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1
Mills, Timothy J. "Content modelling in multimedia information retrieval systems : the Cobra retrieval system." Thesis, University of Cambridge, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.442405.
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Al-Maskari, Azzah. "Evaluation of interactive information retrieval systems." Thesis, University of Sheffield, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.538079.
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Zhang, Dengsheng 1963. "Image retrieval based on shape." Monash University, School of Computing and Information Technology, 2002. http://arrow.monash.edu.au/hdl/1959.1/8688.
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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<br>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.
5
Xie, Jinsheng. "On the analysis of selected automated storage and retrieval systems." Diss., Georgia Institute of Technology, 1985. http://hdl.handle.net/1853/25020.
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Watkins, Alfred R. "Continuous media object modeling, storage, and retrieval." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/9184.
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Portabella, Arnús Marcos. "Wind Field Retrieval from Satellite Radar Systems." Doctoral thesis, Universitat de Barcelona, 2002. http://hdl.handle.net/10803/734.
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Wind observations are essential for determining the atmospheric flow. In particular, sea-surface wind observations are very useful for many meteorological and oceanographic applications. In this respect, most of the satellite remote-sensing radar systems can provide sea-surface wind information. This thesis reviews the current wind retrieval procedures for such systems, identifies the most significant unresolved problems, and proposes new methods to overcome such problems.<br/>In order to invert the geophysical model function (GMF), which relates the radar backscatter measurement with the wind speed and direction (unknowns), two independent measurements over the same scene (wind cell) are at least needed. The degree of independence of such measurements is given by the azimuth (view) angle separation among them. This thesis is focused on improving the wind retrieval for determined systems (two or more measurements) with poor azimuth diversity and for underdetermined systems (one single measurement). For such purpose, observations from two different radar systems, i.e., SeaWinds and SAR (Synthetic Aperture Radar), are used.<br/>The wind retrieval methods proposed in this book for determined (Multiple Solution Scheme, denoted MSS) and underdetermined (SAR Wind Retrieval Algorithm, denoted SWRA) systems are based on Bayesian methodology, that is, on maximizing the probability of obtaining the "true" wind given the radar measurements and the a priori wind information (often provided by numerical weather prediction models), assuming that all wind information sources contain errors. In contrast with the standard procedure for determined systems, the MSS fully uses the information obtained from inversion, which turns out to positively impact the wind retrieval when poor azimuth diversity. On the other hand, in contrast with the various algorithms used nowadays to resolve the wind vector for underdetermined systems, the SWRA assumes not only that the system can not be solved without additional information (underdetermination assumption) but also that both the algorithms and the additional information (which are combined to retrieved the wind vector) contain errors and these should be well characterized. The MSS and the SWRA give promising results, improving the wind retrieval quality as compared to the methods used up to now.<br/>Finally, a generic quality control is proposed for determined systems. In general, high-quality retrieved wind fields can be obtained from scatterometer (determined systems) measurements. However, geophysical conditions other than wind (e.g., rain, confused sea state or sea ice) can distort the radar signal and, in turn, substantially decrease the wind retrieval quality. The quality control method uses the inversion residual (which is sensitive to inconsistencies between observations and the geophysical model function that are mainly produced when conditions other than wind dominate the radar backscatter signal) to detect and reject the poor-quality retrievals. The method gives good results, minimizing the rejection of good-quality data and maximizing the rejection of poor-quality data, including rain contamination.
8
Engström, Felix, and Eli Pleaner. "Learning Methods for Improving News Retrieval Systems." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-187492.
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Content providers require an efficient and accurate way of retrieving relevant content with minimal human aid. News retrieval, for instance, often requires human intervention to recognize which text documents are news articles and which are not. The differences between a factual news article and an opinionated blog piece may be subtle, yet are critical for providing informative and relevant content to users. This thesis explores the problem of format classification: the task of classifying text documents based on the format in which they are written, such as a news article, blog entry or forum text. More explicitly, the goal of the thesis is to examine how well state-of-the-art supervised text classifica- tion techniques work for format classification. We select a number of classifiers that have been shown to perform well in other text classification tasks and evaluate their perfor- mance in this unexplored task. Experimental evaluation, performed on a novel dataset created from multiple existing datasets, explores both binary and multi-class classification in a bag-of-words feature space. Based on our experimental results, we have found that state-of-the-art supervised text classification techniques perform acceptably well at format classification. Furthermore, we propose a Gradient Boost model as a candidate classifier for the task of format clas- sification, and provide a discussion of future work.<br>Företag som tillhandahåller innehållshanteringstjänster be- höver effektiva och precisa metoder för att med minsta möjliga mänskliga arbetskraft utvinna relevant innehåll ur stora mängder data. Ett exempel på detta är tjänster för insamlande av nyheter, där nyheter skall utvinnas från olika källor. Som en del av den processen krävs att de kan avgöra om en text är en nyhetsartikel eller någon annan form av text. Skillnaden mellan en nyhetsartikel och en text skriven för en blogg kan vara subtil, men är avgörande för dessa företag. Denna rapport utforskar formatklassifi- cering: uppgiften att klassificera texter baserat på vilket format de är skrivna för. Exempel på format är: nyhet- sartikel, bloggtext eller forumtext. Mer specifikt tar den sig an uppgiften att undersöka hur väl de metoder som idag används i den väl utforskade uppgiften att klassificera texter baserat på ämne fungerar applicerade på formatk- lassificering. Det utforskas med experimentell evaluering på ett nytt dataset som konstruerats genom att kombin- era ett flertal existerande dataset. Detta görs både som en binär- och multiklassificeringsuppgift i en bag-of-word vektorrymd. Ett antal ämnesklassificeringsmetoder väljs baserat på resultat från tidigare forskning, och hur dessa presterar på formatklassificering undersöks. Vi drar slut- satsen att våra resultat visar att de textklassificeringsme- toder vi testat fungerar acceptabelt väl på formatklassifi- cering. Vi föreslår vidare gradient-boost eller multinomial naive bayes för att lösa uppgiften, beroende på om fokus ligger på kvaliteten av klassificeringen eller prestanda. Slut- ligen diskuteras resultaten , de sätts i relation till de begrän- sningar som förelegat och förslag till framtida forskning ges.
