Karimli Elvin Eyvaz,
Master student at the Department of Intellectual Property Law, Faculty of Law,
Baku State University
Email: [email protected]
THE CONCEPT OF BIG DATA AND BIG DATA DATABASES
Big Data is defined by its sheer volumes of heterogeneous data, created at a high speed by multiple sources, which can be handled by sophisticated processors, software, and algorithms. There are five dimensions of Big Data that need to be understood: volume, veracity, velocity, variety, and value.
The first dimension of Big Data is the volume, which is characterized by the sheer size of the data in comparison to smaller data sets. Veracity, the quality, and reliability of the data is crucial since the processing of Big Data is oft automated. Velocity refers to the speed at which data is being created, captured, consumed, and analyzed. Variety includes the entire range of data and its sources, from online platforms and social media to various digital equipment. Finally, the combination of these dimensions creates great value, which is then translated into critical insight and practical value.
As a collection of a vast amount of data from myriad sources, Big Data corpus utilizes the sophisticated and analytical tools, which are commonly referred as to “Big Data Analytics”. Namely, Big Data Analytics incorporates the Text and Data Mining process which commonly hinges on the Artificial Intelligence algorithm. Accordingly, one needs to delve into the analysis of the TDM process.
As per the European Union, text and data mining is defined as “any automated analytical technique aimed at analyzing text and data in digital form in order to generate information which includes but is not limited to patterns, trends and correlations”. As inferred from this definition, TDM process could be expounded as a three-step process, including:
- identification and duplication of the materials from a wide range of sources;
- the extraction of relevant data; and
- the restructuring of data into a new pattern or correlation.
By incorporating TDM process, as a starting point the Big Data corpus is firstly comprised of the myriad sources of data. At the end of the TDM process, the gathered data can be transformed into patterns or correlations. In the light of the TDM process, this article argues that the following subject-matters relating to Big Data could be differentiated from each other:
- The data formulating the Big Data corpus
- Big Data as a database
- The outputs of Big Data Analytics or Big Data patterns
Among these subject-matters, this article has focused on the Big Data databases in the light of the sui-generis protection under the EU legislation. For that reason, there is a reason to provide the specificities of the Big Data databases.