Towards Ecosystems based on Open Data as a Service
Kiev Gama and Bernadette Farias Lóscio
Centro de Informática, Universidade Federal de Pernambuco (CIn/UFPE), Recife, Brazil
Keywords: Open Data, Software Ecosystem, Business Ecosystem, Data as a Service.
Abstract: Despite several efforts in contests throughout the world that encourage local communities to develop
applications based on government Open Data, the solutions resulting from such initiatives do not have
longevity, lacking maintenance and rapidly falling into disuse. This is due mainly to the lack of investment
or even a model for monetizing the use of such applications. Therefore, it is necessary to develop a model
that fosters the value chain for Open Data aiming an economically self-sustained ecosystem. Such
ecosystem should promote new businesses through the creation of systems and applications focused on
citizens. This article discusses the creation of software ecosystems for services and applications
underpinned by a platform based on Open Data as a Service.
1 INTRODUCTION
Recently, a global trend is being observed on the
release of data access in public administration, so far
restricted to government organizations that hold
these data. Increasingly, public institutions are
planning and implementing strategies for open
government data, in order to increase transparency
and efficiency of government as well as increasing
citizen participation (Huijboom and Van den Broek,
2011). The data in question range from high-level
administrative data to city infrastructure data. While
the former focuses on data such as public
expenditure, tax revenues and similar information,
the former consists of diverse domains such as
rainfall indices; data about schools, demographics,
sewage, transportation, water and electricity; public
spaces geopositioning data; waste collection points,
and so forth.
The United States and the United Kingdom
pioneered the efforts around Open Data (OD),
releasing their portals in 2009 (Hogge, 2010). Many
countries followed the global trend on OD, and
nowadays, Open Data portals can be found in many
places. The main idea behind Open Data consists of
promoting the transparency and the empowerment of
the citizens through the free access to government
data. Though, besides of promoting transparency
and enabling the empowerment of citizens, Open
Data may also enable the creation of significant
economic value. Recently, a research developed by
the McKinsey Global Institute shows that Open Data
could help to generate more than $3 trillion in value
every year in different domains of the global
economy (McKinsey, 2013).
Townsend (2013) points out competitions as a
way to stimulate the usage of Open Data in various
cities of many countries – USA (Washington D.C.,
New York, San Francisco, Portland), Canada
(Edmonton), Netherlands (Amsterdam), Ireland
(Dublin), to cite a few. The format of these contests
usually ranges from medium duration (a few
months) to short duration (hackathons, that only last
a few days). Most applications in such scenarios end
up being quickly abandoned. In the long run, very
few applications resulting from such contests are
actually scalable and sustainable (in an economical
perspective).
As discussed in (Goldstein and Dyson, 2013),
just providing an Open Data access is not enough to
unlock significant amounts of economic value. In
order to achieve its full economic potential, an Open
Data initiative must be part of a whole ecosystem,
which is mainly composed by Open Data providers
and OD consumers that collaborate to promote the
initiative. An Open Data ecosystem should foster
new businesses through the creation of systems and
applications focused on improving productivity of
existing companies or government institutions as
well as improving the wellbeing of citizens. Equally
important, an IT-based platform must also be
provided to promote the interactions between the
actors of the Open Data ecosystem. Furthermore,
659
Gama K. and Farias Lóscio B..
Towards Ecosystems based on Open Data as a Service.
DOI: 10.5220/0004974506590664
In Proceedings of the 16th International Conference on Enterprise Information Systems (ICEIS-2014), pages 659-664
ISBN: 978-989-758-028-4
Copyright
c
2014 SCITEPRESS (Science and Technology Publications, Lda.)
this platform should enable the creation in large
scale of innovative products and services through the
use of standard formats and common tools.