9
Liu, Chi-Cheng. "Implementation of parallel intelligent information retrieval systems." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/mq30509.pdf.
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Tang, Wai-hung, and 鄧偉雄. "An anonymity scheme for file retrieval systems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B40887972.
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Pollitt, A. S. "An expert systems approach to document retrieval." Thesis, University of Huddersfield, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.372852.
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Revutska, H. I., and V. L. Malinkina. "Automated information retrieval systems to the internet." Thesis, КНУТД, 2016. https://er.knutd.edu.ua/handle/123456789/5039.
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Agbele, Kehinde Kayode. "Context-awareness for adaptive information retrieval systems." Thesis, University of the Western Cape, 2014. http://hdl.handle.net/11394/3845.
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Philosophiae Doctor - PhD<br>This research study investigates optimization of IRS to individual information needs in order of relevance. The research addressed development of algorithms that optimize the ranking of documents retrieved from IRS. In this thesis, we present two aspects of context-awareness in IR. Firstly, the design of context of information. The context of a query determines retrieved information relevance. Thus, executing the same query in diverse contexts often leads to diverse result rankings. Secondly, the relevant context aspects should be incorporated in a way that supports the knowledge domain representing users’ interests. In this thesis, the use of evolutionary algorithms is incorporated to improve the effectiveness of IRS. A context-based information retrieval system is developed whose retrieval effectiveness is evaluated using precision and recall metrics. The results demonstrate how to use attributes from user interaction behaviour to improve the IR effectiveness
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Tang, Wai-hung. "An anonymity scheme for file retrieval systems." Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/hkuto/record/B40887972.
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Janicki, James H. "Retrieval from an image knowledge base /." Online version of thesis, 1993. http://hdl.handle.net/1850/12196.
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Estall, Craig. "A study in distributed document retrieval." Thesis, Queen's University Belfast, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.328342.
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LaBouve, Eric. "Relevance Analysis for Document Retrieval." DigitalCommons@CalPoly, 2019. https://digitalcommons.calpoly.edu/theses/1976.
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Document retrieval systems recover documents from a dataset and order them according to their perceived relevance to a user’s search query. This is a difficult task for machines to accomplish because there exists a semantic gap between the meaning of the terms in a user’s literal query and a user’s true intentions. Even with this ambiguity that arises with a lack of context, users still expect that the set of documents returned by a search engine is both highly relevant to their query and properly ordered. The focus of this thesis is on document retrieval systems that explore methods of ordering documents from unstructured, textual corpora using text queries. The main goal of this study is to enhance the Okapi BM25 document retrieval model. In doing so, this research hypothesizes that the structure of text inside documents and queries hold valuable semantic information that can be incorporated into the Okapi BM25 model to increase its performance. Modifications that account for a term’s part of speech, the proximity between a pair of related terms, the proximity of a term with respect to its location in a document, and query expansion are used to augment Okapi BM25 to increase the model’s performance. The study resulted in 87 modifications which were all validated using open source corpora. The top scoring modification from the validation phase was then tested under the Lisa corpus and the model performed 10.25% better than Okapi BM25 when evaluated under mean average precision. When compared against two industry standard search engines, Lucene and Solr, the top scoring modification largely outperforms these systems by upwards to 21.78% and 23.01%, respectively.
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Gómez, Luis Alfonso. "A two-level model for information retrieval systems." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/mq24469.pdf.
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Zhao, Dianguo. "Information retrieval in digital libraries : the systems aspect." Thesis, De Montfort University, 1997. http://hdl.handle.net/2086/4124.
