Scalable system and method for real-time predictions and anomaly detection
First Claim
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1. A method for detecting an event or anomaly in real-time and for triggering an action based thereon, the method comprising:
- receiving a stream of data from data sources, the data including at least two categorical features and a real-value measure;
performing sketching on the categorical features using min-wise hashing to create sketches of the data;
learning a regression tree on the sketches so as to minimize a desired loss function;
determining whether the event or anomaly exists; and
triggering the action based on at least one of a type, location or magnitude of the determined event or anomaly.
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Abstract
A method detects an event or anomaly in real-time and triggers an action based thereon. A stream of data is received from data sources. The data includes at least two categorical features and a real-value measurement. Sketching is performed on the features using min-wise hashing to create sketches of the data. A regression tree is learnt on the sketches so as to estimate a mean squared error. It is determined whether an event or anomaly exists based on the mean squared error. An action is triggered based on at least one of a type, location or magnitude of the determined event or anomaly.
26 Citations
16 Claims
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1. A method for detecting an event or anomaly in real-time and for triggering an action based thereon, the method comprising:
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receiving a stream of data from data sources, the data including at least two categorical features and a real-value measure; performing sketching on the categorical features using min-wise hashing to create sketches of the data; learning a regression tree on the sketches so as to minimize a desired loss function; determining whether the event or anomaly exists; and triggering the action based on at least one of a type, location or magnitude of the determined event or anomaly. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A system for detecting an event or anomaly in real-time and for triggering an action based thereon, the system comprising at least one server or processor configured to:
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receive a stream of data from data sources, the data including at least two categorical features and a real-value measure; perform sketching on the categorical features using min-wise hashing to create sketches of the data; learn a regression tree on the sketches so as to minimize a desired loss function; determine whether the event or anomaly exists; and trigger the action based on at least one of a type, location or magnitude of the determined event or anomaly. - View Dependent Claims (12, 13, 14, 15)
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16. A tangible, non-transitory computer medium having instructions thereon which when run on at least one processor or server cause the following steps to be performed:
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receiving a stream of data from data sources, the data including at least two categorical features and a real-value measure; performing sketching on the categorical features using min-wise hashing to create sketches of the data; learning a regression tree on the sketches so as to minimize a desired loss function; determining whether the event or anomaly exists; and triggering the action based on at least one of a type, location or magnitude of the determined event or anomaly.
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Specification