Relevant bibliographies by topics / Retrieval and management

Academic literature on the topic 'Retrieval and management'

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Published: 4 June 2021
Last updated: 30 January 2023

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Journal articles on the topic "Retrieval and management"

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Tokumi, Yuta, Junki Hakamata, and Masataka Tokumaru. "Development of a Nutritional Management System for a Healthy Eating Habits Support System." Journal of Advanced Computational Intelligence and Intelligent Informatics 17, no. 2 (March 20, 2013): 324–34. http://dx.doi.org/10.20965/jaciii.2013.p0324.

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In this study, we propose a Nutritional Management System (NMS) that optimizes nutritional balance using a tabu search. Contemporary recipe retrieval systems generally retrieve a recipe either by using a keyword or by recommending a popular recipe. However, these recipe retrieval systems yield the same retrieval results for different users, and thus, the results do not necessarily reflect an individual user’s tastes. In addition, the search results delivered by many recipe retrieval systems do not always describe the nutritional details of the recipes. Therefore, we developed a Healthy Eating Habits Support System for resolving these issues. This system is composed of an NMS and a Kansei Retrieval System (KRS). The NMS retrieves nutritionally balanced menus using a tabu search. The KRS learns a user’s taste preferences, and selects menus suitable for a user’s tastes from among the menus retrieved by the NMS. The KRS needs multiple nutritionally balanced menus to learn a user’s taste preferences. Thus, in this study, we simulated scenarios to examine whether the NMS can retrieve multiple nutritionally balanced menus in the long-term without duplication, using quasi recipe data and real recipe data. When we used quasi recipe data in a simulation with a very large number of recipes, the NMS could retrieve a sustained quantity of menus in the long-term. In addition, when we used real recipe data, the NMS could quickly retrieve menus over the long-term. Therefore, the effectiveness of the NMS was confirmed.
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Shen, Jin Xing. "Ontology-Based Semantic Retrieval for Management Information System." Applied Mechanics and Materials 278-280 (January 2013): 2069–72. http://dx.doi.org/10.4028/www.scientific.net/amm.278-280.2069.

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In order to achieve semantic retrieval for scientific research information in WWW, this paper applies an ontology-based framework to information retrieval system for management information system. After analyze the limitations of traditional method, bring a semantic search forward, and mainly introduce the thought of the semantic retrieval as well as the way to constitute ontology entity and the language that describes it. Moreover, semantic retrieval system based on ontology is also given. The application to retrieve project information shows that the framework can overcome the localization of other ontology’s models, and this research facilitates the semantic retrieval of management information through semantic retrieval concepts on the Semantic Web.
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Ben Ayed, Alaidine, Ismaïl Biskri, and Jean-Guy Meunier. "An End-to-End Efficient Lucene-Based Framework of Document/Information Retrieval." International Journal of Information Retrieval Research 12, no. 1 (January 2022): 1–14. http://dx.doi.org/10.4018/ijirr.289950.

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In the context of big data and the 4.0 industrial revolution era, enhancing document/information retrieval frameworks efficiency to handle the ever‐growing volume of text data in an ever more digital world is a must. This article describes a double-stage system of document/information retrieval. First, a Lucene-based document retrieval tool is implemented, and a couple of query expansion techniques using a comparable corpus (Wikipedia) and word embeddings are proposed and tested. Second, a retention-fidelity summarization protocol is performed on top of the retrieved documents to create a short, accurate, and fluent extract of a longer retrieved single document (or a set of top retrieved documents). Obtained results show that using word embeddings is an excellent way to achieve higher precision rates and retrieve more accurate documents. Also, obtained summaries satisfy the retention and fidelity criteria of relevant summaries.
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Bergman, Ofer, Tamar Israeli, and Steve Whittaker. "Factors hindering shared files retrieval." Aslib Journal of Information Management 72, no. 1 (December 16, 2019): 130–47. http://dx.doi.org/10.1108/ajim-05-2019-0120.

