Microsoft technology licensing, llc (20240338354). METHOD AND SYSTEM FOR LOCK AFTER QUALIFICATION FOR UPDATE QUERIES simplified abstract
Contents
METHOD AND SYSTEM FOR LOCK AFTER QUALIFICATION FOR UPDATE QUERIES
Organization Name
microsoft technology licensing, llc
Inventor(s)
Chaitanya Sreenivas Ravella of Surrey (CA)
Hanumantha Rao Kodavalla of Sammamish WA (US)
Prashanth Purnananda of Bellevue WA (US)
Craig Steven Freedman of Sammamish WA (US)
Vasileios Papadimos of Seattle WA (US)
METHOD AND SYSTEM FOR LOCK AFTER QUALIFICATION FOR UPDATE QUERIES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240338354 titled 'METHOD AND SYSTEM FOR LOCK AFTER QUALIFICATION FOR UPDATE QUERIES
Simplified Explanation: The patent application discusses techniques for anomaly detection using sparse judgmental samples in a database environment.
- Generating a qualified timestamp for the execution of a transaction operation.
- Determining if a row in a table is not locked by another transaction based on the timestamp.
- Checking if the row meets a condition of the transaction operation.
- Updating the row by acquiring a lock if it meets the condition.
Key Features and Innovation: - Anomaly detection using sparse judgmental samples - Timestamp-based row locking mechanism - Condition-based row updating process
Potential Applications: - Database management systems - Transaction processing systems - Anomaly detection in data analytics
Problems Solved: - Efficient anomaly detection in databases - Ensuring data integrity during transaction operations
Benefits: - Improved data security - Enhanced transaction processing efficiency - Reduced risk of data inconsistencies
Commercial Applications: Title: Timestamp-based Anomaly Detection System for Database Management This technology can be used in various industries such as finance, healthcare, and e-commerce for secure and efficient data management.
Prior Art: Further research can be conducted in the field of database anomaly detection and transaction processing systems to explore similar technologies and innovations.
Frequently Updated Research: Stay updated on advancements in database management systems, anomaly detection techniques, and transaction processing technologies to enhance the efficiency and security of data operations.
Questions about Anomaly Detection in Databases: 1. How does the qualified timestamp help in detecting anomalies in database transactions? 2. What are the potential challenges in implementing sparse judgmental samples for anomaly detection in databases?
Original Abstract Submitted
example aspects include techniques for anomaly detection via sparse judgmental samples. these techniques may include generating a qualified timestamp corresponding to execution of a first operation of a first transaction over a database and determining, based on the qualified timestamp, that a row of a table is not locked by a second operation of a second transaction over the database. in addition, the techniques may include determining that the row meets a condition of the first operation. further, the techniques may include updating, based on the qualified timestamp, the row in response to the row meeting the condition, the updating including acquiring a lock on the row of the table.
