Search

The increasing availability of data search tools brings opportunities for non-expert users. Among these users, interdisciplinary researchers and data journalists represent a growing population whose work can lead to societal benefit. Through in-depth interviews, we examine what strategies and approaches researchers and journalists adopt to search online data, how they apply current technology to facilitate dataset search, and the barriers and difficulties that they encounter in their work with data. Our findings reveal that with technological limitations in the aspects of searchability, interactivity and usability, dataset search for non-experts remains a challenge. We have found that little attention has been paid to non-experts' emerging data need, significantly constraining the design and development of technological tools for supporting non-expert users. Our findings underline the critical impact of the design, development and deployment of technological tools to enable the meaningful use of today's increasingly available data toward a civil society. [ABSTRACT FROM AUTHOR]

Optical character recognition (OCR) is the process of converting an image of text into a machineencoded text format. For example, when we scan a form or document, the computer saves the scan as an image file. A text editor cannot be used to edit, search, or count words in an image file. OCR helps convert an image into a text document and content is stored as text data. Optical character recognition (OCR) provides the added functionality of being able to edit and search these documents. Such data can be used for analysis, optimization, process automation and productivity improvement. It saves time and reduces errors. We inform our thousands of users that the OCR-passed digital collections of old and new digitized periodicals, editions dedicated to the anniversaries of periodicals are replenished thanks to daily hard work. As of 26.10.2023 #armocr #ocr has received around 93,691 PDFs or 1,496,939 pages: newspaper, magazine, abstracts, NLA publication. These repositories can serve as a unique online library of educational resources. [ABSTRACT FROM AUTHOR]

Secret sharing is a promising approach to protecting the confidentiality of outsourced data. However, the existing secret sharing-based data outsourcing schemes involve several security and performance issues, including vulnerability to some inferences and attacks. In particular, most of them leak the order of the secret values. In this paper, a nondeterministic encryption scheme, called order-shuffling encryption (OSE), is first proposed. It generates searchable encrypted values that do not leak the order of the secret values. Then, a new secure data outsourcing scheme is elaborated on secret sharing and OSE. The proposed data outsourcing scheme generates some searchable shares using OSE and some other non-searchable and random shares from each secret value to be outsourced. It provides resistance to various inferences and attacks and supports the server-side execution of a wide variety of queries. Extensive evaluations and analyses demonstrate that the proposed secure data outsourcing scheme is quite practical and efficient. [ABSTRACT FROM AUTHOR]

This study investigates the search processes of users accessing public websites representing historic clothing collections, examining where their searches are supported by the metadata in the collection databases and what factors could make their experience more inclusive. With IRB approval from four universities, we performed a recorded experiment with twenty adults: ten students of historic dress and ten fashion professionals. Four tasks included search scenarios and images representing diverse historic garments. Results indicate that both the descriptive metadata and search features on collection websites present challenges for the typical user search process. Users search for historical dress content the way they perform online shopping, relying on images, lay terms, and user-friendly search filters. Factors that influenced individual users' experience included technology and ease of guessing, revealing a hesitation around international garments for fear of being culturally offensive if guessing incorrectly. Inconsistency of terminology used by different websites meant that even users who were familiar with professional clothing vocabulary faced challenges. Conclusions indicate a need for enhanced metadata, better use of international nomenclature standards, effective search filters, and basic images in databases. Furthermore, community involvement will improve public search tools and facilitate learning about different cultures. [ABSTRACT FROM AUTHOR]

In this paper, two new practical attacks on some secret sharing-based data outsourcing schemes are first introduced, and several other security and performance issues with the existing schemes are also explored. The existing and new attacks exploit the information about the share range boundaries or the correspondences between the secret values and shares. A range expansion technique is then proposed to thwart one of the attacks. It expands the ranges in every range predicate in the submitted queries in order to hide the share range boundaries from any query observer. Next, a mapping method is proposed to thwart the other attacks. It maps each secret value to a mapping value using a secret one-to-many mapping with a finite set of linear mapping rules so that the tuples of shares are generated from the mapping values rather than directly from the secret values. The proposed mapping method works as an additional layer of security and addresses any attack based on the correspondences between the secret values and shares. At the same time, it preserves the homomorphism property of secret sharing. Finally, a new secure data outsourcing scheme is elaborated on secret sharing, the proposed mapping method, and the proposed range expansion technique. The proposed scheme is resistant to various attacks and also some inferences. It supports the fully server-side or a partially server-side query execution of most types of queries. The experimental results confirm that the proposed scheme is quite practical and efficient. [ABSTRACT FROM AUTHOR]