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Purpose Personal information management (PIM) is an activity in which people store information items in order to retrieve them later. The purpose of this paper is to test and quantify the effect of factors related to collection size, file properties and workload on file retrieval success and efficiency. Design/methodology/approach In the study, 289 participants retrieved 1,557 of their shared files in a naturalistic setting. The study used specially developed software designed to collect shared files’ names and present them as targets for the retrieval task. The dependent variables were retrieval success, retrieval time and misstep/s. Findings Various factors compromise shared files retrieval including: collection size (large number of files), file properties (multiple versions, size of team sharing the file, time since most recent retrieval and folder depth) and workload (daily e-mails sent and received). The authors discuss theoretical reasons for these negative effects and suggest possible ways to overcome them. Originality/value Retrieval is the main reason people manage personal information. It is essential for retrieval to be successful and efficient, as information cannot be used unless it can be re-accessed. Prior PIM research has assumed that factors related to collection size, file properties and workload affect file retrieval. However, this is the first study to systematically quantify the negative effects of these factors. As each of these factors is expected to be exacerbated in the future, this study is a necessary first step toward addressing these problems.
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Li, Zhongjian, Jun Xiang, Lei Wang, Ning Zhang, Ruru Pan, and Weidong Gao. "Yarn-Dyed Fabric Image Retrieval Using Colour Moments and the Perceptual Hash Algorithm." Fibres and Textiles in Eastern Europe 27, no. 5(137) (October 31, 2019): 60–69. http://dx.doi.org/10.5604/01.3001.0013.2900.

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Due to the variety of yarn colours and arrangement, it is a challenging problem to retrieve a yarn-dyed fabric image. In this paper, yarn-dyed fabric samples are captured by the DigiEye system first, and then pattern images of the fabric images captured are simulated by pattern design software based on extracted structure parameters of the yarn-dyed fabric. For the simulated pattern image, an effective algorithm is proposed to retrieve these kinds of images by combining the colour moments method and perceptual hash algorithm. Then the pattern images retrieved are mapped back to the yarn-dyed fabric image so as to realise the yarn-dyed fabric image retrieval. In the algorithm proposed, the colour moments method is adopted to extract the colour features, and the perceptual hash algorithm is utilised to calculate the spatial features of the simulated pattern images. Then the two kinds of image features are used to compute the similarity between the input original image and each target image based on the Euclidean distance and Hamming distance. Relevant images can be retrieved in dependence on the similarity value, which is determined by calculating the optimum weighted value of the colour features’ similarity and spatial features’ similarity. In order to measure the retrieval efficiency of the method proposed, the accuracy rate and retrieval rate of image retrieval were computed in experiments using a PATTERN image database with 300 images. The experimental results show that the average accuracy rate of the method proposed is 85.30% and the retrieval rate - 53.51% when the weighted value of the colour feature similarity is fixed at 0.45 and the spatial feature similarity is 0.55. It is shown that the method presented is effective to retrieve pattern images of yarn-dyed fabric.
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Logan, Caitriona, Niamh O’Connell, John Kavanagh, Niall McEniff, Mark Ryan, Michael Guiney, Orla Seery, James O’Donnell, Kevin Ryan, and Barry White. "Out of Sight, out of Mind? An Audit Which Proposes a Follow-Up and Management Pathway for Inferior Vena Cava Filters." Thrombosis 2016 (March 27, 2016): 1–3. http://dx.doi.org/10.1155/2016/6538456.

