Business Intelligence Applications (BIA) are used by international companies
to serve customers, retails and merchants over the internet all the time. BIA based on Electronic Commerce (EC) techniques, management and intelligent information systems to make better decisions. There are urgent needs to optimize the search space for selecting the most appropriate decision; therefore, the objective of this study is to maximize the output results of the BIA though proposing a new approach named Multi–Criteria Business Intelligence (MCBI) for fulfilling this purpose.
The MCBI approach is used to enhance the BIA by applying Multi-Criteria
Decision Making (MCDM). The major contribution of the MCBI approach is to
improve business decisions and Business Intelligence Decision Support System
(BIDSS) for BIA. MCBI approach presents a methodology to evaluate and select
business decisions according to MCBI model. This methodology is based on the
quantitative and qualitative methodologies to produce recommended business
decisions.
MCBI approach consists of a set of models, descriptions and analysis
methodologies that systematically exploit the available data to retrieve information,knowledge and advice useful in supporting complex decision making processes according to many criteria and alternatives. We have implemented the MBCI approach on a system named “national food security” as a case study to verify its improvement in the results.
The recommended business decisions are the appropriate and optimal
decisions to be implemented. The proposed model for MCBI approach consists of five major stages; the first stage determines business objectives, problem definition and main goals by business owners and BI system stakeholders. The second stage collects business data and treatments heterogeneous data. The third stage builds unified business databases to perform analytical and mining processing for business data through Business Intelligence Processing Unit (BIPU) in fourth stage. Finally, the fifth stage evaluates the business decisions to select the appropriate and optimal solutions to be implemented.

In the presented work, a tool that augments each paragraph in a document or publication with metadata describing that paragraph was developed. The class of documents targeted by this work is those which cover similar concepts within some given domain the augmentation process is based on knowledge from the domain, which is acquired dynamically from indexed documents. The search process described in this work results only in paragraph(s) which match the entered search criteria, being returned. Moreover the search engine of the tool exists as a web service to give the search facility to any other agent or any search system, to use the search engine of the tool regardless of the platform or operating system.

The aim of this work is to improve the translation quality of state-of-the-art statistical machine translation systems. We achieve this by optimizing the training process, which is an important phase in building SMT systems. We want to optimize the training process as much as we can to get the most out of the training data available. This can be achieved in two ways: One way requires linguistic resources such as lexicons and morphological analyzers, while the other one makes no use of any linguistic resources.

Abstract

with the explosion of the World Wide Web, a wealth of data on many different subjects has become available online. Because of the magnitude of information currently available analyzing it and understanding is an immense task that has motivated much research efforts.

Some scientific branches have evolved to help summarize, analyze and understand the huge quantity of data. Examples of such branches include data and web mining, text summarization, information retrieval and information extraction, among others.

Information extraction, the field on which this thesis is focused, aims to extract certain information from unstructured or semi structured text and put it in a predefined form or a database, in order to facilitate information finding.

In the presented work, a tool that augments each paragraph in a document or publication with metadata describing that paragraph was developed. The class of documents targeted by this work is those which cover similar concepts within some given domain the augmentation process is based on knowledge from the domain, which is acquired dynamically from indexed documents. The search process described in this work results only in paragraph(s) which match the entered search criteria, being returned. Moreover the search engine of the tool exists as a web service to give the search facility to any other agent or any search system, to use the search engine of the tool regardless of the platform or operating system.

This thesis is interested in researching the statistical machine translation approach and trying to apply it to the problem of the technical translation domain. We proposed a statistical machine translation system and our experiments using a small corpus of size 20,000 sentences suggested that this system outperforms the well-established word based statistical machine translation system. In a small experiment, we showed that the output of the proposed system is better than the suggestions CAT tools supply to the human translator, and we suggest a new architecture to replace the fuzzy match suggestions found in the available commercial CAT tools. In future work we need to do more experimentation using the new suggested architecture for the CAT tools. Also we need to enhance the statistical model used in our machine translation system by adding a syntax language model and to experiment the effect of this language model on the performance of the system.