CIAO-WPS

Automatic and Intelligent Orchestration of Geospatial Web Services using Semantic

Web (Web 3.0) Technologies

Chet Bing Tan, Geoff West, David A. McMeekin and Simon Moncrieff

Curtin University, Cooperative Research Centre for Spatial Information, Bentley, Australia

Keywords:

Semantic Web, Web 3.0, Ontologies, Metadata, Web Processing Services, WPS, CIAO-WPS.

Abstract:

Current geospatial datasets and web services are disparate, obscure and difﬁcult to expose to the world. With

the advent of geospatial processes utilizing temporal data and big data, along with datasets continually increas-

ing in size, the problem of under-exposed datasets and web services is ampliﬁed. Current text search capabil-

ities do not sufﬁciently expose web services and datasets for use in on-the-ﬂy geospatial use cases. End users

are required to know the exact location of these online resources, their format and what they do. For example,

to locate an OGC (Open Geospatial Consortium - http://www.opengeospatial.org)-compliant WPS (Web Pro-

cessing Service) that performs ﬂood modelling, a Google Search for “Flood Modelling WPS” is insufﬁcient

to ﬁnd relevant results. This paper proposes the integration of semantic web concepts and technologies into

geospatial datasets and web services, making it possible to link these datasets and services via functionality,

the inputs required and the outputs produced. To do so requires the extensive use of metadata to allow for

a standardised form of description of their function. There are already ISO (International Organization for

Standardization - www.iso.org) standards in place (ISO 19115-1:2014) that specify the schema required for

describing geographic information and services. The use of ontologies and AI (Artiﬁcial Intelligence) then

allows for the intelligent determination of which web services and datasets to use, and in what order they are

to be used to achieve the desired ﬁnal output. This research aims to provide a method to automatically and

intelligently chain together web services and datasets to assist in a geospatial analyst’s productivity. A sim-

ple prototype termed CIAO-WPS (Chet’s Intelligent, Automatically-Orchestrated Web Processing Services)

is created as a proof of concept, using the Python programming language. The prototype seeks to reinforce

ideas in regards to pathing and cost constraints, as well as explore overlooked designs.

1 INTRODUCTION

ACIL Tasman (ACIL Tasman, 2008) determined that

inefﬁcient access to geospatial data is estimated to

have reduced the direct productivity impacts in certain

sectors by around $0.5 billion, and that reductions in

these inefﬁciencies will contribute to Australia’s eco-

nomic, social and environmental development goals.

This is not only due to the current exposure issues

of geospatial datasets and web services, but also the

manual processing and workﬂows involved in chain-

ing together these resources to achieve a task.

Current geospatial workﬂows that include geospa-

tial web services (for example, ﬂood prediction within

a given area) are manual, requiring the chaining to-

gether and intervention between the running of pro-

cesses to be manually performed to ensure the rele-

vant output is generated. The possible introduction

of human error as a result of manually processing

and searching the datasets and/or web services also

increases the probability that the output generated is

based on out-dated or even irrelevant data.

There are currently many inefﬁciencies with pro-

cessing and using geospatial data. Yu and Liu (Yu

and Liu, 2013) have documented these similar inefﬁ-

ciencies in their attempts to implement a new system

that republishes real-world data as linked geo-sensor

data. Janowicz (Janowicz and Blomqvist, 2012) has

also observed in their survey of semantically-enabled

DSS (Decision Support Systems), that the users of

said system still require a lot of work in increasing

the efﬁciency and productivity of the intelligent sys-

tem. The research outcomes of this project aligns with

the conclusions of the survey.

The ability to automatically and intelligently or-

chestrate multiple geospatial web services to provide

intelligent and useful output from a complex user

Tan, C., West, G., McMeekin, D. and Moncrieff, S.

CIAO-WPS - Automatic and Intelligent Orchestration of Geospatial Web Services using Semantic Web (Web 3.0) Technologies.

In Proceedings of the 2nd International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2016), pages 71-79

ISBN: 978-989-758-188-5

query will greatly assist a geospatial analyst’s efﬁ-

ciency, accuracy and productivity. Being able to or-

chestrate geospatial datasets and WPS to achieve a

task will be adaptable to other ﬁelds as well. Integrat-

ing Semantic Web concepts and technologies into var-

ious non-geospatial datasets and Web Services will al-

low for automatic orchestration and hence, increased

productivity. As an example, Kauppinen (Kauppinen

et al., 2014) has documented how semantic technolo-

gies are being integrated within Brazilian Amazon

Rainforest data, that has led to increased productiv-

ity and efﬁciency in that area.

