resemble the structure of relational DB and queries requires fewer joins but it potentially contains an 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 property is low, otherwise it is particularly expansive in computational terms. Hexastores are particular structure 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 type 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 kind of data but instances of the graph. Generally, each instance has property that describes itself (datatype properties) and properties that relates it to other objects (object properties), so the datatype property 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 object 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 useage 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
3 https://www.w3.org/DesignIssues/LinkedData.html
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
10 https://www.insight-center.org/
12 https://data.europa.eu/euodp/en/home
13 https://www.europeandataportal.eu/
15 http://data.unescap.org/sdg/
18 http://cassandra.apache.org/
19 https://commons.apache.org/proper/commons-rdf/
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/
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,