The effective creation of an Open Data
ecosystem along with an IT platform to enable the
communication between data providers and data
consumers are fundamental issues for the success of
an Open Data initiative. In this paper, we are
interested on those issues and we propose a
promising approach that can facilitate the
provisioning of Open Data, allowing a flexible and
easy way to reuse it. In a world where the IT
industry started to design Cloud Computing systems
that deliver everything as a service (Banerjee et al.,
2011), this article specifically discusses the creation
of ecosystems underpinned by a platform based on
the concept of Open Data as a Service. We also
argue that the use of such solution contributes
significantly to the generation of the expected
economic value of the Open Data. The remainder of
this paper is organized as follows: Section 2
provides some background on Open Data and its
challenges, followed by a brief overview on data as
a service and business ecosystems; Section 3
introduces our perspective concerning the Open
Data ecosystem; Section 4 presents our view of a
platform for Open Data as a Service; and, finally,
Section 5 concludes this paper.
2 BACKGROUND
2.1 Open Data
Nowadays, Open Data portals can be found in most
developed countries (e.g, United Kingdom, France,
Germany) as well as in developing economies (e.g.,
Brazil, Russia, India). Open Data is typically
presented through raw bulks of data, in machine-
readable format, that can be used by citizens and
institutions as they wish (e.g., development of
systems and applications, data analysis using
software tools, etc).
Data consolidation and creation of these portals
that provide masses of raw data already follow
precepts dictated by the open government initiatives.
An open format is considered independent of data
technology platforms; it is based on open standards
and tools, and made available to the public without
restrictions (Dietrich et al., 2009). The information
portals on the Internet providing that data, often
make it available through files of various formats
(XML, CSV, JSON), sometimes accompanied by
metadata describing them.
This strategy of publishing data enables third
parties to build applications that use these data
sources to correlate them, analyze them and extract
information relevant to the objective of each use.
However, access to such data sources is typically
done manually. Those who wish to use such data
need to access the portal, copy the bulks of data,
then extract what is relevant and finally integrate the
data into their applications which must access local
databases with full or partial copies of the acquired
Open Data.
2.2 Open Data Challenges
Despite of the several Open Data portals that have
been released in the last few years, substantial
challenges need to be addressed when designing and
planning a new Open Data initiative. Identification
of relevant data, integration of distributed data, the
lack of metadata and standards, and the quality of
the released data are good examples of challenges to
be faced when starting an Open Data initiative.
Moreover, once the project is running, it will be
necessary to deal with new issues, including, for
example, stale data as well as problems related with
the scalability, availability and reliability of the
adopted Open Data solutions. Most of the existing
Open Data initiatives use solutions like CKAN,
Socrata and Data Hub, which focus basically on
providing platforms for just storing and providing
access to Open Data. In general, those solutions may
be classified as data-centric catalogues and do not
offer appropriate tools to address the previously
mentioned limitations.
According to the McKinsey report (McKinsey,
2013) some high level key issues have to be
addressed in order to realize the full Open Data
value potential, including: i) Investment in
technology to provide suitable tools for collecting
and sharing data, and to perform data analyses to
uncover important insights; ii) Development of
standards to facilitate the integration of data
distributed in multiple data sources; iii) Releasing of
metadata to make Open Data more usable; and iv)
Creation of an Open Data marketplace to provide
clear channels for sharing data and to build a
community with group norms and rules.
In this context, new Open Data solutions are
required to deal with the limitations of existing
approaches and to help to unlock the expected Open
Data economic value. Considering the increasing
interest of the IT industry on service-centric
approaches and the design of Cloud Computing
systems that deliver everything as a service
ICEIS2014-16thInternationalConferenceonEnterpriseInformationSystems
660
(Banerjee et al, 2011), we argue that those solutions
should deliver Open Data as a Service on top of a
Cloud Computing infrastructure. Specifically, an
Open Data solution must offer:
Services that can be easily combined for
constructing applications, including data
publication services and data analysis services;
An infrastructure that can provide scalability,
availability and reliability on the available data
services;
APIs for easily allowing to crowdsense data
that can replace or complement stale Open
Data;
Transparently linking and integration of data
from different sources.
