Relevant bibliographies by topics / Information retrieval Database searching

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Information retrieval Database searching'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "Information retrieval Database searching"

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Updegrove, Natalie A. "Database searching: Information retrieval for nutrition professionals." Journal of Nutrition Education 22, no. 5 (1990): 241–47. http://dx.doi.org/10.1016/s0022-3182(12)80731-x.

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Gratch, Bonnie G. "Information Retrieval Technology in Education." Journal of Educational Technology Systems 13, no. 4 (1985): 271–80. http://dx.doi.org/10.2190/05kp-3nfu-c08t-yffr.

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Microcomputer technology has had a tremendous effect on the way information and research in education can be retrieved. Since the early 1960's and the availability of online computer searching of the ERIC database, about thirty U.S.-produced databases specifically oriented to educators have become available. Microcomputer technology makes direct access possible to these databases by anyone with a microcomputer, modem, and printer. Such direct access means that educators who have not had the opportunity to use library computer search services may now conduct their own online, interactive searches of the vast body of published literature and other products of educational research and practice. This article identifies and describes many of the educational databases and the information retrieval companies that offer information search services for educators.
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Othman, Roslina, and Nor Sahlawaty Halim. "Retrieval features for online databases: common, unique, and expected." Online Information Review 28, no. 3 (2004): 200–210. http://dx.doi.org/10.1108/14684520410543643.

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The aims of this study were to identify the retrieval features for online databases; difficulties faced by users; and retrieval features expected by users. A total of 25 databases were surveyed and 40 users were interviewed after the training sessions. Common retrieval features included Boolean operators, phrase searching, match of exact words or phrases, field specific and limit fields searches, truncation, and wildcard. Even though features are offered in many systems, their interpretation and implementation are different. Unique features included lateral searching, density and frequency of terms, reference link, and searching via table of content. The expected features included relevance feedback and term weighting other than those already offered by ACM Digital Library and IEEE Xplore. Such expectations were influenced by the users' background in ICT. Difficulties included application of the retrieval features in searching. Database providers must include the expected features, synonyms linked to terms in the thesaurus, and extensive search examples.
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Aung, Z., and K. L. Tan. "Rapid 3D protein structure database searching using information retrieval techniques." Bioinformatics 20, no. 7 (2004): 1045–52. http://dx.doi.org/10.1093/bioinformatics/bth036.

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McNab, Alison. "Call Resources: Electronic Retrieval of Information." ReCALL 3, no. 4 (1991): 4–9. http://dx.doi.org/10.1017/s0958344000002494.

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The retrieval of information by electronic means can involve several media: remote searching of large commercially-produced databases, utilising CD-ROM and multimedia applications, and electronic mail. Both the information retrieval techniques used in this process and the information retrieved are of relevance to those involved in computer-assisted language learning.
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Srinivasa Reddy, K., R. Anandan, K. Kalaivani, and P. Swaminathan. "A comprehensive survey on content based image retrieval system and its application in medical domain." International Journal of Engineering & Technology 7, no. 2.31 (2018): 181. http://dx.doi.org/10.14419/ijet.v7i2.31.13436.

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Content Based Image Retrieval (CBIR) is an important and widely used technique for retrieval of different kinds of images from large database. Collection of information in database are available in different formats such as text, image, graph, chart etc. Here, our focus is on information which is available in the form of images. Searching and retrieval of the image from a large amount of database is difficult problem because it uses the image visual information such as shape, text and color for indexing and representation of an image. For efficient CBIR system, there is a need to develop different kinds of retrieval methods using feature extraction, similarity matching etc. Text Based Image Retrieval systems are used in many hospitals, but for large databases these are inefficient. To solve this problem, CBIR systems are proposed to retrieve matching images from database using automated feature extraction method. At present, medical imaging field finds extensive growth in the generation and evaluation of various types of medical images which are high inconsistency, usually fused and the combination of various minor composition structures. For easy retrieval, need to be development of feature extraction and image classification methods. Different methods are used for different kinds of medical images. The Radiology department and Cardiology department are the largest producers of medical images and the patient abnormal images can be stored with the normal images. CBIR uses query image as input and it retrieves the images, which are similar to the query more efficiently and effectively. This paper provides a comprehensive Survey about CBIR system and its one of the major application in medical domain.
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Shama Rani and Jaiteg Singh. "Getting Bulk Data Through Google: An empirical study." Journal of Technology Management for Growing Economies 7, no. 2 (2016): 39–48. http://dx.doi.org/10.15415/jtmge.2016.72002.

