Semantic Enhancement for Enterprise Data Management

Abstract

Abstract. Taking customer data as an example, the paper presents an approach to enhance the management of enterprise data by using Semantic Web technologies. Customer data is the most important kind of core business entity a company uses repeatedly across many business processes and systems, and customer data management (CDM) is becoming critical for enterprises because it keeps a single, complete and accurate record of customers across the enterprise. Existing CDM systems focus on integrating customer data from all customer-facing channels and front and back office systems through multiple interfaces, as well as publishing customer data to different applications. To make the effective use of the CDM system, this paper investigates semantic query and analysis over the integrated and centralized customer data, enabling automatic classification and relationship discovery. We have implemented these features over IBM Websphere Customer Center, and shown the prototype to our clients. We believe that our study and experiences are valuable for both Semantic Web community and data management community.

Semantic Technologies for Enterprise Cloud Management

“…For instance, [7] presents an approach to the collaborative management of systems monitoring information based on Semantic MediaWiki. From a more general, wiki-independent perspective, the benefits of semantic technologies for enterprise data management have been exemplarily discussed in the domain of customer data management in [10]. All these works are complementary to our work, which has its focus on the domain and challenges of enterprise cloud management.…”

Modelling provenance in hydrologic science: a case study on streamflow forecasting

“…subclass relationships) that are using in our provenance ontology. To handle this, we can either pre-compute inference results with the help of an OWL reasoner (Ma et al 2009) or perform reasoning at query time by extending the SPARQL queiy engine (Ma et al 2008). In the former case, more storage space is needed and the reasoning engine is independent of query processing; while in the latter case longer response times are expected and the reasoning engine needs to be bundled with query processing services.…”

A Review on RDB to RDF Mapping for Semantic Web

“…The real time applicability of semantic web is extended to areas far than web. The applications of semantic web in data exchange and integration from different data sources is elaborated in [2][3][4][5][6].…”