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Create dashboards and data story with
the Data & Analytics frameworks
Michele Petito3[0000-0002-5463-5322], Fallucchi Francesca1,2[0000-0002-3288-044X],
and De Luca Ernesto William1,2[0000-0003-3621-4118]
1DIII, Guglielmo Marconi University, Rome, Italy
2DIFI, Georg Eckert Institute Braunschweig, Germany
3 Università di Pisa, Italy
[f.fallucchi,ew.deluca]@unimarconi.it
[fallucchi,deluca]@gei.de
michele.petito@unipi.it
Abstract. In recent years, many data visualization tools have appeared on the
market that can potentially guarantee citizens and users of the Public Administra-
tion (PA) the ability to create dashboards and data stories with just a few clicks,
using open and unopened data from the PA. The Data Analytics Framework
(DAF), a project of the Italian government launched at the end of 2017 and cur-
rently being tested, has the goal to improve and simplify the interoperability and
exchange of data between Public Administrations, thanks to its big data platform
and the integrated use of data visualization tools and semantic technologies. The
DAF also has the objective of facilitating data analysis, improving the manage-
ment of Open Data and facilitating the spread of linked open data (LOD) thanks
to the integration of OntoPiA, a network of controlled vocabularies and ontolo-
gies, such as "IoT Events", an ontology for representing and modelling the
knowledge within the domain of the Internet of Things. This paper contributes to
the enhancement of the project by introducing a case study created by the author,
concerns tourism of Sardinia (a region of Italy). The case study follows a process
in the DAF in 5 steps, starting from selection of the dataset to the creation phase
of the real dashboard through Apache Superset (a business intelligence tool) and
the related data story. This case study is one of the few demonstrations of use on
a real case of DAF and highlights the ability of this national platform to transform
the analysis of a large amount of data into simple visual representations with clear
and effective language.
Keywords: Data & Analytics Framework, Data Visualization, Dashboard,
Business intelligence, Open Data.
1 Introduction
Public sector is rich of data. In recent years, the open data dataset in Italy has increased
considerably: in 2018 there were about 15,000 and in 2019 the number is increased to
over 25,000 [1]. Unfortunately this dataset growth has not been accompanied by an
2
improvement in the quality of open data portals that represent only simple catalogs. The
analysis and implementation phase of the dashboards can only take place using third-
party tools, thus not favoring the sharing of knowledge and the birth of new ideas. But
the biggest problem concerns the fragmentation of data that limits the analysis and the
interpretation of national social and economic phenomena [2] [3]. Therefore, in order
to make the most of the data potential, it is necessary to abandon the silo approach and
adopt a systemic vision that favors access and data sharing.
In this scenario, the big data platform DAF [4] designed by the Digital Transfor-
mation Team [5], has the challenge to provide a single point of access for government
data and support increased public participation, collaboration and cooperation. The
DAF is an infrastructure of the Italian Government established in September 2016, rep-
resents Italy's latest effort to valorize public information assets. The objective of the
DAF is to overcome these difficulties by using big data platforms to store in a unique
repository the data of the PAs, implementing ingestion procedures, promoting stand-
ardization and interoperability. So, thanks to a framework for distributed applications
such as Apache Hadoop [6], the DAF allows the exploitation of enormous public sector
data that describes the realities of citizens and businesses to generate insights and in-
formation hidden in it [7] [8].
Furthermore, the DAF promotes semantic interoperability, according to the new Eu-
ropean Interoperability Framework (EIF) [9]. To enhance interoperability DAF make
use of an ecosystem of ontologies and controlled vocabularies (OntoPiA) [10]). Every
dataset in DAF is accompanied by metadata that describes the dataset and its internal
structure. It will be the user's responsibility to define the ontological information and
controlled vocabularies associated with the data structure, through the meaning of the
semantic tags. A tagging system will allow to drive the user to the correct use of con-
trolled vocabularies and to ensure that all datasets can be effectively connected to-
gether.
In this paper, we will focus in particular on DAF data visualization technologies,
providing a comparative analysis with other tools and platforms on the market (see
Section 2). In Section 3 we will present the general architecture of the Data & Analytics
Framework and OntoPiA, the network of ontologies and controlled vocabularies. Sec-
tion 4 focuses on the process of building a dashboard through Apache Superset. Finally,
a case of use of the DAF will be presented for the construction of a dashboard starting
from a dataset on tourism in the Sardinia Region (see Section 5). We conclude the paper
with some future developments.
