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COMPUTER NETWORKS AND ISDN SYSTEMS 1998年第1-7期30卷 612-615页

作者： Cao, L Leong, MK Lu, Y Low, HB Kent Ridge Digital Labs Singapore 119597 Singapore

A crucial issue in the design of global Internet systems is the presence of many languages for data exchange, indexing, searching, and retrieval. It is especially significant in the library community where there is a trend towards incorporating multilingual bibliographic collections into regional and international databases for resource sharing through the WWW. Such systems are distributed and heterogeneous, with libraries using different proprietary systems and various flavours of standards such as MARC. This paper proposes an architecture for searching distributed heterogeneous multi-Asian language bibliographic sources, and describes a successful pilot implementation of the system. (C) 1998 Published by Elsevier Science B.V. All rights reserved.

关键词： distributed search multilingual retrieval bibliographic digital library Z39.50

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Improving Disease Early Detection and Prevention by Multiplex Longitudinal Outcomes Marker Modeling

Improving Disease Early Detection and Prevention by Multiple...

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International Conference on eHealth, Telemedicine, and Social Medicine

作者： Conley, Edward C. Owens, David R. Cardiff Univ Univ Hosp Llandough Diabet Res Unit Cardiff S Glam Wales

ISBN: (纸本)9781424433605

We are interested in understanding how high quality information management systems can improve disease prevention and screening services, particularly in the prevention of diabetes and its complications. As part of this effort, we are exploiting standards-driven distributed data collation, processing and computational technologies to deduce comparative performance of disease outcomes markers. In the system design, data is sent to an individualized risk management information service (e.g. as part of a home or mobile-based telehealth service). Data originating across the patient path' is quality controlled to improve the accuracy of risk models. Delivery of these functions 'at scale' requires the development of new data trend management and decision support tools. Semantic standardization, particularly in representation of disease progression features for data aggregation and distributed search is a key requirement. We discuss some of the current challenges surrounding outcomes marker modeling to support disease prevention and screening services.

关键词： disease prevention health technologies outcomes modeling risk analysis Disease Controlled Health Care Technology Risk Management risk analysis distributed search decision support tool unified retrieval models

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Scalable distributed Information Retrieval Model Based on Topic Map and Mobil Agent

Scalable Distributed Information Retrieval Model Based on To...

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IEEE International Symposium on IT in Medicine and Education

作者： Xia, Li-xin Wang, Zhong-yi Chen, Chen Cent China Normal Univ Dept Informat Management Wuhan Peoples R China

ISBN: (纸本)9781424425105

In view of the problems of centralized search engine and based on analyzing the technology of topic map, OAI, mobile Agent and P2P, we design and implement a Scalable distributed Retrieval Model based on Topic Map and Mobile Agent. The model can effectively improve the utilization rate of resources, increase search depth, overcome server failure and heterogeneity of network, and has a strong capacity for expansion, fault-tolerant and parallel processing

关键词： topic maps information retrieval models distributed search search engines PARALLEL PROCESSING (COMPUTERS) Scalability Mobile agents

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An Approach to Manage Semantic Heterogeneity in Unstructured P2P Information Retrieval Systems

An Approach to Manage Semantic Heterogeneity in Unstructured...

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12th IEEE International Conference on Peer-to-Peer Computing (P2P)

作者： Cerqueus, Thomas Cazalens, Sylvie Lamarre, Philippe Univ Nantes LINA UMR 6241 F-44035 Nantes France Univ Nantes LINA UMR 6241 INRIA Sophia F-44035 Nantes France Univ Nantes LINA UMR 6241 INSA Lyon F-69621 Villeurbanne France

ISBN: (纸本)9781467328623

In unstructured information retrieval P2P systems, semantic heterogeneity comes from the use of different ontologies. Semantic interoperability refers to the ability of peers to communicate with each others. We take into account these notions separately, as raising two different problems. Hence we propose two independent and complementary solutions. The GoOD-TA protocol aims at reducing heterogeneity through ontology-driven topology adaptation. DiQuESh is a top-k algorithm for distributed information retrieval that is intended to ensure interoperability. This distinction enables highlighting their respective benefits on the IR performances and leads to a modular architecture. For our experiments we obtained a set of actively used real-world ontologies through the NCBO BioPortal. We implemented GoOD-TA and DiQuESH in Java and used the PeerSim simulator. We first show that GoOD-TA nicely reduces the semantic heterogeneity related to the system topology, handles the evolution of peers' descriptors, and is suitable for dynamic systems. Then, GoOD-TA and DiQuESh are run simultaneously, with a significant increase of precision and recall. This enables to identify the indirect contribution of heterogeneity reduction obtained with GoOD-TA to improving interoperability.

