Abstract: Access is obtained to a plurality of information technology services problem tickets. At least a first subset of the tickets include free text tickets with server names embedded in unstructured text fields. The server names are extracted from the first subset of the tickets via a statistical machine learning technique. Using the extracted server names, those of the first subset of the tickets from which the server names have been extracted are linked to corresponding server entries in a configuration information database to facilitate resolution of problems associated with the first subset of the tickets from which the server names have been extracted; and/or at least one of the extracted server names is identified as missing from a list of known server names.

Abstract: Feature sequences are extracted, as individual letters separated by spaces, from a digital representation of a proper name in a first language to obtain a first orthographic feature sequence set; and from a digital representation of a proper name in a second language to obtain a second orthographic feature sequence set. The first and second orthographic feature sequence sets (a transliteration pair) are compared to determine a similarity score, based on a similarity model including a plurality of conditional probabilities of known orthographic feature sequences in the first language given known orthographic feature sequences in the second language and a plurality of conditional probabilities of known orthographic feature sequences in the second language given known orthographic feature sequences in the first language. Based on at least one threshold value, it is determined whether the transliteration pair belong to an identical actual proper name.

Abstract: Aspects of the present invention provide a tool for analyzing and remediating an update-related failure. In an embodiment, a failure state of a computer system that has been arrived at as a result of an update is captured. A semantic diff that includes the difference between the failure state and at least one of an original state or a completion state is then computed. This semantic diff is transformed into a feature vector format. Then the transformed semantic diff is analyzed to determine a remediation for the update. Failure and/or resolution signatures can be constructed using the semantic diff and contextual data, and these signatures can be used in comparison and analysis of failures and resolutions.

Abstract: A method for predicting contract renewal ahead of contract expiration includes receiving comments and interview transcripts by a sentiment analysis program to generate sentiments, where the comments and interview transcripts are received from a plurality of clients who are contractees to one or more service contracts, combining the sentiments with contract assessment survey scores and historical renewal and growth data for the service contracts to generate a contract renewal and growth prediction model, providing a contract that is up for expiration to the predictive model, and providing the comments, interview transcripts, and risk assessment survey scores to the predictive model, where the predictive model outputs a prediction of renewal and growth for the contract up for expiration, and an analysis of root causes for the predictions.

Abstract: Access is obtained to a plurality of information technology services problem tickets. At least a first subset of the tickets include free text tickets with server names embedded in unstructured text fields. The server names are extracted from the first subset of the tickets via a statistical machine learning technique. Using the extracted server names, those of the first subset of the tickets from which the server names have been extracted are linked to corresponding server entries in a configuration information database to facilitate resolution of problems associated with the first subset of the tickets from which the server names have been extracted; and/or at least one of the extracted server names is identified as missing from a list of known server names.

Abstract: Aspects of the present invention provide a tool for analyzing and remediating an update-related failure. In an embodiment, a failure state of a computer system that has been arrived at as a result of an update is captured. A semantic diff that includes the difference between the failure state and at least one of an original state or a completion state is then computed. This semantic diff is transformed into a feature vector format. Then the transformed semantic diff is analyzed to determine a remediation for the update. Failure and/or resolution signatures can be constructed using the semantic diff and contextual data, and these signatures can be used in comparison and analysis of failures and resolutions.

Abstract: Aspects of the present invention provide a tool for analyzing and remediating an update-related failure. In an embodiment, a failure state of a computer system that has been arrived at as a result of an update is captured. A semantic diff that includes the difference between the failure state and at least one of an original state or a completion state is then computed. This semantic diff is transformed into a feature vector format. Then the transformed semantic diff is analyzed to determine a remediation for the update. Failure and/or resolution signatures can be constructed using the semantic diff and contextual data, and these signatures can be used in comparison and analysis of failures and resolutions.