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Gerber, Howard L. "Optimization information retrieval from disparate menu driven systems." Thesis, Massachusetts Institute of Technology, 1989. http://hdl.handle.net/1721.1/61052.
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Vermilyer, Robert. "Knowledge Discovery in Content-Based Image Retrieval Systems." NSUWorks, 2005. http://nsuworks.nova.edu/gscis_etd/898.
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The advent of the World Wide Web and digital photography has led to a phenomenal increase in the number and complexity of stored images. Accordingly, the ability to browse and retrieve images based upon image content is of rapidly growing importance. The goals of this research project are to develop a Content-Based Image Retrieval (CBIR) system that combines dynamic, user-driven search capabilities with artificial intelligence techniques and to examine the system's effectiveness. The experimental method will be used to test the specific hypotheses and various research questions proposed in this research project. All of the experiments will be conducted using a CBIR prototype system that incorporates intelligent User Interface Agents (UIA). The UlAs will use both neural networks and an expert reasoning system. The actual experiments will be conducted using a task-oriented approach, with both descriptive and analytical statistics used to assess the results. In addition, a new evaluation CBIR metric will be proposed and applied. It is expected that this research will benefit CBIR research and CBIR system development by: 1) demonstrating the effectiveness of providing users with an interface that allows them to sketch an image, provides a relevance feedback mechanism that is based on providing similar images, and offers query refinement suggestions; 2) presenting a reusable modular design approach that can be used to create CBIR systems; 3) showing how AI techniques, particularly intelligent User Interface Agents, can be used effectively in CBIR systems; 4) proposing a "standard" CBIR user interface; and 5) proposing a new CBIR evaluation metric. The results of this research project should advance the current state of CBIR in that it designs, implements and evaluates an interactive CBIR system that uses image input and incorporates both the user's interactive guidance and artificial intelligence techniques to access images.
22
Williams, Michael J. "Training Aviation Maintenance Technicians with Information Retrieval Systems." NSUWorks, 2000. http://nsuworks.nova.edu/gscis_etd/924.
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The goal of this dissertation has been to examine changes in computing technology skills possessed by aircraft maintenance technicians. The perspective of this project has been from that of a Federal Aviation Administration (FAA) approved Aviation Maintenance Technician School (AMTS). Specifically, the AMTS curriculum at Embry-Riddle Aeronautical University (ERAU) in Daytona Beach, Florida, provided participants for the study. Recommendations for integrating computing technology training into the AMTS curriculum at ERAU are being proposed for planned changes to the existing curriculum. With the increased availability of computerized documentation and reference materials for the aviation industry, many technical training institutions are becoming aware of the importance of computing technology skills to the aircraft technician. With the continuing explosion of the Internet and availability of other technology-based systems, the question of what skills are already possessed will be significant in the development of any new training. A literature review has been conducted to determine the current status of computing technology within both the aviation maintenance field and as used by aircraft technician training programs. The use of such technology is quite extensive and is rapidly gaining acceptance throughout the aviation industry. In an effort to determine the computing technology skills currently possessed by aircraft maintenance students, a study was conducted that compared computer familiarity and user accuracy. The study consisted of two separate phases conducted two years apart. Identical computer software and hardware was used for each phase of the study. Instruments used included a survey of current computer skills, exercises to determine computer use, and a Subjective evaluation of two different FAA documentation software packages. Analysis of the data determined that computing technology skills possessed by AMTS students at ERAU have not significantly changed in the two years spanning the two phases of this study. These results are being used to provide recommendations for changes to AMTS curricula concerning the level of computing technology skills to be presented. While there is presently no such training required by the FAA for approved AMTSs, results from this study point to a need for exposure to computers throughout the AMTS curriculum.
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Maleki-Dizaji, Saeedeh. "Evolutionary learning multi-agent based information retrieval systems." Thesis, Sheffield Hallam University, 2003. http://shura.shu.ac.uk/6856/.
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The volume and variety of information available on the Internet has experienced exponential growth, presenting a difficulty to users wishing to obtain information that accurately matches their interests. A number of factors affect the accuracy of matching user interests and the retrieved documents. First, is the fact that users often do not present queries to information retrieval systems in the form that optimally represents the information they want. Secondly, the measure of a document's relevance is highly subjective and variable between different users. This thesis addresses this problem with an adaptive approach that relies on evolutionary user-modelling. The proposed information retrieval system learns user needs from user-provided relevance feedback. The method combines a qualitative feedback measure obtained using fuzzy inference, and quantitative feedback based on evolutionary algorithms (Genetic Algorithms) fitness measures. Furthermore, the retrieval system's design approach is based on a multi-agent design approach, in order to handle the complexities of the information retrieval system including: document indexing, relevance feedback, user modelling, filtering and ranking the retrieve documents. The major contribution of this research are the combination of genetic algorithms and fuzzy relevance feedback for modelling adaptive behaviour, which is compared against conventional relevance feedback. Novel Genetic Algorithms operators are proposed within the context of textual; the encoding and vector space model for document representation is generalised within the same context.