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Insertion of an IVC filter can be a safe and effective way to avoid PE in thrombosis patients who cannot be anticoagulated. If temporary filters are not promptly removed they can become difficult to remove, causing avoidable complications and often requiring lifelong warfarin. In this study, two sequential audits of retrieval of temporary IVC filters were conducted before and after the implementation of a coordinated management strategy for IVC filter follow-up. 33 filter placements were examined over a 15-month period (Group A). Following implementation of the strategy a comparable 15-month period in which 33 IVC filters were placed was audited (Group B). Following implementation, failed retrievals dropped from 15% to 9%. The number successfully retrieved did not change at 45%. The number made permanent from the outset following expert discussion increased from 12% to 39%. The number of filters with no attempted retrieval and no consultation about retrieval decreased from 27% to 9% (these patients were lost to follow-up with multiple contact attempts made). In Group B 100% of placed IVC filters were followed up appropriately. The proposed model is an easily implemented plan to avoid patient morbidity caused by temporary IVC filters made unintentionally permanent by loss to follow-up.
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Rajagopal, Prabha, Taoufik Aghris, Fatima-Ezzahra Fettah, and Sri Devi Ravana. "Clustering of Relevant Documents Based on Findability Effort in Information Retrieval." International Journal of Information Retrieval Research 12, no. 1 (January 6, 2023): 1–18. http://dx.doi.org/10.4018/ijirr.315764.

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A user expresses their information need in the form of a query on an information retrieval (IR) system that retrieves a set of articles related to the query. The performance of the retrieval system is measured based on the retrieved content to the query, judged by expert topic assessors who are trained to find this relevant information. However, real users do not always succeed in finding relevant information in the retrieved list due to the amount of time and effort needed. This paper aims 1) to utilize the findability features to determine the amount of effort needed to find information from relevant documents using the machine learning approach and 2) to demonstrate changes in IR systems' performance when the effort is included in the evaluation. This study uses a natural language processing technique and unsupervised clustering approach to group documents by the amount of effort needed. The results show that relevant documents can be clustered using the k-means clustering approach, and the retrieval system performance varies by 23%, on average.
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Bērziša, Solvita. "Project Management Knowledge Retrieval: Project Classification." Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 2 (August 5, 2015): 33. http://dx.doi.org/10.17770/etr2011vol2.968.

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Project management knowledge contains a wide range of information that can be accumulated from theory and practice. This knowledge is not always readily available to project manager and that can leave a significant impact on project management efficiency and success. Therefore, this knowledge is necessary to store in the project management knowledge repository and then to retrieve it when necessary. To find this knowledge it is necessary to define attributes for searching relevant projects and knowledge associated with these projects. The objective of this paper is to develop a set of project classification attributes that can be used to describe project characteristics and use them in similarity definition. The project classification attributes are defined as a part of the architecture of project management knowledge retrieval. They are identified by analyzing several project management methodologies and are validated by classifying twenty two empirical information technology projects.
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Lin, Lin, and Mei-Ling Shyu. "Correlation-Based Ranking for Large-Scale Video Concept Retrieval." International Journal of Multimedia Data Engineering and Management 1, no. 4 (October 2010): 60–74. http://dx.doi.org/10.4018/jmdem.2010100105.

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Motivated by the growing use of multimedia services and the explosion of multimedia collections, efficient retrieval from large-scale multimedia data has become very important in multimedia content analysis and management. In this paper, a novel ranking algorithm is proposed for video retrieval. First, video content is represented by the global and local features and second, multiple correspondence analysis (MCA) is applied to capture the correlation between video content and semantic concepts. Next, video segments are scored by considering the features with high correlations and the transaction weights converted from correlations. Finally, a user interface is implemented in a video retrieval system that allows the user to enter his/her interested concept, searches videos based on the target concept, ranks the retrieved video segments using the proposed ranking algorithm, and then displays the top-ranked video segments to the user. Experimental results on 30 concepts from the TRECVID high-level feature extraction task have demonstrated that the presented video retrieval system assisted by the proposed ranking algorithm is able to retrieve more video segments belonging to the target concepts and to display more relevant results to the users.
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Thomas, Rachel Cafarchio. "Record retrieval." Nursing Management (Springhouse) 45, no. 2 (February 2014): 34–37. http://dx.doi.org/10.1097/01.numa.0000442638.63206.be.

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Dissertations / Theses on the topic "Retrieval and management"

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Romano, Nicholas C., Dmitri G. Roussinov, Jay F. Nunamaker, and Hsinchun Chen. "Collaborative Information Retrieval Environment: Integration of Information Retrieval with Group Support Systems." HICSS, 1999. http://hdl.handle.net/10150/105688.

