Data warehousing energizes your enterprise
Datamation
The data warehouse toolkit: practical techniques for building dimensional data warehouses
The data warehouse toolkit: practical techniques for building dimensional data warehouses
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Summarizing text documents: sentence selection and evaluation metrics
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Communications of the ACM
Data mining: concepts and techniques
Data mining: concepts and techniques
Concept-based knowledge discovery in texts extracted from the Web
ACM SIGKDD Explorations Newsletter
Probabilistic techniques for phrase extraction
Information Processing and Management: an International Journal
Document Warehousing and Text Mining: Techniques for Improving Business Operations, Marketing, and Sales
Building the Data Warehouse
Data Warehousing in the Real World: A Practical Guide for Building Decision Support Systems
Data Warehousing in the Real World: A Practical Guide for Building Decision Support Systems
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
Ontologies for conceptual modeling: their creation, use, and management
Data & Knowledge Engineering
IEEE Internet Computing
IEEE Internet Computing
What Are Ontologies, and Why Do We Need Them?
IEEE Intelligent Systems
MDX Solutions: with Microsoft SQL Server Analysis Services 2005 and Hyperion Essbase
MDX Solutions: with Microsoft SQL Server Analysis Services 2005 and Hyperion Essbase
A conceptual model for multidimensional analysis of documents
ER'07 Proceedings of the 26th international conference on Conceptual modeling
Toward total business intelligence incorporating structured and unstructured data
Proceedings of the 2nd International Workshop on Business intelligencE and the WEB
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During the past decade, data warehousing has been widely adopted in the business community. It provides multi-dimensional analyses on cumulated historical business data for helping contemporary administrative decision-makings. However, many data warehousing query language in present only provides on-line analytical processing (OLAP) for numeric data. For example, MDX (Multi-Dimensional eXpressions) has been proposed as a query language to allow describing multi-dimensional queries over databases with OLAP capabilities. Nevertheless, it is believed there is only about 20% information can be extracted from data warehouses concerning numeric data only, the other 80% information is hidden in non-numeric data or even in documents. Therefore, many researchers now advocate it is time to conduct research works on document warehousing to capture complete business intelligence. Document warehouses, unlike traditional document management systems, include extensive semantic information about documents, cross-document feature relations, and document grouping or clustering to provide a more accurate and more efficient access to text-oriented business intelligence. In this paper, we extend the structure of MDX into a new one containing complete constructs for querying document warehouses. The traditional MDX only contains SELECT, FROM, and WHERE clauses, which is not rich enough for document warehousing. In this paper, we present how to extend the language constructs to include GROUP BY, HAVING, and ORDER BY to design an SQL-like query language for document warehousing. The work is essential for establishing an infrastructure to help combining text processing with numeric OLAP processing technologies. Hopefully, the combination of data warehousing and document warehousing will be one of the most important kernels of knowledge management and customer relationship management applications.