EIAW: Towards a Business-Friendly Data Warehouse Using Semantic Web Technologies

Abstract

Abstract. Data warehouse is now widely used in business analysis and decision making processes. To adapt the rapidly changing business environment, we develop a tool to make data warehouses more business-friendly by using Semantic Web technologies. The main idea is to make business semantics explicit by uniformly representing the business metadata (i.e. conceptual enterprise data model and multidimensional model) with an extended OWL language. Then a mapping from the business metadata to the schema of the data warehouse is built. When an analysis request is raised, a customized data mart with data populated from the data warehouse can be automatically generated with the help of this built-in knowledge. This tool, called Enterprise Information Asset Workbench (EIAW), is deployed at the Taikang Life Insurance Company, one of the top five insurance companies of China. User feedback shows that OWL provides an excellent basis for the representation of business semantics in data warehouse, but many necessary extensions are also needed in the real application. The user also deemed this tool very helpful because of its flexibility and speeding up data mart deployment in face of business changes.

Ontological On-line Analytical Processing for Integrating Energy Sensor Data

“…Xie et al [55] realized that there were rapid changes in business environment; they have developed a tool based on semantic web technologies to make data warehouses business-friendly. They proposed an extended OWL language to represent the business metadata that includes conceptual enterprise data model and multidimensional model, so the business semantics can be modelled and used to automatically generate a customized data mart with the required data and an OLAP cube metadata.…”

“…(deftemplate Sensor (multislot Sensor_Reading) (multislot Time) (slot sensorID) (slot Location)) (slot-setSensor1Sensor_Reading21,33, 44,55,33,32) The data in the ontology can be downloaded to Jess for reasoning.…”

SemPI: A semantic framework for the collaborative construction and maintenance of a shared dictionary of performance indicators

“…While the ontological representation of the aggregation structure of PIs, typical of the multidimensional model, is largely studied [24][25][26][27], the compound nature and consequent dependency among indicators is much less explored. In [28] formulas are specified through a proprietary script language aimed at the automatic generation of customised data marts, capable to calculate complex measures from data on the basis of their definitions.…”

Modelling Architecture for Multimedia Data Warehouse

“…[1] proposed hierarchical way to store semantic data, they use two repositories of metadata which describes data in hierarchical manner in XML files. The authors of [4] have built a data warehouse which has two ontologies, one for the specific business terms and one for the technical terms, specific to the aggregation and knowledge extraction tools. This requires a one-time collaboration between the business experts and data warehouse designers, to produce a mapping between two ontologies.…”