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Kolonias, I. "Cognitive vision systems for video understanding and retrieval." Thesis, University of Surrey, 2007. http://epubs.surrey.ac.uk/843661/.
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This thesis addresses the problem of creating computer vision systems that will facilitate high-level, user-friendly interpretation of an observed scene, and which will be readily adaptable to a wide range of computer vision tasks. Hence, the notion of injecting cognitive capabilities to traditional computer vision systems is central to this work. Initially, the requirements of creating a cognitive vision system will be examined. This will lead us to the conclusion that the two main enabling components for such systems are the following: a unified framework for reasoning in the context of the observed scene; and a multi-layered memory architecture that will aid the reasoning framework in recalling and storing all relevant information about the observed scene. Regarding the apparatus used for reasoning in video sequences, it will be argued that it must be characterised by its ability to be applied at all levels of information processing (from raw input data to high-level abstractions concerning the evolution of the observed scene), support and exploit any combination of spatial and temporal dependencies (i.e. context) present among the input data, and deliver good reasoning performance when applied at any categorical domain. On the other hand, the requirements the reasoning engine sets will be used as a guideline for the design of a memory architecture conducive to the former. Therefore, the latter must be able to handle arbitrary input data types, depending on the scope of the current cognition task. It must also allow for both forward and feedback interaction with the reasoning framework, as contextual information extracted from the observed scene at a later stage may assist the reasoning engine in altering a decision made in previous stages - just like humans do when presented with contradicting evidence. To further emulate the mechanisms that enable human cognition, forgetting processes were also embedded in the memory infrastructure. For this particular feature, different layers of memory storage facilitate forgetting at different speeds; the system forgets raw input and low-level feature data very quickly, whereas high-level concepts about the evolution of the observed scene are retained over relatively long term. Finally, the overall proposed system has been implemented and tested on a real-world application - the annotation of broadcast tennis video sequences. In this sample application, the goal was to create a cognitive vision system that would keep track of the score for the duration of the broadcast match, based on the main components described above. The results obtained from processing a set of sequences captured off-the-air indicate that the overall approach achieves far superior results to simply segmenting the video sequence into shots and analysing each one separately, taken out of the context of the match. This demonstrates that the ability to adapt by discovering and exploiting context is paramount to the efficiency of any future computer vision system, and is, in no small part, a feature that sets biological cognitive vision systems apart from their machine-based counterparts.
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CATENA, MATTEO. "Energy efficiency in large scale information retrieval systems." Doctoral thesis, Gran Sasso Science Institute, 2017. http://hdl.handle.net/20.500.12571/9645.
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Web search engines are large scale information retrieval systems, which provide easy
access to information on the Web. High performance query processing is fundamental
for the success of such systems. In fact, web search engines can receive billions
of queries per day. Additionally, the issuing users are often impatient and expect
sub-second response times to their queries (e.g., 500 ms). For such reasons, search
companies adopt distributed query processing strategies to cope with huge volumes
of incoming queries and to provide sub-second response times.
Web search engines perform distributed query processing on computer clusters
composed by thousands of computers and hosted in large data centers. While data
center facilities enable large-scale online services, they also raise economical and environmental concerns. Therefore, an important problem to address is how to reduce
the energy expenditure of data centers. Moreover, another problem to tackle is how
to reduce carbon dioxide emissions and the negative impact of the data centers on
the environment.
A large part of the energy consumption of a data center could be accounted to
inefficiencies in its cooling and power supply systems. However, search companies already adopt state-of-the art techniques to reduce the energy wastage of such systems,
leaving little room for more improvements in those areas. Therefore, new approaches
are necessary to mitigate the environmental impact and the energy expenditure of
web search engines.One option is to reduce the energy consumption of computing resources to mitigate the energy expenditure and carbon footprint of a search company. In particular,
reducing the energy consumption of CPUs represents an attractive venue for web
search engines. Currently, CPU cores frequencies are typically managed by operating system components, called frequency governors. We propose to delegate the
CPU power management from the OS frequency governors to the query processing
application. Such search engine-specific governors can reduce up to 24% a server
power consumption, with only limited (but uncontrollable) drawbacks in the quality
of search results with respect to a system running at maximum CPU frequency.
Since users can hardly notice response times that are faster than their expectations
we advise that web search engine should not process queries faster than user expectations and, consequently, we propose the Predictive Energy Saving Online Scheduling
(PESOS) algorithm, to select the most appropriate CPU frequency to process a query
by its deadline, on a per-core basis. PESOS can reduce the CPU energy consumption of a query processing server from 24% up to 48% when compared to a high
performance system running at maximum CPU core frequency.