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To store the information in a database is one of the major tasks. The efficient storage of data is important for future use. Information retrieval is a method of gathering information related to input queries from the various sources or stored databases. To retrieve the information, a search engine plays an important role. A web search engine creates an index to match queries. The quality of information is improved with the help of search engine. For retrieving the information, a search engine comprises some modules such as query processor, a searching and matching function, document processor and page rank capability. This paper focuses on the retrieval of web documents against input queries and stores them in to database. A Google search API can be used to fetch the results. It analyses the data by processing through these modules and downloads the content available in different formats.
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Arunlal, S. L., N. Santhi, and K. Ramar. "Design and Implementation of Content-Based Natural Image Retrieval Approach Using Feature Distance." International Journal of Image and Graphics 20, no. 02 (2020): 2050014. http://dx.doi.org/10.1142/s021946782050014x.

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Generally, the database is a gathering of data that is arranged for simple storage, retrieval and modernize. This data comprises of numerous structures like text, table, and image, outline and chart and so on. Content-based image retrieval (CBIR) is valuable for calculating the huge amount of image databases and records and for distinguishes retrieving similar images. Rather than text-based searching, CBIR effectively recovers images that are similar like query image. CBIR assumes a significant role in various areas including restorative finding, industry estimation, geographical information satellite frameworks (GIS frameworks), and biometrics; online searching and authentic research, etc. Here different medical database images are considered to the CBIR procedure is done by the proposed strategy. The proposed method considers the input features are shape, texture feature, wavelet feature, and SIFT feature. To retrieve the input image based on the features, the suggested method utilizes artificial neural network (ANN) structure. Back-propagation technique, which is an organizational structure for learning is utilized for training the neural network framework. Trial demonstrates that the proposed work improves the results of the retrieval system. From the outcomes minimizes the image retrieval time and maximum Precision 87.3% in distance based ANN process.
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Davis, G. L., Edward F. Gilman, and Howard W. Beck. "An Electronically Based Horticultural Information Retrieval System." HortTechnology 6, no. 4 (1996): 332–36. http://dx.doi.org/10.21273/horttech.6.4.322.

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A large horticultural database and an electronic retrieval system for extension education programs were developed using compact disk-read only memory (CD-ROM) and World Wide Web (WWW) as the medium for information delivery. Object-oriented database techniques were used to organize the information. Conventional retrieval techniques including hypertext, full text searching, and expert systems were integrated into a complete package for accessing information stored in the database. A multimedia user interface was developed to provide a variety of capabilities including computer graphics and high resolution digitized images. Information for the CD-ROM was gathered from extension publications that were tagged using the standard generalized markup language (SGML)-based document markup language (International Standards Organization, 1986). Combining funds from the state legislator with grants from the USDA and other institutions, the CD-ROM system has been implemented in all 67 county extension offices in Florida and is available to the public as a for-sale CD-ROM. Public access is also available to most of the database through the WWW.
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Safran, C. "Searching for Answers on a Clinical Information System." Methods of Information in Medicine 34, no. 01/02 (1995): 79–84. http://dx.doi.org/10.1055/s-0038-1634587.

