Search results

The purpose of this paper is twofold: first, to compare the lists of publishers in SPI (Spain) and the lists of VIRTA (Finland), in order to determine some of the potential uses of a merged list, such as complementing each other; and, second, to assess the effects of cross-field variability in the SPI rankings on the potential uses identified in the previous objective.

VIRTA and SPI lists were matched and compared in terms of level and number of submissions (VIRTA) and prestige (SPI).

There is a set of international publishers common to both information systems, but most publishers are nationally oriented. This type of publisher is still highly relevant for scholars. Consequently, a merge of national lists would provide useful information for all stakeholders involved in terms of grounding information for the rating of foreign, non-international publishers. Nevertheless, several issues should be considered in an eventual merging process, such as the decisions related to the use of field-specific rankings or general rankings.

If merged, ratings ought to be kept separately. Ratings of national publishers can be imputed in other systems’ evaluation process, thus making the merging process potentially useful.

This research explores obstacles and opportunities for merging scholarly publishers’ lists from an empirical perspective. It provides groundwork for future efforts toward supra-national combinations of publishers’ lists.

In federated search, a query is sent simultaneously to multiple resources and each one of them returns a list of results. These lists are merged into a single list using the results merging process. In this work, the authors apply machine learning methods for results merging in federated patent search. Even though several methods for results merging have been developed, none of them were tested on patent data nor considered several machine learning models. Thus, the authors experiment with state-of-the-art methods using patent data and they propose two new methods for results merging that use machine learning models.

The methods are based on a centralized index containing samples of documents from all the remote resources, and they implement machine learning models to estimate comparable scores for the documents retrieved by different resources. The authors examine the new methods in cooperative and uncooperative settings where document scores from the remote search engines are available and not, respectively. In uncooperative environments, they propose two methods for assigning document scores.

The effectiveness of the new results merging methods was measured against state-of-the-art models and found to be superior to them in many cases with significant improvements. The random forest model achieves the best results in comparison to all other models and presents new insights for the results merging problem.

In this article the authors prove that machine learning models can substitute other standard methods and models that used for results merging for many years. Our methods outperformed state-of-the-art estimation methods for results merging, and they proved that they are more effective for federated patent search.

lis‐link is the primary electronic discussion forum for the Library and Information Science (LIS) community in the UK. The list has been active since May 1991, and now has a membership of over 2500 individuals, generating over 3000 messages annually. This article examines the historical origins of the list, the purpose and community it serves, and illustrates the development of lis‐link over time.

The Quick Search Librarian is an inexpensive, specialized file manager for capturing, sorting and searching journal literature and other serials materials. Although it searches a file faster than a scalded cat, the search implementation and operations are cumbersome. It would therefore not be a good choice for applications such as ad hoc inquiries, that require immediate, easy access to search results. Not would it be a good choice for producing print‐ready bibliographic materials. However, the package can be an acceptable information management tool when quick turnaround is not required, and where it is used frequently enough so the user becomes thoroughly familiar with the search codes and procedures.

The purpose of this paper is to propose efficient algorithms for structural grouping over Extensible Markup Language (XML) data, called TOPOLOGICAL ROLLUP (T‐ROLLUP), which are to compute aggregation functions based on XML data with multiple hierarchical levels. They play important roles in the online analytical processing of XML data, called XML‐OLAP, with which complex analysis over XML can be performed to discover valuable information from XML.

Several variations of algorithms are proposed for efficient T‐ROLLUP computation. First, two basic algorithms, top‐down algorithm (TDA) and bottom‐up algorithm (BUA), are presented in which the well‐known structural‐join algorithms are used. The paper then proposes more efficient algorithms, called single‐scan by preorder number and single‐scan by postorder number (SSC‐Pre/Post), which are also based on structural joins, but have been modified from the basic algorithms so that multiple levels of grouping are computed with a single scan over node lists. In addition, the paper attempts to adopt the algorithm for parallel execution in multi‐core environments.

Several experiments are conducted with XMark and synthetic XML data to show the effectiveness of the proposed algorithms. The experiments show that proposed algorithms perform much better than the naïve implementation. In particular, the proposed SSC‐Pre and SSC‐Post perform better than TDA and BUA for all cases. Beyond that, the experiment using the parallel single scan algorithm also shows better performance than the ordinary basic algorithm.

