Linked Data Visualization. Laura Po

Чтение книги онлайн.

      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 enroling 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 anlysis 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 focusses 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.