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Linked Data Visualization
Techniques, Tools, and Big Data

Synthesis Lectures on Data,
Semantics, and Knowledge
Editors
Ying Ding, University of Texas at Austin
Paul Groth, University of Amsterdam
Founding Editor Emeritus
James Hendler, Rensselaer Polytechnic Institute
Synthesis Lectures on Data, Semantics, and Knowledge is edited by
Ying Ding of the University of Texas at Austin and Paul Groth of the
University of Amsterdam. The series focuses on the pivotal role that
data on the web and the emergent technologies that surround it play
both in the evolution of the World Wide Web as well as applications
in domains requiring data integration and semantic analysis. The
large-scale availability of both structured and unstructured data on
the Web has enabled radically new technologies to develop. It has
impacted developments in a variety of areas including machine
learning, deep learning, semantic search, and natural language
processing. Knowledge and semantics are a critical foundation for
the sharing, utilization, and organization of this data. The series aims
both to provide pathways into the field of research and an
understanding of the principles underlying these technologies for an
audience of scientists, engineers, and practitioners.
Topics to be included:
• Knowledge graphs, both public and private
• Linked Data
• Knowledge graph and automated knowledge base construction
• Knowledge engineering for large-scale data

• Machine reading
• Uses of Semantic Web technologies
• Information and knowledge integration, data fusion
• Various forms of semantics on the web (e.g., ontologies,
language models, and distributional semantics)
• Terminology, Thesaurus, & Ontology Management
• Query languages
Linked Data Visualization: Techniques, Tools, and Big Data
Laura Po, Nikos Bikakis, Federico Desimoni, and George
Papastefanatos
2019
Ontology Engineering
Elisa F. Kendall and Deborah L. McGuinness
2019
Demistifying OWL for the Enterprise
Michael Uschold
2018
Validating RDF Data
Jose Emilio Labra Gayo, Eric Prud’hommeaux, Iovka Boneva, and
Dimitris Kontokostas
2017
Natural Language Processing for the Semantic Web
Diana Maynard, Kalina Bontcheva, and Isabelle Augenstein
2016
The Epistemology of Intelligent Semantic Web Systems
Mathieu d’Aquin and Enrico Motta
2016
Entity Resolution in the Web of Data

Vassilis Christophides, Vasilis Efthymiou, and Kostas Stefanidis
2015
Library Linked Data in the Cloud: OCLC’s Experiments with New
Models of Resource Description
Carol Jean Godby, Shenghui Wang, and Jeffrey K. Mixter
2015
Semantic Mining of Social Networks
Jie Tang and Juanzi Li
2015
Social Semantic Web Mining
Tope Omitola, Sebastián A. Ríos, and John G. Breslin
2015
Semantic Breakthrough in Drug Discovery
Bin Chen, Huijun Wang, Ying Ding, and David Wild
2014
Semantics in Mobile Sensing
Zhixian Yan and Dipanjan Chakraborty
2014
Provenance: An Introduction to PROV
Luc Moreau and Paul Groth
2013
Resource-Oriented Architecture Patterns for Webs of Data
Brian Sletten
2013
Aaron Swartz’s A Programmable Web: An Unfinished Work
Aaron Swartz
2013
Incentive-Centric Semantic Web Application Engineering

Elena Simperl, Roberta Cuel, and Martin Stein
2013
Publishing and Using Cultural Heritage Linked Data on the Semantic
Web
Eero Hyvönen
2012
VIVO: A Semantic Approach to Scholarly Networking and Discovery
Katy Börner, Michael Conlon, Jon Corson-Rikert, and Ying Ding
2012
Linked Data: Evolving the Web into a Global Data Space
Tom Heath and Christian Bizer
2011

Copyright © 2020 by Morgan & Claypool
All rights reserved. No part of this publication may be reproduced, stored in a
retrieval system, or transmitted in any form or by any means—electronic,
mechanical, photocopy, recording, or any other except for brief quotations in
printed reviews, without the prior permission of the publisher.
Linked Data Visualization: Techniques, Tools, and Big Data
Laura Po, Nikos Bikakis, Federico Desimoni, and George Papastefanatos
www.morganclaypool.com
ISBN: 9781681737256 paperback
ISBN: 9781681737263 ebook
ISBN: 9781681738345 epub
ISBN: 9781681737270 hardcover
DOI 10.2200/S00967ED1V01Y201911WBE019
A Publication in the Morgan & Claypool Publishers series
SYNTHESIS LECTURES ON DATA, SEMANTICS, AND KNOWLEDGE
Lecture #19
Series Editors: Ying Ding, University of Texas at Austin
Paul Groth, University of Amsterdam
Founding Editor Emeritus: James Hendler, Rensselaer Polytechnic Institute
Series ISSN
Print 2160-4711 Electronic 2160-472X

Linked Data Visualization
Techniques, Tools, and Big Data
Laura Po
University of Modena and Reggio Emilia, Italy
Nikos Bikakis
University of Ioannina, Greece
Federico Desimoni
University of Modena and Reggio Emilia, Italy
George Papastefanatos
ATHENA Research Center, Greece
SYNTHESIS LECTURES ON DATA, SEMANTICS, AND KNOWLEDGE
#19

ABSTRACT
Linked Data (LD) is a well-established standard for publishing and
managing structured information on the Web, gathering and bridging
together knowledge from different scientific and commercial
domains. The development of Linked Data Visualization techniques
and tools has been adopted as the established practice for the
analysis of this vast amount of information by data scientists,
domain experts, business users, and citizens.
This book covers a wide spectrum of visualization topics,
providing an overview of the recent advances in this area, focusing
on techniques, tools, and use cases of visualization and visual
analysis of LD. It presents core concepts related to data visualization
and LD technologies, techniques employed for data visualization
based on the characteristics of data, techniques for Big Data
visualization, tools and use cases in the LD context, and, finally, a
thorough assessment of the usability of these tools under different
scenarios.
The purpose of this book is to offer a complete guide to the
evolution of LD visualization for interested readers from any
background and to empower them to get started with the visual
analysis of such data. This book can serve as a course textbook or
as a primer for all those interested in LD and data visualization.
KEYWORDS
linked data, data visualization, visual analytics, big data, visualization
tools, web of data, semantic web, data exploration, information
visualization, usability evaluation, human-computer interaction

