Search results

Items of information that have been stored in a computer normally need to be accessed via their contents. In principle this is always possible by doing an exhaustive scan of the entire file of information, but to achieve the access efficiently we use some sort of organizing principle, a file organization or file structure, to reduce the amount anning. Typically the items retrieved are a response to a request which fully or partially specifies their contents. Often the file organization requires pre‐processing of the body of information so that a secondary body of information (an index or directory) may be created which in some sense reveals the contents of the file. So, ultimately file structures are time saving devices, where we pay for the time saved by extra storage. They enable us quickly to find items of information by completely or partially specifying their contents.

A grid information retrieval model has benefits for sharing resources and processing mass information, but cannot handle conceptual heterogeneity without integration of semantic information. The purpose of this research is to propose a concept‐based retrieval mechanism to catch the user's query intentions in a grid environment. This research re‐ranks documents over distributed data sources and evaluates performance based on the user judgment and processing time.

This research uses the ontology lookup service to build the concept set in the ontology and captures the user's query intentions as a means of query expansion for searching. The Globus toolkit is used to implement the grid service. The modification of the collection retrieval inference (CORI) algorithm is used for re‐ranking documents over distributed data sources.

The experiments demonstrate that this proposed approach successfully describes the user's query intentions evaluated by user judgment. For processing time, building a grid information retrieval model is a suitable strategy for the ontology‐based retrieval model.

Most current semantic grid models focus on construction of the semantic grid, and do not consider re‐ranking search results from distributed data sources. The significance of evaluation from the user's viewpoint is also ignored. This research proposes a method that captures the user's query intentions and re‐ranks documents in a grid based on the CORI algorithm. This proposed ontology‐based retrieval mechanism calculates the global relevance score of all documents in a grid and displays those documents with higher relevance to users.

As an emerging tool for data discovery, data retrieval systems fail to effectively support users' cognitive processes during data search and access. To uncover the relationship between data search and access and the cognitive mechanisms underlying this relationship, this paper examines the associations between affective memories, perceived value, search effort and the intention to access data during users' interactions with data retrieval systems.

This study conducted a user experiment for which 48 doctoral students from different disciplines were recruited. The authors collected search logs, screen recordings, questionnaires and eye movement data during the interactive data search. Multiple linear regression was used to test the hypotheses.

The results indicate that positive affective memories positively affect perceived value, while the effects of negative affective memories on perceived value are nonsignificant. Utility value positively affects search effort, while attainment value negatively affects search effort. Moreover, search effort partially positively affects the intention to access data, and it serves a full mediating role in the effects of utility value and attainment value on the intention to access data.

Through the comparison between the findings of this study and relevant findings in information search studies, this paper reveals the specificity of behaviour and cognitive processes during data search and access and the special characteristics of data discovery tasks. It sheds light on the inhibiting effect of attainment value and the motivating effect of utility value on data search and the intention to access data. Moreover, this paper provides new insights into the role of memory bias in the relationships between affective memories and data searchers' perceived value.

Compilation of numeric data has been of interest to mankind since ancient times. Collections of astronomic observations permitted the production of systematic calendars for great nations like the Mayans, Egyptians, Babylonians and Jews. Extensive sets of data were available to early navigators and pioneer physicists of medieval times. A well known example of data compilation was the celebrated Alfonsine Tables. The tables were completed in 1252 under the direction of Isaac ben Said and Yehuda ben Moshe Cohen during the rule of Alfonso X. of Castile. For three hundred years this was considered to be the best planetary data collection. Another famous astronomical data compilation was Caroline Herschel's Catalogue of Stars published in 1798. In the last decade there has been an increased need for precise information by government agencies, private industries, the business world and academic institutions. The continuing acknowledgement of the need for quantitative data contributed to the quick development and marketing of many databases that contain numerical and other factual information.

