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標題Title: Towards_a_SOA_Infrastructure_for_Statistically_Analysing_Public_Health_Data
作者Authors: 邱保欽,Pierpaolo Vittorini..等
上傳單位Department: 資訊管理系
上傳時間Date: 2009-11-17
上傳者Author: 邱保欽
審核單位Department: 資訊管理系
審核老師Teacher: 陳志達
檔案類型Categories: 課堂報告In-class Report
關鍵詞Keyword: SOA
摘要Abstract: To respond to the need for interoperable information systems in
public health, several proposals based on XML-related technologies
are currently available. For instance, the CDA [8] is an architecture
developed by the HL7 organization for representing and
managing clinical documents, while the PHIN [12] is a CDC infrastructure
whose aim is to automatically exchange XML data between
public health partners through ebXML compliant SOAP web
services [16, 11].
Despite the large efforts spent in developing standards and infrastructures
– though not conclusive – useful to achieve more effective
interoperability among public health information systems,
to the best of our knowledge, there are no researches produced
so far to statistically analyse biomedical data represented as XML
documents. Among the languages which can query XML documents,
XPath can perform only basic statistics (e.g. mean, minimum,
maximum [13]), while it is known that high-level tests are
mandatory for every common analysis. Thus, the sole current possibility
is to convert the data stored into such documents into a tabular
format, and to use a standard statistical package to perform the
analysis.
To overcome this limitation, the paper proposes a complete SOA
infrastructure, with major concern with the following components:
(i) the XFNSE web-service which contains a list of operations implementing
the statistical analyses reported in [6]; (ii) a prototype
of a service consumer – called JXFNSE – which uses the operations
exposed by XFNSE to statistically analyse a dataset; (iii) an
eXist [14] module which extends XPath by adding a list of functions
“tracing” the XFNSE operations.
Finally, by computing several performances, the authors discuss
the drawbacks of using services while analysing large datasets, and
show a possible improvement in terms of a caching mechanism.

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