Contents
Preface
Acknowledgments
1 Introduction
1.1 The Power of Visualization on Linked Data
1.2 The Web of Linked, Open, and Semantic Data
1.3 Principles of Linked Data
1.4 The Linked Open Data Cloud
1.5 Web of Data in Numbers
1.6 The Value and Impact of Linked and Open Data
1.7 Semantic Web Technologies
1.8 Conclusions
2 Principles of Data Visualization
2.1 Data Visualization Design Process
2.2 Data Visualization Types
2.2.1 Visualizing Patterns over Time
2.2.2 Visualizing Proportions
2.2.3 Visualizing Graph Relationships
2.2.4 Visualizing Data on Maps
2.3 Interactive Visualization
2.4 Visualization in Big Data Era
2.4.1 How Does the Visualization of Big Data Differ from Traditional
Ones?
2.4.2 Visualization Systems and Techniques
2.5 Conclusions
3 Linked Data Visualization Tools
3.1 Evolution Over Time
3.2 Browsers and Exploratory Tools
3.3 Tools Using Multiple Visualization Types
3.4 Graph-Based Visualization Tools
3.5 Domain, Vocabulary-Specific, and Device-Oriented Visualization Tools
3.6 Ontology Visualization Tools
3.7 Conclusions

4 Visualization Use Cases
4.1 User Needs on LD Visual Exploration
4.2 Use Cases
4.3 Modeling Use Cases
4.3.1 T-Box Related Use Cases
4.3.2 A-Box Related Use Cases
4.3.3 T-Box and A-Box Related Use Cases
4.4 Conclusions
5 Empirical Evaluation of Linked Data Visualization Tools
5.1 Basic Characteristics of the Tools
5.2 Evaluation
5.2.1 Evaluation of T-Box Use Cases
5.2.2 Evaluation of A-Box Use Cases
5.2.3 Evaluation of A-Box and T-Box Uses Cases
5.2.4 Evaluation Summary
5.3 Different Tools for Different Tasks
5.4 Conclusions
6 Conclusions and Future Challenges
6.1 Future Challenges
Bibliography
Authors’ Biographies

Preface
The Linked Data Principles defined by Tim Berners-Lee promise that
a large portion of Web Data will be usable as one big interlinked RDF
database. Today, we are assisting the staggering growth in both the
production and consumption of Linked Data (LD) coming from
diverse domains such as health and biology, humanities and social
sciences, or open government. In the early phases of LD adoption,
most efforts focused on the representation and publication of large
volumes of privately held data in the form of Linked Open Data
(LOD), contributing to the generation of the Linked Open Data
Cloud.
Nowadays, given the wide adoption and availability of a very
large number of LD sources, it is crucial to provide intuitive tools for
researchers, data scientists, and domain experts as well as business
users and citizens to visualize and interact with increasingly large
datasets. Visual analytics integrates the analytic capabilities of the
computer and the abilities of the human analyst, allowing novel
discoveries and empowering individuals to take control of the
analytical process. LD visualization aims to provide graphical
representations of datasets or of some information of interest
selected by a user, with the aim of facilitating their analysis and
generating insights into complex interconnected information.
Visualization techniques can vary according to the domain, the type
of data, the task that the user is trying to perform, as well as the
characteristics of the user (e.g., skills).
This book presents the principles of LD visualization, as well as
demonstrates and evaluates state-of-the-art LD visualization tools.
Moreover, future challenges and opportunities in the field of Big
(Linked) Data visualization are presented.
The book is written for everyone who wants to explore and
exploit LD, whether undergraduate and post-graduate students, data

scientists, semantic technology developers, or UI & UX designers
who wish to gain some practical experience with LD tools. Previous
knowledge of Semantic Web technologies such as RDF, OWL,
SPARQL, or programming skills is not required. The purpose of this
book is to empower readers of any background to get started with
their own experiments on the LOD Cloud, select the most
appropriate LD tool for each scenario, and be aware of the
challenges and techniques related to Big Linked Data exploration.
Since readers are likely to have a wide variety of different
backgrounds, each chapter presents an overview of its content at
the beginning. A reader who wishes to have a quick overview can
start with the first page of each chapter. When the material in any
section becomes more advanced, the reader can skip to the
beginning of the next section without losing continuity. Chapter 1
introduces the Web of Linked Data, describing the phenomenon of
the production and consumption of LD, the social and economic
impact that this data has, and the effect that visualization tools can
have in facilitating the understanding and exploitation of such data.
Moreover, it presents the principles of LD and the technologies of the
Semantic Web Stack. Chapter 2 addresses how data can be
presented in visual form, focusing on interactive and specialized
visualizations of proportions, relationships, and spatial data. Further,
it introduces the new challenges and methods related to Big Data
Visualization. Chapter 3 surveys the variety of linked data
visualization tools. Chapter 4 defines and models a set of
visualization use cases based on the users’ requirements in LD
exploration. Chapter 5 describes a wide empirical evaluation of the
tools introduced in Chapter 3. Here, a practical evaluation of the
tools will be shown in order to describe their characteristics and
limitations as well as formalize how the tools handle the use cases
described in Chapter 4. Chapter 6 reports some conclusions and
open issues and suggests research challenges and promising trends
for the future.
Papastefanatos

Acknowledgments
Sincere thanks go to Dr. Jakub Klímek for his careful reading of the
manuscript and his constructive remarks. The work carried out in
this book was partially supported by the Networking on Linked Data
project funded by the “Enzo Ferrari” Engineering Department of the
University of Modena and Reggio Emilia within FAR2019 as well as
by the VisualFacts project (#1614) funded by the 1st Call of the
Hellenic Foundation for Research and Innovation Research Projects
for the support of post-doctoral researchers.
Papastefanatos
March 2020

CHAPTER 1
Introduction
Linked data provides the basis for knowledge to be distributed,
networked, and shared. The term Linked Data (LD) refers to a set of
best practices for publishing and interlinking structured data on the
Web. Creating a connection between data and its contexts could
lead to the development of intelligent search engines which could
explore the Web, moving from a keyword-based approach to a
meaning-based approach. Researches can be more accurate by
exploiting the relations between words. LD can provide a benefit in
several research areas like in the medical field for structuring the
connections between various illnesses and the relative cures, in the
scientific literature for structuring the citations between the million of
documents published online. The potentialities of exploitation of LD
are countless.
On the other hand, given the wide availability of LD sources, it is
crucial to provide intuitive tools enabling users without semantic
technology background to explore, analyze, and interact with
increasingly large datasets. Visual analytics integrates the analytic
capabilities of the computer and the abilities of the human analyst,
allowing novel discoveries and empowering individuals to take
control of the analytical process. LD visualization aims to provide
graphical representations of datasets with the aim to facilitate their
analysis and the generation of insights out of complex
interconnected information.
In this chapter, we will introduce why visualization is a powerful
means for linked data exploration, then, the principles and
technologies that are the bases for the creation of LD are presented,
and we also depict the incredible impact that LD can have in the real
world.