关键词： Semantics Ontology distributed search Peers P2RY4 gene peer to peer file sharing Information Retrieval Systems Interoperability information retrieval COMMUNICATING Java Java programming language

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Learning to Rank for Non Independent and Identically distributed Datasets 24

Learning to Rank for Non Independent and Identically Distrib...

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International Conference on the Theory of Information Retrieval (ICTIR)

作者： Cecchetti, Jacopo Tonellotto, Nicola Perego, Raffaele Univ Pisa Pisa Italy ISTI CNR Pisa Italy

ISBN: (纸本)9798400706813

With the growing data privacy concerns, federated machine learning algorithms capable of preserving the confidentiality of sensitive information while enabling collaborative model training across decentralized data sources are attracting increasing interest. In this paper, we address the problem of collaboratively learning effective ranking models from non-independently and identically distributed (non-IID) training data owned by distinct search clients. We assume that the learning agents cannot access each other's data, and that the models learned from local datasets might be biased or underperforming due to a skewed distribution of certain document features or query topics in the learning-to-rank training data. Thus, we aim to instill in the local ranking model learned from local data the knowledge from other models to obtain a more robust ranker capable of effectively handling documents and queries underrepresented in the local collection. To achieve this, we explore different methods for merging the ranking models, thus obtaining in each client a model that excels in ranking documents from the local data distribution but also performs well on queries retrieving documents having distributions typical of a partner's node. In particular, our findings suggest that by relying on a linear combination of the local models, we can improve IR models effectiveness by up to +17.92% in NDCG@10 (moving from 0.619 to 0.730), and by up to +19.64% in MAP (moving from 0.713 to 0.853).

关键词： Learning to Rank Non-IID distributed search

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Cost Partitioning for Multi-agent Planning 11

Cost Partitioning for Multi-agent Planning

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11th International Conference on Agents and Artificial Intelligence (ICAART)

作者： Stolba, Michal Urbanovska, Michaela Fiser, Daniel Komenda, Antonin Czech Tech Univ Dept Comp Sci Karlovo Namesti 13 Prague 12135 Czech Republic

ISBN: (纸本)9789897583506

Similarly to classical planning, heuristics play a crucial role in Multi-Agent Planning (MAP). Especially, the question of how to compute a distributed heuristic so that the information is shared effectively has been studied widely. This question becomes even more intriguing if we aim to preserve some degree of privacy, or admissibility of the heuristic. The works published so far aimed mostly at providing an ad-hoc distribution protocol for a particular heuristic. In this work, we propose a general framework for distributing heuristic computation based on the technique of cost partitioning. This allows the agents to compute their heuristic values separately and the global heuristic value as an admissible sum. We evaluate the presented techniques in comparison to the baseline of locally computed heuristics and show that the approach based on cost partitioning improves the heuristic quality over the baseline.

关键词： Multi-agent Planning distributed search distributed Heuristic Computation

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Balancing Latency and Quality in Web search 15

Balancing Latency and Quality in Web Search

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15th IEEE International Conference on Networking, Architecture and Storage (NAS)

作者： Zhou, Liang Ramakrishnan, K. K. Univ Calif Riverside Comp Sci & Engn Dept Riverside CA 92521 USA

ISBN: (纸本)9781728177441

Selecting the right time budget for a search query is challenging because a proper balance between the search latency, quality and efficiency has to be maintained. State-of-the-art approaches leverage a centralized sample index at the aggregator to select the Index Serving Nodes (ISNs) to maintain quality and responsiveness. In this paper, we propose Cottage, a coordinated framework between the aggregator and ISNs for latency and quality optimization in web search. Cottage has two separate neural network models at each ISN to predict the quality contribution and latency, respectively. Then, these prediction results are sent back to the aggregator for latency and quality optimizations. The key task is integration of the predictions at the aggregator in determining an optimal dynamic time budget for identifying slow and low quality ISNs to improve latency and search efficiency. Our experiments on the Solr search engine prove that Cottage can reduce the average query latency by 54% and achieve a good P@10 search quality of 0.947.