To reduce the carbon footprint of web search engines, another option consists in
using green energy to partially power their data centers. Stemming from these observations, we propose a new query forwarding algorithm that exploits both the green
energy sources available at different data centers and the differences in market energy
prices. The proposed solution maintains a high query throughput, while reducing by
up to 25% the energy operational costs of multi-center search engines.
26
Brooks, H. M. "An intelligent interface for document retrieval systems : Developing the problem description and retrieval strategy components." Thesis, City University London, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.373324.
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Hyland, Peter. "Supporting the retrieval and analysis of external statistical data by casual and novice users." Access E-Book, 2001. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20011220.095735/index.html.
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Taraszewski, Stephen A. "Understanding Knowledge Storage/Retrieval System Success: An Analytic Network Process Perspective." Cleveland State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=csu1493926537562139.
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Betts, M. P. "Coordinated information retrieval for building contractors' tendering." Thesis, London South Bank University, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.378209.
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Staniaszek, Michal. "Feature-Feature Matching For Object Retrieval in Point Clouds." Thesis, KTH, Datorseende och robotik, CVAP, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-170475.
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In this project, we implement a system for retrieving instances of objects from point clouds using feature based matching techniques. The target dataset of point clouds consists of approximately 80 full scans of office rooms over a period of one month. The raw clouds are reprocessed to remove regions which are unlikely to contain objects. Using locations determined by one of several possible interest point selection methods, one of a number of descriptors is extracted from the processed clouds. Descriptors from a target cloud are compared to those from a query object using a nearest neighbour approach. The nearest neighbours of each descriptor in the query cloud are used to vote for the position of the object in a 3D grid overlaid on the room cloud. We apply clustering in the voting space and rank the clusters according to the number of votes they contain. The centroid of each of the clusters is used to extract a region from the target cloud which, in the ideal case, corresponds to the query object. We perform an experimental evaluation of the system using various parameter settings in order to investigate factors affecting the usability of the system, and the efficacy of the system in retrieving correct objects. In the best case, we retrieve approximately 50% of the matching objects in the dataset. In the worst case, we retrieve only 10%. We find that the best approach is to use a uniform sampling over the room clouds, and to use a descriptor which factors in both colour and shape information to describe points.
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Veerasamy, Aravindan. "Visualization and user interface techniques for interactive information retrieval systems." Diss., Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/9244.
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Bae, Soo Hyun. "Information retrieval via universal source coding." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/26573.
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Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009.<br>Committee Chair: Juang, Biing-Hwang; Committee Member: Al-Regib, Ghassan; Committee Member: Linda Wiils; Committee Member: Mersereau, Russell; Committee Member: Pappas, Thrasyvoulos. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Reck, Ryan. "Suffix Trees for Document Retrieval." DigitalCommons@CalPoly, 2012. https://digitalcommons.calpoly.edu/theses/773.
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This thesis presents a look at the suitability of Suffix Trees for full text indexing and retrieval. Typically suffix trees are built on a character level, where the tree records which characters follow each other character. By building suffix trees for documents based on words instead of characters, the resulting tree effectively indexes every word or sequence of words that occur in any of the documents. Ukkonnen's algorithm is adapted to build word-level suffix trees. But the primary focus is on developing Algorithms for searching the suffix tree for exact and approximate, or fuzzy, matches to arbitrary query strings. A proof-of-concept implementation is built and compared to a Lucene index for retrieval over a subset of the Reuters RCV1 data set.
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Melder, Karl Henry. "PC-Gipsy : a usable PC-based image processing system /." This resource online, 1990. http://scholar.lib.vt.edu/theses/available/etd-01262010-020041/.
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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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Alaei, Fahimeh. "Texture Feature-based Document Image Retrieval." Thesis, Griffith University, 2019. http://hdl.handle.net/10072/385939.