In the next section, we start illustrating how visualization is a
good way of interacting with the corresponding very large amounts
of complex, interlinked, multi-dimensional data. The evolution of the
web from Web 1.0 to Web 4.0. is depicted in Section 1.2. We
highlight the principles of LD in Section 1.3; after this, we describe
the Linked Data Cloud (Section 1.4) that draws datasets that have
been published according to those principles. Sections 1.5 and 1.6
are devoted to assessing the impact of LD in our life and the
opportunities they can generate. Finally, in Section 1.7, we introduce
the theoretical basis of LD by describing the Semantic Web
technologies.
1.1 THE POWER OF VISUALIZATION ON
LINKED DATA
On any kind of data visualization enables serendipity and
exploration. On LD it allows users to start understanding data
previously unknown and to get the picture of the dataset in their
mind, or to penetrate in some portions of the source. Moreover,
visualization over LD is probably the only way to enable users
without technical skills to grasp the meaning of the content of LD
sources. Furthermore, also domain experts can take advantage of a
visual exploration of the dataset resulting in reduction in time.
Following the idea of Tim-Berners Lee, each resource should
have a unique name that starts with HTTP. It means that reality can
be replicated over the Internet. Each resource of the world can have
its digital alter ego. Moreover, the pillar of linked data is that
resources should be connected to other resources.
The simplest form of relationship are personal relations; John is a
friend of Martin, Martin is the son of Peter, and somehow John is
remotely connected to Martin. However, this can be extended to
every existing field. Biology, Sociology, and Art are only a few areas
in which LD can be deployed. LD has the power to universally
express everything. However, how to visualize everything? One
possible choice is to learn Semantic Web technologies, write SPARQL
queries and then analyze the results. Despite the difficulty of writing

SPARQL queries, this approach can be used only when the results
are limited, since the information that can be displayed on a screen
are limited. The other possible approach is to exploit the power of
visualization.
The first tests on graphic visualization date back to 1890. In
1890, Herman Hollerith revolutionized the world of data analysis with
a creative and innovative idea: he used punch cards to collect and
analyze the U.S. census data. Using punch cards saved two years
and five million dollars over the manual tabulation techniques used
in the previous census while enabling more thorough analysis of the
data [Blodgett and Schultz, 1969]. We currently face an analogous
development in the filed of LD. Since 2006, many researchers
developed original solutions for solving the task of LD visualization
and now we can exploit different tools and different visualization
layouts.
Listing 1.1: Query for extracting relations between classes
For example, how can a user understand the content of the
Wikipathways dataset1
? Assuming that the user wants to know the
contents of the dataset, he/she could formulate a SPARQL query to
extract the classes and relations similar to the Listing 1.1 and then
analyze the results, as shown in Figure 1.1.
Adopting a graphical visualization, instead, can simplify a lot the
analysis of the results. For example, the previous information can be
obtained through one of the visualizations provided by the tool H-
BOLD (Figure 1.2). As it can be seen, displaying the same
information with a graph, it is easier to understand the connections
and paths among the classes.

Figure 1.1: Results of the query in Listing 1.1.
Figure 1.2: HBOLD schema visualization of the Wikipathways
dataset.
A crucial and impressive aspect of LD is that information is
interlinked with different sources. Therefore, starting from the URI of
a resource it is possible to display not only the information that
describes the resource within the dataset, but also information from
outside datasets. Figure 1.3 depicts how a LD visualization tool is
able to create a collage of information from disparate sources. In
that example, LodView2
has been exploited to merge all information
about London. Starting from the URI of the resource London in
Dbpedia (http://dbpedia.org/resource/London), the tool looks at all

the outcoming links and illustrates all the labels and pictures
associated with the URIs of these links. What is displayed is an
overview of images and information related to London from different
data sources.
Figure 1.3: LodView visualization of London.
1.2 THE WEB OF LINKED, OPEN, AND
SEMANTIC DATA
Tim Berners-Lee had a grand vision for the Internet when he began
development of the World Wide Web in 1989 [Gillmor, 2004, Chapter
2]. He envisioned a read/write Web. However, what had emerged in
the 1990s was an essentially read-only Web, the so-called Web 1.0.
The users’ interactions with the Web were limited to the search and
the reading of information. The lack of active interaction between
users and the Web lead, in 1999, to the birth of the Web 2.0. For the
first time, common users were able to write and share information
with everyone. This era empowered users with a few new concepts
like blogs, social media, and video-streaming platforms like Twitter,
Facebook, and Youtube.

Over time, users started to upload textual and multimedia
content at an incredibly high rate and, as a consequence, more and
more people started to use the Web for several different purposes.
The high volume of web pages and the higher number of requests
required Web applications to find new ways for handling documents.
Machines needed to understand what data they are handling. The
main idea was to provide a context to the documents in a machine-
readable format. This new revolution, the Web 3.0, is called
Semantic Web or Web of Data.
With the advent of the Semantic Web, users started to publish
content together with metadata, i.e., other data that provide some
context about the main data in a machine-understandable way. The
machine-readable descriptions enable content managers to add
meaning to the content. In this way, a machine can process
knowledge itself, instead of text, using processes similar to human
deductive reasoning and inference, thereby obtaining more
meaningful results and helping computers to perform automated
information gathering and research. Making data understandable to
machines implies, anyway, the sharing of a common data structure.
To solve this issue, the RDF (Resource Description Framework) was
the language proposed by the W3C for achieving a common data
structure.
The Semantic Web also allows creating links among data on the
Web. So that a person or machine can explore the Web of data. With
Linked Data, when you have some of it, you can find other, related,
data. Like the Web of hypertext, the Web of data is constructed with
documents on the Web and the links between arbitrary things are
described by RDF. The URIs identify any kind of object or concept.
Connecting your own data to other information already present
on the Web resulted in at least two important consequences. The
first is the possibility to add even more information and provide a
more extended context, and the second is the creation of a global
network of LD, the Giant Global Graph.
Alongside the arise of the Semantic Web, the Web shifted from a
web pages-oriented Web to a data-oriented Web (Figure 1.4). Users
of the Web started to publish data online and governments foresee