To advance automated and intelligent orchestra-

tion, certain features and speciﬁcations must be added

to the current WPS standard to allow for machine in-

terpretation of these Web Services, to be able to ef-

fectively and reliably determine which Web Services

are appropriate for completing a given task. Critical

information that paves the way for automatic orches-

tration are currently not deﬁned in the WPS speciﬁca-

tion and this research aims to set an example for the

addition of metadata and functions that allow for au-

tomatic orchestration as the next logical step for WPS.

This paper explores automated orchestration

methods of web services and data from multiple, dis-

parate sources; in contrast to the current widespread

method of supplying all the data and services to the

end user and leaving it to them to manually analyse

and process the vast amounts of varying kinds of data,

and determine what processing needs to be executed.

Natural language processors and ontologies are pro-

posed to build the required Artiﬁcial Intelligence to

automatically chain together the resources to produce

useful output for the end user.

2 BACKGROUND

In the last decade, the Web has been moving to-

wards Service-Oriented Computing architecture sup-

porting automated use (Ameller et al., 2015) (Huhns

and Singh, 2005). This architecture aims to build a

network of interoperable and collaborative applica-

tions, independent of platform, called services (Pa-

pazoglou and Georgakopoulos, 2003) (Pugliese and

Tiezzi, 2012). The geospatial world is also mov-

ing away from the traditional desktop application

paradigm to processing and accessing data on-the-ﬂy

from the Web using Web Processing Services, as out-

lined by Granell et al. (Granell et al., 2012). As Web

Service technology has matured in recent years, an

increasing amount of geospatial content and process-

ing capabilities are available online as Web Services

(Zhao et al., 2012). These Web Services enable inter-

operable, distributed, and collaborative geoprocessing

to signiﬁcantly enhance the abilities of users to col-

lect, analyze and derive geospatial data, information,

and knowledge over the Internet (Zhao et al., 2012).

Current geospatial workﬂows and processes rely

on manual human intervention in searching for the

relevant and/or required datasets and Web Services

(Granell et al., 2012). These workﬂows also require

human analysis of the output at each stage of process-

ing, and manual determination of which Web Process-

ing Service to use next on the data to achieve the ﬁnal

required output (Granell et al., 2012). This has been

observed to lead to inefﬁciencies in the accuracy and

currency of the data as we are relying on a human user

to search for these ill-exposed datasets and Web Ser-

vices. For example, a human user will tend to have a

bias towards a dataset or Web Service that he/she has

used before, regardless of the currency of the data or

the frequency of updating of the data, a phenomenon

known as the Mere-repeated-exposure paradigm (Za-

jonc, 2001). Geospatial Web Services and datasets

that may be vital in contributing to the ﬁnal result

may also be left unexposed due to current search tech-

nologies not being able to expose Web Services sufﬁ-

ciently. The way that Web Services are searched for is

by functional and non-functional requirements as well

as interactive behaviour (Wu, 2015a), which require

more than simple keyword matching, as per current

search algorithms.

2.1 The Semantic Web (Web 3.0)

The Semantic Web aims to create a web of infor-

mation that is machine-readable and not just human-

readable (Berners-Lee et al., 2001). This allows ma-

chines to automatically ﬁnd, combine and act upon

information found on the Web (Pulido et al., 2006).

The objective of the Semantic Web is accom-

plished by integrating semantic content into web

pages that helps describe the contents and context of

the data in the form of metadata (data about data)

(Handschuh and Staab, 2003). This greatly improves

the quality of the data so that a machine is able to un-

derstand what the data is for, what it can be used for

and what other things are linked to it (Harth, 2004)

(Berners-Lee et al., 2001). This allows the machine

to process and use the data, instead of the current

paradigm of relying on a human to interpret, process

and understand data.

Ontologies are a core component of the Semantic

Web, and are required by machines to be able to in-

telligently reason and infer data (Pulido et al., 2006).

An ontology is a set of data elements within a domain

that are linked together to denote the types, proper-

GISTAM 2016 - 2nd International Conference on Geographical Information Systems Theory, Applications and Management

ties and relationships between them (Beydoun et al.,

2014). Ontologies contribute to resolve semantic het-

erogeneity by providing a shared comprehension of a

given domain of interest (Nacer and Aissani, 2014).

In knowing the relationships that exist between data,

search can be expanded to incorporate relationships

that exist between the data as well as traditional string

matching. The search is now a semantically intelli-

gent search.

Put simply, the Semantic Web aims to create a web

of knowledge and information that is both machine

and human-readable (Pulido et al., 2006)(Berners-

Lee et al., 2001). This consequently allows the ca-

pability for machines to automatically ﬁnd, combine

and act upon information found on the Semantic Web.