This paper proposes a fuzzy based clustering search approach for an ERP system and searching mechanisms along with most appropriate metrics. In real time scenarios it is cumbersome to search for a particular piece of data from a data mart used by ERP applications. The situation becomes worse when it is difficult to search for the string when numerous strings are available. This work proposes a clustering approach which uses a fuzzy inference engine to make the search more effective and fast. The proposed technique reduces the processing time by 82% and the memory usage by 8%, compared to the conventional technique of searching the students' or faculty data from the experimental ERP data sets. This paper also provides the comprehensive list of the metrics which can be considered to be incorporated as part of the framework and deployment of any ERP system. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to propose a model to anticipate the success in the use of a Knowledge Management System (KMS) by doctoral researchers. Doctoral researchers who are preparing their doctoral dissertation are requested to prepare a tool to manage the knowledge they are collecting. The tool is based on data base techniques, and the researchers will use this tool to collect data about the knowledge they use. Doctoral researchers will perceive satisfaction in the use of this tool, depending on internal aspects that they could previously perceive, such as ease of use, usefulness, or quality. Also, there could be external aspects such as rewards, trust and social norms that could affect the perceived satisfaction. As a conclusion, the correct identification of internal and external aspects can improve the success in the use of a KMS. [ABSTRACT FROM AUTHOR]

Background: Digital health technologies (DHTs), such as electronic health records and prescribing systems, are transforming health care delivery around the world. The quality of information in DHTs is key to the quality and safety of care. We developed a novel clinical information quality (CLIQ) framework to assess the quality of clinical information in DHTs.Objective: This study explored clinicians' perspectives on the relevance, definition, and assessment of information quality dimensions in the CLIQ framework.Methods: We used a systematic and iterative eDelphi approach to engage clinicians who had information governance roles or personal interest in information governance; the clinicians were recruited through purposive and snowball sampling techniques. Data were collected using semistructured online questionnaires until consensus was reached on the information quality dimensions in the CLIQ framework. Responses on the relevance of the dimensions were summarized to inform decisions on retention of the dimensions according to prespecified rules. Thematic analysis of the free-text responses was used to revise definitions and the assessment of dimensions.Results: Thirty-five clinicians from 10 countries participated in the study, which was concluded after the second round. Consensus was reached on all dimensions and categories in the CLIQ framework: informativeness (accuracy, completeness, interpretability, plausibility, provenance, and relevance), availability (accessibility, portability, security, and timeliness), and usability (conformance, consistency, and maintainability). A new dimension, searchability, was introduced in the availability category to account for the ease of finding needed information in the DHTs. Certain dimensions were renamed, and some definitions were rephrased to improve clarity.Conclusions: The CLIQ framework reached a high expert consensus and clarity of language relating to the information quality dimensions. The framework can be used by health care managers and institutions as a pragmatic tool for identifying and forestalling information quality problems that could compromise patient safety and quality of care.International Registered Report Identifier (irrid): RR2-10.1136/bmjopen-2021-057430. [ABSTRACT FROM AUTHOR]

Issues related to searchability and ease of access have plagued fan fiction since its inception. This paper discusses the predominate forms of fan-mediated indexing and descriptive metadata, commonly referred to as folksonomy or tagging, and compares the benefits and disadvantages of each model. These models fall into three broad categories: free tagging, controlled vocabulary, and hybrid folksonomy. Each model has distinct advantages and shortcomings related to findability, results filtering, and creative empowerment. Examples for each are provided. Possible ramifications to fan fiction from improved metadata and access are also discussed.

Abstract: While a great deal is known about the topology of social networks, there is much less agreement about the geographical structure of these networks. The fundamental question in this context is: how does the probability of a social link between two individuals depend on the physical distance between them? While it is clear that the probability decreases with the distance, various studies have found different functional forms for this dependence. The exact form of the distance dependence has crucial implications for network searchability and dynamics: Kleinberg (2000) [15] shows that the small-world property holds if the probability of a social link is a power-law function of the distance with power −2, but not with any other power. We investigate the distance dependence of link probability empirically by analyzing four very different sets of data: Facebook links, data from the electronic version of the Small-World experiment, email messages, and data from detailed personal interviews. All four datasets reveal the same empirical regularity: the probability of a social link is proportional to the inverse of the square of the distance between the two individuals, analogously to the distance dependence of the gravitational force. Thus, it seems that social networks spontaneously converge to the exact unique distance dependence that ensures the Small-World property. [Copyright &y& Elsevier]

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This article reviews current federated search and discovery products. The authors propose that federated search and discovery services are vital tools to increase searchability and discoverability of library resources, and that they are particularly useful for undergraduate students as a starting point of their information research process. The unifying access to a library's subscribed resources will create new opportunities for librarians to help patrons to advance their skills. [ABSTRACT FROM PUBLISHER]

The article enumerates the technologically naïve humanist's with list for the type of text analysis tools to be developed by the Text Analysis Portal for Research (TAPoR) project. TAPoR is of course intended to be more than a website. Text Analysis Portal has its primary goal to develop new tools for working with texts.

Issues related to searchability and ease of access have plagued fan fiction since its inception. This paper discusses the predominate forms of fan-mediated indexing and descriptive metadata, commonly referred to as folksonomy or tagging, and compares the benefits and disadvantages of each model. These models fall into three broad categories: free tagging, controlled vocabulary, and hybrid folksonomy. Each model has distinct advantages and shortcomings related to findability, results filtering, and creative empowerment. Examples for each are provided. Possible ramifications to fan fiction from improved metadata and access are also discussed. [ABSTRACT FROM AUTHOR]