2 Related works
Research supports an increasing focus on visual imagery, in all its forms, as a way of
communicating with and engaging with online audiences. Data visualization
1
allows
PA to obtain useful trends and information with maximum simplicity and speed. The
1
The science of visual representation of ‘data’, which has been abstracted in some schematic form, includ-
ing attributes or variables for the units of information.
3
DAF project embraces this approach by integrating Superset [11], a business intelli-
gence tool for data representation. There are many other tools in the same category such
as Microsoft Power BI [12], Tableau [13], Google Data Studio [14] e Ploty.ly [15] that
offer the possibility of use in the cloud via API and that could allow integration with
the DAF, even if with important limitations.
Public Tableau [13] allows the creation of complex dashboards with great flexibility,
without requiring specific technical skills. But the use of the free version is allowed
only through a desktop application.
Google Data Studio [14] is very intuitive but does not allow the use of more than
one dataset within the same dashboard. It has few data connectors (only for MySql [16],
and for PostgreSQL [17]) and the quality of the dashboards is not comparable to that of
the competitors in the sector.
Plot.ly [15] allows the creation and sharing of quick interactive dashboards. But it
only accepts datasets with a maximum size of 5 MB and the published graphics must
be public (for them to be private it is necessary to pay a subscription). Among the open
source categories we have instead distinguished three data visualization tools: Superset
[11], Metabase [18] and Redash [19]. All these projects meet the requirements [20] that
an OGD
2
visualization tool should possess. Superset and Redash are very similar. Both
are powerful and give the possibility to connect to a large number of data sources. In
addition, they have a powerful interface for writing and executing SQL queries.
Once saved, queries can also be used as a basis for the creation of dashboards. Su-
perset supports a larger number of authentication systems than Redash. For example, it
includes LDAP
3
, the system used for the unique authentication of all modules in the
DAF. Although Redash is an excellent project in rapid evolution, Superset [11] has
been chosen for three main reasons: the presence of LDAP authentication, the high
number of views, and the support of the Python language (used throughout the DAF
project).
Superset [11] is an open source product hosted on the Apache Foundation Github
4
platform and is developed using Flask [21] a very lean Python framework [22] for web
development. The part that generates the interactive graphs instead makes use of NVD3
[23] a javascript library built on D3.js [24]. Any dashboard created with Superset con-
sists of a series of graphs (called slices). Each of these can be resized, moved relative
to the others, or shown in full screen. In addition, each dataset represented in a graph
can also be exported in CSV or JSON format or through SQL queries. The "slices" are
created starting from a table available in the many data sources that Superset is able to
manage. Superset provides two main interfaces: the first is the Rich SQL IDE (Interac-
tive Development Environment) called Sql Lab
5
with which the user can have immedi-
ate and flexible access to data or write specific SQL queries, the second is a data explo-
ration interface that allows the conversion of data tables into rich visual insights.
2
Open Government Data. http://www.oecd.org/gov/digital-government/open-government-data.htm
3
Lightweight Directory Access Protocol. http://foldoc.org/ldap
4
Superset Github repository. https://github.com/apache/incubator-superset
5
https://superset.incubator.apache.org/sqllab.html
4
3 Data & Analytics Framework (DAF)
DAF has a complex architecture which integrates different components [25]. Fig. 1
shows a simplified view of two relevant related characteristics: the interoperability be-
tween the components mediated by the use of microservices and the use of docker con-
tainer
6
technology that isolates the components for greater security.
The Dataportal is the main point of access to the DAF and its functionalities. It is
characterized by a public section [26] and a private section [27]. In the public section
(accessible via https://dataportal.daf.teamdigitale.it/) anyone can browse the data sto-
ries
7
and dashboards associated with the data in the national catalog. The private sec-
tion is accessed only after login, thus allowing only accredited users to exploit the func-
tionality of querying, analyzing and sharing data.
