Research Challenges of Open Data as a Service for Smart Cities

Leonard Walletzk

, Franti

ska Romanovsk

, Angeliki Maria Toli

and Mouzhi Ge

1,2

Faculty of Informatics, Masaryk University, Brno, Czech Republic

Institute of Computer Science, Masaryk University, Brno, Czech Republic

Bartlett School of Construction and Project Management, University College London, London, U.K.

Keywords:

Open Data, Data Life Cycle, Open Data as a Service.

Abstract:

The open data are considered to be an important building block of Smart City services. Based on the data

availability in the cities, companies are building innovative smart services to facilitate the city development.

However, most of the practitioners are focusing only on the open data usage but paying little attention to the

processes related to the data publication. Thus, there is a lack of understanding the whole process of open data

life cycle such as before the data can be open, when they are published and when they need to be archived.

Those phases in the open data life cycle are critical for assuring the data accuracy, availability and relevance

because the data are analysed, changed, anatomized or generally processed during the whole life cycle. This

also creates a set of research challenges for open data. Therefore, in the context of smart cities, this paper

proposes to consider Open Data as a Service and identiﬁes the research challenges along with the open data

life cycle.

1 INTRODUCTION

The term smart city gained popularity in the early

2010s to demonstrate how technological advances

along with data that have the signiﬁcant potential

to improve city planning and management (Step

anek

et al., 2017). There is a variety of data sources that can

be used in smart city services. For example, smart city

projects regularly collect data via Internet of Things

(IoT) platforms, where the collected data can further

be published as open data. Thus, while the develop-

ment of smart cities increases the open data, open data

also facilitate the smart city applications. In smart

cities, open data can be deﬁned as data that are avail-

able and accessible for use and reproduction by any

party (Lindman et al., 2013). Those open data can

bring various advantages to smart cities, for example,

governmental open data will promote transparency

and accountability (McDermott, 2010), improve the

decision making capacity and create innovation op-

portunities for both governmental and private actors.

Moreover, open data can also improve the provision

of public services and lead to the improvement of cit-

izens’ quality of life (Pereira et al., 2017). Due to high

utility of open data, open data initiatives in smart cit-

ies are considered to have a signiﬁcant impact on the

domains of governance, economy and transport and

mobility (Ojo et al., 2015b). However, literature sug-

gests that the actual effect of open data on ongoing

smart city projects has been elusive to assess. For ex-

ample, the smart city program of Rio de Janeiro has

been slow in achieving its goals regarding the open

data (Angelidou, 2017).

Open data can help to increase the positive im-

age and motivate the creation of the innovative ap-

plications. For example, in the Brno city (second

largest city in Czech Republic with 380.000 inhab-

itants), open data show that total length of the roads

of bikers was around 63km in 2018 and Brno is an-

nouncing that is having about 136km of the roads for

bikers in 2019 open city data. This helps the city to

build an image that the city is planning more cycling

lanes and thus more and more applications are de-

veloped to support bikers in Brno in 2020. From this

real-world scenario, it can be seen that open data can

help citizens and organizations to understand the city

and build the smart city together. With the increas-

ing importance of open data, most organizations are

considering the open data only as a data source and

usually ignore how the life cycle of the open data and

how to help to improve the healthy life cycle of open

data. Therefore, this paper views the open data as a

service and discusses how to create Open Data as a

Service in smart cities.

468

Walletzký, L., Romanovská, F., Toli, A. and Ge, M.

Research Challenges of Open Data as a Service for Smart Cities.

DOI: 10.5220/0009578604680472

In Proceedings of the 10th International Conference on Cloud Computing and Services Science (CLOSER 2020), pages 468-472

ISBN: 978-989-758-424-4

The rest of the paper is organized as follows, Sec-

tion 2 reviews the open data and its life cycle. Then

from the service perspective, Section 3 discusses the

open data process, related service and value propos-

itions. Section 4 proposes a set of research chal-

lenges of Open Data as a Service for Smart Cities,

ﬁnally, section 5 concludes the paper and outlines fu-

ture works.

2 SERVICE VIEW TO OPEN DATA

In order to understand the relations between smart cit-

ies and open data, the British Standards Institution

envisions smart city as a city that is citizen-centred,

collaborative, digital and deﬁned by open data (BSI,

2014). Open data can be deﬁned as data and con-

tent that can be used, modiﬁed and shared with no re-

strictions, by anyone and for any scope (ODI, 2017).

