Engineering topics

Akella, Ram

Publications and source records attributed to Akella, Ram.

4 records

NASA NTRS2006

Anomaly Detection in Large Sets of High-Dimensional Symbol Sequences

This paper addresses the problem of detecting and describing anomalies in large sets of high-dimensional symbol sequences. The approach taken uses unsupervised clustering of sequences using the normalized longest common subsequence (LCS) as a similarity measure, followed by detailed analysis of outliers to detect anomalies. As the LCS measure is expensive to compute, the first part of the paper discusses existing algorithms, such as the Hunt-Szymanski algorithm, that have low time-complexity. We then discuss why these algorithms often do not work well in practice and present a new hybrid algorithm for computing the LCS that, in our tests, outperforms the Hunt-Szymanski algorithm by a factor of five. The second part of the paper presents new algorithms for outlier analysis that provide comprehensible indicators as to why a particular sequence was deemed to be an outlier. The algorithms provide a coherent description to an analyst of the anomalies in the sequence, compared to more normal sequences. The algorithms we present are general and domain-independent, so we discuss applications in related areas such as anomaly detection.

Budalakoti, Suratna↗

NASA NTRS2006

Enabling the Discovery of Recurring Anomalies in Aerospace System Problem Reports using High-Dimensional Clustering Techniques

This paper describes the results of a significant research and development effort conducted at NASA Ames Research Center to develop new text mining techniques to discover anomalies in free-text reports regarding system health and safety of two aerospace systems. We discuss two problems of significant importance in the aviation industry. The first problem is that of automatic anomaly discovery about an aerospace system through the analysis of tens of thousands of free-text problem reports that are written about the system. The second problem that we address is that of automatic discovery of recurring anomalies, i.e., anomalies that may be described m different ways by different authors, at varying times and under varying conditions, but that are truly about the same part of the system. The intent of recurring anomaly identification is to determine project or system weakness or high-risk issues. The discovery of recurring anomalies is a key goal in building safe, reliable, and cost-effective aerospace systems. We address the anomaly discovery problem on thousands of free-text reports using two strategies: (1) as an unsupervised learning problem where an algorithm takes free-text reports as input and automatically groups them into different bins, where each bin corresponds to a different unknown anomaly category; and (2) as a supervised learning problem where the algorithm classifies the free-text reports into one of a number of known anomaly categories. We then discuss the application of these methods to the problem of discovering recurring anomalies. In fact the special nature of recurring anomalies (very small cluster sizes) requires incorporating new methods and measures to enhance the original approach for anomaly detection. ?& pant 0-

Srivastava, Ashok, N.↗

NASA NTRS2006

Discovering Atypical Flights in Sequences of Discrete Flight Parameters

This paper describes the results of a novel research and development effort conducted at the NASA Ames Research Center for discovering anomalies in discrete parameter sequences recorded from flight data. Many of the discrete parameters that are recorded during the flight of a commercial airliner correspond to binary switches inside the cockpit. The inputs to our system are records from thousands of flights for a given class of aircraft and destination. The system delivers a list of potentially anomalous flights as well as reasons why the flight was tagged as anomalous. This output can be analyzed by safety experts to determine whether or not the anomalies are indicative of a problem that could be addressed with a human factors intervention. The final goal of the system is to help safety experts discover significant human factors issues such as pilot mode confusion, i.e., a flight in which a pilot has lost situational awareness as reflected in atypicality of the sequence of switches that he or she throws during descent compared to a population of similar flights. We view this work as an extension of Integrated System Health Management (ISHM) where the goal is to understand and evaluate the combined health of a class of aircraft ar a given destination.

Budalakoti, Suratna↗

NASA NTRS2006

Data Mining SIAM Presentation

This viewgraph document describes the data mining system developed at NASA Ames. Many NASA programs have large numbers (and types) of problem reports.These free text reports are written by a number of different people, thus the emphasis and wording vary considerably With so much data to sift through, analysts (subject experts) need help identifying any possible safety issues or concerns and help them confirm that they haven't missed important problems. Unsupervised clustering is the initial step to accomplish this; We think we can go much farther, specifically, identify possible recurring anomalies. Recurring anomalies may be indicators of larger systemic problems. The requirement to identify these anomalies has led to the development of Recurring Anomaly Discovery System (ReADS).

Srivastava, Ashok↗