Shulou(Shulou.com)06/03 Report--

The big data processing process mainly includes data collection, data preprocessing, data storage, data processing and analysis, data display/data visualization, data application and other links, among which data quality runs through the whole ×× process, and each data processing link will have an impact on the quality of big data. Generally, a good big data product should have a large amount of data scale, fast data processing, accurate data analysis and prediction, excellent visual charts and concise and understandable result interpretation. Based on the above links, this paper will analyze the impact of different stages on big data quality and its key influencing factors.

I. Data collection

During data collection, data sources affect the authenticity, integrity, consistency, accuracy, and security of big data quality. For Web data, most of them are collected by web crawler, which requires time setting for crawler software to ensure the timeliness and quality of collected data. For example, you can use the value-added API settings of Yihaiju acquisition software to flexibly control the start and stop of acquisition tasks.

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II. Data preprocessing

In the process of big data collection, there are usually one or more data sources, including homogeneous or heterogeneous databases, file systems, service interfaces, etc., which are easily affected by noise data, missing data values, data conflicts, etc. Therefore, it is necessary to preprocess the collected big data sets first to ensure the accuracy and value of big data analysis and prediction results.

The preprocessing of big data mainly includes data cleaning, data integration, data reduction and data conversion, which can greatly improve the overall quality of big data and embody the quality of big data process. Data cleaning technology includes data inconsistency detection, noise data identification, data filtering and correction, which is conducive to improving the consistency, accuracy, authenticity and availability of big data.

Data integration is the integration of data from multiple data sources to form a centralized and unified database, data cube, etc. This process is conducive to improving the integrity, consistency, security and availability of big data.

Data reduction is to reduce the scale of data set without damaging the accuracy of analysis results and simplify it, including dimension reduction, data reduction, data sampling and other technologies. This process is conducive to improving the value density of big data, that is, improving the value of big data storage.

Data transformation processing includes rule-based or metadata-based transformation, model-based and learning-based transformation and other technologies, which can achieve data unification through transformation, which is conducive to improving the consistency and availability of big data.

In a word, data preprocessing is conducive to improving the consistency, accuracy, authenticity, availability, integrity, security and value of big data, and the related technology in big data preprocessing is the key factor affecting the quality of big data process.

III. Data processing and analysis

1. Data processing

The distributed processing technology of big data is related to storage form and business data type. The main computing models for big data processing include MapReduce distributed computing framework, distributed memory computing system, distributed stream computing system, etc. MapReduce is a batch distributed computing framework, which can analyze and process massive data in parallel. It is suitable for processing all kinds of structured and unstructured data. Distributed memory computing system can effectively reduce the overhead of data reading, writing and moving, and improve the performance of big data processing. Distributed stream computing systems process data streams in real time to ensure the timeliness and value of big data.

In short, no matter what kind of big data distributed processing and computing system, it is conducive to improving the value, availability, timeliness and accuracy of big data. The type and storage form of big data determine the data processing system it adopts, and the performance and quality of the data processing system directly affect the value, availability, timeliness and accuracy of the quality of big data. Therefore, when processing big data, it is necessary to select the appropriate storage form and data processing system according to the type of big data to optimize the quality of big data.

2. Data analysis

Big data analysis technology mainly includes distributed statistical analysis technology of existing data and distributed mining and deep learning technology of unknown data. Distributed statistical analysis can be completed by data processing technology, while distributed mining and deep learning technology can be completed in the big data analysis stage, including clustering and classification, association analysis, deep learning, etc., which can mine data association in large data sets and form description patterns or attribute rules for things. The accuracy of data analysis and prediction can be improved by constructing machine learning models and massive training data.

Data analysis is a key link in big data processing and application, which determines the value and availability of big data sets, as well as the accuracy of analysis and prediction results. In the process of data analysis, appropriate data analysis technology should be selected according to big data application scenarios and decision-making needs to improve the availability, value and accuracy of big data analysis results.

IV. Data visualization and application

Data visualization refers to the process of displaying the results of big data analysis and prediction to users in an intuitive way of computer graphics or images, and can be processed interactively with users. Data visualization technology helps to discover the regularity information hidden in a large amount of business data to support management decisions. Data visualization can greatly improve the intuitiveness of big data analysis results, which is convenient for users to understand and use. Therefore, data visualization is a key factor affecting the availability and understandability of big data.

Big data application refers to the process of applying the big data results obtained after analysis and processing to management decision-making, strategic planning, etc. It is the inspection and verification of big data analysis results, and the big data application process directly reflects the value and availability of big data analysis and processing results. Big data applications play a guiding role in analyzing and processing big data.

Before a series of operations such as big data collection and processing, through full investigation of application scenarios and in-depth analysis of management decision-making demand information, the goals of big data processing and analysis can be clarified, thus providing clear directions for big data collection, storage, processing, analysis and other processes, and ensuring the availability, value and user needs of big data analysis results.

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