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Artificial Intelligence Lab, Department of MIS, University of Arizona
Observations of Information Retrieval (IR) system user experiences reveal a strong desire for collaborative search while at the same time suggesting that collaborative capabilities are rarely, and then only in a limited fashion, supported by current searching and visualization tools. Equally interesting is the fact that observations of user experiences with Group Support Systems (GSS) reveal that although access to external information and the ability to search for relevant material is often vital to the progress of GSS sessions, integrated support for collaborative searching and visualization of results is lacking in GSS systems. After reviewing both user experiences described in IR and GSS literature and observing and interviewing users of existing IR and GSS commercial and prototype systems, the authors conclude that there is an obvious demand for systems supporting multi-user IR.. It is surprising to the authors that very little attention has been given to the common ground shared by these two important research domains. With this in mind, our paper describes how user experiences with IR and GSS systems has shed light on a promising new area of collaborative research and led to the development of a prototype that merges the two paradigms into a Collaborative Information Retrieval Environment (CIRE). Finally the paper presents theory developed from initial user experiences with our prototype and describes plans to test the efficacy of this new paradigm empirically through controlled experimentation.
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Bernstein, Yaniv, and ybernstein@gmail com. "Detection and management of redundancy for information retrieval." RMIT University. Computer Science and Information Technology, 2006. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20070417.160526.

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The growth of the web, authoring software, and electronic publishing has led to the emergence of a new type of document collection that is decentralised, amorphous, dynamic, and anarchic. In such collections, redundancy is a significant issue. Documents can spread and propagate across such collections without any control or moderation. Redundancy can interfere with the information retrieval process, leading to decreased user amenity in accessing information from these collections, and thus must be effectively managed. The precise definition of redundancy varies with the application. We restrict ourselves to documents that are co-derivative: those that share a common heritage, and hence contain passages of common text. We explore document fingerprinting, a well-known technique for the detection of co-derivative document pairs. Our new lossless fingerprinting algorithm improves the effectiveness of a range of document fingerprinting approaches. We empirically show that our algorithm can be highly effective at discovering co-derivative document pairs in large collections. We study the occurrence and management of redundancy in a range of application domains. On the web, we find that document fingerprinting is able to identify widespread redundancy, and that this redundancy has a significant detrimental effect on the quality of search results. Based on user studies, we suggest that redundancy is most appropriately managed as a postprocessing step on the ranked list and explain how and why this should be done. In the genomic area of sequence homology search, we explain why the existing techniques for redundancy discovery are increasingly inefficient, and present a critique of the current approaches to redundancy management. We show how document fingerprinting with a modified version of our algorithm provides significant efficiency improvements, and propose a new approach to redundancy management based on wildcards. We demonstrate that our scheme provides the benefits of existing techniques but does not have their deficiencies. Redundancy in distributed information retrieval systems - where different parts of the collection are searched by autonomous servers - cannot be effectively managed using traditional fingerprinting techniques. We thus propose a new data structure, the grainy hash vector, for redundancy detection and management in this environment. We show in preliminary tests that the grainy hash vector is able to accurately detect a good proportion of redundant document pairs while maintaining low resource usage.
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Pongsuwan, Wuttipong. "Design and implementation of a multimedia DBMS : retrieval management." Thesis, Monterey, California: Naval Postgraduate School, 1990. http://hdl.handle.net/10945/34924.

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Approved for public release; distribution unlimited.
Current conventional Database Management Systems (DBMS) manage only alphanumeric data. However, data to be stored in the future is expected to include some multimedia form, such as images, graphics, sounds or signals. The structure and the semantics of the media data and the operations on that data are complex. It is not clear what requirements are needed in a DBMS to manage this kind of data. It is also not clear what is needed in the data model to support this kind of data; nor what the user interface should be for such a system. The goal of the Multimedia Database Management System project in the computer science department of the Naval Post Graduate School is to build into a Database Management System (DBMS) the capability to manage multimedia data, as well as the formatted data, and define operations on multimedia data. This thesis, focusing only on the media data of image and sound, first describes the operations of such a system, then discusses the general design of it, and finally outline the detailed design and implementation of the retrieval operation.
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Dogan, Ebru. "Content-based Audio Management And Retrieval System For News Broadcasts." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12611018/index.pdf.