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Abstract:We examined observational data on the use of a clinical computing system in an effort to develop an empirical model of clinicians’ information needs. Clinicians turn to information systems most often to review the results of diagnostic studies. After that clinicians turn to information systems most often for communication. Bibliographic retrieval is the third most frequent reason for use, but is an order of magnitude less frequent than either results inquiry or electronic mail. Secondary retrieval of aggregate data from a clinical database is two orders of magnitude less common than primary retrieval. In a study at our hospital, clinicians gained access to an information resource during 16% of all general medical visits of patients with human immunodeficiency virus (HIV) infection while the patient was present. Insofar as our narrow focus on the primary care of HIV infection models the larger context of clinical medicine, clinicians are most often looking at information relating to therapeutics. Whatever knowledge structures are adopted, they must accommodate this observed need for therapeutic information.
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Dissertations / Theses on the topic "Information retrieval Database searching"

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Robins, David B. (David Bruce). "Shifts of Focus Among Dimensions of User Information Problems as Represented During Interactive Information Retrieval." Thesis, University of North Texas, 1998. https://digital.library.unt.edu/ark:/67531/metadc935560/.

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The goal of this study is to increase understanding of information problems as they are revealed in interactions among users and search intermediaries during information retrieval. Specifically, this study seeks to investigate: (a) how interaction between users and search intermediaries reveals aspects of user information problems; (b) to explore the concept of representation with respect to information problems in interactive information retrieval; and (c) how user and search intermediaries focus on aspects of user information problems during the course of searches. This project extends research on interactive information retrieval, and presents a theoretical framework that synthesizes rational and non-rational questions concerning mental representation as it pertains to user's understanding of information problems.
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Hendry, David G. "Extensible information-seeking environments." Thesis, Robert Gordon University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.337903.

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Taylor, Steven Joseph. "Children's usage of an information retrieval system /." Access Digital Full Text version, 1992. http://pocketknowledge.tc.columbia.edu/home.php/bybib/11230162.

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Thesis (Ed.D.)--Teachers College, Columbia University, 1992.<br>Includes tables. Typescript; issued also on microfilm. Sponsor: Robert O. McClintock. Dissertation Committee: Robert P. Taylor. Includes bibliographical references (leaves 149-157).
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Chen, Hsinchun, and Jinwoo Kim. "GANNET: A machine learning approach to document retrieval." M.E. Sharpe, Inc, 1994. http://hdl.handle.net/10150/105547.

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Artificial Intelligence Lab, Department of MIS, University of Arizona<br>Information science researchers have recently turned to new artificial intelligence-based inductive learning techniques including neural networks, symbolic learning and genetic algorithms. An overview of the new techniques and their usage in information science research is provided. The algorithms adopted for a hybrid genetic algorithms and neural nets based system, called GANNET, are presented. GANNET performed concept (keyword) optimization for user-selected documents during information retrieval using the genetic algorithms. It then used the optimized concepts to perform concept exploration in a large network of related concepts through the Hopfield net parallel relaxation procedure. Based on a test collection of about 3,000 articles from DIALOG and an automatically created thesaurus, and using Jaccard's score as a performance measure, the experiment showed that GANNET improved the Jaccard's scores by about 50% and helped identify the underlying concepts that best describe the user-selected documents.
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Porter, Brandi. "Information Retrieval Strategies of Millennial Undergraduate Students in Web and Library Database Searches." NSUWorks, 2009. http://nsuworks.nova.edu/gscis_etd/357.

