A method of building a model for predicting failure of a machine, including parsing (41) daily machine event logs of one or more machines to extract data for a plurality of features, parsing (42) service notifications for the one or more machine to extract failure information data, creating (43) bags from the daily machine event log data and failure information data for multiple instance learning by grouping daily event log data into the bags based on a predetermined predictive interval, labeling each bag with a with a known failure as positive, and bags without known failures as negative, where a bag is a set of feature vectors and an associated label, where each feature vector is an n-tuple of features, transforming (44) the multiple instance learning bags into a standard classification task form, selecting (45) a subset of features from the plurality of features, and training (46) a failure prediction model using the selected subset of features.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method of building a model for predicting failure of a machine, the method comprising the steps of: parsing daily machine event logs of one or more machines to extract data for a plurality of features; parsing service notifications for the one or more machine to extract failure information data; creating bags from the daily machine event log data and failure information data for multiple instance learning by grouping daily event log data into the bags based on a predetermined predictive interval, labeling each bag with a with a known failure as positive, and bags without known failures as negative, wherein a bag is a set of feature vectors and an associated label, wherein each feature vector is an n-tuple of features; transforming the multiple instance learning bags into a standard classification task form; selecting a subset of features from the plurality of features; and training a failure prediction model using the selected subset of features. 2. The method of claim 1, wherein features include event codes, event code variations, and numerical values associated with the event codes and event code variations. 3. The method of claim 1, wherein transforming the multiple instance learning bags into a standard classification task from comprises transforming each feature vector in a bag with a negative label into a negative example, and for each bag with a positive label, creating a meta-positive example by computing a mean of the feature vectors in the bag. 4. The method of claim 1, wherein selecting a subset of features from the plurality of features comprises: creating multiple subsets by randomly subsampling negative bags and including all positive bags, learning a sparse linear classifier on each subset, and averaging weights from each sparse linear classifier and selecting features with the highest absolute weights. 5. The method of claim 1, wherein training a failure prediction model using the selected subset of features comprises learning the failure prediction model using the selected subset of features and all event log instances for the selected subset of features. 6. The method of claim 5, wherein learning the failure prediction model comprises finding a set of weights w that minimizes λ 2   w  1 2 + Σ j  max  { 1 - y j  w T  x j , 0 } , wherein λ>0 is a user-specified regularization parameter, yj ε{+1, −1} is a label for bag j, and xj is a vector of the selected subset of features for bag j. 7. The method of claim 1, further comprising using said failure prediction model on new instances from the daily machine event log wherein an alert is triggered if a prediction value is greater than a predetermined threshold. 8. The method of claim 1, further comprising evaluating said failure prediction model by outputting a maximal prediction score of all instances in a bag. 9. A computer-implemented method of building a model for predicting failure of a machine, the method comprising the steps of: parsing daily machine event logs of one or more machines to extract data for a plurality of features; parsing service notifications for the one or more machine to extract failure information data; creating bags from the daily machine event log data and failure information data for multiple instance, wherein a bag is a set of feature vectors and an associated label, wherein each feature vector is an n-tuple of features; transforming the multiple instance learning bags into a standard classification task form by transforming each feature vector in a bag with a negative label into a negative example, and for each bag with a positive label, creating a meta-positive example by computing a mean of the feature vectors in the bag; selecting a subset of features from the plurality of features; and training a failure prediction model using the selected subset of features. 10. The method of claim 9, wherein creating bags from the daily machine event log data and failure information data for multiple instance learning comprises grouping daily event log data into the bags based on a predetermined predictive interval, labeling each bag with a with a known failure as positive, and bags without known failures as negative. 11. The method of claim 9, wherein selecting a subset of features from the plurality of features comprises: creating multiple subsets by randomly subsampling negative bags and including all positive bags, learning a sparse linear classifier on each subset, and averaging weights from each sparse linear classifier and selecting features with the highest absolute weights. 12. The method of claim 9, wherein training a failure prediction model using the selected subset of features comprises learning the failure prediction model using the selected subset of features and all event log instances for the selected subset of features by finding a set of weights w that minimizes λ 2   w  1 2 + Σ j  max  { 1 - y j  w T  x j , 0 } , wherein λ>0 is a user-specified regularization parameter, yjε{+1, −1} is a label for bag j, and xj is a vector of the selected subset of features for bag j. 13. A non-transitory program storage device readable by a computer, tangibly embodying a program of instructions executed by the computer to perform the method steps for building a model for predicting failure of a machine, the method comprising the steps of: parsing daily machine event logs of one or more machines to extract data for a plurality of features; parsing service notifications for the one or more machine to extract failure information data; creating bags from the daily machine event log data and failure information data for multiple instance learning by grouping daily event log data into the bags based on a predetermined predictive interval, labeling each bag with a with a known failure as positive, and bags without known failures as negative, wherein a bag is a set of feature vectors and an associated label, wherein each feature vector is an n-tuple of features; transforming the multiple instance learning bags into a standard classification task form; selecting a subset of features from the plurality of features; and training a failure prediction model using the selected subset of features. 14. The computer readable program storage device of claim 13, wherein features include event codes, event code variations, and numerical values associated with the event codes and event code variations. 15. The computer readable program storage device of claim 13, wherein transforming the multiple instance learning bags into a standard classification task from comprises transforming each feature vector in a bag with a negative label into a negative example, and for each bag with a positive label, creating a meta-positive example by computing a mean of the feature vectors in the bag. 16. The computer readable program storage device of claim 13, wherein selecting a subset of features from the plurality of features comprises: creating multiple subsets by randomly subsampling negative bags and including all positive bags, learning a sparse linear classifier on each subset, and averaging weights from each sparse linear classifier and selecting features with the highest absolute weights. 17. The computer readable program storage device of claim 13, wherein training a failure prediction model using the selected subset of features comprises learning the failure prediction model using the selected subset of features and all event log instances for the selected subset of features. 18. The computer readable program storage device of claim 17, wherein learning the failure prediction model comprises finding a set of weights w that minimizes λ 2   w  1 2 + Σ j  max  { 1 - y j  w T  x j , 0 } , wherein λ>0 is a user-specified regularization parameter, yj ε{+1, −1} is a label for bag j, and xj is a vector of the selected subset of features for bag j. 19. The computer readable program storage device of claim 13, the method further comprising using said failure prediction model on new instances from the daily machine event log wherein an alert is triggered if a prediction value is greater than a predetermined threshold. 20. The computer readable program storage device of claim 13, the method further comprising evaluating said failure prediction model by outputting a maximal prediction score of all instances in a bag.