in opening data, a way for enrolling the citizen in the governative life
of the city.
The volume of data is growing exponentially everywhere. Each
minute, 149,513 emails are sent, 3.3 million Facebook posts are
created, 65,972 Instagram photos are uploaded, 448,800 Tweets are
constructed, and 500 hours of YouTube videos are uploaded. The
tremendous increase of data through the Internet of Things
(continuous increase of connected devices, sensors, and
smartphones), has contributed to the rise of a “data-driven” era.
Moreover, future predictions argue that by 2020, every person will
generate 1.7 megabytes in just a second.
Each sector is affected by this dizzying increase in available data
and this means that Big Data analysis techniques must be
implemented for mining data. Big Data is formed of large, diverse,
complex, longitudinal, and distributed data sets generated from
various instruments, sensors, Internet transactions, email, video,
click streams, and other sources, whereas open-linked data focuses
on the opening and the combining of data. The data can be released
both by public organizations and by private organizations or
individuals. Big Data analytics can be used to promote better
utilization of resources and improved personalization. Naturally, there
are no barriers between Big Data, Linked Data, and Open Data. It
means that when a dataset is at the same time open, structured in
node-edge fashion, and tremendously big, it can be referred as a
BOLD (Big, Open, and Linked Data) source.

Figure 1.4: Transition from the Web of Documents to the Web of
Data.
As a consequence, the arisen of the Web of Data gave birth to
new specialized figures that can boost the value of those data. Data
analysts, which are able to analyze and discover patterns from the
data, Data Scientists, which try to predict the future based over past
data, or the Chief Data Officer (CDO), who has the duty of defining
and governing the data improvements strategy for supporting the
achievement of corporate objectives, are only a few figures born for
handling with the Web of Data.
Now, in 2019, we are already entering the fourth-generation
internet, the Internet of Things, or the Web of intelligence
connections. It is talked to be the web of the augmented reality for
interacting at the same time with the real world and the online
world. Domotic houses, smart domestic appliances, and voice
assistants are only a few applications that will take place in the
following years. Although interesting, the innovations of the Web 4.0
are out of the scope of this book and will not be addressed.

1.3 PRINCIPLES OF LINKED DATA
The term Linked Data was coined in 2006 from one of the creators
of the Web, Sir Tim Berners-Lee. At the same time, he published a
note3
listing four rules for publishing LD.
1. Use URIs as names for things. This is the first rule for
publishing LD. This rule is the first milestone for creating a
system where all resources could be univocally identified. The
term resource refers both to real-world objects than web
pages.
2. Use HTTP URIs so that people can look up those names. The
second rule adopted the HTTP protocol as the mean for
reaching resources and their information. Thanks to it, users
are able to look for a specific object and get all the information
they need as a result. Moreover, considering the fact that the
resources should also be machine-readable, it is possible to
exploit the content negotiation system for obtaining different
representations of the requested resource.
3. When someone looks up a URI, provide useful information
using the standard. This means the resource’s information
should be returned to the requester in an RDF compliant
format.
4. Include links to other URIs so that they can discover more
things. The last rule emphasizes the fact that resources should
be connected to other resources in order to create what can
be considered as the successor of WWW, the Giant Global
Graph. This rule is the enabler of the great connectivity of
Linked Data. Starting from a resource, the users of the Web
have the possibility to jump from an object to another
resource as they desire.
Some time later, more precisely at the TED4
conference in 2009,
the same Tim Berners-Lee restated the principles defined in 2006 as

three “extremely simple” rules.5
1. All kinds of conceptual things, they have names now that start
with HTTP.
2. If I take one of those HTTP names and I look it up … I will
get back some data in a standard format which is kind of
useful data that somebody might like to know about that
thing, about the event, …
3. When I get back that information it’s not just got somebody’s
height and weight and when they were born, it’s got
relationships. And when it has relationships, whenever it
expresses a relationship, then the other thing that it’s related
to is given one of those names that start with HTTP, so that I
can go ahead and look that thing up.
Shortly before the birth of Linked Data principles, Open Data
arose and there were defined some principles. The first appearance
of the term “Open Data” dates back in 1995 in a document of
American scientific agency. That document stated that geophysical
and environmental data transcends political border so they promoted
a complete and open exchange of scientific information between
different countries. However, a formal definition of the term Open
Data wait until 2005 with the Open Definition 2.1.6
This document
holds several characteristics for data to be considered open and it
can be summarized as: “Knowledge is open if anyone is free to
access, use, modify, and share it—subject, at most, to measures that
preserve provenance and opennes.” Moreover, a more specific
definition of the term Open Government Data7
had to wait for 2007
where 30 advocates gathered in Sebastopol, California. The meeting
was meant to design a set of principles of open government data but
the same logic could be inherited by all kinds of Open Data. At the
end of the meeting, it was stated that government data is
considered open if it is compliance with the following principles.

• Complete. All public data is made available. Public data is data
that is not subject to valid privacy, security or privilege
limitations.
• Primary. Data is as collected at the source, with the highest
possible level of granularity, not in aggregate or modified
forms.
• Timely. Data is made available as quickly as necessary to
preserve the value of the data.
• Accessible. Data is available to the widest range of users for
the widest range of purposes.
• Machine processable. Data is reasonably structured to allow
automated processing.
• Non-discriminatory. Data is available to anyone, with no
requirement of registration.
• Non-proprietary. Data is available in a format over which no
entity has exclusive control.
• License-free. Data is not subject to any copyright, patent,
trademark, or trade secret regulation. Reasonable privacy,
security, and privilege restriction may be allowed.
Well aware of the advantages both Linked Data and Open Data
offered, it didn’t take long before someone started encouraging to
fuse Linked Data with Open Data. In fact, in 2010, the same Tim
Berners-Lee published an extension of its note containing a star
rating system for publishing Linked Open Data (LOD). Every rule of
this rating system is a specialization of the previous one, it means
that a five-star dataset satisfies all the criteria.
Available on the Web (whatever format) but with an
open license, to be Open Data. Documents are now
publicly available online. Everyone can read, edit, save,
share, and print them but unless building a custom
parser, it is hard to extract data.