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Millennial students make up a large portion of undergraduate students attending colleges and universities, and they have a variety of online resources available to them to complete academically related information searches, primarily Web based and library-based online information retrieval systems. The content, ease of use, and required search techniques are different between the two information retrieval systems. Students often prefer searching the Web, but in doing so often miss higher quality materials that may be available only through their library. Furthermore, each system uses different information retrieval algorithms for producing results, so proficiency in one search system may not transfer to another. Web based information retrieval systems are unable to search and retrieve many resources available in libraries and other proprietary information retrieval systems, often referred to as the Invisible Web. These are resources that are not available to the general public and are password protected (from anyone not considered to be an affiliated user of that particular organization). These resources are often licensed to libraries by third party vendors or publishers and include fee-based access to content. Therefore, many millennial students may not be accessing many scholarly resources available to them if they were to use Web based information retrieval systems. Investigation of how millennial students approach searches for the same topic in both systems was conducted. The goal was to build upon theory of why students search using various techniques, why they often choose the Web for their searches, and what can be done to improve library online information retrieval systems. Mixed qualitative methods of data gathering were used to elicit this information. The investigation showed that millennial undergraduate students lacked detailed search strategies, and often used the same search techniques regardless of system or subject. Students displayed greater familiarity and ease of use with Web based IR systems than online library IR systems. Results illustrated suggestions for search design enhancements to library online information retrieval systems such as better natural language searching and easier linking to full text articles. Design enhancements based on millennial search strategies should encourage students to use library-based information retrieval systems more often.
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Pikas, Christina K. "Blog Searching for Competitive Intelligence, Brand Image, and Reputation Management." Information Today, Inc, 2005. http://hdl.handle.net/10150/106424.

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Reviews why it is important to search blogs for competitive intelligence, reputation management, and brand image management. Describes the structure of blogs and how to format searches in several search engines to effectively retrieve this information.
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Masters, Gary E. (Gary Everett). "The Effects of Increased Equipment Speed on Online Database Searching Practices." Thesis, North Texas State University, 1987. https://digital.library.unt.edu/ark:/67531/metadc332315/.

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This study reports changes in online database searching at North Texas State University when equipment speed was increased. Data were from database vendor invoices and price and sale data of online equipment. The hypotheses examined the relationship between the decrease in the cost of online equipment and the change to faster online equipment and the change in the number of databases that changed for online types. The change in equipment was related to changes in the number of offline prints per hour, the average time per search, the average number of descriptors per search, the number of searches per month, and the rank order of database use over the studied period. The increase in the number of databases with billed types was related to the number of online billed types produced. The number of prints was related to the number of billed types. Time spent online was examined for annual seasonal cycles. The major statistical tool was time-series analysis, although other methods were applied.
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Draeger, Marco. "Use of probabilistic topic models for search." Thesis, Monterey, California : Naval Postgraduate School, 2009. http://edocs.nps.edu/npspubs/scholarly/theses/2009/Sep/09Sep_Draeger.pdf.

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Thesis (M.S. in Operations Research)--Naval Postgraduate School, September 2009.<br>Thesis Advisor(s): Squire, Kevin M. "September 2009." Description based on title screen as viewed on November 5, 2009. Author(s) subject terms: Document modeling, information retrieval, semantic search, Bayesian nonparametric methods, hierarchical Bayes. Includes bibliographical references (p. 67-71). Also available in print.
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Chen, Junjie. "Learning hash codes for multimedia retrieval." HKBU Institutional Repository, 2019. https://repository.hkbu.edu.hk/etd_oa/684.

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The explosive growth of multimedia data in online media repositories and social networks has led to the high demand of fast and accurate services for large-scale multimedia retrieval. Hashing, due to its effectiveness in coding high-dimensional data into a low-dimensional binary space, has been considered to be effective for the retrieval application. Despite the progress that has been made recently, how to learn the optimal hashing models which can make the best trade-off between the retrieval efficiency and accuracy remains to be open research issues. This thesis research aims to develop hashing models which are effective for image and video retrieval. An unsupervised hashing model called APHash is first proposed to learn hash codes for images by exploiting the distribution of data. To reduce the underlying computational complexity, a methodology that makes use of an asymmetric similarity matrix is explored and found effective. In addition, the deep learning approach to learn hash codes for images is also studied. In particular, a novel deep model called DeepQuan which tries to incorporate product quantization methods into an unsupervised deep model for the learning. Other than adopting only the quadratic loss as the optimization objective like most of the related deep models, DeepQuan optimizes the data representations and their quantization codebooks to explores the clustering structure of the underlying data manifold where the introduction of a weighted triplet loss into the learning objective is found to be effective. Furthermore, the case with some labeled data available for the learning is also considered. To alleviate the high training cost (which is especially crucial given a large-scale database), another hashing model named Similarity Preserving Deep Asymmetric Quantization (SPDAQ) is proposed for both image and video retrieval where the compact binary codes and quantization codebooks for all the items in the database can be explicitly learned in an efficient manner. All the aforementioned hashing methods proposed have been rigorously evaluated using benchmark datasets and found to outperform the related state-of-the-art methods.
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Evans, Mary Marcum. "The influence of retrieval system on the outcomes of ERIC searches by graduate students /." Full-text version available from OU Domain via ProQuest Digital Dissertations, 1995.