Fig. 1. Logical architecture of the DAF
The Dataportal communicates with the rest of the system through the Kong API
Gateway [28] and the MICROSERVICE LAYER. The docker container layer repre-
sented in point 1 of Fig. 1, manages the analysis, cataloging and display of data. This
layer encapsulates some docker containers, including Superset [29], Metabase [18], Ju-
pyter [30] and CKAN [31]. The latter implements a component called a harvester,
which allows the DAF to collect all datasets within the DAF. In addition, CKAN per-
forms data catalog functions and allows the download of datasets. Jupyter is instead a
very useful tool for data scientists, as it allows to perform operations such as data clean-
ing and transformation, numerical simulations, statistical modeling, machine learning
and to run Scala and Python applications on the big data platform of DAF thanks to
integration with Apache Spark. On the left side of Fig. 1, the Superset docker container
is shown: as you can see, in this container there are also databases as PostgreSQL [17],
used to store the tables and Redis [32] to manage the cache. Centralized authentication
is guaranteed by the FreeIPA LDAP [33], an open source solution for integrated identity
6
https://www.docker.com/resources/what-container
7
Data stories are an extension of the dashboards, which allow you to express what you can see from the
views.
5
management. The point 2 of Fig. 1 shows a second layer of docker containers consisting
of the platforms OpenTSDB [34], Livy [35] and Nifi [36]. Finally, in the lower part we
find the Hadoop storage and computational layer [37] that contains the entire storage
platform provided by Hadoop (HDFS [38], Kudu [39] and HBase [38]).
The microservices layer, described in the previous study [40], provides the semantic
microservices and allow the implementation of semantic technologies, namely the
standardization, production, and publication of LOD. These processes can be achieved
thanks to the presence of OntoPiA [10] a network of remote ontologies and vocabular-
ies, published on Github. OntoPiA allows the DAF to provide the catalog of controlled
vocabularies. Moreover it favors the function of semantic tagging and the reuse of con-
trolled ontologies and vocabularies by companies and PAs. The network is based on
the standards of the Semantic Web and it is aligned with the so-called Core Vocabulary
[41] of the European Commission's ISA2 program.
4 Building dashboard process
The process of creating a dashboard in the DAF, inspired by the KDD model [42], is
structured in five steps. The first two phases are described in section 4.1 and concern
the selection (step 1), the analysis and verification of the quality of the dataset (step 2).
In section 4.2 the remaining three steps are presented: configuration of local tables (step
3), creation of slices (step 4) and creation of the dashboard and data story (step 5).
4.1 Selection and dataset analysis (step 1-2)
In the first step you look for the dataset to analyze. This activity can be carried out
in two ways: through the public portal [26], as anonymous visitor, or from the private
portal [27]. Using the search form you can search with keywords or browse through
the categories that describe the domain. Once the dataset has been identified, we move
on to the second step consisting in carrying out a first analysis of the data to evaluate a
part of the quality characteristics foreseen by the ISO / IEC 25024 [43] and described
in action no. 9 of the National guidelines for the enhancement of public information
8
.
4.2 Superset’s tables configuration and visualization (step 3-5)
The last three steps of the DAF dashboard creation process are mainly realized in
Superset. Before proceeding to the realization of the slices, it is necessary to modify the
settings of the fields (or columns in the Superset jargon) of the table. This means that it
will be necessary to verify the correct assignment of the field types (INT, BIGINT,
CHAR, DATETIME etc.), establish the dimensions on which to perform the aggrega-
tions and define the relative metrics.
8
https://lg-patrimonio-pubblico.readthedocs.io
6
Once the step of settings of local tables is finished, it is possible to proceed with the
realization of the first slice (step 4). Superset has no less than 34 different types of
graphics
9
.
The last step is the realization of the dashboard which consists of a personalized
composition of the individual slices.
5 Case study
The case study we created in the DAF on February 4, 2018 during the Open Sardinia
Contest
10
, concerns the development of a dashboard and the related data story based on
the Sardinia Region's tourism dataset. Specifically, we describe the 5 steps of the pro-
cess for creating a dashboard illustrated in the previous paragraph. In particular, the
phenomenon to be studied and the analysis of the relative dataset will be introduced
(see paragraph 5.1), the whole development activity in Superset will be illustrated (see
paragraph 5.2), concerning the configuration of the dataset, the realization of the slices
and the dashboard. In paragraph 5.3 the case of data story associated with the dashboard
is presented.
5.1 Sardinia Tourism dataset
The tourism industry in general occupies an important place in the economy of a
country and tourism activities are a potential source of employment, so it is good to
know the volume of tourism and its characteristics. This is important for the local gov-
ernment to answer questions such as:
What is the origin of tourism between June and September?
What is the tourist period preferred by the Germans in the Olbia area?
What is the accommodation capacity in the south of the island?
Which types of accommodation are present?
This allows the local government to decide how and where to spend public money.