There are currently well-deﬁned, open standards

in place for moving towards Web 3.0, where resources

and ontologies are shared (World Wide Web Consor-

tium, 2001). The advantage of this is that there is

no reinvention of the wheel, however there is still

signiﬁcant development required in integrating Se-

mantic Web techniques into spatial applications. To

aid in the movement towards semantic spatial data

manipulation, the OGC has established standards for

storing, discovering and processing geospatial infor-

mation (Janowicz et al., 2010). Having these stan-

dards (W3C -World Wide Web Consortium and OGC

- Open Geospatial Consortium) to work with creates

the ability to simplify any data collected from multi-

ple sources that are usually stored in their own unique

proprietary formats, and create a standardised format

of the spatial data and processes for use both in re-

search and in industry.

2.2 The OGC WPS Standard

Web Processing Services, as their name suggests, pro-

vide services over the Internet to consumers, be it data

access or processing. They are client-side platform-

independent and have standardized input and output

protocols so that consumers are able to utilize these

Web Services (Wu, 2015b).

It is not uncommon for a company website to be

converted into an interactive, completely-automated,

web-based application (such as those for stock trad-

ing, electronic commerce, on-line banking, travel

agencies, etc) (Kov

acs and Kutsia, 2012). It is worth

noting that to achieve reliable application develop-

ment, appropriate speciﬁcation and veriﬁcation tech-

niques and tools are required. Systematic, formal ap-

proaches to the analysis and veriﬁcation of a web ser-

vice or application can deal with the problems of this

particular domain by automated and trustworthy tools

that also incorporate semantic aspects (Kov

acs and

Kutsia, 2012).

In the geospatial world, the OGC is the main stan-

dards body for geospatial data and technology. The

standard published by the OGC in regards to Web

Processing Services is currently in its second itera-

tion (v2.1), that brings about enhanced features to im-

prove the functionality of web services, especially in

regards to asynchronous operation and error handling.

However there has been little work on the orches-

tration of Web Services. This research requires the

adding of functionality to the standard.

The OGC WPS standard sets out the groundwork

for exposing features, inputs and outputs and process-

ing of a geospatial web service (Lopez-Pellicer et al.,

2012). Loosely deﬁned, it simply describes the syntax

and minimal requirements of a geospatial Web Pro-

cessing Service.

An example of a WPS is a service that provides

polygon intersection capabilities. A user provides two

or more polygons as input to the WPS and receives a

polygon of their intersections as output, or a NULL

value if the polygons provided do not intersect. The

WPS is speciﬁc as to what format and coordinate ref-

erence systems are used in the polygons that it pro-

cesses.

Using and extending the WPS speciﬁcation for or-

chestration within a workﬂow is not a foreign con-

cept in the geospatial community. For example, a

partially manual system of orchestrating PyWPS (A

Python implementation of WPS) within Taverna has

been proposed to assist in their efforts to assess mod-

elling in urban areas (De Jesus et al., 2012). This

workbench for mapping business processes and work-

ﬂows to chains of web services to complete geospatial

tasks shows promise and feasibility.

By integrating Semantic Web concepts in the form

of ontologies and metadata tags, as well as improve-

ments and expansion of the OGC WPS standard (and

possibly other open standards as seen ﬁt), it is possi-

ble to expose all these useful datasets and Web Ser-

vices that the user hasn’t considered through better

metadata and linking them through ontologies. The

use of Semantic Web technologies allows us to look

for meaning, rather than simple keyword matching.

While this has been achieved to a certain degree

in generic, publically-available search engines such

as Google by utilizing semantic analysis algorithms

(Cilibrasi and Vit

anyi, 2007), there has been little de-

velopment in the geospatial area.

CIAO-WPS - Automatic and Intelligent Orchestration of Geospatial Web Services using Semantic Web (Web 3.0) Technologies

Figure 1: A system ﬂow diagram of how a user query is processed through CIAO-WPS.

3 APPROACH

To automate the orchestration process, there are sev-

eral essential aspects that must be developed to assist

in this regard.

Firstly, NLP (Natural Language Processing)

methods require investigation to determine the mean-

ing of a user query and semantically search for infor-

mation using ontologies. In situations where a rel-

evant ontology is not available, a classic text search

may be executed and an ontology dynamically gener-

ated on-the-ﬂy from the search results using an off-

the-shelf product. In this scenario, ontologies may be

used akin to a workﬂow, where information and pro-

cess interdependencies are linked.