2.3 Data as a Service
Software services are a promising approach that can
facilitate the provisioning of Open Data, allowing a
flexible and easy way to reuse it. Service-Oriented
Computing (SOC) (Papazoglou, 2003) offers an
approach to build applications through services as
building blocks. Services consist of self-descriptive
elements that are platform-independent and hosted
in a distributed environment. A service could consist
in any type of functionality, whether simple or
complex. Service-oriented architectures (SOA) act
as a logical model to represent how services should
be delivered and consumed through service
contracts. The applications delivered as services
over the Internet have long been referred to as
Software as a Service (SaaS), while the datacenter
hardware and software delivering such services is
what is called a Cloud (Armbrust et al., 2010).
Although the Open Data handbook (Dietrich et
al., 2009) suggests the use of software services to
provide access to data only in cases where they
change frequently, using this approach to also access
data that do not necessarily change in a regular basis
would provide several advantages. Besides the
ability to reuse existing functionality, the
construction of applications would be facilitated by
the usage of services. There would be no need to
copy the entire mass of relevant data (e.g. mappings
of schools, streets, census data). The typical scenario
for third party application is using only a small
fraction of that data, which would be available
through services.
The Web has evolved to become a
programmable Web, where a new generation of
applications is based on mashups (Yu and Woodard,
2009). They consist on applications that are created
by integrating data from different sources. The
availability of data as service is one of the enablers
for an open programmable Web (Maximilien,
Ranabahu and Gomadam, 2008). Others have
discussed the approach to provide data as services.
The concept of Information-as-a-Service (IaaS)
discussed by Dan and colleagues (Dan et al., 2007),
is referred by Truong and Dustdar (Truong and
Dustdar, 2009) as Data as a Service (DaaS).
Typically, it is available in the form of infrastructure
that extracts data from other sources and provide
services in "slices" of smaller data, under a specific
context (e.g., stock market data). There are basically
two categories of service delivery in that case: (1)
access to data based on other data sources, and (2)
access to data held in own infrastructure provided,
allowing, in addition to the consultations, the
addition, deletion and modification of information.
2.4 Business Ecosystems
The concept of business ecosystem was introduced
by James F. Moore in the early 1990s (Moore,
1993). Moore defines a business ecosystem as: “An
economic community supported by a foundation of
interacting organizations and individuals: the
organisms of the business world. This economic
community produces goods and services of value to
customers, who are themselves members of the
ecosystem. The member organizations also include
suppliers, lead producers, competitors, and other
stakeholders. Over time, they coevolve their
capabilities and roles, and tend to align themselves
with the directions set by one or more central
companies”
Two important issues of this definition are: the
collaborative nature of a business ecosystem and the
diversity of actors participating in the ecosystem.
The first one is related with the idea that member
organisations or parts of the ecosystem evolve in
alignment, instead of the traditional view where
companies are part of a single industry and try to
evolve individually. Moore (1993) argues that in a
business ecosystem, companies co-evolve
capabilities around a new innovation: they work
cooperatively and competitively to support new
products, satisfy customer needs, and eventually
incorporate the next round of innovations. Other
important issue is that a business ecosystem should
include not only partners and subcontractors but also
complementors, competitors, customers, and
potential collaborator companies, as well as public
bodies, local incubators, investors, and even research
institutes and universities (Moore, 1993).
TowardsEcosystemsbasedonOpenDataasaService
661
Based on those perspectives, in the next section
we present our definition for an Open Data
ecosystem.
3 OPEN DATA ECOSYSTEM
Recently, Open Data ecosystems have been the
subject of a lot of discussions. The McKinsey report
on Open Data, for example, highlights the need of a
vibrant ecosystem in order to transform Open Data
into valuable tools (McKinsey, 2013). However, to
the best of our knowledge, the concept has not been
formally defined. In this section, we present some of
our preliminary ideas concerning the definition of an
Open Data ecosystem. In our proposal, we consider
the two issues discussed in the previous section: the
collaborative nature and the diversity of actors
participating in a business ecosystem. We also argue
that in order to promote the effective collaboration
among the actors and to allow the quickly
development of new products and services an IT-
based platform is required.