For example, with regard to the first question, if a high percentage of German tourism
is highlighted, the Mayor could establish the installation of tourist road signs in German
or provide German language courses for City employees. Regarding the second ques-
tion, if you find that the German tourism is concentrated mainly in the months of Sep-
tember and June, the local administrator may choose for example to enhance tourist
services in the areas and months involved, extending the opening hours of offices or
municipality health districts.
In the open data catalog of the Sardinia Region (published on dati.regione.sarde-
gna.it) it is possible to find the datasets related to the movements of the clients in the
hospitality establishments in the years between 2013 and 2016. The data derive from
the communications for statistical and obligatory purposes by law, which the accom-
modation facilities do to the Sardinia Region. Finally, the Region transmits the data to
9
https://superset.incubator.apache.org/gallery.html#visualizations-gallery
10
Contest dedicated to open data, promoted by the Sardinia Region and held in the period from 16/10/2017
to 21/01/2018. http://contest.formez.it/
7
ISTAT (The Italian National Institute of Statistics), according to the current legal re-
quirements.
The following datasets (in CSV format) are those selected for the case study:
(1) Tourist movements in Sardinia by municipality;
(2) Tourist movements in Sardinia by province;
(3) Tourist movements in Sardinia by macro type of accommodation facility;
(4) Capacity for accommodation facilities in Sardinia.
The CSV (1-3) collect the arrivals (number of hosted customers) and the visit dura-
tion (number of nights spent) of tourists in Sardinia, divided by the tourist's origin and
type of accommodation. The CSV (4) collects the capacities of the receptive structures
of the Sardinia Region. The capacity measures the consistency in terms of number of
accommodation facilities and related beds and rooms.
Through data portal it is possible to perform a series of operations including down-
loading the dataset (in JSON format [44] and limited to 1000 records), obtaining the
endpoint API, access the analysis tools and display dashboards related. It is also possi-
ble to access to CKAN [31], a module integrated in DAF which allows to see all
metadata associated to the current dataset.
5.2 Sardinia Tourism dashboard
After selecting the dataset, it is possible to open Superset very intuitively by clicking
on a button on the web interface. At this point we have reached the third step of the
process of creating the dashboard described in paragraph 4. In this phase the Superset
tables must be configured for a specific slice. As an example, let's consider the type of
slice Table view named “Arrivi e presenze totali per provenienza turista”, one of slices
created for this use case.
The datasource's configuration form of Table view (see Fig. 2) is divided into three
tabs (Detail, List Columns and List Metrics) and allows the modification of the table
parameters. The initial tab shows some basic information such as the table name and
the associated slices. The second tab displays the fields in the table, for example, prov-
ince, macro-typology, arrivals, etc. The first and third columns respectively contain the
name of the field (e.g. province, macro-typology, arrivals, etc.) and the type of data
contained (STRING, INT, DATETIME, etc.).
Fig. 2. Edit table form
8
Superset automatically assigns the correct properties in relation to the type of data de-
clared, but you can decide to change them or increase the details. Special attention
should be paid to DATETIME columns. Formatting and orders by day, month and year
can change. Superset allows you to customize them thanks to all the combinations that
can be created with the management of dates in Python. In the specific case of the table
in Fig. 2, the format "% Y-% m-% d% H:% M:% S" was used for the calculated field
"_anno_mese", ie a timestamp useful for a time based analysis on month / year. The
"_anno_mese" field is not a standard field, but is dynamically generated by the function
"Expression = concat (cast (year as string), '-', cast (month as string), '-01
00:00:00')"and from the option "Datetime Format =% Y-% m-% d% H:% M:% S": this
sets the timestamp format according to the Python datetime string pattern. Here the
syntax can vary depending on the database used: the DAF uses Apache Impala [45] the
engine for Hadoop SQL queries [37]. If you use SQLite [46] (the default Superset da-
tabase) the setting to use would be "Expression = year || '-' || month "and" Datetime
Format =% Y-% d ". This happens because SQLite accepts a date format of type Year
+ Month that does not exist in Impala. The only way to get the same result in Impala is
to use a timestamp format and concatenate the "year" and "month" fields with the string
'-01 00:00:00'.
Now let's consider the "year" field: on this the “Is temporal” option has been set
which tells Superset to treat the values to describe variable phenomena over time. On
this same field, "Datetime format =% Y" was set, in order to tell Superset to treat the
values as years. If in the ingestion phase (as happened in this case) the field should be
of an incorrect type (for example INT) it is possible to use the option "Database expres-
sion = date_part ('year', '{}')" to perform the conversion from INT to DATETIME. The
Impala function "date_part" in addition to casting from INT to DATETIME, extracts
the year from the timestamp.
