Explain the clustering Big data and also gives classification of Big data.
Clustering is an essential data mining and tool for analyzing big data. There are difficulties for applying clustering techniques to big data duo to new challenges that are raised with big data. As Big Data is referring to terabytes and petabytes of data and clustering algorithms are come with high computational costs, the
question is how to cope with this problem and how to deploy clustering techniques to big data and get the results in a reasonable time. This study is aimed to review the trend and progress of clustering algorithms to cope with big data challenges from very first proposed algorithms until today’s novel solutions. The algorithms
and the targeted challenges for producing improved clustering algorithms are introduced and analyzed, and afterward, the possible future path for more advanced algorithms is illuminated based on today’s available technologies and frameworks.
1. Structured data
Structured Data is used to refer to the data which is already stored in databases, in an ordered manner. It accounts for about 20% of the total existing data and is used the most in programming and computer-related activities.
There are two sources of structured data- machines and humans. All the data received from sensors, web logs, and financial systems are classified under machine-generated data. These include medical devices, GPS data, data usage statistics captured by servers and applications and the huge amount of data that usually move through trading platforms, to name a few.
Human-generated structured data mainly includes all the data a human input into a computer, such as his name and other personal details. When a person clicks a link on the internet, or even makes a move in a game, data is created- this can be used by companies to figure out their customer behavior and make the appropriate decisions and modifications.
2. Unstructured data
While structured data resides in traditional row-column databases, unstructured data is the opposite- they have no clear format in storage. The rest of the data created, about 80% of the total account for unstructured big data. Most of the data a person encounters belong to this category- and until recently, there was not much to do to it except storing it or analysing it manually.
Unstructured data is also classified based on its source, into machine-generated or human-generated. Machine-generated data accounts for all the satellite images, the scientific data from various experiments and radar data captured by various facets of technology.
Human-generated unstructured data is found in abundance across the internet, since it includes social media data, mobile data and website content. This means that the pictures we upload to out Facebook or Instagram handles, the videos we watch on YouTube and even the text messages we send all contribute to the gigantic heap that is unstructured data.
3. Semi-structured data.
The line between unstructured data and semi-structured data has always been unclear, since most of the semi-structured data appear to be unstructured at a glance. Information that is not in the traditional database format as structured data, but contain some organizational properties which make it easier to process, are included in semi-structured data. For example, NoSQL documents are considered to be semi-structured, since they contain keywords that can be used to process the document easily.
Big Data analysis has been found to have a definite business value, as its analysis and processing can help a company achieve cost reductions and dramatic growth. So it is imperative that you do not wait too long to exploit the potential of this excellent business opportunity.
