Dell products l.p. (20240249164). SYSTEM AND METHOD FOR DETECTION OF TRANSIENT DATA DRIFT WHILE PERFORMING ANOMALY DETECTION simplified abstract

SYSTEM AND METHOD FOR DETECTION OF TRANSIENT DATA DRIFT WHILE PERFORMING ANOMALY DETECTION

Organization Name

dell products l.p.

Inventor(s)

OFIR Ezrielev of Be'er Sheva (IL)

SYSTEM AND METHOD FOR DETECTION OF TRANSIENT DATA DRIFT WHILE PERFORMING ANOMALY DETECTION - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240249164 titled 'SYSTEM AND METHOD FOR DETECTION OF TRANSIENT DATA DRIFT WHILE PERFORMING ANOMALY DETECTION

Simplified Explanation: The patent application discusses methods and systems for identifying transient data drift during anomaly detection in a distributed environment.

• An anomaly detector and data collectors work together to identify transient data drift.
• The system uses pairs of inference models to detect data drift and determine if it is transient.
• If transient data drift is identified, the system replaces the inference models to improve accuracy.

Key Features and Innovation:

• Utilizes an anomaly detector and data collectors to identify transient data drift.
• Uses pairs of inference models to detect and analyze data drift.
• Automatically replaces inference models to improve accuracy in detecting transient data drift.

Potential Applications:

• Anomaly detection in distributed systems.
• Monitoring data drift in real-time.
• Enhancing the accuracy of anomaly detection algorithms.

Problems Solved:

• Identifying transient data drift in distributed environments.
• Improving the accuracy of anomaly detection systems.
• Enhancing the efficiency of data monitoring processes.

Benefits:

• Early detection of transient data drift.
• Improved accuracy in anomaly detection.
• Enhanced performance of distributed systems.

Commercial Applications: The technology can be applied in various industries such as cybersecurity, finance, healthcare, and manufacturing to improve anomaly detection systems and ensure data integrity.

Prior Art: Readers can explore prior research in anomaly detection, data drift detection, and distributed systems to understand the evolution of this technology.

Frequently Updated Research: Stay updated on the latest advancements in anomaly detection algorithms, data drift analysis, and distributed computing to enhance the capabilities of this technology.

Questions about Transient Data Drift: 1. What are the key components of the system used to identify transient data drift? 2. How does the system determine if a data drift is transient or not?

Original Abstract Submitted

methods and systems for identifying transient data drift while performing anomaly detection in a distributed environment are disclosed. to identify transient data drift, a system may include an anomaly detector and one or more data collectors. the anomaly detector may identify a first data drift using a first pair of inference models. the anomaly detector may obtain additional data from the one or more data collectors and determine whether a second data drift has occurred using a second pair of inference models. if a second data drift has occurred, the anomaly detector may utilize the first pair of inference models to determine whether the first data drift was a transient data drift. if the first data drift was a transient data drift, the second pair of inference models may be replaced with the first pair of inference models.