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Storing and manipulating documents in digital form to contribute to a paperless society has been the propensity of emerging technology. There has been notable growth in the variety and quantity of digitised documents, which have often been scanned/photographed and archived as images without any labelling or sufficient index information. The growth of these kinds of document images will undoubtedly continue with new technology. To provide an effective way for retrieving and organizing these document images, many techniques have been implemented in the literature. However, designing automation systems to accurately retrieve document images from archives remains a challenging problem. Finding discriminative and effective features is the fundamental task for developing an efficient retrieval system. An overview of the literature reveals that research on document image retrieval using texture-based features has not yet been broadly investigated. Texture features are suitable for large volume data and are generally fast to compute. In this study, the effectiveness of more than 50 different texture-based feature extraction methods from four categories of texture features - statistical, transform-based, model-based, and structural approaches - are investigated in order to propose a more accurate method for document image retrieval. Moreover, the influence of resolution and similarity metrics on document image retrieval are examined. The MTDB, ITESOFT, and CLEF_IP datasets, which are heterogeneous datasets providing a great variety of page layouts and contents, are considered for experimentation, and the results are computed in terms of retrieval precision, recall, and F-score. By considering the performance, time complexity, and memory usage of different texture features on three datasets, the best category of texture features for obtaining the best retrieval results is discussed. The effectiveness of the transform-based category over other categories in regard to obtaining higher retrieval result is proven. Many new feature extraction and document image retrieval methods are proposed in this research. To attain fast document image retrieval, the number of extracted features and time complexity play a significant role in the retrieval process. Thus, a fast and non-parametric texture feature extraction method based on summarising the local grey-level structure of the image is further proposed in this research work. The proposed fast local binary pattern provided promising results, with lower computing time as well as smaller memory space consumption compared to other variations of local binary pattern-based methods. There is a challenge in DIR systems when document images in queries are of different resolutions from the document images considered for training the system. In addition, a small number of document image samples with a particular resolution may only be available for training a DIR system. To investigate these two issues, an under-sampling concept is considered to generate under-sampled images and to improve the retrieval results. In order to use more than one characteristic of document images for document image retrieval, two different texture-based features are used for feature extraction. The fast-local binary method as a statistical approach, and a wavelet analysis technique as a transform-based approach, are used for feature extraction, and two feature vectors are obtained for every document image. The classifier fusion method using the weighted average fusion of distance measures obtained in relation to each feature vector is then proposed to improve document image retrieval results. To extract features similar to human visual system perception, an appearance-based feature extraction method for document images is also proposed. In the proposed method, the Gist operator is employed on the sub-images obtained from the wavelet transform. Thereby, a set of global features from the original image as well as sub-images are extracted. Wavelet-based features are also considered as the second feature set. The classifier fusion technique is finally employed to find similarity distances between the extracted features using the Gist and wavelet transform from a given query and the knowledge-base. Higher document image retrieval results have been obtained from this proposed system compared to the other systems in the literature. The other appearance-based document image retrieval system proposed in this research is based on the use of a saliency map obtained from human visual attention. The saliency map obtained from the input document image is used to form a weighted document image. Features are then extracted from the weighted document images using the Gist operator. The proposed retrieval system provided the best document image retrieval results compared to the results reported from other systems. Further research could be undertaken to combine the properties of other approaches to improve retrieval result. Since in the conducted experiments, a priori knowledge regarding document image layout and content has not been considered, the use of prior knowledge about the document classes may also be integrated into the feature set to further improve the retrieval performance<br>Thesis (PhD Doctorate)<br>Doctor of Philosophy (PhD)<br>School of Info & Comm Tech<br>Science, Environment, Engineering and Technology<br>Full Text
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Müller, Michael. "Retrieval and Analysis of Software Systems from SCM Repositories." Thesis, Växjö University, School of Mathematics and Systems Engineering, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:vxu:diva-1603.
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<p>One source of input data for software evolution research is data stored inside a software configuration management repository. The data includes different versions of a software system’s source code as well as version history metadata, such as check-in dates or log messages. Inherently, extracting this data manually is a time- and labor intensive task. The subsequent preprocessing step and the appropriate storage of the results, necessary to utilize the data for further analysis, is an additional effort for the researcher.</p><p>The goal of this thesis is to design and implement a front-end plug-in for an existing software comprehension tool, the VizzAnalyzer, providing the capability to extract and analyze multiple versions and evolutional information of software systems from SCM repositories and to store the results. Thereby, the implemented solution provides the infrastructure for software evolution research.</p>
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Lin, Xing. "Modern GIR Systems : Framework, Retrieval Model and Indexing Techniques." Licentiate thesis, KTH, Geoinformatik och Geodesi, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-34787.
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Geographic information is one of the most important and the most common types of information in human society. It is estimated that more than 70% of all information in the world has some kind of geographic features. In the era of information explosion, information retrieval (IR) tools, such as search engines, are the main tools people used to quickly find the information they need nowadays. Because of the importance of geographic information, recent efforts have been made either by expanding the traditional IR to support a spatial query, or building a GIR in a brand new architecture from the ground such as the SPIRIT project. To some degree, these existing GIR systems could solve users’ information search need with a spatial filter, especially when the users are looking for information on something within a relatively large extent.Despite its advantage on processing geographical information and queries over conventional IR systems, modern GIR systems are also facing challenges including a proper representation and extraction of geographical information within documents, a better information retrieval model for both thematic and geographical information, a fast indexing mechanism for rapid search within documents by thematic and geographical hints, and even a new architecture of system.The objective of this licentiate research is to provide solutions to some of these problems in order to build a better modern GIR system in the future. The following aspects have been investigated in the thesis: a generic conceptual framework and related key technologies for a modern GIR system, a new information retrieval model and algorithm for measuring the relevance scores between documents and queries in GIR, and finally a new better indexing technique to geographically and thematically index the documents for a faster query processing within modern GIR.Concerning the proposed conceptual framework for modern GIR, it includes three modules: (1) the user interface module, (2) the information extractor, storage and indexer module and (3) the query processing and information retrieval module. Two knowledge bases, Gazetteer and Thesaurus, play an important role in the proposed framework. A digital map based user interface is proposed for the input of user information search needs and representation of retrieval results. Key techniques required for the implementation of a modern GIR using the proposed framework are a proper representation of document and query information, a better geographical information extractor, an innovative information retrieval model and relevance ranking algorithm, and a combined indexing mechanism for both geographical and thematic information.The new information retrieval model is established based on a Spatial Bayesian Network consisting of place names appeared in a single document and the spatial relationships between them. The new model assesses the geographical relevance between GIR document and query by the geographical importance and adjacency of the document geo-footprint versus the geographical scope of the user’s query.Regarding the indexing mechanism for modern GIR systems, a Keyword-Spatial Hybrid Index (KSHI) is proposed for the single and overall geo-footprint model, in which there is only one single geo-footprint for each document to retrieve from. A Keyword-Spatial Dual Index (KSDI) is proved to be more appropriate for a GIR system which allows for multiple geo-footprints within a single document.In addition to theoretical analysis, necessary experiments have also been carried out to evaluate the efficiency of proposed new information retrieval model and indices. Both the theoretical analysis and results of experiments show the potentials of proposed solution and techniques.<br>QC 20110630
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Marinheiro, Rui Miguel Neto. "Using context to integrate hypermedia with information retrieval systems." Thesis, University of Southampton, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390725.
