Efforts to develop Arabic decision support systems have been plagued with problems of how to search for information in a database.  Standard Arabic, which is used consistently in written language, has 29 letters, some of which can be adjusted with five different diacritics.  In addition, the alphabet consists of several sets of homophones, a rich morphology, and standardized spelling of Arabic names is error-prone.   Finally, there are almost 20 encodings currently in use for Arabic.  Thus, in order to create accurate queries of the database in a DSS, there needs to be some preprocessing of the input data.  Some have experimented with eliminating the diacritics.  Otair, Al-Sardi and Al-Gialain (2008), however, have developed a more promising intermediary product that attempts to understand the request before transforming them into SQL queries.  Their approach processes the words using a stem-based morphological analysis.  The tool, called the Arabic Query Analyzer (which is DMBS and application independent), has been fully implemented and has shown tangible performance metrics.   A related effort by El-Haj and Hammo (2008) built a query-oriented text summarization system to respond to natural language queries in Arabic.  Such a system could help decision makers understand the range of documents, both internal and on the internet, that might be of help to in a choice context.  They too have promising results.