Today it is very often necessary to print different documents from company information systems. These documents, that are designed to represent the company, contain information that is usually stored in databases. The goal of this thesis is to show the methods for the automatic conversion of data into formatted documents. My aim is to show some methods, how to automatically generate printouts and how to create templates for them in an easy way. For the purpose of this thesis, I've developed a small web application, which stands for an information system of a logistics company. The application allows creating documents with the help of templates. All documents are generated in generally common formats.

This thesis describes WebDoc, an automated system that classifies Web documents according to the Library of Congress classification system. This work is an extension of an early version of the system that successfully generated indexes for journal articles. The unique features of Web documents, as well as how they will affect the design of a classification system, are discussed. We argue that full-text analysis of Web documents is inevitable, and contextual information must be used to assist the classification. The architecture of the WebDoc system is presented. We performed experiments on it with and without the assistance of contextual information. The results show that contextual information improved the system?s performance significantly.

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Obsahem této práce je návrh metody porovnání webových stránek na základě vizuálních rysů. Na začátku jsou popsány možné přístupy k porovnávání dokumentů s ohledem na jejich použití. Dále jsou prezentovány přístupy porovnávání zaměřené na vizuální vzhled dokumentů. Zde jsou nejdříve popsány metody pro porovnávání z vyrendrovaného obrázku dokumentu a pak přístup pomocí zdrojového kódu. Tato práce je dále detailně zaměřena na získání vizuální rysů ze zdrojového kódu dokumentu. Je zde popsán návrh nové metody pro porovnávání dokumentů na základě vizuálních rysů, která využívá strukturální popis dokumentu. Součástí je taky popis implementace aplikace a dosažené výsledky. V závěru jsou informace o možném rozšíření navržené metody a dalším pokračování.

Finding near-duplicate documents is an interesting problem but the existing methods are not suitable for large scale datasets and memory constrained systems. In this work, we developed approaches that tackle the problem of finding near-duplicates while improving query performance and using less memory. We then carried out an evaluation of our method on a dataset of 70M web documents, and showed that our method works really well. The results indicated that our method could achieve a reduction in space by a factor of 5 while improving the query time by a factor of 4 with a recall of 0.95 for finding all near-duplicates when the dataset is in memory. With the same recall and same reduction in space, we could achieve an improvement in query-time by a factor of 4.5 while finding first the near-duplicate for an in memory dataset. When the dataset was stored on a disk, we could achieve an improvement in performance by 7 times for finding all near-duplicates and by 14 times when finding the first near-duplicate.

When users are searching for documents, they must sift through a collection of potentially relevant documents assessing, categorizing and prioritizing them based on the current task at hand, a process we refer to as document triage. Since users' time is precious, as much information as possible should be presented to them to aid the process of document triage. This thesis presents a simple visualization and a set of features that can help users in identifying information of interest. As a part of this thesis, the System Layer of the Visual Knowledge Builder (VKB) was developed as a tab strip container. Each of the tabs presents a different type of information about Web Documents. The types of information currently included in VKB are: a summary of the Web Document, keywords based on users' interests provided by the Interest Profile Manager (IPM), popular keywords from a social bookmarking site, metadata about the Web Document, a list of outgoing links of the Web Document, and the history of the Web Document. We performed a heuristic evaluation to assess the usefulness of the new visualization and features. During the evaluation, participants were asked to rate the usefulness of each of the new web document features over a scale of 1 to 7, where a value of 1 indicated strong disagreement, and 7 indicated strong agreement. Our results indicate that the document summary, the keywords from IPM, popular tags, and the history of the Web Document are expected to be most useful during the process of document triage.

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