Figure 1 shows our perspective for an Open Data
ecosystem. The main actors of our model are:
government, application developers, small and
medium enterprises (SMEs), startups, civil society,
universities, funding agencies and investors. Each
one of them would play at least one of the following
roles: data provider, data consumer, data
aggregator and data sponsor.
The government interacts with all the other
actors and it can play different roles. Its main role is
as a data provider, i.e., it is responsible for
providing free access to the governmental data. On
the other hand, the government is also a consumer of
solutions developed by companies or single
application developers. Finally, the government
promotes the Open Data initiative by providing
communication channels between the other actors as
well as encouraging the development of Open Data
products. Universities are mainly responsible for
promoting the innovation and the development of
specialized knowledge. They act like aggregators
bringing other actors together to form a shared
understanding and to promote the development of
standard formats and platforms. The civil society,
together with the government, is mainly responsible
for identifying new demands for Open Data access,
acting like an indirect data provider. Moreover, the
civil society is also a data consumer, once it
consumes data and information produced by market
solutions. Application developers, startups and
SMEs compose the market, whose main role is as a
data consumer. The market is the responsible for the
development of Open Data based products and,
similar to universities, it should also promote the
innovation and the development of Open Data
solutions in large scale. Funding agencies and
investors can be seen as Open Data sponsors. They
are responsible for promoting the Open Data
initiative through both public funding programs and
private investments.
Open‐Data‐as‐
a‐Service
Platform
Government
Developers,SMEs,
Startups
Universities
CivilSociety
Funding
agenciesand
investors
Figure 1: Open Data ecosystem.
Other fundamental component of our proposal is
the Open-Data-as-Service platform, which allows
the interactions among a subset of actors in the
ecosystem. In our context, this platform allows them
to play their different roles as data providers, data
consumers and data aggregators, by means of
services and applications built on top of those
services. The platform would promote the
integration of these different actors, allowing the
development of products and services based on
Open Data that can provide practical benefits to
users (e.g., civic apps). Therefore, a marketplace for
services and applications can be built around the
platform, allowing monetizing the solutions and thus
generating economic value from Open Data.
Considering the important role that the software
platform plays in the ecosystem, our proposal can be
considered as a Software ecosystem, which is a
special type of business ecosystems (Jansen &
Cusumano, 2013). In a Software ecosystem, the
relationships among actors “are frequently
underpinned by a common technological platform or
market and operate through the exchange of
information, resources and artifacts” (Jansen et al.,
2009).
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4 A PLATFORM FOR
OPEN DATA AS A SERVICE
Currently, no data-as-a-service approach focuses on
providing Open Data. From a service-oriented
perspective, what one may find is limited to Open
Data portals following the recommendation from the
World Wide Web Consortium (W3C) (Bennet &
Harvey, 2009), that suggests exposing Open Data
through application programming interfaces (API)
based on Web Services (REST or SOAP). In our
perspective, OD should not only be exposed as
services, but all building blocks of the platform can
be built based on services. The platform would
provide core services and allow developers to
consume such services for: integrating OD providers
with the platform; building their own services (e.g.
aggregating value based on OD) for offering them in
the same platform; constructing applications using
Open Data services. Figure 2 illustrates the different
service layers comprising our vision of such
platform, as detailed next in bottom-up order:
Virtualization Services: This is the basic Cloud
Computing infrastructure that will provide
virtualized services such as elastic storage, thus
ensuring availability, reliability and scalability;
Linked Open Data Publication Services: This
service layer is responsible for services involving
mediation and integration of data, as well as services
for storing ontologies (i.e., ontologies catalogs),
metadata, matching and merging ontologies, data
transformation (e.g., XML to RDF) and data
publication. Platform users (developers) can plug
their own services in this layer for extracting data
from their data sources, which can either be data
originated from conventional databases or
crowdsensed information (e.g., obtained from end
users through the usage of Apps);
Query Services: this category of service is
responsible for providing transparent access to
distinct data sources, with services responsible for
query rewriting (e.g.: REST to SPARQL) and query
routing.
