Analysis of Computer Information Processing Technology Under the Background of Intelligent Big Data

Abstract

With advancements in GPS, remote sensing, and computational simulation, an enormous volume of spatiotemporal data is being collected at an increasing speed from various application domains, spanning Earth sciences, agriculture, smart cities, and public safety. Such emerging geospatial and spatiotemporal big data, coupled with recent advances in deep learning technologies, foster new opportunities to solve problems that have not been possible before. For instance, remote sensing researchers can potentially train a foundation model using Earth imagery big data for numerous land cover and land use modeling tasks. Coastal modelers can train AI surrogates to speed up numerical simulations. However, the distinctive characteristics of spatiotemporal big data pose new challenges for deep learning technologies. This vision paper introduces various types of spatiotemporal big data, discusses new research opportunities in the realm of deep learning applied to spatiotemporal big data, lists the unique challenges, and identifies several future research needs.

Research on Artificial Intelligence Applications Based on Data Mining Algorithms in the Era of Big Data

“…Data mining algorithms are essentially statistical methods based on statistics, but data mining algorithms are different from general statistical methods, that is, data mining is for non-random samples, while statistical methods are for random sampling samples, which results in the results of data mining algorithms. The conclusion is more scientific [7] . At present, there are many kinds of data mining algorithms, and practical applications include shopping basket, MBR, decision tree, cluster analysis, etc.…”

“…Data mining is an interdisciplinary subject, involving many fields such as statistics, machine learning, and high-performance computing. Complete data mining usually includes several key steps such as data cleaning, data integration, data specification, data change, knowledge discovery, pattern evaluation, and knowledge representation [5] . The purpose of data cleaning is to eliminate data noise and data inconsistency, and make it Comply with data mining algorithm specifications; data integration is the combination of different data sources, and sometimes it is necessary to eliminate the data redundancy in them; data protocol is to extract relevant data from the collected data as needed to reduce the consumption of data mining as much as possible The purpose of data transformation is to use techniques such as smoothing and aggregation processing to transform data into a form suitable for mining; knowledge discovery is the use of various data mining algorithms to mine useful new information from the data; pattern evaluation is the use of measurement methods The secondary results of knowledge discovery are evaluated to verify whether the data mining results are correct; knowledge identification is to display the mining knowledge in a visual manner.…”

“…The purpose of data cleaning is to eliminate data noise and data inconsistency, and make it Comply with data mining algorithm specifications; data integration is the combination of different data sources, and sometimes it is necessary to eliminate the data redundancy in them; data protocol is to extract relevant data from the collected data as needed to reduce the consumption of data mining as much as possible The purpose of data transformation is to use techniques such as smoothing and aggregation processing to transform data into a form suitable for mining; knowledge discovery is the use of various data mining algorithms to mine useful new information from the data; pattern evaluation is the use of measurement methods The secondary results of knowledge discovery are evaluated to verify whether the data mining results are correct; knowledge identification is to display the mining knowledge in a visual manner. The process of data mining usually needs to be repeated many times, or one of the steps needs to be adjusted or re-executed [6] .…”