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Books on the topic "Information retrieval Database searching"

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Hawkins, Donald T. Online information retrieval bibliography, 1983-1986. Learned Information, 1987.

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Information retrieval: SciFinder. 2nd ed. Wiley, 2009.

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Kessler, Carsten. Context-aware semantics-based information retrieval. IOS Press, 2010.

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Tenopir, Carol. Retrieval performance in a full text journal article database. University Microfilms International, 1985.

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Wood, Frances E. Guidelines for teachers of online information retrieval. United Nations Educational, Scientific and Cultural Organization, 1988.

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Online reference and information retrieval. 2nd ed. Libraries Unlimited, 1987.

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British Computer Society. Information Retrieval Specialist Group. Research Colloquium. Information retrieval: New systems and current research. Taylor Graham, 1994.

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Zakharov, V. P. Informat︠s︡ionnye sistemy: Dokumentalʹnyĭ poisk : uchebnoe posobie. Sankt-Peterburgskiĭ gos. universitet, 2002.

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Kemp, D. Alasdair. Computer-based knowledge retrieval. Aslib, 1988.

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Services, DIALOG Information. Searching DIALOG: The complete guide. DIALOG Information Services, 1987.

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Book chapters on the topic "Information retrieval Database searching"

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Li, C. S., A. S. Pollitt, and M. P. Smith. "Multilingual MenUSE - A Japanese front-end for searching English Language databases and vice versa." In 14th Information Retrieval Colloquium. Springer London, 1993. http://dx.doi.org/10.1007/978-1-4471-3211-0_2.

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Amati, Giambattista. "Information Retrieval." In Encyclopedia of Database Systems. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_915.

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Amati, Giambattista. "Information Retrieval." In Encyclopedia of Database Systems. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_915.

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Amati, Giambattista. "Information Retrieval." In Encyclopedia of Database Systems. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_915-2.

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Jones, Christopher B., and Ross S. Purves. "Geographical Information Retrieval." In Encyclopedia of Database Systems. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_177.

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Yi, Xun. "Private Information Retrieval." In Encyclopedia of Database Systems. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_80752.

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Amati, Giambattista. "Information Retrieval Models." In Encyclopedia of Database Systems. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_916.

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Rasmussen, Edie. "Information Retrieval Operations." In Encyclopedia of Database Systems. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_940.

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Amati, Giambattista. "Information Retrieval Models." In Encyclopedia of Database Systems. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_916.

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Rasmussen, Edie. "Information Retrieval Operations." In Encyclopedia of Database Systems. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_940.

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Conference papers on the topic "Information retrieval Database searching"

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Yu, Chunsheng, Qingjin Peng, and Lushen Wu. "A VR-Based Data Retrieval System." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-84325.