Data can be characterized as open when they meet

the following criteria: accessible -thus typically pub-

lished on the web- , provided in a machine-readable

format and under a license that allows to access, use

and share them (Transport Systems Catapult, 2017).

Open data initiatives support the smart city objectives

and are considered to have a signiﬁcant impact on the

domains of governance, economy and transport and

mobility (Ojo et al., 2015a). They can be viewed as

a way to restore the separation between government

and civic society by stimulating public organizations

to act as a more open system (Janssen et al., 2012).

Smart city projects regularly collect data via IoT

platforms, which are frequently made open (Ahlgren

et al., 2016). These data are often sourced by govern-

mental bodies from citizens through sensors, kiosks,

meters, smart phones and smart appliances (Harrison

et al., 2010) and are made available by numerous

cities through open data portals, based on the inter-

net and connected to data hubs (Caird and Hallett,

2019). An example of such cities is London, UK.

London offers a number of open data sources such

as the London Datastore, Transport for London (TfL)

and data.gov.uk, the main themes of which are: demo-

graphics, employment and skills, environment, trans-

parency, housing, health, transport, business and eco-

nomy and education (Ghahremanlou et al., 2019).

Literature suggests that open data operate in smart

city ecosystems (Hall et al., 2012; Poikola et al.,

2011; Zuiderwijk et al., 2014), characterized by in-

terdependencies between issues (Zuiderwijk et al.,

2014). Such issues may be related to pre-existing net-

works, power and information asymmetries and vari-

ance of training and capacity (Hall et al., 2012).

(Zuiderwijk et al., 2014) present an open government

data scenario to demonstrate how open data ecosys-

tems operate, which correlates to data lifecycle man-

agement. This demonstrates how governmental or-

ganizations can create or collect, subsequently store

and curate, then verify and remove sensitive inform-

ation from the data that are then made available to

users. These data are frequently made available, via

data integrating actors referred to as infomediaries.

Subsequently, users can return to the governmental

organizations with further data requests or to discuss

the datasets.

According to (Zuiderwijk et al., 2012) the process

of opening the data can be split up into ﬁve steps. The

ﬁrst two steps are focusing on the responsibilities of a

city/organization, such as creating the data and open-

ing the data. The next two steps focus on users (ﬁnd-

ing the open data and using them) and the last step

closes the cycle on a border between user and organ-

ization in which they discuss and provide feedback on

the open data (see Figure 1). This describes the open

data as an ongoing process with a constant need for

improvement of the service they provide for the users;

however, it does not discuss closing the open data.

By that is meant the withdrawal of the published

data due to various reasons, e.g., change in legisla-

tion. Only one (Demchenko et al., 2013) of the ten

models describing the data life cycle in (Charalabidis

et al., 2018) mentions a step after sharing/publishing

the data.

3 OPEN DATA LIFE CYCLE AND

VALUE PROPOSITION

From the perspective of Open Data as a Service, we

propose to simplify the open data life cycle into three

stages: before open data, open data and after open

data. although it is important to describe the open

data in each of the three stages, we found that most

literature is focused on the open data application and

lacking to pay attention to before open data and after

open data stage.

From the perspective of Service dominant logic

(Lusch and Vargo, 2014), we conduct our analysis

from product view to service view in order to identify

the value of the data and their usefulness to their

users. The important characteristic of open data ser-

vice is the value proposition. The value proposition

refers to the potential value the customer can obtain

by usage of particular service. Therefore, considering

the three basic steps of open data life cycle, we can

investigate the value proposition that is designed for

Open Data as a Service.

Before Open Data: value proposition is based on

Research Challenges of Open Data as a Service for Smart Cities

469

Figure 1: Open data interactions between organizations and users (Zuiderwijk et al., 2012).

the possibility and ability of the data to be trans-

formed into Open Data.

Open Data: value proposition is focused on the

ability of data to create value, for example, to be an in-

put to some innovative application or provide relevant

information to stakeholders.

After Open Data: value proposition is about sav-

ing historical values and being able to provide retro-

spective analyses.

It can be seen that each part of open data life

cycle has different value proposition, also focused

to the different stakeholders. Therefore, the service of

open data, its features and aspects, must be analysed

separately, because the consequences and results are

different in each phase of life cycle.

