Semantic net based large scale offline data analysis framework

Abstract

The invention relates to a semantic net based large scale offline data analysis framework. The large scale offline data analysis framework includes a data acquisition layer, a body layer, a data storage layer, a semantic layer, a data analysis layer and an application layer. A data source includes dynamic data and static data, and the static data includes data and database internal logic semantic and structure type. The static data is established into a body model in the analysis framework; the static data is extracted and modeled, and then the static data orients a user or an upper analysis task in a semantic service manner. The large scale offline data analysis framework can effectively improve the ability to organizing multi-source heterogeneous offline data and has a uniform interface to upper data; and application users or data analysis workers can access a lower data source through a semantic interface without knowing all the information of different data sources, and relevant data information is acquired. The large scale offline data analysis framework can effectively update the whole data source from a global perspective by correction of the body structure having changed content and update and inference service built in an application tool.

Description

technical field [0001] The invention relates to a large-scale offline data analysis framework based on semantic web. Background technique [0002] With the maturity of the new generation of information technology, the concepts of the Internet of Things, mobile Internet, and cloud computing are gradually accepted by individuals and enterprises. A large amount of data and information is growing at the PB level every day. How to process, analyze and obtain big data Valuable knowledge has become the ultimate focus of research by major institutions and companies. Big data is an important resource for enterprises to reconstruct the value chain, tap potential economic growth points, and drive independent innovation in the new era. Big data research also involves various fields, such as transportation, medical care, finance, Internet, public management, industry, service industry and scientific research in universities. [0003] Similarly, the processing and analysis of big data r...

AI Technical Summary

Problems solved by technology

Due to the existence of various data acquisition devices in the same field, the original data is stored in different physical addresses in space. At the same time, the same type of data is not stored in a standardized manner, which brings great difficulties to data retrieval and calculation. ;

[0007] (3) Data traceability is difficult

Data collection and integration processes are diverse, and different collection departments only focus on their own data needs. Therefore, data collected by different departments in the same field has strong semantic ambiguity, which makes information exchange difficult and cannot be reused.

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