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The audio signals can provide rich semantic cues for analyzing multimedia content, so audio information has been recently used for content-based multimedia indexing and retrieval. Due to growing amount of audio data, demand for efficient retrieval techniques is increasing. In this thesis work, we propose a complete, scalable and extensible audio based content management and retrieval system for news broadcasts. The proposed system considers classification, segmentation, analysis and retrieval of an audio stream. In the sound classification and segmentation stage, a sound stream is segmented by classifying each sub segment into silence, pure speech, music, environmental sound, speech over music, and speech over environmental sound in multiple steps. Support Vector Machines and Hidden Markov Models are employed for classification and these models are trained by using different sets of MPEG-7 features. In the analysis and retrieval stage, two alternatives exist for users to query audio data. The first of these isolates user from main acoustic classes by providing semantic domain based fuzzy classes. The latter offers users to query audio by giving an audio sample in order to find out the similar segments or by requesting expressive summary of the content directly. Additionally, a series of tests was conducted on audio tracks of TRECVID news broadcasts to evaluate the performance of the proposed solution.
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Chen, Min. "Knowledge assisted data management and retrieval in multimedia database sistems." FIU Digital Commons, 2007. http://digitalcommons.fiu.edu/etd/2139.

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With the proliferation of multimedia data and ever-growing requests for multimedia applications, there is an increasing need for efficient and effective indexing, storage and retrieval of multimedia data, such as graphics, images, animation, video, audio and text. Due to the special characteristics of the multimedia data, the Multimedia Database management Systems (MMDBMSs) have emerged and attracted great research attention in recent years. Though much research effort has been devoted to this area, it is still far from maturity and there exist many open issues. In this dissertation, with the focus of addressing three of the essential challenges in developing the MMDBMS, namely, semantic gap, perception subjectivity and data organization, a systematic and integrated framework is proposed with video database and image database serving as the testbed. In particular, the framework addresses these challenges separately yet coherently from three main aspects of a MMDBMS: multimedia data representation, indexing and retrieval. In terms of multimedia data representation, the key to address the semantic gap issue is to intelligently and automatically model the mid-level representation and/or semi-semantic descriptors besides the extraction of the low-level media features. The data organization challenge is mainly addressed by the aspect of media indexing where various levels of indexing are required to support the diverse query requirements. In particular, the focus of this study is to facilitate the high-level video indexing by proposing a multimodal event mining framework associated with temporal knowledge discovery approaches. With respect to the perception subjectivity issue, advanced techniques are proposed to support users’ interaction and to effectively model users’ perception from the feedback at both the image-level and object-level.
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Al-Qahtani, Meshal. "An empirical study for enhanced information retrieval and management techniques." Thesis, University of the West of Scotland, 2017. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.739193.

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Andric, Mirjana Andrija. "Exploiting metadata links to support information retrieval in document management systems." Thesis, University of Southampton, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.427370.

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Xie, Haiyan. "User model driven architecture for information retrieval in construction project management." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0008374.

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Chen, Hsinchun. "From Information Retrieval to Knowledge Management Enabling Technologies and Best Practices." Elsevier, 1999. http://hdl.handle.net/10150/106079.

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Artificial Intelligence Lab, Department of MIS, University of Arizona
In this era of the Internet and distributed multimedia computing, new and emerging classes of information technologies have swept into the lives of office workers and everyday people. As technologies and applications become more overwhelming, pressing, and diverse, several well-known information technology problems have become even more urgent. Information overload, a result of the ease of information creation and rendering via Internet and WWW, has become more evident in people’s lives. Significant variations of database formats and structures, the richness of information media text, audio, and video , and an abundance of multilingual information content also have created various information interoperability problems - structural interoperability, media interoperability, and multilingual interoperability.
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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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Books on the topic "Retrieval and management"

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Candan, K. Selçuk. Data management for multimedia retrieval. Cambridge: Cambridge University Press, 2010.