4 OPEN DATA AS A SERVICE

According to (Zuiderwijk et al., 2014), the following

aspects of open data has been identiﬁed:

• Availability and Access: collecting data; deciding

what to collect, when and by whom it should be

collected; not opening the data

• Find Ability: the ability of data and metadata to

be easily found by users, as well as easily browsed

and searched

• Usability: Ensuring the correctness of the data

and trustworthiness of the source. The right

amount of services to use the data.

• Understand Ability: The ability of the data to be

understood by users (e.g., visualisation) suppor-

ted by good and user friendly API. Knowledge of

users to use the data.

• Quality: Reliable and accurate data.

• Linking and Combining Data: The ability of the

data to be linked and combined.

• Comparability and Compatibility: Uniﬁed and

standardized format of data for easier comparison.

Uniform policy for publishing.

• Metadata: Commonly agreed metadata with re-

cognizable structure, their extent and provision.

From our applied research results, based on cooper-

ation with public authorities and city representatives,

we identiﬁed three critical aspects:

Security and Privacy. This aspects is mostly underes-

timated by municipalities and stakeholders. This is a

very complex problem, combining issues of data pri-

vacy, authorization and availability. As the data are

public available, the data privacy and data anonymiz-

ation become very important to protect the citizen’s

personal information (Mbarek et al., 2020).

Quality. Data quality research can be traced back to

two decades ago (Ge and Helfert, 2006; Helfert et al.,

2009b), in the context of open data, while the open

data facilitate to share the information and knowledge

among the Internet peers, it also suffers from a large

variety of the data quality problems (Helfert et al.,

2009a; Ge et al., 2011). Consider the Wikipedia as

an example, (Lewoniewski et al., 2017) report that

for the same fact number such population of certain

city, different language versions of the same Wikipe-

dia articles can be different. Further, the contents for

the same topic in different language versions also vary

signiﬁcantly.

CLOSER 2020 - 10th International Conference on Cloud Computing and Services Science

470

Quantity or Volume. Not only data quality is import-

ant, but also data quantity plays a critical role. For

the most of the people from municipalities, open data

are represented only in the form of tables (more spe-

ciﬁcally in csv format). However there are more con-

cerns, for example, we can have a data streams where

only part of the data is relevant, or we can have the

Big Data, where only some of data are important for

decision-making (Ge et al., 2018). To further reﬁne

this research issue, it is also hard to identify which

part of Big Data is important for a speciﬁc applica-

tion or use case. Compared to other Vs of Big Data,

the volume of big open data should be prioritized.

It can be seen that each aspect can play an im-

portant role in the life cycle of the open data. Along

the open data life cycle, data access and usability may

vary. Before open data, the critical aspect is to cre-

ate and process open data, we need to assure to ob-

tain the right data in the right time. When the data is

opened, it is important to concern the usability for the

city and third-party organizations. It is representing

the feature to be used to create a value for the users of

applications, linked to open data. After opening the

data, the related questions can be that what data can

be stored and what data can be deleted? If they are

stored, for how long they should be available?

We found that the research of open data for smart

cities can be done in the cooperation with municipalit-

ies in the different stages of Open Data development.

If the research would be done in the municipality of

the same level, some of critical aspects of problems

can be missed or ignored. If this analysis can be done,

it will clarify the process of Open Data publication,

help the developers of Open Data platforms to design

their products more precisely and feed the data ser-

vice need of all relevant stakeholders during whole

data life cycle.

5 CONCLUSION

In this position paper, we have proposed to underpin

the importance of the open data life cycle and con-

sider open data as a service. We have revisited how

the open data are generated and used along its life

cycle in smart cities. From the service perspective,

we have investigated the value proposition of the open

data in the phase of before open data, open data and

after open data. Based on studying the open data from

service perspective, we have reviewed and identiﬁed

as a set of research issues for open data as a service,

where we especially propose to focus on three critical

aspects for open data in smart cities: security, quality

and quantity. Each aspects have been discussed to fa-

cilitate the future research of open data in smart cities,

and also help to develop the concept of Open Data as

a Service for smart cities.

ACKNOWLEDGEMENTS

The work is supported from European Regional De-

velopment Fund Project CERIT Scientiﬁc Cloud (No.

CZ.02.1.01/0.0/0.0/16 013/0001802).

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