To some extent, big data is the cutting-edge technology of data analysis. In short, the ability to quickly obtain valuable information from various types of data is big data technology. It is important to understand this point, and it is this point that makes the technology have the potential to reach many enterprises.
Big data has brought an unprecedented information explosion to the Internet. It not only changes the data application mode of the Internet, but also deeply affects people's production and life. People deep in the era of big data have realized that big data has changed the understanding of data analysis from "backward analysis" to "forward analysis", which has changed people's thinking mode, but at the same time, big data has also proposed to us It solves the problems of data collection, analysis and use. While solving these problems, it also means that big data has begun to develop in depth.
What is big dataBig data refers to a collection of data whose content cannot be captured, managed, and processed with conventional software tools within a certain period of time. Big data technology refers to the ability to quickly obtain valuable information from various types of data. Technologies applicable to big data include massively parallel processing databases, data mining grids, distributed file systems, distributed databases, cloud computing platforms, the Internet, and scalable storage systems.
Big data can be divided into fields such as big data technology, big data engineering, big data science, and big data applications. At present, the most talked about is big data technology and big data applications. Engineering and scientific issues have not been taken seriously. Big data engineering refers to the systematic engineering of big data planning, construction, operation and management; big data science focuses on discovering and verifying the laws of big data and its relationship with nature and social activities during the development and operation of big data networks.
In recent years, the IT communication industry, including information technologies such as the Internet, the Internet of Things, and cloud computing, has developed rapidly. The rapid growth of data has become a severe challenge and precious opportunity faced by many industries. Therefore, the modern information society has entered into big data. era. In fact, big data changes not only people's daily life and work patterns, business operations and business models, but also fundamental changes in scientific research models.
In a general sense, big data refers to a collection of data that cannot be perceived, acquired, managed, processed, and serviced with conventional machines and hardware and software tools within a certain period of time. Network big data refers to the big data that is generated by the interaction and integration of the ternary world of "human, machine, and things" in cyberspace and available on the Internet. Applying data to life and production can effectively help people or companies make more accurate judgments on information in order to take appropriate actions. Data analysis is the process of organizing purposefully collecting data, analyzing data, and turning it into information. It refers to the process by which individuals or enterprises use analytical methods to process data in order to solve problems such as decision-making or marketing in life and production.
The processing and analysis of big data is becoming the node of the new generation of information technology fusion applications. Mobile Internet, Internet of Things, social networking, digital home, e-commerce, etc. are the application forms of new generation information technology, and these applications continue to generate big data. Cloud computing provides a storage and computing platform for these massive and diversified big data. Through the management, processing, analysis and optimization of data from different sources, and feedback of the results to the above-mentioned applications, tremendous economic and social value will be created.
The use of big data will become a key factor in improving core competitiveness. Decisions in all walks of life are changing from "business-driven" to "data-driven". The analysis of big data can enable retailers to grasp market dynamics in real time and respond quickly; it can provide decision-making support for merchants to formulate more accurate and effective marketing strategies; it can help companies provide consumers with more timely and personalized services; In the field of public utilities, big data has also begun to play an important role in promoting economic development and maintaining social stability.
The methods and means of scientific research in the era of big data will undergo major changes. For example, sampling survey is the basic research method of social sciences. In the era of big data, real-time monitoring and tracking of massive behavioral data generated by research objects on the Internet can be carried out for mining and analysis, revealing regularities, and proposing research conclusions and countermeasures.
Visual analysis of big dataThe data is structured, including the relational database in the original data, and the data is semi-structured, such as the well-known text, graphics, and image data, as well as data of different configurations of the network. Through the analysis of various data, it is possible to clearly discover different types of knowledge structure and content, including generalized knowledge that reflects representations; it is used to reflect the aggregation mode of data or distinguish its belonging according to the attributes of the object. The characteristic knowledge of the category; the difference knowledge that describes differences and extreme special cases; the relevance knowledge that reflects the dependence or correlation between an event and other events.
The users of big data analysis include big data analysis experts as well as ordinary users, but the most basic requirement of both of them for big data analysis is visual analysis, because visual analysis can intuitively present the characteristics of big data, and it can be easily accessed. What the reader accepts is as simple and clear as looking at pictures.
One of the final application areas of big data analysis is predictive analysis. The characteristics are extracted from big data. Through scientific model establishment, predictive analysis allows analysts to make some predictive results based on the results of visual analysis and data mining. judgment. One of the final application areas of big data analysis is predictive analysis. Visual analysis and data mining are both preliminary work. As long as the characteristics and connections of information are excavated from big data, a scientific data model can be established. Through the model Bring in new data to predict future data. As a subset of data mining, memory computing efficiency drives predictive analysis, bringing real-time analysis and insight, and enabling real-time transaction data streams to be processed more quickly. The real-time transaction data processing mode can strengthen the enterprise's monitoring of information, and also facilitate the enterprise's business management and information update and circulation.
In addition, the predictive analysis capabilities of big data can help companies analyze future data information and effectively avoid risks. After the predictive analysis of big data, both individuals and companies can understand and manage big data better than before.
Data mining refers to the knowledge discovery in the database. Its history can be traced back to the first KDD International Academic Conference held in Detroit, USA in 1989. The first International Academic Conference on Knowledge Discovery and Data Mining (DM) is At the conference held in Canada in 1995, the data stored in the database was vividly compared to ore deposits, so the term "data mining" quickly spread. The purpose of data mining is the process of finding useful and suitable data from a large amount of data in a chaotic database, and revealing its hidden and unknown potential value information. In fact, data mining is only one step in the entire KDD process.
The theoretical core of big data analysis is data mining algorithms. Various data mining algorithms based on different data types and formats can more scientifically present the characteristics of the data itself. It is precisely because these are recognized by statisticians all over the world. All kinds of statistical methods (which can be called truth) can go deep into the data and unearth recognized value. Another aspect is that these data mining algorithms can process big data faster. If an algorithm takes several years to reach a conclusion, then the value of big data will be out of the question.
There is no uniform definition of data mining. Among them, “data mining is a process of extracting from incomplete, unclear, massive, and noisy practical application data with great randomness. "The process by which people acquire knowledge or patterns that are potentially useful" is a widely accepted definition.
Big data analysis is the evolution of business intelligence. Today, sensors, GPS systems, QR codes, social networks, etc. are creating new data streams. All of these can be discovered, and it is this kind of true breadth and depth of information that creates countless opportunities. To make big data tangible, so that small and medium-sized enterprises can gain a competitive advantage by being closer to customers, data integration and data management are the core.
Big data is everywhere. Big data is used in various industries. We are already big data. Our daily food, clothing, housing and transportation are big data.
Insulated Copper Tube Terminals
Insulated Copper Tube Terminals,High quality insulated terminal,copper tube terminal
Taixing Longyi Terminals Co.,Ltd. , https://www.longyiterminals.com