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Candan, K. Selçuk. Data management for multimedia retrieval. New York: Cambridge University Press, 2010.

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Luisa, Sapino Maria, ed. Data management for multimedia retrieval. Cambridge: Cambridge University Press, 2010.

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Feng, David Dagan, Wan-Chi Siu, and Hong-Jiang Zhang, eds. Multimedia Information Retrieval and Management. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05300-3.

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Metadata for information management and retrieval. London: Facet, 2004.

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Computational Information Retrieval Conference (2000 Raleigh, N.C.). Computational information retrieval. Philadelphia, PA: SIAM, 2001.

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Ingwersen, Peter. Information retrieval interaction. London: Taylor Graham, 1992.

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Korfhage, Robert R. Information storage and retrieval. New York: Wiley Computer Pub., 1997.

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Center for Intelligent Information Retrieval, ed. Advances in information retrieval: Recent research from the Center for Intelligent Information Retrieval. New York: Kluwer Academic, 2002.

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Liu, Qianhong. Document Processing and Retrieval: Texpros. Boston, MA: Springer US, 1996.

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Book chapters on the topic "Retrieval and management"

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Koster, Paul, and Willem Jonker. "Digital Rights Management." In Multimedia Retrieval, 321–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72895-5_12.

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Baldini, Domenico, Cristina Lavopa, Maria Matteo, and Antonio Malvasi. "Oocyte Retrieval." In Pick Up and Oocyte Management, 151–78. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28741-2_10.

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Riggert, Wolfgang. "Dokumentensuche – Information Retrieval." In ECM – Enterprise Content Management, 99–112. Wiesbaden: Springer Fachmedien Wiesbaden, 2019. http://dx.doi.org/10.1007/978-3-658-25923-5_7.

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Baldini, Domenico, Domenico Carone, Antonino Guglielmino, and Sandro Gerli. "Oocytes Retrieval Theater." In Pick Up and Oocyte Management, 73–81. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28741-2_5.

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Prabhakaran, B. "Multimedia Storage and Retrieval." In Multimedia Database Management Systems, 25–51. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6235-1_2.

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Breen, Sean, Michel Manago, Stefan Wess, and Wolfgang Wilke. "Information retrieval." In Applying Knowledge Management, 163–77. Elsevier, 2003. http://dx.doi.org/10.1016/b978-155860760-6/50007-8.

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Jančar, Nina, and Helena Ban Frangež. "Oocyte retrieval." In Management of Infertility, 205–9. Elsevier, 2023. http://dx.doi.org/10.1016/b978-0-323-89907-9.00027-2.

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"7. Experience Retrieval." In Experience Management, 187–218. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45759-3_7.

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Rodrigues de Oliveira, Renan, Fábio Moreira Costa, Cedric Luiz de Carvalho, and Ana Paula Ambròsio. "Semantic Retrieval of Documents From Digital Repositories and Twitter Integration in the Moodle Environment." In Information Retrieval and Management, 2083–111. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5191-1.ch093.

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Virtual learning environments represent an important step in enabling distance and blended education. Moodle's structure for content enables the identification of well-defined learning contexts. Nevertheless, Moodle currently does not provide standard features to leverage the use of such contextual information, nor does it provide a standard built-in search facility. This chapter presents a context-sensitive Moodle plug-in for the search of external resources that allows semantic-based retrieval of documents from any external repository that offers an OAI-PMH standard compliant interface. As a way to increase their potential use, the plug-in also retrieves Twitter messages (known as tweets), since social networks are shown as an important tool to support education. Retrieved resources are presented in order of importance according to both the query terms provided by the user and the current context derived from the Moodle content structure. Searches are semantically expanded by evaluating the query terms according to a specific ontology associated with the context.
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"Knowledge Management." In Understanding Information Retrieval Systems, 131–44. Auerbach Publications, 2011. http://dx.doi.org/10.1201/b11499-14.