关键词： distributed search time budget search quality search latency search efficiency

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SPIRIX: A Peer-to-Peer search Engine for XML-Retrieval

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7th International Workshop of the Initiative for the Evaluation of XML Retrieval

作者： Winter, Judith Drobnik, Oswald Goethe Univ Frankfurt Inst Informat Frankfurt Germany

ISBN: (纸本)9783642037603

At INEX 2008 we presented SPIRIX, a Peer-to-Peer search engine developed to investigate distributed XML-Retrieval. Such investigations have been neglected by INEX so far: while there is a variety of successful and effective XML-Retrieval approaches, all current solutions are centralized search engines. They do not consider distributed scenarios, where it is undesired or impossible to hold the whole collection on one single machine. Such scenarios include search in large-scale collections, where the load of computations and storage consumption is too high for one server. Other systems consist of different owners of heterogeneous collections willing to share their documents without giving up full control over their documents by uploading them on a central server. Currently, there are research solutions for distributed text-retrieval or multimedia-retrieval. With IN EX and innovative techniques for exploiting XML-structure, it is now time to extend research to distributed XML-Retrieval. This paper reports on SPIRIX performance at INEX'08.

关键词： distributed XML-Retrieval INEX distributed search XML Information Retrieval Efficiency Peer-to-Peer

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Wide-Area Transposition-Driven Scheduling

Wide-Area Transposition-Driven Scheduling

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10th Annual IEEE International Symposium on High Performance distributed Computing

作者： Romein, JW Bal, HE Vrije Univ Amsterdam Dept Math & Comp Sci Fac Sci Amsterdam Netherlands

ISBN: (纸本)0769512968

distributed search of state spaces containing cycles is a challenging task and has been studied for years. Traditional parallel search algorithms either ignore the cyclic nature of the state space and waste much time in duplicated search effort, or rely on heavy communication to reduce duplicate work, resulting in a large communication overhead. Both methods perform poorly, even when using a fast, local interconnect, A recently developed task-distribution scheme, Transposition-Driven Scheduling (TDS), performs much better, since it communicates asynchronously and efficiently, suppresses duplicate search effort. TDS, however, requires bandwidths of megabytes per second per processor In this paper, we investigate how cyclic state spaces can be searched efficiently on a meta-computing system containing multiple clusters, connected by high-latency, low-bandwidth wide-area links. This is quite a challenge, because the wide-area links provide neither the bandwidth required for TDS, nor the latency required for traditional distributed search algorithms. We propose a scheme that strongly reduces communication between clusters, at the expense of some duplicate search effort. Performance measurements for several applications show that the new scheme outperforms traditional schemes by a wide margin.

关键词： meta computing distributed search distributed supercomputing Transposition-Driven Scheduling

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distributed case-based learning 4

Distributed case-based learning

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4th International Conference on MultiAgent Systems

作者： Prasad, MVN VerticalNet Adv Prod & Res Grp Palo Alto CA 94304 USA

ISBN: (纸本)0769506259

Multi-agent systems exploiting case-based reasoning techniques have to deal with the problem of retrieving episodes that are themselves distributed across a set of agents. From a Gestalt perspective, a good overall case may not be the one derived from the summation of best subcases. in this paper,ve deal with issues involved in learning and exploiting the learned knowledge in multiagent case-based systems. We propose a novel algorithm called OA* which composes optimal overall cases from distributed case components, and prove its optimality. We then experiment with OA* in a transportation domain on a grid world. We provide empirical results that provide strong evidence of the effectiveness of OA* for the distributed case-based learning task.

关键词： multi-agent learning distributed search

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