Data extraction from highway data model

Transcription

1 Data extraction from highway data model Tuyen Le a and H. David Jeong b a Ph.D. Student, Dept. of Civil, Construction and Environmental Engineering, Iowa State University. Ames, ttle@iastate.edu b Associate Professor, Dept. of Civil, Construction and Environmental Engineering, Iowa State University. Ames, djeong@iastate.edu 2015 Mid-Continent Transportation Research Symposium Today's Innovation, Tomorrow's Best Practice August 19 20, 2015 Gateway Hotel and Conference Center Ames, IA

2 Outline Background Research hypothesis Research methodology GraHIQ development Example Summary

3 Background

4 Highway information model Virtual model of a structure in digital world 4D 3D 5D n-d

5 Highway information model Design Construction Asset management Visualization Sequencing/Simulation Emergency simulation Clash detection Cost estimation Operation simulation

6 Data sharing: Issue Architect Owner Engineer Interoperability issue Asset Manager Contractor Project Manager

7 Data sharing: Issue Designer s systems Documents Drawings Contractor s systems Documents Drawings Asset manager systems Paper based exchange Re-creation issue Error-prone Time-consuming Costly Estimated cost for U.S. capital >=$15.8 billion/year (Gallaher et al. 2004).

8 Data sharing: Solution Owner Asset Manager Architect LandXML Project Manager Engineer Contractor LandXML: International standard XML data schema Shared data model for Civil projects Covering wide range of lifecycle information Open data format

9 Limitations Neutral format alone is insufficient (Froese 2003; East et al. 2012), since Landxml is a rich dataset, but a transaction requires only a subset. XML cannot achieve the required level of interoperability (Wang et al. 2005), and its query language (XQuery) is still challenging to new users. Xquery code

10 The emergence of Graph database Graph DB XML Relational

11 Research hypothesis

12 Research hypothesis Input Output Data requirements LandXML document How? RDF Query LandXML subset Research hypothesis: RDF database and query can simplify the data extraction process from a highway data model.

13 Methodology RDF and related tools

14 RDF (Resource Description Framework) RDF structure: triple RDF ontology RDF query: SPARQL RDF Graph Facebook found_by Mark Zuckerberg married married Priscilla Chan SELECT?z FROM NAMED <facebook.ttl> WHERE { Triple Subject predicate Object?x _:found_by?y.?y _:married?z.} Ontology Company Found_By Person married

15 Research objectives & Tools Objectives and tools Roadway ONTOLOGY using Ontology Web Language (OWL) Converting data from Landxml to RDF format A data QUERY protocol using SPARQL

16 GraHIQ Graph Highway Information Query Framework

17 Proposed framework Graph Highway Information Query (GraHIQ) Input Data requirement Landxml document User GraHIQ LandXML Ontology 1 Jena SPARQL 3 Data wrapper 2 Searching path Output data subset RDF graph

18 Query process: User guide Step 1: Convert Landxml file to RDF file using GraHIQ Step 2: Find informal searching path using GraHIQ Step 3: Construct SPARQL searching path by user

19 GraHIQ-Module 1 LandXML Ontology Derived from LandXML Limited to roadway geometry information

20 GraHIQ-Module 2 Data translator Matching rules for translating from Landxml to RDF

21 GraHIQ-Module 3 Data query Built upon Jena SPARQL API in Java Input are searching path and RDF dataset Output is a RDF subset

22 Example

23 Example Step 1: Translating design dataset from Landxml to RDF format

24 Example Step 2: constructing informal searching path EX: What type of seal coat of route 202? Route haspavement Pavement hascoat Coat

25 Example Step 3: constructing formal searching path Route haspavement Pavement hascoat Coat FROM NAMED <rdf_design.ttl> WHERE { {?Route design:hasname?routename. Path?Route design:haspavement?pavement.?pavement design:hascoat?coat.} Input file

26 Example Result

27 Summary & Future direction

28 Summary Paper based exchange is time consuming, errorprone. Existing open exchange format is complicated, user unfriendly. This paper proposes a graph based method for extracting subset of data. Software protocols were developed to assist the data extraction process. Sample project was performed to validate the proposed method.

29 Future direction Apply the proposed method on a real project. Automatize the process of identifying searching path using natural language processing. Expand the data schema/ontology to other domains throughout project life cycle. Develop a novel mechanism for data integration and extraction from isolated data sources.

30 THANK YOU!