In our previous work, we proposed the ontology-based integration of data warehousing to make existing data warehouse system more user-friendly, adaptive and automatic. This paper further outlines a high-level picture of the ontological engineering in data warehousing. Its basic theory includes building ontology profiles for warehousing in terms of domain ontology and problemsolving ontology, an...

The aim is to study the data warehouse specific security needs which means focusing on the long life cycle of the data and the data security and privacy issues. These requirements needs to be fulfilled despite of the unpredictable future uses of the data. The basic assumption is that data warehouse server is running on the dedicated server, and the basic information system security is properly ...

Traditional data warehouses have played a key role in decision support system until the recent past. However, the rapid growing of the data generation by the current applications requires new data warehousing systems: volume and format of collected datasets, data source variety, integration of unstructured data and powerful analytical processing. In the age of the Big Data, it is important to f...

During the past decade, data warehousing has been widely adopted in the business community. It provides multidimensional analyses on cumulated historical business data for helping contemporary administrative decision-making. Nevertheless, it is believed that only about 20% information can be extracted from data warehouses concerning numeric data only, the other 80% information is hidden in non-...

In order to analyze market trends and make reasonable business plans, a company’s local data is not sufficient. Decision making must also be based on information from suppliers, partners and competitors. This external data can be obtained from the Web in many cases, but must be integrated with the company’s own data, for example, in a data warehouse. To this end, Web data has to be mapped to th...

There is a growing awareness that high quality of data is a key to today‟s business success and dirty data that exits within data sources is one of the reasons that cause poor data quality. To ensure high quality, enterprises need to have a process, methodologies and resources to monitor and analyze the quality of data, methodologies for preventing and/or detecting and repairing dirty data. How...

More data, especially unstructured data, is available to users than ever. There is so much data available that it is diicult for users to make use of their data in its raw form. To handle the diversity of data types, we have designed and prototyped a multidatabase/warehouse system. The system has been especially designed to facilitate the interaction of structured and unstructured data. The sys...