Available as machine-readable structured data (e.g.,
Excel instead of image scan of a table, …). Data are now
accessible to machines but they remain bound to a
proprietary file format. Extracting data means depending
on proprietary software.
Available through non-proprietary format (e.g., CSV
instead of Excel, …). Data are now fully accessible to
everyone (both humans and machines) but they are still
bound in documents and not freely accessible from the
Web.
Use open standards from W3C (RDF and SPARQL) to
identify things so that people can point at your stuff.
Every resource has its own URI that identifies it
univocally. Users can look them up through HTTP
requests and read, edit, and share those data freely.
Generally, the data are represented through RDF format
however they can be converted in other formats easily.
Link your data to other people’s data to provide context.
Data are now fully connected to other resources and
their value increases. Both publishers and consumers
benefit from the network effect,8
the higher is the
number of consumers than the higher is the value of the
data.
1.4 THE LINKED OPEN DATA CLOUD
The LOD Cloud9
is a diagram that depicts the Linked Data datasets
publicly available online. The diagram is updated regularly and it is
maintained by the Insight Center for Data Analytics10
which is one of
the biggest data science research center in Europe.
Everyone can upload datasets in the cloud but it will only be
accepted and added to the cloud if it matches with the LOD Cloud
principles, which are a slightly different version of the LD principles
described in the section above. In order of being published, a
dataset must respect the following rules.

1. There must be resolvable http:// (or https://) URIs.
2. They must resolve, with or without content negotiation, to
RDF data in one of the popular RDF formats (RDFa, RDF/XML,
Turtle, N-Triples).
3. The dataset must contain at least 1000 triples.
4. The dataset must be connected via RDF links to a dataset that
is already in the diagram. This means, either your dataset
must use URIs from the other dataset, or vice versa. They
arbitrarily require at least 50 links.
5. Access the entire dataset must be possible via RDF crawling,
via an RDF dump, or via a SPARQL endpoint.
Moreover, the maintainers of the LOD cloud developed an ad-hoc
rating system for evaluating the quality of the published dataset.
Although all the datasets respect the five rules described above, it is
not assured that every dataset has the same characteristics.
Generally, the evaluation metrics takes into account several
metadata associated with the dataset like the presence or the
absence of a SPARQL endpoint, the information about the author,
the presence or the absence of the information of the author, the
presence or the absence of metadata (and eventually the kind of
metadata provided) and so forth. At the end of the process, each
dataset is associated with a number of stars ranging from 1–5. The
higher is the number of stars then higher is the quality of the
dataset.
The Linked Data Cloud was, initially, created in May in 2007, at
that time, it was composed of only 12 datasets. The LOD cloud
contained the following.
• DBpedia which is a Linked Data version of Wikipedia.
• Geonames which contains a Linked Data version of
geographical data.
• DBLP which contains a Linked Data version of academic data.

• Project Guttenberg and RDF Book Mashup which contains RDF
data about books.
• Revyu which contains reviews in the form of LD.
• MusicBrainz, DBtune, and Jamendo which contain RDF data
about the music business.
• FOAF (acronym of Friend of a Friend) which is an ontology
containing LD that describes information about people, their
relations, their activities, and, more generally, social network
data.
• World Factbook and U.S. census data that contain governative
data in the form of RDF triples.
The cloud shows which datasets are related to other datasets
and a qualitative indication of the number of properties which
connect a dataset to another. A thin line indicates that two datasets
are connected with a low number of properties while a thick line
represents a high number of relations connecting those datasets.
As time passed by, more and more institutions started to publish
their data according to the Linked Data Principles described in
Section 1.3 and the cloud got immense. After only half a year, the
Linked Data Cloud doubled in terms of the number of the dataset
published and reached the incredible number of 295 datasets on
September 2011. There is no data about the dimension of the cloud
in 2012 and 2013 but in 2014 the cloud counted up to 570 datasets.
Again, no data is available for 2015 and 2016 but from 2017 the LOD
cloud started to be updated regularly and there is plenty of
information. The first record of 2017, dated January 26th, reported
that the number of datasets increased to 1.146 (the double of the
number of datasets present in the cloud in 2014). During the
following years, the race of publishing Linked Data slowed down. In
fact, recent updates of March 29, 2019 showed that the number of
datasets present in the Linked Data Cloud is 1.239. Figure 1.5
represents the actual Linked Data Cloud. Despite the time elapsed

and the increasing number of datasets, DBPedia is still the biggest
and the best representative datasets of the LOD Cloud.
Figure 1.5: Linked Open Data Cloud (March 29, 2019).
As it can be perceived from Figure 1.5 the cloud is depicted as a
partially connected graph. Each node of the graph represents a
dataset and the link between two nodes indicates that some kind of
property connects elements from different datasets. For helping the
users during the navigation of the LOD cloud, given the fact that
each dataset is different to others both in size and in the domain it
covers, the maintainers of the LOD cloud decided to improve the
graph through the adoption of visual notation. The number of triples
contained in a dataset is used for calculating the dimension of its
node while the domain is used to color the inside of the nodes.
Moreover, to provide further aid during the navigation, each domain
is subsequently divided into distinct subsections.

1.5 WEB OF DATA IN NUMBERS
The Linked Open Data Cloud is probably the best representation of
the Web of data. However, Figure 1.5 does not reflect the volume of
data it contains. Each node of the graph, despite the small
dimension, contains an incredibly amount of data. For example,
DBPedia alone contains more than 9.5 billion triples. Clearly, DBPedia
is one of the biggest datasets around but it is not the only one that
reaches an incredibly high number of triples. Geonames,
LinkedGeoData, BabelNet are only a few other examples of huge
datasets. Along with several metadata, the LOD cloud collects the
number of triples which compose every single dataset.
Unfortunately, the number of the triples is not present for all the
datasets, but analyzing in details the dataset whose triples count is
given, it results that the mean number of triples for each dataset is
approximately equal to 176 million triples. It ends up in a total of
202 billion triples counted over 1151 datasets!
The number of Linked Data has risen in the last years also
because the efforts of governments, to be more transparent and
responsive to citizens’ demands, have been increasing [Attard et al.,
2015], and this, in most cases, resulted in the publication of (linked)
Open Data. Many online data portals exist and play a fundamental
role in the expansion of the Web of Data. Portals like DataHub,11
the
EU Open Data Portal,12
the European Data Portal,13
Data.Gov,14
Asia-
Pacific SDG Data Portal15
act as repositories for all kind of datasets
(agricultural, economy, education, environment, government, justice,
transports, …) of different countries so that everyone could freely
access those data. The only limitations to the usage of the data are
defined by the licenses under which the data have been published
but generally, they are not particularly restrictive. Thanks to those
portals, the amount of data accessible through the Web is insane.
Gathering together all the datasets those portal contains it is easy to
exceed the threshold of one million datasets. However, despite the
incredibly high number of datasets, their dimension is limited. Some
datasets could be pretty big but the greatest part of them occupies a
few kilobytes in space.

