{"title":"面向大规模数据库频繁模式监测的动态回忆自适应织机的智能开发","authors":"st Sindhu, G. Madhuri, T. Mahesh","doi":"10.1109/ICDCECE57866.2023.10150481","DOIUrl":null,"url":null,"abstract":"The Dynamic Recollection Adaptive Loom (DRAL) is a groundbreaking technology that provides real-time monitoring and analysis of frequent patterns in data streams. This technology is based on the concept of dynamic memory, which allows the system to quickly adapt to changing patterns and data flows and automatically adjust to new patterns and trends. DRAL is designed to provide a comprehensive and efficient way of detecting, analyzing and responding to frequent patterns in data streams. It uses a combination of machine learning algorithms and data mining techniques to accurately detect and analyze patterns in data streams. This technology is able to rapidly detect outliers and anomalies in the data stream and quickly identify frequent patterns. Additionally, it can quickly respond to changes in the data stream and provide datadriven recommendations for optimization and future predictions. DRAL also provides a robust and secure data management platform that enables users to securely store and manage their data streams in a secure and efficient manner. This technology also provides a comprehensive security framework that ensures the confidentiality and integrity of the data streams. It enables users to easily monitor and manage their data streams and quickly respond to any changes.","PeriodicalId":221860,"journal":{"name":"2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Smart Development with Dynamic Recollection Adaptive Loom for Frequent Pattern Monitoring in Large Scale Databases\",\"authors\":\"st Sindhu, G. Madhuri, T. Mahesh\",\"doi\":\"10.1109/ICDCECE57866.2023.10150481\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Dynamic Recollection Adaptive Loom (DRAL) is a groundbreaking technology that provides real-time monitoring and analysis of frequent patterns in data streams. This technology is based on the concept of dynamic memory, which allows the system to quickly adapt to changing patterns and data flows and automatically adjust to new patterns and trends. DRAL is designed to provide a comprehensive and efficient way of detecting, analyzing and responding to frequent patterns in data streams. It uses a combination of machine learning algorithms and data mining techniques to accurately detect and analyze patterns in data streams. This technology is able to rapidly detect outliers and anomalies in the data stream and quickly identify frequent patterns. Additionally, it can quickly respond to changes in the data stream and provide datadriven recommendations for optimization and future predictions. DRAL also provides a robust and secure data management platform that enables users to securely store and manage their data streams in a secure and efficient manner. This technology also provides a comprehensive security framework that ensures the confidentiality and integrity of the data streams. It enables users to easily monitor and manage their data streams and quickly respond to any changes.\",\"PeriodicalId\":221860,\"journal\":{\"name\":\"2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-04-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDCECE57866.2023.10150481\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDCECE57866.2023.10150481","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Smart Development with Dynamic Recollection Adaptive Loom for Frequent Pattern Monitoring in Large Scale Databases
The Dynamic Recollection Adaptive Loom (DRAL) is a groundbreaking technology that provides real-time monitoring and analysis of frequent patterns in data streams. This technology is based on the concept of dynamic memory, which allows the system to quickly adapt to changing patterns and data flows and automatically adjust to new patterns and trends. DRAL is designed to provide a comprehensive and efficient way of detecting, analyzing and responding to frequent patterns in data streams. It uses a combination of machine learning algorithms and data mining techniques to accurately detect and analyze patterns in data streams. This technology is able to rapidly detect outliers and anomalies in the data stream and quickly identify frequent patterns. Additionally, it can quickly respond to changes in the data stream and provide datadriven recommendations for optimization and future predictions. DRAL also provides a robust and secure data management platform that enables users to securely store and manage their data streams in a secure and efficient manner. This technology also provides a comprehensive security framework that ensures the confidentiality and integrity of the data streams. It enables users to easily monitor and manage their data streams and quickly respond to any changes.