Analysis on the Development Prospect and Strategy of Big Data

Big data, also known as massive data, refers to the information that is so large that it cannot be retrieved, managed, processed and collated into more positive information to help enterprises make business decisions within a reasonable time through the human brain or even mainstream software tools.

Features of Big Data

Large amount of data, many types of data,

It requires strong real-time performance and great value of data. Big data exists in all walks of life, but a lot of information and consultation are complicated. We need to search, process, analyze, summarize and summarize its deep-seated laws.

Acquisition of Big Data

The development of science and technology and the Internet are pushing forward the advent of the big data era. Every day, a large number of data fragments are generated in all walks of life. Data measurement units have developed from Byte, KB, MB, GB and TB to PB, EB, ZB, YB and even BB, NB and DB. In the era of big data, data collection is no longer a technical problem, but how can we find its inherent laws in the face of so many data.

Mining and Processing of Big Data

Big data cannot be calculated and estimated by human brain, or processed by a single computer. Distributed computing architecture must be adopted, relying on cloud computing distributed processing, distributed database, cloud storage and virtualization technology. Therefore, cloud technology must be used for big data mining and processing.

Application of Big Data

Big data can be applied to all walks of life to analyze and sort out the huge data collected by people and realize the effective use of information. For example, if we look for major genes related to milk yield at the gene level of dairy cows, we can scan the whole genome of dairy cows first. Although we have obtained all phenotypic information and gene information, due to the huge amount of data, we need to use big data technology to analyze and compare and mine major genes. There are many more examples.

Significance and Prospect of Big Data

In general, big data is the mining of large, dynamic and sustainable data through the application of new systems, new tools and new models, thus obtaining things with insight and new values. In the past, when faced with huge data, we may be blinded and unable to understand the true nature of things, thus obtaining erroneous inferences in scientific work. With the advent of the era of big data, all the truth will be revealed to us.

Big Data Development Strategy

Traditional data methods, whether traditional OLAP

Neither technology nor data mining technology can meet the challenge of big data. First, the implementation efficiency is low. Traditional data mining technology is based on centralized underlying software architecture development, which is difficult to parallelize, so TB is being processed

The efficiency of data above grade level is low. Secondly, the accuracy of data analysis is difficult to improve with the increase of data volume, especially for unstructured data.

Among all the digital data of human beings, only a very small part ( about 1% of the total data ) of numerical data has been deeply analyzed and mined ( e.g. regression, classification, clustering ). Large Internet enterprises have carried out shallow analysis ( e.g. sorting ) on semi-structured data such as webpage indexes and social data, accounting for nearly the total amount.

60% of unstructured data such as voice, picture and video are still difficult to analyze effectively.

Therefore, the development of big data analysis technology needs to make breakthroughs in two aspects. One is to carry out efficient in-depth analysis of massive structured and semi-structured data to mine tacit knowledge, such as understanding and identifying semantics, emotions and intentions from text web pages composed of natural languages. The second is to analyze unstructured data, transform massive complex multi-source voice, image and video data into machine – recognizable information with clear semantics, and then extract useful knowledge from it.

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