In the business and the operation management, business strategies and operation guidelines are manually recorded on paper in large repositories. The data include standard operating procedures, work instructions, technical documents, manufacturing specifications and training materials. However, many companies are facing problems of document managements including a lack of updated information, document duplication, file misplacement and inconsistency, etc. Furthermore, accumulated working experiences, considered as parts of the most valuable resources of companies, are not well preserved after resignations and retirements of senior employees. This critical situation is apparent especially in those companies established over decades and affects the operation efficiency and the future expansion. This study helps understand the needs of document managements and shares experiences of document management implementations in the company. The standard processes of the document management includes source establishment, information review, data examination and format, file security and restoration and user feedback. In fact, most of companies do not carry out the entire processes. Companies can develop their own document management systems according to their business formats and operation demands. With the advent of computers, companies build up document management concepts by applying software like MIS. In addition, companies can also use ISO document management processes as the paradigm for the document management establishment. The document management gives companies abilities to learn faster and take better reactions in the market. Efficiency becomes the competitive advantage of the company which is able to outperform competitors in the industry.

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Nowadays, academic researchers maintain a personal library of papers, which they would like to organize based on their needs, e.g., research, projects, or courseware. Clustering techniques are often employed to achieve this goal by grouping the document collection into different topics. Unsupervised clustering does not require any user effort but only produces one universal output with which users may not be satisfied. Therefore, document clustering needs user input for guidance to generate personalized clusters for different users. Semi-supervised clustering incorporates prior information and has the potential to produce customized clusters. Traditional semi-supervised clustering is based on user supervision in the form of labeled instances or pairwise instance constraints. However, alternative forms of user supervision exist such as labeling features. For document clustering, document supervision involves labeling documents while feature supervision involves labeling features. Their joint of use has been called dual supervision. In this thesis, we first explore and propose a framework to use feature supervision for interactive feature selection by indicating whether a feature is useful for clustering. Second, we enhance the semi-supervised clustering with feature supervision using feature reweighting. Third, we propose a unified framework to combine document supervision and feature supervision through seeding. The newly proposed algorithms are evaluated using oracles and demonstrated to be more helpful in producing better clusters matching a single user's point of view than document clustering without any supervision and with only document supervision. Finally, we conduct a user study to confirm that different users have different understandings of the same document collection and prefer personalized clusters. At the same time, we demonstrate that document clustering with dual supervision is able to produce good personalized clusters even with noisy user input. Dual supervision is also demonstrated to be more effective in personalized clustering than no supervision or any single supervision. We also analyze users' behaviors during the user study and present suggestions for the design of document management software.

The dissertation describes the improvement of the document management in an organization without additional financial expenses. / В диссертации рассматривается совершенствование документационного обеспечения организации без дополнительных финансовых затрат.

Cílem této práce je představení a zhodnocení přínosů technologií Microsoft SharePoint. Stěžejními produkty jsou zde Windows SharePoint Services a jejich nadstavba Microsoft Office SharePoint Portal Server. Popsána je základní architektura, základní stavební prvky, webové části, principy bezpečnosti nebo možnosti vyhledávání a customizace. Samostatná kapitola je věnována i možnostem vývoje webových aplikací na platformě SharePoint.

With the emergence and proliferation of Internet services and e-commerce applications, a tremendous amount of information is accessible online, typically as textual documents. To facilitate subsequent access to and leverage from this information, the efficient and effective management¡Xspecifically, text categorization¡Xof the ever-increasing volume of textual documents is essential to organizations and person. Existing text categorization techniques focus mainly on categorizing monolingual documents. However, with the globalization of business environments and advances in Internet technology, an organization or person often retrieves and archives documents in different languages, thus creating the need for cross-lingual text categorization. Motivated by the significance of and need for such a cross-lingual text categorization technique, this thesis designs a technique with two different category assignment methods, namely, individual- and cluster-based. The empirical evaluation results show that the cross-lingual text categorization technique performs well and the cluster-based method outperforms the individual-based method.

Text categorization deals with the automatic learning of a text categorization model from a training set of preclassified documents on the basis of their contents and the assignment of unclassified documents to appropriate categories. Most of existing text categorization techniques deal with monolingual documents (i.e., all documents are written in one language) during the text categorization model learning and category assignment (or prediction). However, with the globalization of business environments and advances in Internet technology, an organization or individual often generates/acquires and subsequently archives documents in different languages, thus creating the need for cross-lingual text categorization (CLTC). Existing studies on CLTC focus on the prediction-corpus translation-based approach that lacks of a systematic mechanism for reducing translation noises; thus, limiting their cross-lingual categorization effectiveness. Motivated by the needs of providing more effective CLTC support, we design a training-corpus translation-based CLTC approach. Using the prediction-corpus translation-based approach as the performance benchmark, our empirical evaluation results show that our proposed CLTC approach achieves significantly better classification effectiveness than the benchmark approach does in both Chinese

Knowledge is an increasingly important asset for organizational competition. In order to manage organizational knowledge effectively, document management is the first step. Therefore, how to manage organizational documents is an issue of interest to many enterprises. In order to manage documents effectively on the computer, it is necessary to understand the content of these documents, which needs a clear description of the included concepts. Ontology is a method for describing concepts and their relations and the metadata of factual data to help users understand the knowledge in a document. Ontology can also be a bridge for knowledge exchange between the user and the computer. Therefore, the construction of ontology is important for knowledge and document management. This research proposes a method for the construction of event ontology, which can be used to construct the ontology of an event. The method includes text pre-processing and event ontology construction. Test pre-processing module includes POS tagger, word filter, and term analysis. Event ontology module provides a friendly editing environment for the user to build the concepts and attributes of an event to provide the ontology of ¡§who,¡¨ ¡§what,¡¨ ¡§where,¡¨ and ¡§what object.¡¨ To verify the feasibility of the proposed method, a prototype system for ontology construction is built. The Alexander Poison Event was used as an example to demonstrate the value of the prototype system.

Knowledge management is an important weapon for business competition. Many organizations are adopting knowledge management systems. For knowledge management, document management is its key foundation. There is a large amount of procedural knowledge existing in decision documents. This knowledge can illustrate the process and considerations in a decision situation, called episodic. The episodic knowledge can help decision makers understand historical decision process and considerations for future decision making. Therefore, how to discover decision episodes from existing documents is a major research issue in knowledge management. This research proposes a method for episode mining that integrates automatic document summary techniques, knowledge ontology, and index structures to build the relations and processes of events, and use the Gantt Chart and Flow Chart to portray event processes. We build a prototype system and use a news event as our example to illustrate the feasibility of the proposed approach and demonstrate the results.

With the rapid emergence and proliferation of Internet and the trend of globalization, a tremendous number of textual documents written in different languages are electronically accessible online. Efficiently and effectively managing these textual documents written different languages is essential to organizations and individuals. Although poly-lingual text categorization (PLTC) can be approached as a set of independent monolingual classifiers, this naïve approach employs only the training documents of the same language to construct to construct a monolingual classifier and fails to utilize the opportunity offered by poly-lingual training documents. Motivated by the significance of and need for such a poly-lingual text categorization technique, we propose a PLTC technique that takes into account all training documents of all languages when constructing a monolingual classifier for a specific language. Using the independent monolingual text categorization (MnTC) technique as our performance benchmark, our empirical evaluation results show that our proposed PLTC technique achieves higher classification accuracy than the benchmark technique does in both English and Chinese corpora. In addition, our empirical results also suggest the robustness of the proposed PLTC technique with respect to the range of training sizes investigated.