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Li, Zhuoxun. "Information retrieval for automatic link creation in hypertext systems." Thesis, University of Southampton, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.239393.
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Tsui, Carleton 1970. "Multimedia data integration and retrieval in planning support systems." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/70303.
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Chen, Xin. "Human-centered semantic retrieval in multimedia databases." Birmingham, Ala. : University of Alabama at Birmingham, 2008. https://www.mhsl.uab.edu/dt/2008p/chen.pdf.
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Thesis (Ph. D.)--University of Alabama at Birmingham, 2008.<br>Additional advisors: Barrett R. Bryant, Yuhua Song, Alan Sprague, Robert W. Thacker. Description based on contents viewed Oct. 8, 2008; title from PDF t.p. Includes bibliographical references (p. 172-183).
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France, Robert Karl. "An artificial intelligence environment for information retrieval research." Thesis, Virginia Polytechnic Institute and State University, 1986. http://hdl.handle.net/10919/91091.
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The CODER (COmposite Document Expert/Extended/Effective Retrieval) project is a multi-year effort to investigate how best to apply artificial intelligence methods to increase the effectiveness of information retrieval systems. Particular attention is being given to analysis and representation of heterogeneous documents, such as electronic mail digests or messages, which vary widely in style, length, topic, and structure. In order to ensure system adaptability and to allow reconfiguration for controlled experimentation, the project has been designed as a moderated expert system. This thesis covers the design problems involved in providing a unified architecture and knowledge representation scheme for such a system, and the solutions chosen for CODER. An overall object-oriented environment is constructed using a set of message-passing primitives based on a modified Prolog call paradigm. Within this environment is embedded the skeleton of a flexible expert system, where task decomposition is performed in a knowledge-oriented fashion and where subtask managers are implemented as members of a community of experts. A three-level knowledge representation formalism of elementary data types, frames, and relations is provided, and can be used to construct knowledge structures such as terms, meaning structures, and document interpretations. The use of individually tailored specialist experts coupled with standardized blackboard modules for communication and control and external knowledge bases for maintenance of factual world knowledge allows for quick prototyping, incremental development, and flexibility under change. The system as a whole is structured as a set of communicating modules, defined functionally and implemented under UNIX™ using sockets and the TCP/IP protocol for communication. Inferential modules are being coded in MU-Prolog; non-inferential modules are being prototyped in MU-Prolog and will be re-implemented as needed in C++.<br>M.S.
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Jaisimha, M. Y. "Compound document retrieval in noisy environments /." Thesis, Connect to this title online; UW restricted, 1996. http://hdl.handle.net/1773/6007.
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Yeung, Chung Kei. "Ontological model for information systems development methodology." HKBU Institutional Repository, 2006. http://repository.hkbu.edu.hk/etd_ra/702.
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Mullany, Michael John. "An analysis of the relationship between analyst-user cognitive style differences and user resistance to information systems." Master's thesis, University of Cape Town, 1989. http://hdl.handle.net/11427/15855.
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Bibliography: pages 102-109.<br>This study investigated the relationships between user resistance to new information systems and other factors, the chief of which was the differences in cognitive problem-solving styles between systems developers (analysts) and users. In addition, associations were tested between user resistance and the following: system accuracy, system reliability, the analyst's attitude, the analyst-user relationship, analyst-user dissonance, the user's age and the user's length of service with his current employer. All data was collected at confidential interviews with the key users and key analysts of 34 post-implementation systems service were recorded at these interviews. Ages and lengths of User resistance was determined from the number of complaints made by users regarding the systems and their manner of implementation. Cognitive style was measured using the Kirton Adaption-innovation Inventory (KAI). All other parameters were measured as responses to suitably phrased questions, quantified using seven-point scales. A significant positive association between user resistance and analyst-user cognitive style difference was found. A model was then developed which enables the estimation of user resistance prior to system development with the aid of the KAI. Significant negative associations were found to exist between user resistance and system accuracy, and user resistance and system reliability. No relationships between user resistance and either user age or user length of service were found.