There is no clear information about the volume of data already
present in the Web but it is easy to figure out that the number and
the dimension of the datasets could only increase over the time
reaching exabytes of information. Since that information hides a real
treasure, in monetary terms, during the last period several data
analysis tools and big data analytics tools have been developed for
supporting the job of data scientists. One of the leading companies
in this sector is the apache foundation that developed a high number
of applications perfectly suited for handling big data like Apache
Hadoop,16
Apache Spark,17
Apache Cassandra,18
Apache Commons
RDF,19
Apache Jena,20
and many others.
1.6 THE VALUE AND IMPACT OF LINKED AND
OPEN DATA
The impact of Open Data at the economic, political, and social levels
has become clear in the recent years. The European Data Portal21
publishes every year several studies and reports about the situation
of Open Data in Europe. They distinguished the benefits coming
from Open Data in direct and indirect benefits. In their study
[Carrara et al., 2015], they defined direct benefits as “monetised
benefits that are realized in market transactions in the form of
revenues and Gross Value Added (GVA), the number of jobs involved
in producing a service or product, and cost savings” and indirect
benefits as “new goods and services, time savings for users of
applications using Open Data, knowledge economy growth,
increased efficiency in public services and growth of related
markets.” In the same document they estimate that the direct value
of the Open Data market in the European Union is 55.3 billion Euros,
with a potential growth between 2016 and 2020 of 36.9% to a value
of 75.7 billion Euros, and that the overall Open Data market reaches
is estimated to be between 193 and 209 billion Euros, with an
estimated projection of 265–286 billion Euros for 2020. They also
quantified the economic benefits by looking other three indicators:
number of jobs created, cost savings, and efficiency gains. The
forecasted number of direct Open Data jobs is expected to rise from

75,000 of 2016 to nearly 100,000 jobs by 2020. Moreover, thanks to
the positive economic effect on innovation and the development of
numerous tools to increase efficiency, not only the private sector, but
also the public sector is expected to experience an increased level of
cost savings through Open Data to a total of 1.7 billion Euros by
2020. They also estimated an augmentation of 7.000 saved lives
thanks to a quicker response, a decreasing of 5.5% in road fatalities,
a decreasing of 16% in energy usage, etc.
Another important document that assesses the value of Open
Data is Manyika et al. [2013]. That document, created in 2013,
estimates the value of the world wide Open Data market is about 3
trillions dollar annually (1.1 trillion for the U.S. market, 0.7 trillion for
the European market, and 1.7 for the others). The value is
calculated over seven domains of interest (Education,
Transportation, Consumer Products, Electricity, Oil and Gas, Health
Care, Consumer finance). The staggering difference between the
previous values implies that calculating the value of Open Data is not
an easy task and that the value is highly dependent on the field in
study. To the best of our knowledge, there is no actual estimation of
the value of the U.S. Open Data market.
1.7 SEMANTIC WEB TECHNOLOGIES
In order to unlock the full potential of Linked Data and to
understand how to extract the maximum profit from them, it is
important to dive into the technologies that have favored the birth of
Linked Data [Bikakis et al., 2013]. Semantic Web is built upon a
series of different technologies that have been piled up. All of these
technologies form the Semantic Web Stack. Figure 1.6 represents
the stack and highlights the logical structure (Concept and
Abstraction) and the technologies adopted (Specification and
Solutions) for the creation of the Semantic Web.

Figure 1.6: Semantic Web Stack.
The first layer of the stack is clearly the media for the information
transfer, the Web platform. The idea behind the Semantic Web was
to create a globally distributed database. This means that it is
necessary to univocally identify the resources and that is necessary
to adopt an universally accepted encoding system in order to identify
thing even between countries that adopt different writing systems.
This first step was accomplished by the adoption of URI (Uniform
Resource Identifies). With the advent of RDF1.1, in 2014, the actual
naming convention standard became the IRI (International Resource
Identifier). IRIs are sequences of Unicode characters and supports
any character of any languages. This is a quite important progress in
the multi-cultural context of the Internet.
Once defined how to identify and how to access the resources, it
is mandatory to create them and provide additional information. The
Resource Description Framework (RDF) is the model adopted for
solving this task and it is a general purpose language for
representing information about resources. RDF has a very simple
and flexible data model, based on the central concept of the RDF
statement. RDF statements describe simple facts as triples in the
form of Subject – Predicate – Object consisting of the resource being
described (the subject), a property (the predicate), and a property
value (the object). In particular, the subject can either be an IRI or a

Blank node, the predicate must be an IRI and the object can be an
IRI, Blank node, or RDF Literal. A Blank node is a placeholder that
stands for a resource to which no IRI nor literal is given. A collection
of RDF statements (or else RDF triples) can be intuitively understood
as a directed labeled graph, where the resources are nodes and the
statements are arcs connecting two nodes (from the subject node to
the object node). Finally, a set of RDF triples is called RDF Dataset or
RDF Graph.
RDF data can be written down in a number of different formats,
known as serialization. The first standard serialization format is
called RDF/XML and it is based on XML tags system. Although the
RDF/XML is still in use, other RDF serialization are now preferred
because they are more human-friendly. The other serialization
formats include:
• RDFa: notation for embedding RDF metadata in XHTML web
pages;
• N-Triples: an intuitive and line-based format. It express each
triple of an RDF graph on a different line;
• N3 (Notation 3): a serialization format developed by Tim
Berners-Lee and designed to be compact and human-readable;
• Turtle (Terse RDF Triple Language): a compact and human-
friendly format. It is a subset of N3;
• TriG: extension of Turtle notation;
• N-Quads: a superset of N-Triples, for serializing multiple RDF
graphs. The fouth element of the “triple” contains the name of
the graph to which the statement belongs; and
• JSON-LD: the standard JSON based serialization format that
superseded RDF/JSON format. It can be used for writing RDF
triples in a JSON style.
The third layer of the stack aims at structuring the data. The
former RDF model and its extension, the RDFS (RDF Schema), were
designed to describe, using a set of reserved terms called the RDFS

