the implementation of iso tc 211 standards in geospatial ... · – paradigm shift for...

CSISS Center for Spatial Information Science

and Systems

The Implementation of ISO TC 211

Standards in Geospatial Web Service

Environments

Meixia Deng & Liping Di Center for Spatial Information Science and Systems (CSISS)

George Mason University (GMU)

4400 University Drive, MSN 6E1

Fairfax, VA 22030

mdeng@gmu.edu

http://csiss.gmu.edu

and Systems

Outline

• Background

– Changes, challenges and new demands in Earth Sciences

– Urgent needs for geospatial web service environments and

• Limitations of traditional approaches and systems

• To meet new demands and challenges

• Principles of a Geospatial Web Service Environment

– Fundamentals

– Architecture Design and Implementation Guidance

• Standards In Action (Examples)

– Implementation of common data environment (CDE) and

common service environment (CSE) with OGC/ISO standards

– Implementation of Geospatial Data Provenance With ISO

19115 and ISO 19115-2 Lineage Model

• Discussion

and Systems

Changes, Challenges and New Demands in Earth

Sciences

• Changing Earth science (ES) for a Changing Planet – Shaped by rapid advances in technology

– Increasingly data-intensive & compute-intensive

– ES research, education and application require easy access to and use of

large volumes of multi-source ES data

– Computer-based data visualization, processing and analysis and model

simulation have become standard methods in ES studies

• Imminent Challenges in ES – How to efficiently transform Earth observations (data) into applicable

products (information), and intelligible results and discoveries (knowledge)?

– How to effectively provide reasoned solutions to problems we face with

climate change, environment protection, natural hazards, and other global

issues?

• New Demands

– Paradigm shift for methodological changes in ES

– Societal demands – e.g. efficiency, quality, and personalized

products and services for better understanding the Earth

and Systems

Urgent Need for Geospatial Web Service

Environments

• To meet the demands and challenges of ES

– Traditional approaches and systems have many

limitations

• Insufficient in enabling access, integration and analysis

of historical and real-time information from various

sources in a reliable, accurate, consistent, seamless and

understandable way and in the fastest possible time.

• Big Data and geoprocessing-modeling issues

• To encourage sharing and collaboration

• To engage community and citizen scientists

• To promote scientific democracy

and Systems

Why A Geospatial Web Service Environment

• Advantages

– Leveraging the latest advances in Web service,

geospatial interoperability and cyberinfrastructure

– Overcoming major limitations of current systems in

• data, information and knowledge services,

• near real-time and on-demand capability

• Interoperability among data, service and systems

– Approaching to an information system that can

provide complete information to support research,

education, and applications (e.g., decision-

making support).

and Systems

CSISS Efforts

• During the past decade, CSISS has dedicated to

developing geospatial Web service technology and

developed a series of operational geospatial web

service environments, which are open and freely

accessible to worldwide users.

– GeoBrain (http://geobrain.laits.gmu.edu)

– DEMExplorer (http://ws.csiss.gmu.edu/DEMExplorer/)

– CropScape (http://nassgeodata.gmu.edu/CropScape/)

– VegScape (http://nassgeodata.gmu.edu/VegScape/)

– GADMFS (the Global Agricultural Drought Monitoring and

Forecasting System) (http://gis.csiss.gmu.edu/GADMFS/)

• Despite very different foci, functionalities,

usages, application areas, and targeted

audiences, all of them share a common

infrastructure.

and Systems

Common Infrastructure of the Environments

and Systems

Fundamentals (1/3)

• Addressed interoperability at data, service (function) and

system levels

– OGC, ISO, W3C and OASIS standards compliant

– Design and implementation of Common Data Environment

(CDE) for providing standard interfaces to discover and

access diverse data and information in distributed data

archives and repositories

– Design and implementation of Common Service

Environment (CSE) for both human and machine users to

discover and invoke geoprocessing services (analytic

functions) at a standard way for processing and analyzing

data obtained from CDE

and Systems

Fundamentals (2/3)

• Addressed service-ability for meeting the Big Data and

geoprocessing-modeling challenges

– To provide sufficient data services: any data available in the

system are coupled with adequate metadata and data pre-

processing services (e.g. sub-setting, reformatting,

resampling, and re-projection) so that the data can be easily

discovered, visualized, customized, retrieved and integrated

through the system

– To provide sufficient information services: adequate

geoprocessing and analysis functions are readily available

and applicable to data in the system as interoperable and

dynamically chainable and executable geospatial Web

services so that online, on-demand and near real-time

geoprocessing and analysis of data can be performed on-

the-fly to generate user-required information products.

and Systems

Fundamentals (3/3)

• Systematic framework – Interoperability - the system has the capability of the plug-in-and-play

over the web so that drought related geospatial resources (e.g., data,

services, service systems) from other sources can be plugged into the

system on-the-fly for meeting the on-demand needs of a wider, more

diverse audiences of both human and machine users (the system is

interoperable at data, service, and system levels).

– Scalability - the system is scalable from the construction

perspective (component-based and modular) and user-

orientation perspective (able to deal with any scale from local to

global per users’ requests).

– Reusability – the system is reusable at function, component, and

system levels.

