Microsoft Technology Licensing, LLC

Gm Global Technolgoy Operations, Inc.

Srivatsan Laxman

Ramarathnam R Venkatesan

Kootaala P Unnikrishnan

Pidaparthy S Sastry

Ryen William White

Basel Q Shadid

Vikram Tankasali

K P Unnikrishnan

P S Sastry

A classification system is described for resilient classification of data. In various embodiments, the classification system divides a full set of the training data into a positive subset and a negative subset. The positive subset contains all training data with a positive classification value and the negative subset contains all training data with a negative classification value. The classification system constructs several subsets of the training data wherein each subset contains data randomly selected from both the positive subset and the negative subset. The classification system then creates at least two classifiers based on each of the randomly created subsets of the training data.

Resilient classification of data

The prediction of future data events using a generative model is disclosed. One disclosed embodiment comprises a method of predicting a search engine switch, by first tracking a sequence of events in a user search. Next, a search engine switch based upon the sequence of events tracked is predicted. Then, in response to predicting a search engine switch and prior to the user requesting a search engine switch, an action is taken toward changing a user experience.

Future data event prediction using a generative model

A classification system is described for resiliently classifying data. In various embodiments, the classification system constructs a combined classifier based on multiple classifiers that are constructed to classify a set of training data. The combined classifier can be constructed in parallel with the multiple classifiers and applied to classify data.

Combining resilient classifiers

A system for temporal data mining includes a computer readable medium having an application configured to receive at an input module a temporal data series having events with start times and end times, a set of allowed dwelling times and a threshold frequency. The system is further configured to identify, using a candidate identification and tracking module, one or more occurrences in the temporal data series of a candidate episode and increment a count for each identified occurrence. The system is also configured to produce at an output module an output for those episodes whose count of occurrences results in a frequency exceeding the threshold frequency.

System and method for mining of temporal data

A system for temporal data mining includes a computer readable medium having an application configured to receive at an input module a temporal data series and a threshold frequency. The system is further configured to identify, using a candidate identification and tracking module, one or more occurrences in the temporal data series of a candidate episode and increment a count for each identified occurrence. The system is also configured to produce at an output module an output for those episodes whose count of occurrences results in a frequency exceeding the threshold frequency.

System and method for temporal data mining

A method, system, and computer program product for fault data correlation in a diagnostic system are provided. The method includes receiving the fault data including a plurality of faults collected over a period of time, and identifying a plurality of episodes within the fault data, where each episode includes a sequence of the faults. The method further includes calculating a frequency of the episodes within the fault data, calculating a correlation confidence of the faults relative to the episodes as a function of the frequency of the episodes, and outputting a report of the faults with the correlation confidence.

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Bengaluru, India

Srivatsan Laxman