This paper focuses on the T‐ROLLUP operation for XML data analysis. For this reason, other operations related to XML‐OLAP, such as CUBE, WINDOWING, and RANKING should also be investigated.

The paper presents an extended version of one of the award winning papers at iiWAS2008.

This paper seeks to disclose the important role of missing documents, broken links and duplicate items in the results merging process of a metasearch engine in detail. It aims to investigate some related practical challenges and proposes some solutions. The study also aims to employ these solutions to improve an existing model for results aggregation.

This research measures the amount of an increase in retrieval effectiveness of an existing results merging model that is obtained as a result of the proposed improvements. The 50 queries of the 2002 TREC web track were employed as a standard test collection based on a snapshot of the worldwide web to explore and evaluate the retrieval effectiveness of the suggested method. Three popular web search engines (Ask, Bing and Google) as the underlying resources of metasearch engines were selected. Each of the 50 queries was passed to all three search engines. For each query the top ten non‐sponsored results of each search engine were retrieved. The returned result lists of the search engines were aggregated using a proposed algorithm that takes the practical issues of the process into consideration. The effectiveness of the result lists generated was measured using a well‐known performance indicator called “TSAP” (TREC‐style average precision).

Experimental results demonstrate that the proposed model increases the performance of an existing results merging system by 14.39 percent on average.

The findings of this research would be helpful for metasearch engine designers as well as providing motivation to the vendors of web search engines to improve their technology.

This study provides some valuable concepts, practical challenges, solutions and experimental results in the field of web metasearching that have not been previously investigated.

This paper aims to identify the number of European companies among the world’s largest telecommunication, media and technology (TMT) companies. Through this, industry trends will be discerned and light shed on whether European companies are losing out to their rivals based in the USA and Asia in relation to the new highly technological economy that is emerging.

The paper begins by outlining the context for the study before detailing the data sources used in the analysis of the world’s largest TMT companies. The data are drawn from successive annual lists of the world’s largest companies published by the Financial Times.

The paper highlights the limited European presence among the world’s largest TMT companies. A significant proportion of these companies provide telecommunication services.

The paper draws on annual lists compiled by the Financial Times. The categorisation adopted in these lists changes over time and does not facilitate the identification of TMT operations in companies where the main activities are in other sectors.

There is a need for more data to be available in the public domain. One aspect of this relates to the need to extend the analysis to include smaller TMT companies, while another is the need for more detailed information regarding companies operating in more than one sector.

The paper extends existing research into structural change within the TMT sector by focussing on Europe. A longitudinal approach is adopted, with the companies uniquely divided into six lines of business.

The paper aims to propose an effective method to process keyword-based queries over graph-structured databases which are widely used in various applications such as XML, semantic web, and social network services. To satisfy users' information need, it proposes an extended answer structure for keyword queries, inverted list indexes on keywords and nodes, and query processing algorithms exploiting the inverted lists. The study aims to provide more effective and relevant answers to a given query than the previous approaches in an efficient way.

A new relevance measure for nodes to a given keyword query is defined in the paper and according to the relevance metric, a new answer tree structure is proposed which has no constraint on the number of keyword nodes chosen for each query keyword. For efficient query processing, an inverted list-style index is suggested which pre-computes connectivity and relevance information on the nodes in the graph. Then, a query processing algorithm based on the pre-constructed inverted lists is designed, which aggregates list entries for each graph node relevant to given keywords and identifies top-k root nodes of answer trees most relevant to the given query. The basic search method is also enhanced by using extend inverted lists which store additional relevance information of the related entries in the lists in order to estimate the relevance score of a node more closely and to find top-k answers more efficiently.

Experiments with real datasets and various test queries were conducted for evaluating effectiveness and performance of the proposed methods in comparison with one of the previous approaches. The experimental results show that the proposed methods with an extended answer structure produce more effective top-k results than the compared previous method for most of the queries, especially for those with OR semantics. An extended inverted list and enhanced search algorithm are shown to achieve much improvement on the execution performance compared to the basic search method.

This paper proposes a new extended answer structure and query processing scheme for keyword queries on graph databases which can satisfy the users' information need represented by a keyword set having various semantics.