– Adaptability - the system is flexible, self-evolvable and

maintainable.

and Systems

Implementation of CDE and CSE (1/5)

• Standards Compliance

ISO/TC 211 standards

W3C and OASIS standards

OGC standards: WCS, WMS, WFS, WPS,

CSW,…

and Systems

• ISO/TC211 Standards Observed

– ISO 19107:2003—Spatial Schema

– ISO 19108:2003—Temporal Schema

– ISO 19115:2003—Metadata

– ISO 19119:2005—Services

– ISO19125-1:2004 (Geographic information - Simple Feature

Access - Part 1: Common Architecture); ISO 19125-2:2004

(Geographic information - Simple feature access - Part 2:

SQL Option);

– ISO 19128 Web Map Service (OGC WMS)

– ISO 19136 (Geographic information - Geography Markup

Language (OGC GML)

– ISO 19142:2010 Web Feature Service (OGC WFS)

– More …

and Systems

• CDE is implemented with

– OGC standards compliant data services

– catalogue federation and component registration services

• CSE has been implemented with

– ISO/OGC standards (e.g. WPS) for geoprocessing

– augmented CSW and CFS with ontology for service discovery

and registry.

– W3C standards (e.g. Web Service Description Language

(WSDL), Simple Object Access Protocol (SOAP) for binding)

and OASIS standards (e.g. WS-BPEL) for Web service and

service chaining

and Systems

• With CDE and CSE, data and services in distributed

systems can be accessed and used over Internet from a

single point of entry. CDE and CSE enable federated

discovery of and access to distributed data and

information coupled with interoperable, personalized and

on-demand data and processing services.

• Catalog Federation and Component Registry Services

– Standards compliant:

• OGC Catalog Service for the Web (CSW) Specification

• ISO 19119 Metadata Profile

• ISO 19115:2003—Metadata

• OASIS ebRIM Profile

• OpenSearch

– Support publishing and discovery of distributed geospatial

data and associated services

and Systems

• OGC Data Services

– Web Map Service

– OGC WMS ( also as ISO 19128 Web Map Service)

– OGC WMS-Tiling (WMTS) built on WMS

– Web Coverage Service (WCS)

- OGC WCS Specification

– Web Feature Service (WFS)

- OGC WFS Specification (also as ISO 19142:2010)

– Coordinate Transformation Services

- OGC CTS

• OGC WPS

– Implemented a large number of WPSs, over 80 of them

based on GRASS

• BPELPower –workflow engine

and Systems

Implementation of Geospatial Data Provenance

• Importance

– data quality and usability evaluation, data trail audition,…

– workflow replication and product reproducibility

– Integration of generation of the geospatial provenance

metadata and execution of geo-processing workflow

promises great benefit since they are complementary.

• Challenges

– The heterogeneity of data and computing resources in the

distributed environment constructed under the service-

oriented architecture (SOA) brings a great challenge to

resource integration.

– Specifically, the issues, such as the lack of interoperability

and compatibility among provenance metadata models and

between provenance and workflow, create obstacles for the

integration of provenance, and geo-processing workflow.

and Systems

Implementation of Geospatial Data Provenance

• A proposed method

– To break the provenance heterogeneity through recording

provenance information in a standard lineage model defined

in ISO 19115:2003 and ISO 19115-2:2009 standards.

– To bridge the gap between provenance and geo-processing

workflow through extending both workflow language and

service interface, making it possible for the automatic

capture of provenance information in the geospatial web

service environment.

• The proposed method is implemented in GeoBrain.

– The testing result from implementation shows that the

geospatial provenance information is successfully captured

throughout the life cycle of geo-processing workflows and

properly recorded in the ISO standard lineage model.

and Systems

Discussion (1/2)

• Why the common infrastructure of an open, three-tier,

component-based, standards-compliant, and SOA- based

geospatial web service system?

– organizing and delivery of large volumes of multi-source

datasets;

– disseminating data, information, and model results quickly to

a wide audience;

– enabling thorough, collaborative, and on-demand analysis to

users’ best advantages.

– highlighting the role of data-intensive science in transforming

raw observations into applicable, intelligible results and

discoveries.

and Systems

Discussion (2/2)

• Why a standards-compliant approach?

– Enabling interoperability in building all the open

environments in CSISS, which proves to be very successful.

• the best option so far for enabling wide interoperability in

building open data and information environments, despite some

existing arguments about the costs vs. benefits of adopting

standards.

– Enabling reusability. Components developed in GeoBrain

have been successfully used for building other environments

in CSISS. Great savings!

• It still remains a grand challenge to enable adequate and

proper interoperability among different data, services, and

systems due to technical, cultural, and organizational

barriers.

and Systems

References (1/2)

• Deng, M., L. Di, W. Han, A. Yagci, C. Peng and Gil Heo, 2013. Web-

service-based Monitoring and Analysis of Global Agricultural Drought,

Photogrammetric Engineering & Remote Sensing (PE&RS), 79 (10),

pp.929-943.

• Di, L., Y. Shao, and L. Kang , 2013. Implementation of Geospatial Data

Provenance in a Web Service Workflow Environment With ISO 19115

and ISO 19115-2 Lineage Model, IEEE Transactions On Geoscience

And Remote Sensing, 51(11), pp. 5082-5089.

• Deng, M. and L. Di, 2013. Building Open Environments To Meet Big

Data Challenges In Earth Sciences, Big Data Techniques and

Technologies in Geoinformatics (Editor: Hassan Karimi), CRC Press,

Chapter 4, pp. 67-88

• Deng, M. and L. Di, 2010. Facilitating Data-intensive Research and

Education in Earth Science - A Geospatial Web Service Approach, LAP

LAMBERT Academic Publishing GmbH & Co. KG, Saarbrücken,

Germany. ISBN: 978-3-8383-9714-6.

and Systems

References (2/2)

• Deng, M. and L. Di, 2010. GeoBrain for Data-Intensive Earth

Science (ES) Education, Advanced Geoinformation Science (C.

Yang, D. Wong, Q. Miao and R. Yang, editors), CRC Press.

• Deng, M. and L. Di, 2009. Building an Online Learning and

Research Environment to Enhance Use of Geospatial Data,

International Journal of Spatial Data Infrastructures Research,

2009, 4:77-95.

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