Analysis and Visualization Services: they offer
support for discovering useful information through
the use of services that encompass traditional data
analysis techniques like data mining and statistical
techniques, as well as visualization techniques like
line graphs, pie and bar charts, interactive maps and
infographics. These services are responsible of
turning raw data into something useful. The idea is
to provide high-level services that allow the most
inexperienced users to quickly start performing
sophisticated analysis and produce great
visualizations from many different types of data.
Service Registry: As illustrated in Figure 2, this
layer communicates with all other layers, being the
central registry that maps the services offered in the
platform. This is the key point of interaction with
service and application developers. In this layer, a
catalog service can group service descriptions by
category and allows for searching and viewing what
services are currently available. During development
time, a user may query the platform to select one or
more desired services and sees their description and
details, or he can develop solutions (services or
applications) that programmatically select the best
available services according to specified criteria
(e.g., Quality of Service) at runtime.
Applications: They are built through mashups or
compositions of different services provided by the
platform. Applications use Open Data with different
purposes, including: to promote the transparency, to
improve accountability, to help people dealing with
mobility and transportation problems and to
encourage citizens to get involved in civic and
community activities.
Service
Registry
Applica ons
Analysis
and
Visualiza on
Services
Query
Services
Linked
Open
Data
Publica on
Services
Virtualiza on
Services
Open
Data
Pla orm
Figure 2: Different service layers of an Open Data
platform.
This platform can be offered as a public cloud
that could be partially maintained by government
and funding agencies, to foster the value chain
around OD. It would rely on the core services
provided with the platform and also allow its
extension through the publication of services from
third parties (i.e., an ecosystem of solutions), which
would enable the creation of a marketplace and
strengthen the sense of community around OD
solutions. The proposed platform addresses the
challenges enumerated in section 2.2, by providing
services for collecting and sharing data, and to
perform data analysis. The platform would also rely
on standards for integrating different data sources
and on metadata for facilitating data publication. As
stated, marketplace around services and applications
can be established. Custom added-value services
TowardsEcosystemsbasedonOpenDataasaService
663
developed by SMEs or startups can charge for
access, as well as applications for niche markets. For
instance, a developer can use consume two distinct
Open Data services offered in the platform: one
providing data from public transportation, and
another one providing real time traffic data. These
services can be composed and with additional
functionality the developer can provide an optimized
routing service from which he can charge and
monetize from. In another scenario, a government
agency may want to use a data transformation
service to upload raw XML data and convert it into
RDF according to the ontology used by the platform.
5 CONCLUSIONS
Nowadays many countries are following a global
trend where public institutions are planning and
implementing strategies for open government data.
As a way to encourage the usage of such data, local
competitions for applications development are being
organized, but the resulting applications are quickly
abandoned and, in the long run, very few of them
can monetize. Such evidence supports the argument
that just providing access to data is not enough to
unlock the potential of extracting significant
economic value from the usage of Open Data. In
order to achieve such goal, many challenges have to
be addressed, such as investing in technology to deal
with OD, and creating a marketplace for sharing
data. Since the IT industry is evolving to systems
that deliver everything-as-a-service, we support the
idea that OD can take advantage of such approach.
Services can help addressing such challenges and be
used as building blocks for the construction of a
platform for Open Data as a Service. The platform
would underpin an ecosystem that involves many
actors interacting and playing different roles. These
interactions would be done through data services and
mashups of such services that would generate other
services (e.g., service compositions, added-value
services) and end-user applications that could be
charged for their use, thus fostering the generation of
business around Open Data.
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