Whole process modeling method of unstructured data management

Abstract

The invention discloses a whole process modeling method of unstructured data management. The method includes five steps: Repository model building, Analysis model building, Index model building, Search model building and Environment model building. Contents of the models are stored as metadata and mapped to a specific data engine for operation. The whole process modeling method can be used for managing unstructured data including texts, audios, videos and images so as to meet management requirements of users for storing, connecting and searching the unstructured data.

Description

technical field [0001] The invention relates to the field of unstructured data management, in particular to a whole-process modeling method for unstructured data management. Background technique [0002] With the development of the Internet and multimedia technology, massive amounts of unstructured data have emerged, such as HTML files on the Internet, multimedia data such as video, audio, and images, and sensor data in industrial production. [0003] Unstructured data is rich in types, complex and diverse in structure, and there is no unified definition of data constraints. Traditional relational data management systems are difficult to meet the requirements of applications for effective management of unstructured data. Internet, news media, process industries and other industries The need for unstructured data management systems is strong. For unstructured data management systems, the modeling of data management process is very important, but there is no mature method at ...

AI Technical Summary

Problems solved by technology

[0005] 1. Extended relational model: Add new data types and corresponding operations on the basis of relational models, but do not support data nesting and type inheritance, making it difficult to reuse models and inefficient for complex data modeling;

[0006] 2. Use object-oriented model: support data nesting and type inheritance, but unstructured data has data modeling requirements at the level of original data, basic information, characteristics, etc., it is difficult to directly use object-oriented model for different levels Differentiate the data, which in turn affects the efficiency of the entire unstructured data management system

[0007] In addition, the above two methods can only be used for data storage modeling, while the unstructured data system includes not only data storage functions, but also management functions such as feature extraction and analysis of raw data, feature-based indexing, and sample query. There is a certain relationship between these functions and data, and the realization of the functions themselves is complex and diverse, so the support of modeling methods is also required

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