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2012 Joint 6th International Conference on Soft Computing and Intelligent Systems (SCIS) and 13th International Symposium on Advanced Intelligent Systems (ISIS) (SCIS-ISIS 2012). November 20-24, 2012, Kobe, Japan

By allowing judgments based on a small number of exemplar documents to be applied to a larger number of unexamined documents, clustered presentation of search results represents an intuitively attractive possibility for reducing the cognitive resource demands on human users of information retrieval systems. However, clustered presentation of search results is sensible only to the extent that naturally occurring similarity relationships among documents correspond to topically coherent clusters. The Cluster Hypothesis posits just such a systematic relationship between document similarity and topical relevance. To date, experimental validation of the Cluster Hypothesis has proved problematic, with collection-specific results both supporting and failing to support this fundamental theoretical postulate. The present study consists of two computational information visualization experiments, representing a two-tiered test of the Cluster Hypothesis under adverse conditions. Both experiments rely on multidimensionally scaled representations of interdocument similarity matrices. Experiment 1 is a term-reduction condition, in which descriptive titles are extracted from Associated Press news stories drawn from the TREC information retrieval test collection. The clustering behavior of these titles is compared to the behavior of the corresponding full text via statistical analysis of the visual characteristics of a two-dimensional similarity map. Experiment 2 is a dimensionality reduction condition, in which inter-item similarity coefficients for full text documents are scaled into a single dimension and then rendered as a two-dimensional visualization; the clustering behavior of relevant documents within these unidimensionally scaled representations is examined via visual and statistical methods. Taken as a whole, results of both experiments lend strong though not unqualified support to the Cluster Hypothesis. In Experiment 1, semantically meaningful 6.6-word document surrogates systematically conform to the predictions of the Cluster Hypothesis. In Experiment 2, the majority of the unidimensionally scaled datasets exhibit a marked nonuniformity of distribution of relevant documents, further supporting the Cluster Hypothesis. Results of the two experiments are profoundly question-specific. Post hoc analyses suggest that it may be possible to predict the success of clustered searching based on the lexical characteristics of users' natural-language expression of their information need.

Thesis deals with analysis and design system for automatic document recognition. The system examines the document and converts it into text data, and shall be preserved information about the initial position of the word in the original document. These data will then be reviewed and some of them will be assigned their importance. The way the data will be assigned is based on rules which may vary according to user needs. According to the data, their assignment and the importance of their position, the system finds a similar document and, if it identifies the current document examined. Powered by TCPDF (www.tcpdf.org)

Thesis deals with analysis and system design for automatic document recognition. The system explores the document and converts it into text data with information about the position of the word in original document. These data will then be reviewed and some of them will be assigned their importance. The way the data will be assigned is based on rules which may vary according to user needs. According to the data, their assignment and the importance of their position, the system finds a similar document and identifies the current document. Powered by TCPDF (www.tcpdf.org)

Digital search engine, taking a search request from user and then returning a result responded to the request to the user, is indispensable for modern humans who are used to surfing the Internet. On the other hand, the digital document PDF is accepted by more and more people and becomes widely used in this day and age due to the convenience and effectiveness. It follows that, the traditional library has already started to be replaced by the digital one. Combining these two factors, a document based search engine that is able to query a digital document database with an input file is urgently needed. This thesis is a software development that aims to design and implement a prototype of such search engine, and propose latent optimization methods for Loredge. This research can be mainly divided into two categories: Prototype Development and Optimization Analysis. It involves an analytical research on sample documents provided by Loredge and a multi-perspective performance analysis. The prototype contains reading, preprocessing and similarity measurement. The reading part reads in a PDF file by using an imported Java library Apache PDFBox. The preprocessing processes the in-reading document and generates document fingerprint. The similarity measurement is the final stage that measures the similarity between the input fingerprint with all the document fingerprints in the database. The optimization analysis is to balance resource consumptions involving response time, accuracy rate and memory consumption. According to the performance analysis, the shorter the document fingerprint is, the better performance the search program presents. Moreover, a permanent feature database and a similarity based filtration mechanism are proposed to further optimize the program. This project has laid a solid foundation for further study in the document based search engine by providing a feasible prototype and enough relevant experimental data. This study figures out that the following study should mainly focuses on improving the effectiveness of the database access, which involves data entry labeling and search algorithm optimization. / Digital sökmotor, som tar en sökfråga från användaren och sedan returnerar ett resultat som svarar på den begäran tillbaka till användaren, är oumbärligt för moderna människor som brukar surfa på Internet. Å andra sidan, det digitala dokumentets format PDF accepteras av fler och fler människor, och det används i stor utsträckning i denna tidsålder på grund av bekvämlighet och effektivitet. Det följer att det traditionella biblioteket redan har börjat bytas ut av det digitala biblioteket. När dessa två faktorer kombineras, framgår det att det brådskande behövs en dokumentbaserad sökmotor, som har förmåga att fråga en digital databas om en viss fil. Den här uppsatsen är en mjukvaruutveckling som syftar till att designa och implementera en prototyp av en sådan sökmotor, och föreslå relevant optimeringsmetod för Loredge. Den här undersökningen kan huvudsakligen delas in i två kategorier, prototyputveckling och optimeringsanalys. Arbeten involverar en analytisk forskning om exempeldokument som kommer från Loredge och en prestandaanalys utifrån flera perspektiv. Prototypen innehåller läsning, förbehandling och likhetsmätning. Läsningsdelen läser in en PDF-fil med hjälp av en importerad Java bibliotek, Apache PDFBox. Förbehandlingsdelen bearbetar det inlästa dokumentet och genererar ett dokumentfingeravtryck. Likhetsmätningen är det sista steget, som mäter likheten mellan det inlästa fingeravtrycket och fingeravtryck av alla dokument i Loredge databas. Målet med optimeringsanalysen är att balansera resursförbrukningen, som involverar responstid, noggrannhet och minnesförbrukning. Ju kortare ett dokuments fingeravtryck är, desto bättre prestanda visar sökprogram enligt resultat av prestandaanalysen. Dessutom föreslås en permanent databas med fingeravtryck, och en likhetsbaserad filtreringsmekanism för att ytterligare optimera sökprogrammet. Det här projektet har lagt en solid grund för vidare studier om dokumentbaserad sökmotorn, genom att tillhandahålla en genomförbar prototyp och tillräckligt relevanta experimentella data. Den här studie visar att kommande forskning bör huvudsakligen inriktas på att förbättra effektivitet i databasåtkomsten, vilken innefattar data märkning och optimering av sökalgoritm.