vocabulary, resources and/or relationships between resources. They
provide constructs for the description of types of objects (classes),
type hierarchies (subclasses), properties that represent object
features (properties), and property hierarchies (subproperty). In
particular, a Class in RDFS corresponds to the generic concept of a
type or category, somewhat like the notion of a class in object-
oriented languages, and is defined using the construct rdfs:Class.
The resources that belong to a class are called its instances. An
instance of a class is a resource having an rdf:type property whose
value is the specific class. Moreover, a resource may be an instance
of more than one class. Classes can be organized in a hierarchical
fashion using the construct rdfs:subClassOf. A property in RDFS is
used to characterize a class or a set of classes and is defined using
the construct rdf:Property. The Web Ontology Language (OWL) was
released in 2004 and is the standard language for defining and
instantiating Web ontologies. OWL and RDFS have several
similarities. Indeed, OWL is defined as a vocabulary like RDF,
however OWL has richer semantics. An OWL Class is defined using
the construct owl:Class and represents a set of individuals with
common properties. Moreover, OWL provides additional constructors
for class definition, including the basic set operations, union,
intersection, and complement that are implemented, respectively, by
the constructs owl:unionOf, owl:intersectionOf, and
owl:complementOf. Regarding the individuals, OWL allows to specify
two individuals to be identical or different through the owl:sameAs
and owl:differentFrom constructs. Unlike RDF Schema, OWL
distinguishes a property whose range is a datatype value
(owl:DatatypeProperty) from a property whose range is a set of
resources (owl:ObjectProperty). In 2009, an extended and
revisioned version of OWL, called OWL 2, became the new W3C
recommendation. The OWL 2 Web Ontology Language (OWL 2) has
a very similar overall structure with OWL 1 and is backward
compatible with it, while it introduces a plethora of new features.
Alongside the developement of OWL, a countless number of
vocabularies have been developed. Just to name a few, VoID22
(Vocabulary of Interlinked Dataset) contains terms for providing

metadata to a dataset, FoaF23
(Friend of a Friend) operates in the
Social Network domains and contains terms for describing people
and their relations, SKOS24
(Simple Knowledge Organization System)
is used for sharing and linking knowledge organization systems like
thesauri or taxonomies while the RDF Data Cube Vocabulary25
can be
used for publishing multi-dimensional data like statistics.
The SPARQL26
Protocol and RDF Query Language (SPARQL) is a
W3C recommendation and it is the standard query language for RDF
data since 2008. SPARQL is one the key technologies of the
Semantic Web and it is used to retrieve and manipulate RDF data
from the knowledge graphs available on the Web. The evaluation of
SPARQL queries is based on graph pattern matching. Graph Patterns
are templates that consist of a series of triples that the SPARQL
engine looks for inside the store.
SPARQL allows four query forms: SELECT, ASK, CONSTRUCT, and
DESCRIBE. The SELECT query form returns a solution sequence, i.e.,
a sequence of variables and their bindings. The ASK query form
returns a Boolean value (yes or no), indicating whether a query
pattern matches or not. The CONSTRUCT query form returns an RDF
graph structured according to the graph template of the query.
Finally, the DESCRIBE query form returns an RDF graph which
provides a “description” of the matching resources. Thus, based on
the query forms, the SPARQL query results may be RDF Graphs,
SPARQL solution sequences, and Boolean values.
Unfortunately, this SPARQL version presented different vacancies
including the lack of the support to data management operators so,
in 2013, the W3C SPARQL working group published SPARQL 1.127
which extended the original SPARQL query language in several
aspects. Precisely, SPARQL 1.1 introduced features for manipulating
the content of the store and introduced the support for nested
queries and aggregation functions.
At last, triples need to be stored in a triplestore. Different
proposals have been developed over the year. Monolithic Triple
Storages are triplestore that store all the triples in a single table.
They are sure easy to implement and work for huge number of
properties but it requires an intelligent index system and several self

join during queries. A slightly lighter version of monolithic storage
implies to associate each URI and Literal with a numerical identifier.
It ends up in two tables; one holds the association URI/Literal—
number and the other contains the triples in a numerical fashion.
Property Tables are triplestore which create a table for each class.
This way, the tuple with the same characteristics are grouped
together. It resembles the structure of relational DB and queries
require fewer joins but it potentially contains a high number of NULL
values. Vertically Partitioned Table triplestore create a two-column
table for every property of the dataset. Each table contains the
subject, in the first column, and the object, in the second column, of
the triples with that specific predicate. This system grants good
performance when then number of properties is low, otherwise, it is
particularly expansive in computational terms. Hexastores are
particular structures that create an index for each possible
combination of triple elements in order to enable efficient processing
at the cost of six times the disk required for storing data.
Quadstores are the natural evolution of the triplestores. The main
difference between them is that the quadstores store tuples of four
elements: Subject, Predicate, Object, and Graph.
Furthermore, the data contained in these structures (both
triplestore and quadstores) tend to be very atomic since the nodes in
the graph are primitive data types like strings, integers, date, etc
and the relations connect those kind of data. Graph Databases
model the graph following an object oriented fashion. The nodes are
not simple primitive kinds of data but instances of the graph.
Generally, each instance has properties that describe itself (datatype
properties) and properties that relates it to other objects (object
properties), so the datatype properties are integrated together
forming a sort of description for the instance while object properties
are treated as the arcs that connect different instances. Therefore, in
graph databases, the nodes are not simple strings but pure objects
with a moltitude of datatype properties. Some popular graph
databases are Neo4j28
and Amazon Neptune.29