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Concurrent Engineering (CE) considers a product design and manufacturing simultaneously. The CE research has led to the product information integration. However, the current CE research focuses on smooth of the product information flow rather than a user-friendly searching process for users to obtain product information quickly. A new data retrieval system for product design and manufacturing is proposed in this paper. The system not only provides an integrated product database, but also uses Virtual Reality (VR) and Web technologies for easing navigation in product data and for accelerating access to useful information. The effective collaboration in a product design and manufacturing can be achieved through the proposed information searching method. Examples are presented in the discussion.
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Lou, Kuiyang, Subramaniam Jayanti, Natraj Iyer, Yagnanarayanan Kalyanaraman, Sunil Prabhakar, and Karthik Ramani. "A Reconfigurable 3D Engineering Shape Search System: Part II — Database Indexing, Retrieval, and Clustering." In ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/detc2003/cie-48188.

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This paper introduces database and related techniques for a reconfigurable, intelligent 3D engineering shape search system, which retrieves similar 3D models based on their shape content. Feature vectors, which are numeric “fingerprints” of 3D models, and skeletal graphs, which are the “minimal representations of the shape content” of a 3D model, represent the shape content. The Euclidean distance of the feature vectors, as well as the distance between skeletal graphs, provides indirect measures of shape similarity between the 3D models. Critical database issues regarding 3D shape search systems are discussed: (a) database indexing, (b) semantic gap, (c) subjectivity of similarity, and (d) database clustering. An Rtree based multidimensional index is used to speed up the feature-vector based search operation, while a decision treebased approach is used for efficiently indexing/searching skeletal graphs. Interactions among users and the search system, such as relevance feedback and feature vector reconfiguration, are used to bridge the semantic gap and to customize the system for different users. Database clustering of the R-tree index is compared with that generated by a selforganizing map (SOM). Synthetic databases and real 3D model databases are employed to investigate the efficiency of the multidimensional index and the effectiveness of relevance feedback.
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Jiang, Shuo, Jianxi Luo, Guillermo Ruiz Pava, Jie Hu, and Christopher L. Magee. "A Convolutional Neural Network-Based Patent Image Retrieval Method for Design Ideation." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22048.

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Abstract The patent database is often used in searches of inspirational stimuli for innovative design opportunities because of its large size, extensive variety and rich design information in patent documents. However, most patent mining research only focuses on textual information and ignores visual information. Herein, we propose a convolutional neural network (CNN)-based patent image retrieval method. The core of this approach is a novel neural network architecture named Dual-VGG that is aimed to accomplish two tasks: visual material type prediction and international patent classification (IPC) class label prediction. In turn, the trained neural network provides the deep features in the image embedding vectors that can be utilized for patent image retrieval and visual mapping. The accuracy of both training tasks and patent image embedding space are evaluated to show the performance of our model. This approach is also illustrated in a case study of robot arm design retrieval. Compared to traditional keyword-based searching and Google image searching, the proposed method discovers more useful visual information for engineering design.
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Li, Zhanjun, Min Liu, and Karthik Ramani. "Review of Product Information Retrieval: Representation and Indexing." In ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/detc2004-57749.

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Engineers spend a significant amount of time searching for the right product information during the design and manufacturing process. A large amount as well as various types of product information have been generated and are available within the engineering databases. But representing and indexing the products effectively and retrieving them efficiently, remains a challenge. The purpose of this survey is to document the current state of research and development. We then identify avenues for exploration and provide a comparison of the advantages, disadvantages, and limitations among the various techniques. This survey concludes by suggesting possible future research directions.
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Karnik, M. V., S. K. Gupta, D. K. Anand, F. J. Valenta, and I. A. Wexler. "Design Navigator System: A Case Study in Improving Product Development Through Improved Information Management." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-84901.

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This paper provides an overview of the Design Navigator system being developed for the Naval Surface Warfare Center, Indian Head. This system addresses the following three information management needs. First, it captures all the relevant information being generated during the product development process in a computer-interpretable form. This eliminates information loss from the design process. Second, it builds fully interconnected information models. Thus ensuring full connectivity between requirements and specifications to various parts and assemblies in the design. Third, it offers multiple modes of searching and retrieving design information in an intuitive and convenient manner. By supporting functionality-based queries, change-based queries, geometry-based queries, and visual navigation of the entire product database, the Design Navigator system is expected to offer maximum flexibility and power to the designers to meet their diverse information retrieval needs.
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"Workshop: Keyword Discovery: Visualizing Your Topic in Research, Thesis and Dissertation Development." In InSITE 2019: Informing Science + IT Education Conferences: Jerusalem. Informing Science Institute, 2019. http://dx.doi.org/10.28945/4335.

