基于分布式图像检索的结果合并

针对目前利用Web页面对数字图书馆进行查询存在的两方面的不足:无法判断从何处和如何精确查找信息,文章提出利用中央服务器在用户和分布式资料库之间充当网关角色,完成用户查询,着重解决如何将不同资料库中信息合并在一起,标准化检索图像的匹配值(数字图书馆所分配的),完成结果合并问题。实验证明,该算法应用于结果合并是可行的。     

SearchPad: explicit capture of search context to support Web search

Experienced users who query search engines have a complex behavior. They explore many topics in parallel, experiment with query variations, consult multiple search engines, and gather information over many sessions. In the process they need to keep track of search context — namely useful queries and promising result links, which can be hard. We present an extension to search engines called SearchPad that makes it possible to keep track of ‘search context' explicitly. We describe an efficient implementation of this idea deployed on four search engines: AltaVista, Excite, Google and Hotbot. Our design of SearchPad has several desirable properties: (i) portability across all major platforms and browsers; (ii) instant start requiring no code download or special actions on the part of the user; (iii) no server side storage; and (iv) no added client–server communication overhead. An added benefit is that it allows search services to collect valuable relevance information about the results shown to the user. In the context of each query SearchPad can log the actions taken by the user, and in particular record the links that were considered relevant by the user in the context of the query. The service was tested in a multi-platform environment with over 150 users for 4 months and found to be usable and helpful. We discovered that the ability to maintain search context explicitly seems to affect the way people search. Repeat SearchPad users looked at more search results than is typical on the Web, suggesting that availability of search context may partially compensate for non-relevant pages in the ranking.     

Rule-based query personalization in digital libraries

Searching a digital library is typically a tedious task. A system can improve information access by building on knowledge about a user acquired in a user profile in order to customize information access both in terms of the information returned in response to a query (query personalization) as well as in terms of the presentation of the results (presentation personalization). In this paper, we focus on query personalization in digital libraries; in particular, we address structured queries involving metadata stored in relational databases. We describe the specification of user preferences at the level of a user profile and the process of query personalization with the use of query-rewriting rules.     

Query-aware location anonymization for road networks

Recently, several techniques have been proposed to protect the user location privacy for location-based services in the Euclidean space. Applying these techniques directly to the road network environment would lead to privacy leakage and inefficient query processing. In this paper, we propose a new location anonymization algorithm that is designed specifically for the road network environment. Our algorithm relies on the commonly used concept of spatial cloaking, where a user location is cloaked into a set of connected road segments of a minimum total length L{\cal L} including at least K{\cal K} users. Our algorithm is “query-aware” as it takes into account the query execution cost at a database server and the query quality, i.e., the number of objects returned to users by the database server, during the location anonymization process. In particular, we develop a new cost function that balances between the query execution cost and the query quality. Then, we introduce two versions of our algorithm, namely, pure greedy and randomized greedy, that aim to minimize the developed cost function and satisfy the user specified privacy requirements. To accommodate intervals with a high workload, we introduce a shared execution paradigm that boosts the scalability of our location anonymization algorithm and the database server to support large numbers of queries received in a short time period. Extensive experimental results show that our algorithms are more efficient and scalable than the state-of-the-art technique, in terms of both query execution cost and query quality. The results also show that our algorithms have very strong resilience to two privacy attacks, namely, the replay attack and the center-of-cloaked-area attack.     

Web document clustering using a hybrid neural network

The list of documents returned by Internet search engines in response to a query these days can be quite overwhelming. There is an increasing need for organising this information and presenting it in a more compact and efficient manner. This paper describes a method developed for the automatic clustering of World Wide Web documents, according to their relevance to the user’s information needs, by using a hybrid neural network. The objective is to reduce the time and effort the user has to spend to find the information sought after. Clustering documents by features representative of their contents—in this case, key words and phrases—increases the effectiveness and efficiency of the search process. It is shown that a two-dimensional visual presentation of information on retrieved documents, instead of the traditional linear listing, can create a more user-friendly interface between a search engine and the user.     

