In current research literature, supply chain management (SCM) is a hot topic breaching the boundaries of many academic disciplines. SCM-related work can be found in the relevant literature for many disciplines. Supply chain management can be defined as effectively and efficiently managing the flows (information, financial and physical) in all stages of the supply chain to add value to end customers and gain profit for all firms in the chain. Supply chains involve multiple partners with the common goal to satisfy customer demand at a profit. While supply chains are not new, the way academics and practitioners view the need for and the means to manage these chains is relatively new. Very little literature can be found on designing supply chains from the ground up or what dimensions of supply chain management should be considered when designing a supply chain. Additionally, we have found that very few tools exist to help during the design phase of a supply chain. Moreover, very few tools exist that allow for comparing supply chain designs. We contribute to the current literature by determining which supply chain management dimensions should be considered during the design process. We employ text mining to create a supply chain design conceptual model and compare this model to existing supply chain models and reference frameworks. We continue to contribute to the current SCM literature by applying a creative application of concepts and results in the field of Stochastic Processes to build a custom simulator capable of comparing different supply chain designs and providing insights into how the different designs affect the supply chain’s total inventory cost. The simulator provides a mechanism for testing when real-time demand information is more beneficial than using first-come, first-serve (FCFS) order processing when the distributional form of lead-time demand is derived from the supply chain operating characteristics instead of using the assumption that lead-time demand distributions are known. We find that in many instances FCFS out-performs the use of real-time information in providing the lowest total inventory cost.

There are several databases collecting information on various types of incidents, and most analyses performed on these databases usually do not expand past basic trend analysis or counting occurrences. This research uses the more robust methods of data mining and text mining to analyze the Hazardous Substances Emergency Events Surveillance (HSEES) system data by identifying relationships among variables, predicting the occurrence of injuries, and assessing the value added by the text data. The benefits of performing a thorough analysis of past incidents include better understanding of safety performance, better understanding of how to focus efforts to reduce incidents, and a better understanding of how people are affected by these incidents. The results of this research showed that visually exploring the data via bar graphs did not yield any noticeable patterns. Clustering the data identified groupings of categories across the variable inputs such as manufacturing events resulting from intentional acts like system startup and shutdown, performing maintenance, and improper dumping. Text mining the data allowed for clustering the events and further description of the data, however, these events were not noticeably distinct and drawing conclusions based on these clusters was limited. Inclusion of the text comments to the overall analysis of HSEES data greatly improved the predictive power of the models. Interpretation of the textual data?s contribution was limited, however, the qualitative conclusions drawn were similar to the model without textual data input. Although HSEES data is collected to describe the effects hazardous substance releases/threatened releases have on people, a fairly good predictive model was still obtained from the few variables identified as cause related.

Document clustering automatically organizes a document collection into distinct groups of similar documents on the basis of their contents. Most of existing document clustering techniques deal with monolingual documents (i.e., documents written in one language). However, with the trend of globalization and advances in Internet technology, an organization or individual often generates/acquires and subsequently archives documents in different languages, thus creating the need for multilingual document clustering (MLDC). Motivated by its significance and need, this study designs a translation-based MLDC technique. Our empirical evaluation results show that the proposed multilingual document clustering technique achieves satisfactory clustering effectiveness measured by both cluster recall and cluster precision.

Text categorization to automatically assign documents into the appropriate pre-defined category or categories is essential to facilitating the retrieval of desired documents efficiently and effectively from a huge text depository, e.g., the world-wide web. Most techniques, however, suffer from the feature selection problem and the vocabulary mismatch problem. A few research works have addressed on text categorization via text summarization to reduce the size of documents, and consequently the number of features to consider, while some proposed using latent semantic indexing (LSI) to reveal the true meaning of a term via its association with other terms. Few works, however, have studied the joint effect of text summarization and the semantic dimension reduction technique in the literature. The objective of this research is thus to propose a practical approach, SBDR to deal with the above difficulties in text categorization tasks. Two experiments are conducted to validate our proposed approach. In the first experiment, the results show that text summarization does improve the performance in categorization. In addition, to construct important sentences, the association terms of both noun-noun and noun-verb pairs should be considered. Results of the second experiment indicate slight better performance with the approach of adopting LSI exclusively (i.e. no summarization) than that with SBDR (i.e. with summarization). Nonetheless, the minor accuracy reduction can be largely compensated for the computational time saved using LSI with text summarized. The feasibility of the SBDR approach is thus justified.

