Bank of America Corporation

DeAundra K Glover

Aaron D Kephart

Charles C Howie

Jesse Price

Darla Nutter

Greg M Lewis

Systems and arrangements for determining whether a scheduled modification of a system, or the like, is a likely cause of an incident are provided. Historical data related to previous incidents may be received and analyzed to identify incidents having a significant business impact and the system, or the like, associated with those incidents. Scheduled modification data may be received and may include a system, or the like, for which a modification has occurred, and a time and date at which the modification is occurred. An incident may be received and the system, or the like associated with the incident may be identified and compared to systems, or the like, identified in the scheduled modification data. If there is a match, a determination may be made as to whether the incident occurred at a time and date of the scheduled modification. If so, the scheduled modification may be identified as a likely cause of the incident.

Near real-time system or network incident detection

Embodiments of the present invention relate to apparatuses, systems, methods and computer program products for a technology configuration system. Specifically, the system typically provides operational data processing of a plurality of records associated with information technology operational activities, for dynamic transformation of data and evaluation of interdependencies of technology resources. In other aspects, the system typically provides technical language processing of the plurality of records for transforming technical and descriptive data, and constructing categorical activity records. The system may be configured to achieve significant reduction in memory storage and processing requirements by performing categorical data encoding of the plurality of records. The system may employ a dynamic categorical data decoding process, which delivers a reduction in processing time when the encoded records are decoded for evaluating the exposure of technology change events to technology incidents and modifying such technology change events.

Computerized system for evaluating technology stability

Systems and arrangements for using temporal analysis to evaluate incidents to determine whether they are likely to cause a significant business impact are provided. Historical data may be analyzed to identify incidents having a significant business impact. The historical data associated with incidents having a significant business impact may be further analyzed to identify a time and/or date at which the incident occurred, as well as the particular system, or the like, impacted by the incident. Normal business hours associated with the system, or the like, may be retrieved and a profile may be generated for the system, or the like. An incident may be received and processed to identify a system, or the like, associated with the incident and profile may be retrieved. The incident data may be compared to the profile to determine whether the incident is likely to cause a significant business impact based, at least in part, on the date and/or time at which it occurred.

Near Real-time system or network incident detection

Computerized system for simulating the likelihood of technology change incidents

Technical language processor administration

Computerized system for evaluating and modifying technology change events

Computerized system for evaluating the impact of technology change incidents

Computerized system for evaluating the likelihood of technology change incidents

Systems and arrangements for pre-processing data to detect and analyze system, application, or the like, incidents to identify incidents likely to cause a significant business impact are provided. Historical data related to incidents that previously occurred may be analyzed to quantify an impact of each incident. The quantified impact may be compared to a threshold and, if the impact above the threshold, the incident may be flagged as having a significant business impact. If the impact is below the threshold, the incident may be flagged as not having a significant business impact. Upon detecting an incident, data from the incident may be compared to the historical data of incidents having a significant business impact. If aspects of the incident are similar to one or more incidents having a significant business impact, the system may determine that the newly received incident is also likely to cause a significant business impact.

System for categorical data dynamic decoding

System for categorical data encoding

Operational data processor

Technical language processor

Systems and arrangements for performing a textual analysis to identify incidents likely to cause a significant business impact are provided. Historical data related to previously occurring incidents may be analyzed to determine a magnitude of impact of the incident. The magnitude may be compared to a predetermined threshold and, if above the threshold, the incident may be flagged as having a significant business impact. If the magnitude is below the threshold, the incident may be flagged as not having a significant business impact. The incidents in each group may then be analyzed to identify keywords, combinations, and/or sequences having a strong correlation to incidents having a significant business impact and incidents not having a significant business impact, which may form inclusion and exclusion criteria. Data from newly received incidents may be compared to the inclusion and exclusion criteria to determine whether the incident is likely to have a significant business impact.

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Waxhaw, NC, United States of America

DeAundra K Glover