Once the dimensions have been established, ie the fields on which to group, the met-
rics (SUM, AVG, etc.) to be applied to these groupings must be defined (third and last
tab of Fig. 2). For this specific case we created "Origin" field and the "sum_arrivi" and
"sum_presenze" metrics were defined and assigned using the SUM(arrivals) and
SUM(visits-duration) functions. The result is a “Table view” with the list of arrivals
and visits duration divided by geographical area of origin of the tourist.
After completing the table configuration phase, we move on to the fourth step of
creating the slices. Superset has no less than 34 different types of graphics. Now sup-
pose we want to create a graph that shows the average number of tourists in Sardinia
and at the same time a graph that shows the trend of visit duration over time. This is
achievable thanks to a slice view as Big number with trendline. Using the
"_anno_mese" field on the abscissa axis and on the ordinates the "_presenzamedia"
metric will get the result as shown in the last graph on the right of the Fig. 3 (point 1).
Many other options can be applied on the slice: for example, in the Filters section
you can add one or more values to exclude in the results. For more complex filters, you
can use Where clause option, which allows you to write conditions directly in SQL.
There is also the possibility to create complex tables (such as pivot tables) or create
dynamic selectable filters that can be used directly by the user on the dashboard.
9
Once we have finished the first slice, we will already be able to proceed with the
creation of the dashboard (fifth step of the process), which initially will naturally con-
tain only one slice. The layout of the Superset dashboards is very flexible: slices can be
easily resized by drag and drop. Between one slice and another you can insert a box
containing text or html to better describe the graph. For the realization the tourist move-
ments dashboard in Sardinia from 2013 to 2016 (Fig. 3) were used 13 slices. For rea-
sons of space, in Fig. 3 only part of the realized dashboard
11
is shown.
Fig. 3. A section of the dashboard of tourist movements in Sardinia
11
The complete dashboard is accessible online at http://bit.ly/storia-turismo-sardo-story
10
5.3 Sardinia tourism data story
With the data story we try to provide an interpretation of a phenomenon from the data.
This narration was published in the Public Data Portal and can be accessed from the
Menu > Community > Story.
Thanks to the filters the user can analyze the data by year, province, macro-typology,
origin and month. For example, analyzing the datasets as a whole (i.e. without setting
filters) it is possible to observe how Sardinian tourism has grown steadily both in terms
of visit duration and arrivals during the four years of survey (2013-2016): 10 million
arrivals in total with an average stay of 3.14 days which generated 47.9 million of nights
in the accommodation facilities of the island (see Fig. 3 point 1).
From the point of view of the foreign tourist, the most important numbers are those
of the Germans (5.91 million) and the French (3.95 million), while the major visit du-
ration from Italy, as shown in the Fig. 3 (point 2), they are those of the residents of
Lombardy (6.45 million) and Lazio (2.92 million). These data, filtered for 2016, are
compatible with an ANSA article [47] that stated that 2016 "was a record year for Sar-
dinian tourism: 2.9 million arrivals with an average stay of 4.6 days which generated
13.5 million of nights in the accommodation facilities of the Island ".
As shown in Fig. 3 (point 3) tourism is mainly distributed in the provinces of Olbia-
Tempio, Cagliari and Sassari with preference for hotel facilities in 74% of cases.
Conclusion
In this document we presented the shortcomings of the some data visualizing tools
on the market with respect to the potential of open source tools integrated into the DAF,
a project requested by the Italian Government to overcome the difficulties of channeling
data stored in local public administrations into a single container (data lake). We have
therefore introduced the DAF architecture and the semantic functionality of OntoPiA.
In particular, we introduced Superset, a data visualization tool which has a central role
in the creation of dashboards. Finally, a dashboard use case was presented using some
datasets from the Sardinia Region present in the DAF. The use case represents not only
one of the first experimental uses of the DAF but also the demonstration of how, fol-
lowing a process in only 5 steps, it is possible to extract information from large data
collections with a simple tool available not only to the PA, but also to businesses and
ordinary citizens. Currently the DAF is still being tested and there are critical issues,
such as the reluctance of some PAs who still do not want to provide their data and
accept the arrival of the new platform. But in general the number of public and private
subjects who are using it is growing and the production release is now imminent as it
is scheduled for December 2019.
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