User-private information retrieval based on a peer-to-peer community

Private information retrieval (PIR) is normally modeled as a game between two players: a user and a database. The user wants to retrieve some item from the database without the latter learning which item is retrieved. Most current PIR protocols are ill-suited to provide PIR from a search engine or large database: (i) their computational complexity is linear in the size of the database; (ii) they assume active cooperation by the database server in the PIR protocol. If the database cannot be assumed to cooperate, a peer-to-peer (P2P) user community is a natural alternative to achieve some query anonymity: a user gets her queries submitted on her behalf by other users in the P2P community. In this way, the database still learns which item is being retrieved, but it cannot obtain the real query histories of users, which become diffused among the peer users. We name this relaxation of PIR user-private information retrieval (UPIR). A peer-to-peer UPIR system is described in this paper which relies on an underlying combinatorial structure to reduce the required key material and increase availability. Extensive simulation results are reported and a distributed key management version of the system is described.     

基于Voronoi-R*的隐私保护路网k近邻查询方法

针对已有的保护位置隐私路网k近邻查询依赖可信匿名服务器造成的安全隐患,以及服务器端全局路网索引利用效率低的缺陷,提出基于路网局部索引机制的保护位置隐私路网近邻查询方法.查询客户端通过与LBS服务器的一轮通信获取局部路网信息,生成查询位置所在路段满足l-路段多样性的匿名查询序列,并将匿名查询序列提交LBS服务器,从而避免保护位置隐私查询对可信第三方服务器的依赖.在LBS服务器端,提出基于路网基本单元划分的分段式近邻查询处理策略,对频繁查询请求路网基本单元,构建基于路网泰森多边形和R^*树的局部Vor-R^*索引结构,实现基于索引的快速查找.对非频繁请求路网基本单元,采用常规路网扩张查询处理.有效降低索引存储规模和基于全局索引进行无差异近邻查询的访问代价,在保证查询结果正确的同时,提高了LBS服务器端k近邻查询处理效率.理论分析和实验结果表明,所提方法在兼顾查询准确性的同时,有效地提高了查询处理效率.     

Examining topic shifts in content-oriented XML retrieval

Content-oriented XML retrieval systems support access to XML repositories by retrieving, in response to user queries, XML document components (XML elements) instead of whole documents. The retrieved XML elements should not only contain information relevant to the query, but also provide the right level of granularity. In INEX, the INitiative for the Evaluation of XML retrieval, a relevant element is defined to be at the right level of granularity if it is exhaustive and specific to the query. Specificity was specifically introduced to capture how focused an element is on the query (i.e., discusses no other irrelevant topics). To score XML elements according to how exhaustive and specific they are given a query, the content and logical structure of XML documents have been widely used. One source of evidence that has led to promising results with respect to retrieval effectiveness is element length. This work aims at examining a new source of evidence deriving from the semantic decomposition of XML documents. We consider that XML documents can be semantically decomposed through the application of a topic segmentation algorithm. Using the semantic decomposition and the logical structure of XML documents, we propose a new source of evidence, the number of topic shifts in an element, to reflect its relevance and more particularly its specificity. This paper has three research objectives. Firstly, we investigate the characteristics of XML elements reflected by their number of topic shifts. Secondly, we compare topic shifts to element length, by incorporating each of them as a feature in a retrieval setting and examining their effects in estimating the relevance of XML elements given a query. Finally, we use the number of topic shifts as evidence for capturing specificity to provide a focused access to XML repositories.     

Processing SPARQL queries over distributed RDF graphs

We propose techniques for processing SPARQL queries over a large RDF graph in a distributed environment. We adopt a “partial evaluation and assembly” framework. Answering a SPARQL query Q is equivalent to finding subgraph matches of the query graph Q over RDF graph G. Based on properties of subgraph matching over a distributed graph, we introduce local partial match as partial answers in each fragment of RDF graph G. For assembly, we propose two methods: centralized and distributed assembly. We analyze our algorithms from both theoretically and experimentally. Extensive experiments over both real and benchmark RDF repositories of billions of triples confirm that our method is superior to the state-of-the-art methods in both the system’s performance and scalability.     

Basic support for cooperative work on the World Wide Web

The emergence and widespread adoption of the World Wide Web offers a great deal of potential in supporting cross-platform cooperative work within widely dispersed working groups. The Basic Support for Cooperative Work (BSCW) project at GMD is attempting to realize this potential through development of web-based tools which provide cross-platform collaboration services to groups using existing web technologies. This paper describes one of these tools, theBSCW Shared Workspace system—a centralized cooperative application integrated with an unmodified web server and accessible from standard web browsers. The BSCW system supports cooperation through “shared workspaces”; small repositories in which users can upload documents, hold threaded discussions and obtain information on the previous activities of other users to coordinate their own work. The current version of the system is described in detail, including design choices resulting from use of the web as a cooperation platform and feedback from users following the release of a previous version of BSCW to the public domain.     

