Cross-Domain Data Processing based on Computer Technology and Big Data

This paper explores how to effectively process and integrate cross domain data through computer technology and big data analysis methods. Due to the storage of data from different fields in independent systems and the lack of effective communication and integration, the data cannot be fully utilized. This paper combines data extraction, applies SQL, regular expression filtering, and K-nearest neighbor interpolation anomaly detection to complete data cleaning and transformation. After storage and compression, the data is optimized for query efficiency using partitioning and sharding strategies to achieve fast reading and writing of cross domain data. Use decision trees and support vector machines for entity matching, while utilizing Spark and Flink for large-scale parallel data processing. The overall process includes data collection, fusion, and subsequent analysis and mining. The experimental results show that compared with single domain data processing methods, cross domain data processing and analysis methods based on computer technology and big data have a data throughput, fusion, and analysis accuracy of over 90%, verifying their effectiveness.