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Deng, Jie. "Emotion-based music retrieval and recommendation." HKBU Institutional Repository, 2014. https://repository.hkbu.edu.hk/etd_oa/82.
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The digital music industry has expanded dramatically during the past decades, which results in the generation of enormous amounts of music data. Along with the Internet, the growing volume of quantitative data about users (e.g., users’ behaviors and preferences) can be easily collected nowadays. All these factors have the potential to produce big data in the music industry. By utilizing big data analysis of music related data, music can be better semantically understood (e.g., genres and emotions), and the user’s high-level needs such as automatic recognition and annotation can be satisfied. For example, many commercial music companies such as Pandora, Spotify, and Last.fm have already attempted to use big data and machine learn- ing related techniques to drastically alter music search and discovery. According to musicology and psychology theories, music can reflect our heart and soul, while emotion is the core component of music that expresses the complex and conscious experience. However, there is insufficient research in this field. Consequently, due to the impact of emotion conveyed by music, retrieval and discovery of useful music information at the emotion level from big music data are extremely important. Over the past decades, researchers have made great strides in automated systems for music retrieval and recommendation. Music is a temporal art, involving specific emotion expression. But while it is easy for human beings to recognize emotions expressed by music, it is still a challenge for automated systems to recognize them. Although some significant emotion models (e.g., Hevner’s adjective circle, Arousal- Valence model, Pleasure-Arousal-Dominance model) established upon the discrete emotion theory and dimensional emotion theory have been widely adopted in the fi of emotion research, they still suffer from limitations due to the scalability and specificity in music domain. As a result, the effectiveness and availability of music retrieval and recommendation at the emotion level are still unsatisfactory. This thesis makes contribution at theoretical, technical, and empirical level. First of all, a hybrid musical emotion model named “Resonance-Arousal-Valence (RAV)” is proposed and well constructed at the beginning. It explores the computational and time-varying expressions of musical emotions. Furthermore, dependent on the RAV musical emotion model, a joint emotion space model (JESM) combines musical audio features and emotion tags feature is constructed. Second, corresponding to static musical emotion representation and time-varying musical emotion representation, two methods of music retrieval at the emotion level are designed: (1) a unified framework for music retrieval in joint emotion space; (2) dynamic time warping (DTW) for music retrieval by using time-varying music emotions. Furthermore, automatic music emotion annotation and segmentation are naturally conducted. Third, following the theory of affective computing (e.g., emotion intensity decay, and emotion state transition), an intelligent affective system for music recommendation is designed, where conditional random fi lds (CRF) is applied to predict the listener’s dynamic emotion state based on his or her personal historical music listening list in a session. Finally, the experiment dataset is well created and pro- posed systems are also implemented. Empirical results (recognition, retrieval, and recommendation) regarding accuracy compared to previous techniques are also presented, which demonstrates that the proposed methods enable an advanced degree of effectiveness of emotion-based music retrieval and recommendation. Keywords: Music and emotion, Music information retrieval, Music emotion recognition, Annotation and retrieval, Music recommendation, Affective computing, Time series analysis, Acoustic features, Ranking, Multi-objective optimization
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Angkasith, Vorapoch. "An intelligent design retrieval system for module-based products." Diss., Columbia, Mo. : University of Missouri-Columbia, 2004. http://hdl.handle.net/10355/4085.
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Thesis (Ph. D.)--University of Missouri-Columbia, 2004.<br>The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (June 30, 2006) Vita. Includes bibliographical references.
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Yoon, Changwoo. "Domain-specific knowledge-based informational retrieval model using knowledge reduction." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0011560.
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Chen, Hsinchun, and Vasant Dhar. "Online Query Refinement on Information Retrieval Systems: A Process Model of Searched System Interactions." ACM, 1990. http://hdl.handle.net/10150/105597.
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Artificial Intelligence Lab, Department of MIS, University of Arizona<br>This article reports findings of empirical research that investigated information searchers online query refinement
process. Prior studies have recognized the information specialists' role in helping searchers articulate and refine queries. Using a semantic network and a Problem Behavior Graph to represent the online search
our study revealed that searchers also refined their own queries in an online task environment. The
information retrieval system played a passive role in assisting online query refinement, which was, however, one
that confirmed Taylor's four-level query formulation model. Based on our empirical findings, we proposed using
process model to facilitate and improve query refinement in an online environment. We believe incorporating
this model into retrieval systems can result in the design of more "intelligent" and useful information retrieval systems.