<Emphasis Type="Italic">I-Quest</Emphasis>: an <Emphasis Type="Italic">i</Emphasis>ntelligent <Emphasis Type="Italic">que</Emphasis>ry <Emphasis Type="Italic">st</Emphasis>ructuring based on user browsing feedback for semantic retrieval of video data

In spite of significant improvements in video data retrieval, a system has not yet been developed that can adequately respond to a user’s query. Typically, the user has to refine the query many times and view query results until eventually the expected videos are retrieved from the database. The complexity of video data and questionable query structuring by the user aggravates the retrieval process. Most previous research in this area has focused on retrieval based on low-level features. Managing imprecise queries using semantic (high-level) content is no easier than queries based on low-level features due to the absence of a proper continuous distance function. We provide a method to help users search for clips and videos of interest in video databases. The video clips are classified as interesting and uninteresting based on user browsing. The attribute values of clips are classified by commonality, presence, and frequency within each of the two groups to be used in computing the relevance of each clip to the user’s query. In this paper, we provide an intelligent query structuring system, called I-Quest, to rank clips based on user browsing feedback, where a template generation from the set of interesting and uninteresting sets is impossible or yields poor results.

保护位置隐私和查询内容隐私的路网K近邻查询方法

位置隐私和查询内容隐私是LBS兴趣点（point of interest,简称POI）查询服务中需要保护的两个重要内容,同时,在路网连续查询过程中,位置频繁变化会给LBS服务器带来巨大的查询处理负担,如何在保护用户隐私的同时,高效地获取精确查询结果,是目前研究的难题.以私有信息检索中除用户自身外其他实体均不可信的思想为基本假设,基于Paillier密码系统的同态特性,提出了无需用户提供真实位置及查询内容的K近邻兴趣点查询方法,实现了对用户位置、查询内容隐私的保护及兴趣点的精确检索;同时,以路网顶点为生成元组织兴趣点分布信息,进一步解决了高强度密码方案在路网连续查询中因用户位置变化频繁导致的实用效率低的问题,减少了用户的查询次数,并能确保查询结果的准确性.最后从准确性、安全性及查询效率方面对本方法进行了分析,并通过仿真实验验证了理论分析结果的正确性.     

Rama: An architecture for Internet information filtering

This paper describes Rama, a first generation experimental information retrieval and filtering system that attempts to recover useful information from various Internet sources including USENIX news and anonymous FTP servers. The focus of the Rama system to date has been on building a distributed query and information retrieval system, which provides an interface to heterogeneous information services. A user of Rama sends one or more asynchronous queries to a Rama server using existing SMTP e-mail clients. The server periodically searches local and remote Internet services. Searches are prefiltered with the use of timestamps. Data objects which are newer than the timestamp are then searched via a query mechanism which relies on a combination of vector-distance, pattern matching operands, and boolean operators. Results are weighted according to how closely they match queries and are posted via e-mail to the user. Input to the e-mail client can be further filtered — one can use the MH mail system and sort input by weight. Results indicate that the current system is useful and extensible. So far we have assumed that existing e-mail systems will be used for input and output and have not attempted to construct special client interfaces. Efforts are underway to extend the system with WWW searching capabilities and construct a special WWW oriented user-interface.     

基于局部呼叫数据建模的相对相似用户分布式查询

在众多以手机呼叫记录（Call Detail Record,CDR）为数据源的分析研究和挖掘应用中,相似用户查询作为基础研究方法占据着重要地位。传统的查询算法多为集中式处理,然而CDR数据分布产生和存储的本质带来了相似用户分布式查询的问题。本文结合真实数据集,分析用户存储于各基站的局部数据与全局数据的关系,提出并实现基于局部呼叫数据建模的相对相似用户分布式查询方法（Rsu-DQ）,使用真实数据设计实验验证所提出方法的准确性和高效性。     

基于关键字密度的XML关键字检索

关键字检索具有友好的用户操作体验,该检索方式已在文本信息检索领域得到了广泛而深入的应用.对XML数据采用关键字检索是目前研究的热点.基于查询语义的XML关键字检索方法存在返回大量与用户查询意图无关的查询片段或者丢失符合用户查询意图的片段这两个问题.针对这些问题,在考虑LCA横向和纵向两个维度的基础上,提出了用户查询意图与LCA相关性的两个规则,根据两个规则定义了LCA的边密度和路径密度,建立了综合的LCA节点评分公式,最后设计TopLCA-K算法对LCA进行排名,并利用中心位置索引CI提高了TopLCA-K算法的效率.实验结果显示,利用所提出的方法返回的查询节点更加符合用户需求.     

