Data Transformation Services Research Articles

SinoPedia-A Linked Data Services platform for decentralized knowledge base.

Knowledge bases are largely developed and utilized in academic and industrial fields, such as DBPedia, VIAF, LoC, Getty, and which are published in the Linked Data format. However, if you want to view different resources on these knowledge bases, you have to switch between different web pages from these resources. Therefore, we proposed a decentralized data hub named SinoPedia, which consists of several linked data services and can re-publish these RDF data in one platform. Firstly, these different Linked Data services include: Linked Data Transformation Service (LDTS), Linked Data Query Service (LDQS), Linked Data Publishing Service (LDPS) and Linked Data Knowledge Service (LDKS). The resource URI is the basis and core of the linked data, thus we will focus on the resource forwarding mechanism in LDPS service which can rewrite resource URI using a global and standard format among knowledge bases. Some knowledge bases were configured in SinoPedia platform in this paper. In addition to the above services, Linked Data Reasoning Service (LDRS) and Linked Data Intelligence Service (LDIS) will be added to the platform in the future. In short, all of these Linked Data Services will form the core framework in order to providing a good linked data application ecosystem.

A framework for modeling and supporting data transformation services over data and knowledge grids with real‐time bound constraints

AbstractData and Knowledge Grids represent emerging and attracting application scenarios for Grid Computing, and pose novel and previously unrecognized challenges to the research community. Basically, Data and Knowledge Grids are found on high‐performance Grid infrastructures, and add to the latter meaningful data‐ and knowledge‐oriented abstractions and metaphors that perfectly marry with innovative requirements of modern complex Intelligent Information Systems. To this end, Service‐oriented Architectures and Paradigms are the most popular for Grids, and on the whole represent an active and widely recognized area of Grid Computing research. In this paper, we introduce the so‐called Grid‐based RTSOA frameworks, which essentially combine Grid Computing with real‐time service management and execution paradigms, and place emphasis for novel research perspectives in data‐intensive e‐science Grid applications on real‐time bound constraints. Grid‐based RTSOA frameworks are then specialized to the particular context of Data Transformation services over Grids, which play a relevant role for both Data and Knowledge Grids. Finally, we complete the main contribution of this paper with a rigorous theoretical model for efficiently supporting Grid‐based RTSOA frameworks, with particular emphasis on the context of Data Transformation services over Grids, along with its comprehensive experimental assessment and analysis. Copyright © 2010 John Wiley & Sons, Ltd.