Secondly, Artiﬁcial Intelligence is explored that

utilizes ontologies to determine what Web Services

and datasets are required to generate an accurate re-

sult to the user’s query. Ontologies may then be

used to link together the functions, inputs and out-

puts of the web resources allowing the AI solution to

determine which order the web services will run in

to achieve an output that will satisfy the end user’s

query.

Finally, we identify enhancements for the OGC’s

open standard for WPS by adding functionality and

supporting metadata to allow for automatic orchestra-

tion by the AI system developed under this research.

An overview of such a proposed system can be

seen in Figure 1.

The user ﬁrstly feeds a complex query in natu-

ral language to the system. The query is then bro-

ken down to simple, modular queries for ease of un-

derstanding using the NLP. These individual queries

are then searched for in any available ontologies, and

if the available ontologies are not available, the dy-

namic ontology generator is used to create ontolo-

gies on-the-ﬂy using documentation and expert do-

main knowledge from trusted sources, obtained via

classic text search from the Internet. Alternatively a

system can also be put in place to query the user for

additional information and details that will help com-

plete the search.

With multiple ontologies on-hand and ready-to-

use, information in regards to the user’s original query

can be searched within the ontologies to obtain links

for, and between datasets and relevant web services.

Using rules of inference and logic processing via AI,

we are able to obtain a chain of Web Services and

datasets in order to satisfy the user query. The se-

quence in which these datasets and Web Services are

invoked in is also important; this is catered for by

the AI. Multiple pathing and costing options can also

be provided for the user to determine the most cost-

efﬁcient path to the end result (processing time or

payment to use). An example of how multiple paths

may lead to the same outcome is shown in Figure 2.

The end result, however, may not always be in the

most relevant form to be utilized by the user. If that

is the case, we can rely on Web Services to transform

the ﬁnal output to a more usable form. For example,

a table of rainfall predictions is not as readily-usable

to a human user in comparison to a percentage chance

of rain in an area, drawn on a map using a WFS (Web

GISTAM 2016 - 2nd International Conference on Geographical Information Systems Theory, Applications and Management

Figure 2: Different paths for the same outcome.

Feature Service) or WMS (Web Map Service) derived

from the table data to show different rainfall chances

in different areas of the map via heat layers.

4 USE CASE SCENARIO

A typical scenario that shows how CIAO-WPS may

be utilized is shown in Figure 3. The following para-

graphs will guide the reader through the diagram.

In this example, the user feeds a complex, natural-

language query into the system. NLP breaks the query

down into three separate sections. “What are the

chances”, “of ﬂooding” and “in my area”.

Based on “in my area”, CIAO-WPS realizes the

statement implies location of the user and therefore

the ﬁrst step would be to obtain the location of the

user as this will provide very important context for

the other queries. This could use simple GPS on the

user’s smart phone/device.

With an ontology such as the SWEET (Semantic

Web for Earth and Environmental Technology) ontol-

ogy readily available to CIAO-WPS, the system looks

up “ﬂooding” within the ontology, that will give links

to elements such as “rain”, “water”, “severe weather

condition”. These elements will further give links to

information such as links to BOM (Bureau of Mete-

orlogy) severe weather warning services or ﬂood pre-

diction Web Services, for example. As for the “in my

Figure 3: A sample use case scenario of CIAO-WPS.

area” section, the location of the user becomes ex-

tremely important at this point as CIAO-WPS will fo-

cus on the vicinity of the user.

“What are the chances” prompts CIAO-WPS that

this is most likely a percentage/likelihood calculation.

This contextual information is fed into the search for

related Web Services and datasets.

With the query broken down, CIAO-WPS will at-

tempt to use traditional text-search tools or further

delve into more specialized ontologies to obtain more

information and methodologies to obtain the relevant

Web Services and datasets. This in turn could re-

veal more critical information such as workﬂows that

provide a more authoritative source of guidance as

to what Web Services and what datasets to access in

what order.

Using AI and OWL-DL, CIAO-WPS attempts to

obtain a path that ﬁts in with user-deﬁned restrictions

and will fulﬁll the user’s query. If a section or piece

of information along the path is unavailable, CIAO-

WPS will notify the user and fallback into a semi-

supervised mode, in which the user may provide the

needed information or direct which path CIAO-WPS

should take.

In this case, we assume that all information is ob-

tained successfully within the user’s set parameters.

The results are accumulated by CIAO-WPS. However

what the system has obtained is simply polygons in

the vicinity with a percentage chance of ﬂooding at-

tached.

