Cloud based linked data platform for Structural Engineering
Experiment Xiaohui Zhang [email protected]
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Outline Motivation The CLDP-SEE Platform Conclusion and Future
Work 2
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Motivation Structural Engineering A discipline analyzing the
force and deformation of buildings by mechanical methods.
Experiment is one of the main means for domain research. Large
amounts of experimental data is accumulated, but be maintained by
each experimental user dispersedly. Due to the complexity and
heterogeneity of the experimental data, the sharing and integrating
with the traditional methods is difficult. 3
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Motivation Linked Data Linked Data is simply about using the
Web to publish structured data and create typed links between data
from different sources. Based on semantic web, linked data uses RDF
to make typed statements that link arbitrary things in the world.
Linked data provides a wonderful approach to publish and consume
data on the web and make the web be a global data space which can
be understood both by computer and human. 4
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Motivation Linked Data for Structure Engineering The data
represented based on semantic can be understood by machines, which
is helpful for the integration and processing of experimental data.
The interlinking among data from different sources is a effective
measure for the heterogeneity. Linked data will make it easy for
the sharing and intelligent processing of experimental data. 5
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Motivation A huge challenge for domain researchers to deploy
and use Linked Data related tools to make operations on the data:
Conversion of data format Publication of experiment data
Integration of experiment data Consuming of linked experiment data
6
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Motivation A centralized platform providing all the functions
needed by experiencing linked data in services is necessary for
domain researchers. A linked data platform based on cloud for
Structural Engineering Experiment (CLDP-SEE) is proposed by this
paper. The publishing, interlinking and consuming of experiment
data is an intact ecosystem of data sharing. CLDP-SEE can lower the
threshold of sharing data with linked data technology for domain
users; promote the growth of the linked data ecosystem and the
development of Structural Engineering discipline. 7
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The CLDP-SEE Platform The application scenario of CLDP-SEE
8
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The CLDP-SEE Platform The operations in application scenario
Uploading and managing the RDF data, setting access control
policies of each datasets. Uploading raw data in traditional
formats, such as CSV, Excel, Relational Database. And then
converting these raw data into RDF. Querying datasets from the
shared data space, private data space according to the authority
and even the datasets from the Web, and then interlinking data
among these datasets to generate a Virtual Data Space. Reasoning
and querying the data in Virtual Data Space. Publishing data with
Linked Data Server. 9
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The CLDP-SEE Platform The Architecture of CLDP-SEE 10
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The CLDP-SEE Platform Portal Layer Provides graphical web
interface for users to experience almost all the functions
providing by CLDP-SEE. 11
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The CLDP-SEE Platform Core Service Layer Data Manage Service:
is mainly used to help users to manage their data. Data Upload Data
Format Transform Dataset Registry Dataset Manage Data Publish
Authority Manage 12
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The CLDP-SEE Platform Core Service Layer Data Link Service
Provides the capabilities of data integration; Coreference
Interlink is responsible for getting the request of users, and
finding the coreference relations between data from different
datasets. The coreference relation of RDF data refers to two
different URI pointing to the same entity. Two methods of
coreference interlinking: Similarity computation: implemented
according to SILk(Isele, R.; Jentzsch, A. & Bizer, C. 2010)
Rules matching: Link Rule Manage service provides graphical
interface for the experts and users to define rules. Links Update
will update the links with the information collected by Dataset
Monitor service. 13
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The CLDP-SEE Platform Core Service Layer Data Reason Service
The rule-based inference is mainly done by this service. Users can
select any datasets from Virtual Data Space, Private Space or
Shared Data Space according to the authority. Inference Rule Manage
supports each user to define and manage their private inference
rules, and check the consistency with default rules provided by
domain experts. Default rules and user-defined rules can be applied
in the inference. 14
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The CLDP-SEE Platform Core Service Layer Data Query Service The
basis of consuming linked experiment data. Two kinds of query
interfaces: navigation query based on SEE ontology query based on
keywords Support users self-defining the scope of query. Query
Engine is responsible for processing the request from self-service
portal, and executes SPARQL query on the datasets selected by
users. 15
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The CLDP-SEE Platform Supporting Service Layer The services in
this layer are mainly supporting the functions of the services in
Core Service Layer. Data service mainly provides the underlying
functions of RDF data management and access. Ontology Manage
service, Dataset Access service Dataset Storage service, Dataset
Monitor. Publish Service mainly supports the Data Publish in Data
Manage Service. Linked Data Server RDF File Server 16
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The CLDP-SEE Platform Supporting Service Layer User Service:
Metadata Manage service: manages the information of users and make
user can update personal materials. Role Manage service: be
provided for platform administrator to manage the roles of users.
Social Network Manage service: manages the friend relationships
among users, and provides personal space for each user. 17
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The CLDP-SEE Platform Data Storage Layer SEE Ontology stores
the unified ontology schema and the data in Shared Data Space. RDF
Datasets stores the datasets in users Private Data Space, and
ensure the isolation between users. Links of Data stores the
relation between the entities from different datasets. Rule Base
default rule bases user defined rules 18
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Conclusion and Future Work CLDP-SEE provides almost all the
services needed by Structural Engineering domain users to manage
and share experiment data based on linked data technology. Future
work: Improving the performance of data linking and inference. More
flexible access control policy and fine- grained access control
model. 19
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Thank You! 20
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Related Works Publication of Linked Data D2R Server
(Prudhommeaux & Seaborne. 2006) publishing the content of
relational databases as RDF. Pubby and Elda: providing Linked Data
interfaces for RDF data sources. 21
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Related Works Searching and Browsing of Linked Data linked data
browser: enables people to view data from one dataset to another by
following RDF links. Tabulator (Berners-Lee et al., 2006) OpenLink
Browser
(http://oat.openlinksw.com/rdfbrowser2/)http://oat.openlinksw.com/rdfbrowser2/
Marbles (http://marbles.sourceforge.net/) linked data engine:
provides service for people querying the Web of Data. Falcons,
Sindice, Swoogle and SWSE 22
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Related Works Interlinking of Linked Data SILK (Robert et al.,
2010) DSNotify(Haslhofer & Popitsch, 2009) LinkedDataBR (Kelli
et al., 2011): a platform used by Brazil for linking open Brazilian
governmental data. Talis: a platform for RDF data sharing via
weaving data with the Web to create a highly available and
adaptable environment. (http://www.talis.com/platform/) 23
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Related Works CLDP-SEE provides services for the storage,
query, publishing and management of RDF data. CLDP-SEE provides
more perfect services with cloud characteristics: More flexible and
personalized self-service model; Query the datasets according to
subject, and ineterlink the data in the result datasets; Elastical
reasoning service on the user-defined datasets; A shared RDF
repository with rich interlinks among data. 24