Shulou(Shulou.com)06/01 Report--

The editor would like to share with you what the key technologies big data has to deal with. I hope you will gain something after reading this article. Let's discuss it together.

The key technologies of big data processing generally include: big data collection, big data preprocessing, big data storage and management, big data analysis and mining, big data presentation and application (big data retrieval, big data visualization, big data application, big data security, etc.).

First, big data's collection technology

Data collection refers to all kinds of structured, semi-structured (or weakly structured) and unstructured massive data obtained through RFID radio frequency data, sensor data, social network interactive data and mobile Internet data, which is the foundation of big data's knowledge service model. The focus is to break through big data collection technologies such as distributed high-speed and highly reliable data crawling or collection and high-speed data full mapping; break through big data integration technologies such as high-speed data parsing, conversion and loading; and design quality evaluation models and develop data quality technology.

Big data collection is generally divided into big data intelligent perception layer: mainly includes data sensing system, network communication system, sensor adaptation system, intelligent identification system and software and hardware resource access system. Realize the intelligent identification, positioning, tracking, access, transmission, signal conversion, monitoring, preliminary processing and management of structured, semi-structured and unstructured massive data. We must focus on the intelligent identification, perception, adaptation, transmission, access and other technologies for large data sources. Basic support layer: provide basic support environment such as virtual server for big data service platform, database of structured, semi-structured and unstructured data and IoT network resources. Focus on the distributed virtual storage technology, big data acquisition, storage, organization, analysis and decision-making operation of the visual interface technology, big data's network transmission and compression technology, big data privacy protection technology and so on.

Second, big data pretreatment technology

It mainly completes the operation of discrimination, extraction and cleaning of the received data. 1) extraction: because the acquired data may have a variety of structures and types, the data extraction process can help us to transform these complex data into a single or easy to process configuration, so as to achieve the purpose of rapid analysis and processing. 2) cleaning: for big data, it is not all valuable, some data are not what we care about, while others are completely wrong interference items, so it is necessary to filter "denoising" the data to extract effective data.

Third, big data storage and management technology

Big data storage and management should use memory to store the collected data, establish the corresponding database, and manage and call. Focus on complex structured, semi-structured and unstructured big data management and processing techniques. It mainly solves several key problems of big data, such as storability, representability, processability, reliability and effective transmission. Develop reliable distributed file system (DFS), energy efficiency optimized storage, computing into storage, big data's de-redundancy and efficient and low-cost big data storage technology; break through distributed non-relational big data management and processing technology, heterogeneous data fusion technology, data organization technology, research big data modeling technology; break through big data index technology; break through big data mobile, backup, replication and other technologies. Develop big data visualization technology.

To develop a new database technology, the database is divided into relational database, non-relational database and database cache system. Among them, non-relational database mainly refers to NoSQL database, which is divided into key-value database, column storage database, graph storage database and document database and so on. Relational database includes traditional relational database system and NewSQL database.

Develop big data security technology. Improve data destruction, transparent encryption and decryption, distributed access control, data audit and other technologies; break through privacy protection and reasoning control, data authenticity identification and forensics, data holding integrity verification and other technologies.

4. Big data's analysis and mining techniques

Big data's analytical techniques. Improve the existing data mining and machine learning technology; develop new data mining technologies such as data network mining, special group mining and graph mining; break through big data fusion technologies such as object-based data connection and similarity connection; break through domain-oriented big data mining technologies such as user interest analysis, network behavior analysis, emotional semantic analysis and so on.

Data mining is a process of extracting hidden, unknown but potentially useful information and knowledge from a large number of, incomplete, noisy, fuzzy and random practical application data. Data mining involves many technical methods and a variety of classifications. According to the mining task, it can be divided into classification or prediction model discovery, data summary, clustering, association rule discovery, sequence pattern discovery, dependency or dependency model discovery, anomaly and trend discovery, etc. According to mining objects, it can be divided into relational database, object-oriented database, spatial database, temporal database, text data source, multimedia database, heterogeneous database, heritage database and Web. According to the mining method, it can be roughly divided into machine learning method, statistical method, neural network method and database method. Machine learning can be subdivided into inductive learning methods (decision tree, rule induction, etc.), case-based learning, genetic algorithm and so on. Among the statistical methods, it can be subdivided into regression analysis (multiple regression, autoregression, etc.), discriminant analysis (Bayesian discrimination, Fischer discrimination, nonparametric discrimination, etc.), clustering analysis (systematic clustering, dynamic clustering, etc.), exploratory analysis (principal component analysis, correlation analysis, etc.) and so on. The neural network method can be subdivided into feedforward neural network (BP algorithm, etc.), self-organizing neural network (self-organizing feature mapping, competitive learning, etc.) and so on. Database methods are mainly multi-dimensional data analysis or OLAP methods, in addition, there are attribute-oriented induction methods.

From the point of view of mining tasks and mining methods, we focus on the following breakthroughs: 1. Visual analysis. Data visualization is the most basic function for both ordinary users and data analysts. The visualization of data can make the data speak by itself and let the user feel the result intuitively. two。 Data mining algorithm. Visualization is the translation of machine language to people, and data mining is the mother tongue of the machine. Segmentation, clustering, outlier analysis and a variety of algorithms allow us to refine data and mine value. These algorithms must be able to cope with the amount of big data, but also have a very high processing speed. 3. Predictive analysis. Predictive analysis allows analysts to make some forward-looking judgments based on the results of graphic analysis and data mining. 4. Semantic engine. Semantic engines need to be designed to have sufficient artificial intelligence to actively extract information from data. Language processing technology includes machine translation, emotion analysis, public opinion analysis, intelligent input, question answering system and so on. 5. Data quality and data management. Data quality and management is a management practice, and data processing through standardized processes and machines can ensure that a predetermined quality analysis result is obtained.

Big data's presentation and application technology

Big data technology can mine the information and knowledge hidden in the massive data, provide a basis for human social and economic activities, so as to improve the operational efficiency of various fields and greatly improve the intensive degree of the whole social economy. In China, big data will focus on the following three major areas: business intelligence, government decision-making, and public service. For example: business intelligence technology, government decision technology, telecom data information processing and mining technology, power grid data information processing and mining technology, meteorological information analysis technology, environmental monitoring technology, police cloud application system (road surveillance, video surveillance, network surveillance, intelligent transportation, anti-telecom fraud, command and dispatching, etc.), large-scale gene sequence analysis and comparison technology, Web information mining technology Multimedia data parallelization processing technology, film and television production rendering technology, cloud computing and massive data processing application technology in other industries.

After reading this article, I believe you have a certain understanding of "what are the key technologies of big data". If you want to know more about it, you are welcome to follow the industry information channel. Thank you for your reading!

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