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Conference papers on the topic "Retrieval and management"

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"Information Retrieval … Knowledge Management." In 2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP). IEEE, 2018. http://dx.doi.org/10.1109/infrkm.2018.8464791.

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Zhai, ChengXiang. "Session details: Information retrieval session 1: adhoc retrieval." In CIKM03: 12th International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2003. http://dx.doi.org/10.1145/3244144.

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Clarke, Charlie. "Session details: Information retrieval session 2: non-text retrieval." In CIKM03: 12th International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2003. http://dx.doi.org/10.1145/3244147.

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Chowdhury, Abdur. "Session details: Information retrieval session 3: cross language retrieval." In CIKM03: 12th International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2003. http://dx.doi.org/10.1145/3244149.

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"Information Retrieval." In 2021 Fifth International Conference on Information Retrieval and Knowledge Management (CAMP). IEEE, 2021. http://dx.doi.org/10.1109/camp51653.2021.9498026.

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"Information retrieval." In 2016 Third International Conference on Information Retrieval and Knowledge Management (CAMP). IEEE, 2016. http://dx.doi.org/10.1109/infrkm.2016.7806334.

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"Information Retrieval." In 2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP). IEEE, 2018. http://dx.doi.org/10.1109/infrkm.2018.8464804.

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Schiller, Jennifer. "Modern meeting management and information retrieval." In Proceeding of the 24th ACM SIGPLAN conference companion. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1639950.1640050.

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Pass, Greg. "Session details: Information retrieval session 4: general retrieval issues I." In CIKM03: 12th International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2003. http://dx.doi.org/10.1145/3244151.

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Conrad, Jack. "Session details: Information retrieval session 5: general retrieval issues II." In CIKM03: 12th International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2003. http://dx.doi.org/10.1145/3244152.

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Reports on the topic "Retrieval and management"

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RIECK, C. A. Project W-211, initial tank retrieval systems, retrieval control system software configuration management plan. Office of Scientific and Technical Information (OSTI), February 1999. http://dx.doi.org/10.2172/781535.

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Klimper, S. C. Tank waste remediation system tank waste retrieval risk management plan. Office of Scientific and Technical Information (OSTI), November 1997. http://dx.doi.org/10.2172/362359.

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McWethy, L. M. Project Management Plan 105-KE Basin sludge retrieval and packaging. Office of Scientific and Technical Information (OSTI), November 1994. http://dx.doi.org/10.2172/10104384.

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Krieg, S. A. ,. Westinghouse Hanford. Initial single shell tank retrieval system project system engineering management plan. Office of Scientific and Technical Information (OSTI), July 1996. http://dx.doi.org/10.2172/657826.

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McDaniel, L. B. Implementation guide for Hanford Tanks Initiative C-106 heel retrieval contract management HNF-2511. Office of Scientific and Technical Information (OSTI), April 1998. http://dx.doi.org/10.2172/353285.

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RIECK, C. A. System Engineering Management and implementation plan for Project W-211, ''Initial tank retrieval systems''. Office of Scientific and Technical Information (OSTI), February 1999. http://dx.doi.org/10.2172/781523.

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Robson, P. G., and K. A. M. Adlam. A menu aided retrieval system (MARS) for use with a relational database management system. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1994. http://dx.doi.org/10.4095/193969.

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VAN BEEK, J. E. System Engineering Management and Implementation Plan for Project W-211 Initial Tank Retrieval Systems (ITRS). Office of Scientific and Technical Information (OSTI), May 2000. http://dx.doi.org/10.2172/803633.

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Draper, J. V. Function analysis of the single-shell Tank Waste Retrieval Manipulator System. Environmental Restoration and Waste Management Program. Office of Scientific and Technical Information (OSTI), August 1993. http://dx.doi.org/10.2172/10184153.

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Kumar, D., M. Wang, R. Rahman, and S. Raghavan. A YANG Data Model for Retrieval Methods for the Management of Operations, Administration, and Maintenance (OAM) Protocols That Use Connectionless Communications. Edited by Q. Wu. RFC Editor, April 2019. http://dx.doi.org/10.17487/rfc8533.

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