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Aim/Purpose: Participants will learn various digital tools to assist them in the discovery of appropriate keywords for their own research and writings as well as in teaching. Background: Experiences teaching undergraduates and graduate students information literacy and retrieval have identified several areas where students have problems in searching databases because of misconceptions as to what they should be using as keywords. Methodology: The use of visual tools to identify keywords. Contribution: The visualization software demonstrated and used in the workshop can enhance the literature review portion of any research project. Recommendations for Practitioners: With the vast expansion of the amount of information available via the Web, it is time to go back to a more considered approach to keywords (as in the past when searching a database could cost hundreds of dollars) rather than just the first words that might occur. Recommendations for Researchers: In addition to using these techniques in your own research, teaching them to research assistants and students and not assume that everyone knows how to use keywords. Impact on Society: These same approaches can be used in continuing education or by independent scholars. Future Research: We plan to demonstrate at the conclusion of the workshop visual tools that can take it another step and help in organizing sources.
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Michelena, Nestor, and Katia Sycara. "Physical Synthesis in Case-Based Design." In ASME 1994 Design Technical Conferences collocated with the ASME 1994 International Computers in Engineering Conference and Exhibition and the ASME 1994 8th Annual Database Symposium. American Society of Mechanical Engineers, 1994. http://dx.doi.org/10.1115/detc1994-0027.

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Abstract Common design practices rely heavily on searching and studying of prior designs, patents, past design rationale, standards and new product announcements. An important step toward automating these activities is the creation of repositories of design information indexed by abstract attributes in addition to low-level structural descriptions. By reusing prior designs or their components an engineer can save design time, by leveraging off previous worked-out solutions, and avoid repeating past mistakes, by accessing information on manufacturing or field failures linked to the retrieved design. Under the case-based design paradigm, an engineer combines parts of different design cases to synthesize a device that satisfies a useful need. Physical synthesis entails taking into account possible interactions between components and sub-assemblies as well as reasoning about the dynamics of the system. In this paper we present a methodology for physical synthesis of design cases and components retrieved by a case-based design tool. Connecting elements for the design cases and components are retrieved from a casebase of connections. Indexing of these connections is based on the mobility restrictions that they impose on the connected parts. The information necessary to accomplish this task is still of a high-level nature, namely, the topology of the artifact and its abstract behavior specification.
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Draganov, Ivo R., Antoaneta A. Popova, and Lubomir L. Ivanov. "Multilingual Names Database Searching Enhancement." In 2008 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). IEEE, 2008. http://dx.doi.org/10.1109/isspit.2008.4775648.

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Kumpulainen, Sanna W., and Heli Kautonen. "Accidentally Successful Searching." In CHIIR '17: Conference on Human Information Interaction and Retrieval. ACM, 2017. http://dx.doi.org/10.1145/3020165.3022124.

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Catao, Vladimir Soares, Marcus Costa Sampaio, and Ulrich Schiel. "Information Retrieval from database queries." In 2014 IEEE/ACS 11th International Conference on Computer Systems and Applications (AICCSA). IEEE, 2014. http://dx.doi.org/10.1109/aiccsa.2014.7073241.

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Reports on the topic "Information retrieval Database searching"

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Poole, Paula M., Marcie S. Kronberg, and Debra Meyers. Automated Airdrop Information Retrieval System-Human Fact ors Database (AAIRS-HFD) (Users Manual). Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada285571.

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Saint-Jean, Felipe. Java Implementation of a Single-Database Computationally Symmetric Private Information Retrieval (cSPIR) Protocol. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada459016.

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