflects the rating correlations. Another alternative way is to calculate the similarity based solely on the relevance Information, namely whether a user has voted an object. The former one uses more Information than the latter, and is intuitively expected to give more accurate rating predictions under the standard collaborative Filtering framework. However, according to the extensive experimental analysis, this letter reports the opposite results that the latter method, making use of only the relevance Information, can outperform the former method, especially when the data set is sparse. Our finding challenges the routine knowledge on Information Filtering, and suggests some alternatives to address the sparsity problem.