1.8 CONCLUSIONS
In this chapter, we have introduced the story of the Web, starting
from the description of a Web of interlinked documents till the Web
of Data. The potential hidden behind the usage of the meaning of
the words can boost the advent of intelligent agents so we explored
the fundamentals that gave birth to the Semantic Web ranging from
RDF to Linked Data to the SPARQL query language to the storage
technologies. Moreover, we have reported some statistics collected
by different Open Data agencies worldwide about the dimension,
value, and impact that Open and Linked Data have been estimated
to reach in the global economic market.
1
https://www.wikipathways.org/index.php/Portal:Semantic_Web
2
https://lodview.it
3
https://www.w3.org/DesignIssues/LinkedData.html
4
https://www.ted.com/
5
https://www.ted.com/talks/tim_berners_lee_on_the_next_Web#t-960912
6
http://opendefinition.org/od/2.1/en/
7
https://public.resource.org/8_principles.html
8
https://en.wikipedia.org/wiki/Network_effect
9
https://lod-cloud.net/
10
https://www.insight-center.org/
11
https://datahub.io/
12
https://data.europa.eu/euodp/en/home
13
https://www.europeandataportal.eu/
14
https://www.data.gov/
15
http://data.unescap.org/sdg/
16
https://hadoop.apache.org/
17
https://spark.apache.org/
18
http://cassandra.apache.org/
19
https://commons.apache.org/proper/commons-rdf/
20
https://jena.apache.org/
21
https://www.europeandataportal.eu
22
https://www.w3.org/TR/void/
23
http://xmlns.com/foaf/spec/
24
https://www.w3.org/2004/02/skos/
25
https://www.w3.org/TR/vocab-data-cube/

26
https://www.w3.org/TR/rdf-sparql-query/
27
https://www.w3.org/TR/sparql11-query/
28
https://neo4j.com/
29
https://aws.amazon.com/it/neptune/

CHAPTER 2
Principles of Data Visualization
Information visualization aims at visually representing different types
of data (e.g., geographic, numerical, text, network) in order to
enable and reinforce cognition. Information visualization offers
intuitive ways for information perception and manipulation that
essentially amplify the overall cognitive performance of information
processing, especially for non-expert users. Visual analytics
combines information visualization with data exploration capabilities.
It enables users to explore and analyze unknown (in terms of
semantics and structure) sets of information, discover hidden
correlations and causalities, and make sense of data in ways that are
not always possible with traditional quantitative data analysis and
mining techniques. This is of great importance, especially given the
massive volumes of digital information concerning nearly every
aspect of human activity that are currently being produced and
collected. The so-called Big Data era refers to this tremendous
volume of information collected by digital means and analyzed to
produce new knowledge in a plethora of scientific domains.
The LOD cloud is one of the main pillars of the so-called Big Data
era. The number of datasets published on the Web, the amount of
information and the interconnections established between disparate
sources being available in the form of LOD are nowadays ever
expanding, making traditional ways of analyzing them insufficient
and posing new challenges in the way humans can explore,
visualize, and gain insights out of them.
In this chapter, we present the basic principles and tasks for data
visualization. We first provide the tasks for the preparation and
visualization of data. We then present the most popular ways for
graphically representing data according to the type of data and then

we provide an overview of the main techniques for users to interact
with the data. Finally, we show the main techniques used for
visualizing and interacting with Big Data.
2.1 DATA VISUALIZATION DESIGN PROCESS
Information visualization requires a set of preprocessing tasks, such
that data can be first extracted from data sources, transformed,
enriched with additional metadata and properly modeled before they
can be visually analyzed. An abstract view of this process along with
its constituent tasks is shown in Figure 2.1. It presents a generic
user-driven end-to-end roadmap of the tasks, problems, and
challenges relating to the visualization of data in general.

Figure 2.1: Process for the visualization of data.
The information is usually present in various formats depending
on the source; the appropriate data extraction and analysis
technique must be selected to transform the raw information into a
more semantically rich, structured format. A set of data processing
techniques are then applied to enhance the quality of the collected
data; these include cleaning data inconsistencies, filling in missing
values and detecting and eliminating duplicates. After that, the data
is enriched and customized with visual characteristics, meaningful
aggregations, and summaries which facilitate user-friendly data
exploration; finally, proper indexing is performed to enable efficient
searching and retrieval. The details for each step are presented
below.
Data Retrieval, Extraction. The first step concerns the retrieval
and extraction of the data to be visualized. Raw data exists in
various formats: e.g., books and reports describe phenomena in
plain text—unstructured information, websites, and social networks
contain annotated text and semi-structured data, whereas open data
sources and corporate databases provide structured information.
Data must first be retrieved in a digital format that is appropriate for
processing (e.g., digital text files from newspapers). The core
modeling concepts and observations are then extracted. Especially
when the source data is in plain text, this is usually performed in an
iterative human-curated way that refines the quality of the extracted
data. For structured data sources, the process involves the extraction
of the source concepts and their mapping to the target modeling.
Linked Data usually comes in structured formats, i.e., conforms
to well-defined ontologies and schemas. Linked data are usually the
result of a data extraction and transformation task, which has turned
raw data into semantically rich data representations. Therefore, LD
visualization usually starts from the next step, that of preparing
already structured data for visualizing them.
Data Preparation. Input data are provided either in databases or
data files (e.g., .csv, .xml). This step involves identifying all the

concepts within the input datasets and representing them in a
uniform data model that supports their proper visualization and
visual exploration. For example, the multidimensional model is
largely employed in social and statistical domains and represents
concepts as observations and dimensions. Observations are
measures of phenomena (e.g., indices) and dimensions are
properties of these observations (e.g., reference period, reference
area). Thus, a first step is to analyze the datasets and identify the
different types of attributes they contain (date, geolocation, numeric,
coded lists, literal). Each attribute is mapped to the corresponding
concept of the multidimensional model, such as dimension,
observation, coded list, etc. In addition, data processing requires a
set of quality improvement activities that eliminate data
inconsistencies and violations in source data. For example, missing
or inconsistent codes are filled in for coded list attributes, date and
time attributes are transformed to the appropriate format, and
numerical values are validated so that wrong values can be
corrected. Moreover, input data from multiple sources usually contain
duplicate facts and a deduplication of the dataset must be
performed. Deduplication is the process of identifying duplicate
concepts within the input dataset based on a set of distinctive
characteristics. A final task concerns the enrichment or the
interlinking of the data with information from external sources. For
example, places and locations are usually extracted as text; they can
subsequently be annotated and enriched with spatial information
(e.g., coordinates, boundaries) from external web services or
interlinked with geospatial linked data sources (e.g., geonames.org)
for their proper representation on maps.
Visual Preparation. This set of tasks involves the enrichment and
customization of the data with characteristics that enable the proper
visualization of the underlying information. These characteristics
extend the underlying data model with visual information. For
example, colors can be assigned to coded values and different types
of diagrams can be bound to different types of data, timelines to
date attributes and maps to geographical values. Thus,

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