As text repositories grow in number and size and global connectivity improves, the amount of online information in the form of free-format text is growing extremely rapidly. In many large organizations, huge volumes of textual information are created and maintained, and there is a pressing need to support efficient and effective information retrieval, filtering, and management. Text categorization is essential to the efficient management and retrieval of documents. Past research on text categorization mainly focused on developing or adopting statistical classification or inductive learning methods for automatically discovering text categorization patterns from a training set of manually categorized documents. However, as documents accumulate, the pre-defined categories may not capture the characteristics of the documents. In this study, we proposed a mining-based category evolution (MiCE) technique to adjust the categories based on the existing categories and their associated documents. According to the empirical evaluation results, the proposed technique, MiCE, was more effective than the discovery-based category management approach, insensitive to the quality of original categories, and capable of improving classification accuracy.

In this thesis, we propose a knowledge map integration system to merge distributed knowledge maps into a global knowledge map based on the concept mapping methodology. This system performs the functions of knowledge map integration and knowledge map maintenance. The knowledge map integration function integrates different local knowledge maps specified by distributed organizations into a global knowledge map, and knowledge seekers can access the overall knowledge structure about the domain knowledge. Besides, the local knowledge maps in different organizations vary dynamically due to accumulation of information. Consequently, the demand for knowledge map maintenance increases in order to keep the global knowledge map up to date. The function of knowledge map maintenance can update the variations of every local knowledge map, and change the global structure simultaneously. The knowledge map integration system is evaluated by master thesis repository at National Central Library, and we obtain good results.

Environmental scanning, which acquires and use the information about event, trends, and changes in an organization¡¦s external environment, is an important process in the strategic management of an organization and permits the organization to quickly adapt to the changes of its external environment. Event detection that detects the onset of new events from news documents is essential to facilitating an organization¡¦s environmental scanning activity. However, traditional feature-based event detection techniques detect events by comparing the similarity between features of news stories and incur several problems. For example, for illustration and comparison purpose, a news story may contain sentences or paragraphs that are not highly relevant to defining its event. Without removing such less relevant sentences or paragraphs before detection, the effectiveness of traditional event detection techniques may suffer. In this study, we developed a summary-based event detection (SED) technique that filters less relevant sentences or paragraphs in a news story before performing feature-based event detection. Using a traditional feature-based event detection technique (i.e., INCR) as benchmark, the empirical evaluation results showed that the proposed SED technique could achieve comparable or even better detection effectiveness (measured by miss and false alarm rates) than the INCR technique, for data corpora where the percentage of news stories discussing old events is high.

In the organism, genes don¡¦t work independently. The interaction of genes shows how the functional task affects. Observing the interaction can understand what the relation between genes and how the disease caused. Several methods are adopted to observe the interaction to construct gene relation network. Existing algorithms to construct gene relation network can be classified into two types. One is to use literatures to extract the relation between genes. The other is to construct the network, but the relations between genes are not described. In this thesis, we proposed a hybrid method based on these two methods. Bayesian network is applied to the microarray gene expression data to construct gene network. Text mining is used to extract the gene relations from the documents database. The proposed algorithm integrates gene network and gene relations into gene relation networks. Experimental results show that the related genes are connected in the network. Besides, the relations are also marked on the links of the related genes.

Automatic text summarization can efficiently and effectively save users¡¦ time while reading text documents. The objective of automatic text summarization is to extract essential sentences that cover almost all the concepts of a document so that users are able to comprehend the ideas the document tries to address by simply reading through the corresponding summary. This research focuses on developing a hybrid automatic text summarization approach, KCS, to enhancing the quality of summaries. This approach basically consists of two major components: first, it employs the K-mixture probabilistic model to calculate term weights in a statistical sense; it then identifies the term relationship between nouns and nouns as well as nouns and verbs, which results in the connective strength (CS) of nouns. With the connective strengths available scores of sentences can be calculated and ranked to be extracted. We conduct three experiments to justify the proposed approach. The quality of summary is examined by its capability of increasing accuracy of text classification,while the classifier employed, the Naïve Bayes classifier, is kept the same through all experiments. The results show that the K-mixture model is more contributive to document classification than traditional TFIDF weighting scheme. It, however, is still no better than CS, a more complex linguistic-based approach. More importantly, our proposed approach, KCS, performs best among all approaches considered. It implies that KCS can extract more representative sentences from the document and its feasibility in text summarization applications is thus justified.

Zugl.: Berlin, Humboldt-Univ., Diss., 2008