This would not be useful to the user and therefore

CIAO-WPS - Automatic and Intelligent Orchestration of Geospatial Web Services using Semantic Web (Web 3.0) Technologies

CIAO-WPS draws the polygons on a map, while col-

oring in the chances of ﬂooding with different inten-

sities using a heat scale. This is much more useful to

a human user than presenting the user with a table of

numbers and coordinates for the edges of the polygon.

5 PROTOTYPE

A proof-of-concept model of CIAO-WPS has been

built, with focus mainly on the chaining of WPS via

metadata added outside of the OGC WPS standard.

An Agile approach has been used for software devel-

opment used in developing CIAO-WPS, prototypes

and proof-of-concepts of modules of the system are

continuously built and discarded to serve as reality

and logic checkers for the research. At the moment

the working proof-of-concept is currently available at

http://research.haxx.net.au/cwps

The prototype was created in Python using classes

to serve as “dummy” WPS with input and output for-

mats and their parameters. Additional information

such as response time and algorithmic complexity

were added as well to explore these ideas further.

In Figure 4, we see a dummy WPS that is a Fire

Modelling WPS, with appropriate metadata in its Get-

Capabilities section and its input (US date in the for-

mat mm/dd/yyyy). It outputs a polygon in vector for-

mat of the predicted ﬁre area. This WPS has metadata

that states that it has a high cost but a quick response

time and a relatively simple algorithmic complexity.

In Figure 5, we see another dummy WPS that also

Figure 4: Dummy Fire Modelling WPS 1.

performs ﬁre predictions, with appropriate metadata

in its GetCapabilities section and its input (US date in

the format mm/dd/yyyy), similar to the ﬁrst ﬁre mod-

elling WPS. It outputs a polygon in vector format of

the predicted ﬁre area. The metadata of this version

states that it has no cost but a slower response time

and a relatively more complex algorithmic complex-

ity. In Figure 6, we see a dummy WPS that is a date

Figure 5: Dummy Fire Modelling WPS 2.

Figure 6: Dummy Date Format Conversion WPS.

format conversion WPS, with appropriate metadata

in its GetCapabilities section and its input (American

GISTAM 2016 - 2nd International Conference on Geographical Information Systems Theory, Applications and Management

date in the format mm-dd-yyyy). It outputs an Amer-

ican date in the format mm/dd/yyyy.

In Figure 7, we see a dummy WPS that is a re-

gional date conversion WPS, with appropriate meta-

data in its GetCapabilities section and its input (Aus-

tralian date in the format dd-mm-yyyy). It outputs an

Figure 7: Dummy AUS to US Date Conversion WPS.

Figure 8: CIAO-WPS Proof-of-concept Search Interface.

American date in the format mm-dd-yyyy.

The interface for this proof-of-concept can be ob-

served in Figure 8, where the user wants to do “ﬁre

modelling”, but only has an Australian date in the for-

mat dd-mm-yyyy as input.

Finally in Figure 9 we have the results displayed.

CIAO-WPS does a custom-tuned BFS (Breadth-First

Search) to explore successful paths and choose the

path that satisﬁes the user’s constraints. At this stage,

the prototype will reveal multiple options for shortest,

simplest, cheapest and most responsive paths. This

idea translates to a semi-supervised operation in the

ﬁnal system.

6 CONCLUSION AND FUTURE

PLANS

This paper has proposed the workings of a system in

development that aims to automate the orchestration

of WPS intelligently using Semantic Web techniques

and concepts. The proof of concept shows the meta-

data required for orchestration that is not present in

the WPS 2.1 standard. The advantages that CIAO-

WPS hopes to achieve are increased productivity and

efﬁciency, reduced manpower in locating resources,

reduction and possible elimination of the manual, hu-

man analysis and linking of related datasets and WPS.

CIAO-WPS, throughout its development, strives to

improve itself from other attempts of orchestration by

adding automation and intelligence.

Future plans for CIAO-WPS is the full integration

and use of ontologies in the decision-making process

of choosing paths and the order in which Web Ser-

vices are run. Using OWL-DL will allow for more

complex constraints such as ﬁle server location re-

strictions. Further improvements to the search algo-

rithm are also planned for better efﬁciency when large

numbers of WPS are involved. This ensures consis-

tent response times for users in time-demanding ap-

plications. Finally, a learning algorithm that ranks

paths based on past successful queries will also be ex-

plored.

ACKNOWLEDGEMENT

The work has been supported by the Cooperative Re-

search Centre for Spatial Information, whose activi-

ties are funded by the Business Cooperative Research

Centres Program.

CIAO-WPS - Automatic and Intelligent Orchestration of Geospatial Web Services using Semantic Web (Web 3.0) Technologies

Figure 9: CIAO-WPS Proof-of-concept Results.

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