This article presents a novel user‐oriented interface for generalised informetric analysis and demonstrates how informetric calculations can easily and declaratively be specified through advanced data modelling techniques. The interface is declarative and at a high level. Therefore it is easy to use, flexible and extensible. It enables end users to perform basic informetric ad hoc calculations easily and often with much less effort than in contemporary online retrieval systems. It also provides several fruitful generalisations of typical informetric measurements like impact factors. These are based on substituting traditional foci of analysis, for instance journals, by other object types, such as authors, organisations or countries. In the interface, bibliographic data are modelled as complex objects (non‐first normal form relations) and terminological and citation networks involving transitive relationships are modelled as binary relations for deductive processing. The interface is flexible, because it makes it easy to switch focus between various object types for informetric calculations, e.g. from authors to institutions. Moreover, it is demonstrated that all informetric data can easily be broken down by criteria that foster advanced analysis, e.g. by years or content‐bearing attributes. Such modelling allows flexible data aggregation along many dimensions. These salient features emerge from the query interface‘s general data restructuring and aggregation capabilities combined with transitive processing capabilities. The features are illustrated by means of sample queries and results in the article.

Hybrid cloud composing of public and private cloud is seen as a solution for storage of health care data characterized by many private and sensitive data. In many hybrid cloud-based solutions, the data are perturbed and kept in public cloud, and the perturbation credentials are kept in private cloud.

Hybrid cloud is a model combing private and public cloud. Security for the data is enforced using this distribution in hybrid clouds. However, these mechanisms are not efficient for range query and retrieval of data from cloud. In this work, a secure and efficient retrieval solution combining K-mean clustering, geometric perturbation and R-Tree indexing is proposed for hybrid clouds.

Compared to existing solution, the proposed indexing on perturbed data is able to achieve 33% reduced retrieval time. The security of indexes as measured using variance of differences was 66% more than existing solutions.

This study is an attempt for efficient retrieval of data with range queries using R-Tree indexing approach.

In this article, the sixth in a series on microcomputer software for information storage and retrieval, test results of nine programs are presented and various properties and qualities of these programs are discussed. We discuss additional programs for information storage and retrieval and for text retrieval from several of the various categories which have been looked at in previous instalments. One new (secondary) type of 1SR software is defined as administrative software. The programs reviewed in this issue are BRS‐Search, dtSearch, InfoBank, Micro‐OPC, Q&A, STN‐PFS, Strix, TINman and ZYindex. All but dtSearch and ZYindex can be regarded as primarily classical retrieval packages; Q&A boasts comprehensive administrative features as well; dtSearch and ZYindex are indexing programs. For ZYindex a new Windows version has been tested. All other programs run under MS‐DOS. For each of the nine programs about 100 facts and test results are tabulated. All the programs are individually discussed as well.

On‐line update combines a bibliography of recent on‐line articles with a search example from a data base producer or an on‐line system vendor.

The purpose of this paper is to use cloud storage in digital preservation by analyzing the pricing and data retrieval models. The author recommends strategies to minimize the costs and believes cloud storage is worthy of serious consideration.

Few articles have been published to show the uses of cloud storage in libraries. The cost is the main concern. An overview of cloud storage pricing shows a price drop once every one or one-and-a-half years. The author emphasize the data transfer-out costs and demonstrate a case study. Comparisons and analysis of S3 and Glacier have been conducted to show the differences in retrieval and costs.

Cloud storage solutions like Glacier can be very attractive for long-term digital preservation if data can be operated within the provider’s same data zone and data transfer-out can be minimized.

Institutions can benefit from cloud storage by understanding the cost models and data retrieval models. Multiple strategies are suggested to minimize the costs.

The paper is intended to bridge the gap of uses of cloud storage. Cloud storage pricing especially data transfer-out pricing charts are presented to show the price drops over the past eight years. Costs and analysis of storing and retrieving data in Amazon S3 and Glacier are discussed in details. Comparisons of S3 and Glacier show that Glacier has uniqueness and advantages over other cloud storage solutions. Finally strategies are suggested to minimize the costs of using cloud storage. The analysis shows that cloud storage can be very useful in digital preservation.

Mountbatten offers a vivid description of the current‐awareness function using the analogy of a very wide conveyor‐belt, representing the information publishers, on which books, periodicals and reports appear at random: ‘The searcher is on a platform just above the belt and as the information material passes underneath he can pick up and read anything that he thinks might be of interest to him. You can imagine his frustration as he realises that for every item he takes time to examine, hundreds of others of possible interest to him have passed by’. Personality and environment will determine whether the individual can find an intelligent compromise between the extremes of neurosis induced by worrying about the material he is missing, or complacency with any system which produces one or two interesting items.