18123673. ANOMALOUS DATA IDENTIFICATION FOR TABULAR DATA simplified abstract (ADOBE INC.)
Contents
ANOMALOUS DATA IDENTIFICATION FOR TABULAR DATA
Organization Name
Inventor(s)
Ramasuri Narayanam of Bangalore (IN)
Shiv Kumar Saini of Bangalore (IN)
Koyel Mukherjee of Bangalore (IN)
Manisha Padala of Hyderabad (IN)
Keshav Vadrevu of San Jose CA (US)
Gautam Choudhary of West Lafayette IN (US)
Atharv Tyagi of Bengaluru (IN)
ANOMALOUS DATA IDENTIFICATION FOR TABULAR DATA - A simplified explanation of the abstract
This abstract first appeared for US patent application 18123673 titled 'ANOMALOUS DATA IDENTIFICATION FOR TABULAR DATA
Simplified Explanation: The patent application describes a system and method for identifying anomalous data in tabular data by generating anomaly scores for data elements and defining evidence sets for attributes in tabular data records.
Key Features and Innovation:
- Generation of anomaly scores for data elements in tabular data records.
- Definition of evidence sets for attributes in tabular data records.
- Identification of anomalous data subsets based on anomaly scores for attributes and tabular data records.
Potential Applications: This technology can be applied in various industries such as finance, healthcare, and cybersecurity for detecting anomalies in large datasets.
Problems Solved: This technology addresses the challenge of efficiently identifying and analyzing anomalous data in tabular data records.
Benefits:
- Improved accuracy in detecting anomalies in tabular data.
- Enhanced data security and fraud detection capabilities.
- Increased efficiency in data analysis and decision-making processes.
Commercial Applications: The technology can be used in financial institutions for fraud detection, in healthcare for identifying irregular patient data, and in cybersecurity for detecting unusual network activity.
Prior Art: Researchers can explore prior art related to anomaly detection in tabular data records, data analysis techniques, and machine learning algorithms.
Frequently Updated Research: Stay updated on advancements in anomaly detection algorithms, data analysis methods, and applications of machine learning in anomaly detection.
Questions about Anomaly Detection in Tabular Data: 1. How does anomaly detection in tabular data records differ from anomaly detection in unstructured data? 2. What are the key challenges in implementing anomaly detection systems in real-time data processing environments?
Original Abstract Submitted
Systems and methods identify anomalous data in tabular data. A set of tabular data records is received. Each tabular data record includes data elements for a numbers of attributes, with each data element providing a value for a corresponding attribute. An anomaly score is generated for each data element of each tabular data record. Additionally, an evidence set is defined for each attribute and each tabular data record based on the anomaly scores for the data elements. An anomaly score is generated for each attribute and each tabular data record using the evidence sets. An output is provided that identifies one or more anomalous data subsets determined based on the anomaly scores for the attributes and tabular data records. Each anomalous data subset identifies a subset of attributes and a subset of tabular data records.
