An Automatic Data Service Generation Approach for Cross-origin Datasets
| Autor: | Gang Xiao, Jiawei Lu, Huang Langyou, Yuanming Zhang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Service layer Data access object Computer science business.industry Cloud computing 02 engineering and technology computer.software_genre Encapsulation (networking) 020901 industrial engineering & automation Dependency graph Data access Data dependency 020204 information systems 0202 electrical engineering electronic engineering information engineering Data mining Data as a service business computer |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030192730 ICWE |
| DOI: | 10.1007/978-3-030-19274-7_27 |
| Popis: | As a unified data access model, data service has become a promising technique to integrate and share heterogeneous datasets. In order to publish overwhelming data on the web, it is a key to automatically extract and encapsulate data services from various datasets in cloud environment. In this paper, a novel data service generation approach for cross-origin datasets is proposed. An attribute dependency graph (ADG) is constructed by using inherent data dependency. Based on the ADG, an automatic data service extraction algorithm is implemented. The extracted atomic data services are further organized into another representation named data service dependency graph (DSDG). Then, a data service encapsulation framework, which includes an entity layer, a data access object layer and a service layer, is designed. Via a flexible RESTful service template, this framework can automatically encapsulate the extracted data services into the RESTful services which can be accessed by the exposed interfaces. In addition, a data service generation system has been developed. Experimental results show that the system has high efficiency and good quality for data service generation. |
| Databáze: | OpenAIRE |
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