Multidimensional ranking for data in digital spatial libraries

Digital spatial libraries currently under development are generating large repositories of data which will continue to grow. As these repositories grow, the situation will inevitably arise in which a digital library user may be confronted with several hundred spatial data sets in response to a particular query. The question then arises as to how the results from this search can be most easily assimilated by the user. Text based materials have benefited from substantial research and experience on ranking of search results. Ranking of spatial data sets has not received the same attention since there has been little motivation for such activity until recently. In this paper we propose a multidimensional ranking scheme based on the three dimensions of space, time, and theme. The multidimensional rank is presented graphically to inform users about how well data sets from a digital spatial library meet their spatial, temporal, and thematic targets.     

RRSi: indexing XML data for proximity twig queries

Twig query pattern matching is a core operation in XML query processing. Indexing XML documents for twig query processing is of fundamental importance to supporting effective information retrieval. In practice, many XML documents on the web are heterogeneous and have their own formats; documents describing relevant information can possess different structures. Therefore some “user-interesting” documents having similar but non-exact structures against a user query are often missed out. In this paper, we propose the RRSi, a novel structural index designed for structure-based query lookup on heterogeneous sources of XML documents supporting proximate query answers. The index avoids the unnecessary processing of structurally irrelevant candidates that might show good content relevance. An optimized version of the index, oRRSi, is also developed to further reduce both space requirements and computational complexity. To our knowledge, these structural indexes are the first to support proximity twig queries on XML documents. The results of our preliminary experiments show that RRSi and oRRSi based query processing significantly outperform previously proposed techniques in XML repositories with structural heterogeneity.

A modular and adaptive framework for large scale video indexing and content‐based retrieval: the SIRSALE system

In this paper, we present the design and the implementation of SIRSALE: a distributed video data management system. SIRSALE allows users to manipulate video streams stored in large distributed repositories, i.e. it provides remote users with functionalities to browse video streams by structures (shots, scenes, sequences, etc.), to annotate the semantic contents of videos and to query the distributed video repositories. One of the main contributions of SIRSALE is its contextual adaptation to the target application, i.e. it is based on a modular data model that allows adapting the system to deal with several semantic contexts. In other words, SIRSALE allows users to define and to use their own semantic data model in order to annotate and query video databases. The key idea behind this is to dynamically adapt the whole system, mainly user interfaces, to stand several semantic data models. The system has been presented to professionals who gave a positive feedback. Copyright © 2006 John Wiley & Sons, Ltd.     

Multi-domain collaborative exploration mechanisms for query expansion in an agent-based filtering framework

Novice users often do not have enough domain knowledge to create good queries for searching information on-line. To help alleviate the situation, exploration techniques have been used to increase the diversity of the search results so that not only those explicitly asked will be returned, but also those potentially relevant ones will be returned too. Most existing approaches, such as collaborative filtering, do not allow the level of exploration to be controlled. Consequently, the search results can be very different from what is expected. We propose an exploration strategy that performs intelligent query processing by first searching usable old queries, and then utilising them to adapt the current query, with the hope that the adapted query will be more relevant to the user’s areas of interest. We applied the proposed strategy to the implementation of a personal information assistant (PIA) set up for user evaluation for 3 months. The experimental results showed that the proposed exploration method outperformed collaborative filtering, and mutation and crossover methods by around 25% in terms of the elimination of off-topic results.     

Exploiting Background Information in Knowledge Discovery from Text

This paper describes the FACT system for knowledge discovery fromtext. It discovers associations—patterns ofco-occurrence—amongst keywords labeling the items in a collection oftextual documents. In addition, when background knowledge is available aboutthe keywords labeling the documents FACT is able to use this information inits discovery process. FACT takes a query-centered view of knowledgediscovery, in which a discovery request is viewed as a query over theimplicit set of possible results supported by a collection of documents, andwhere background knowledge is used to specify constraints on the desiredresults of this query process. Execution of a knowledge-discovery query isstructured so that these background-knowledge constraints can be exploitedin the search for possible results. Finally, rather than requiring a user tospecify an explicit query expression in the knowledge-discovery querylanguage, FACT presents the user with a simple-to-use graphical interface tothe query language, with the language providing a well-defined semantics forthe discovery actions performed by a user through the interface.