International Business Machines Corporation

Kyndryl, Inc.

Pawel Jasionowski

George E Stark

Firas Bouz

Daniel S Riley

Michael H Roehl

Steven J Mazzuca

Daniel Grey Yates

A method, computer system, and a computer program product for estimating the probability of invoking information technology (IT) disaster recovery at a location based on an incident risk is provided. The present invention may include receiving a piece of data associated with an incident at the location. The present invention may also include estimating a similarity value associated with the incident based on a plurality of past incidents from a knowledge base. The present invention may then include receiving a plurality of mined data based on the location. The present invention may further include predicting the incident risk to the location based on the received plurality of mined data and the estimated similarity value to the plurality of past incidents.

Predicting the disaster recovery invocation response time

A system to provide end users with recommendations on improving the quality of the incident management process is provided. A computer device identifies a set of historical incident reports, wherein the historical incident reports identify: (i) incident tickets, (ii) one or more skills associated with personnel assigned to the incident tickets, and (iii) whether the incident tickets were resolved within threshold periods of time to resolve. The computing device trains a machine learning model to predict sets of skills associated with resolving incident tickets within the threshold periods of time to resolve based, at least in part, on the identified set of historical incident reports. The computing device assigns a set of personnel to the new incident ticket based, at least in part, on the predicted set of skills associated with resolving the new incident ticket within the threshold period of time to resolve.

Identification of incident required resolution time

A set of profile parameters to characterize an unknown group of servers is computed. A set of known groups of servers is selected from a historical repository of known group of servers. A subset of known group is selected such that each known group in the subset has a corresponding similarity distance that is within a threshold similarity distance from the unknown group. A decision tree is constructed corresponding to a known group in the subset, by cognitively analyzing a usage of the set of profile parameters of the unknown group in the known group. Using the decision tree a number of problematic servers is predicted in the unknown group. When the predicted number of problematic servers does not exceed a threshold number, a post-prediction action is caused to occur on the unknown group, which causes a reduction in an actual number of problematic servers in the unknown group.

Cognitive prediction of problematic servers in unknown server group

A method of identifying an incident root cause probability that includes identifying, using a monitoring system, a first incident/incident class and generating, using a change management application, a first change request and change class. The method also includes generating, from the change management application, a second change request from a second incident, and where the first and second incidents are in a set of incidents, and where the first and second change requests are in a set of changes, mapping, by a cause analysis application, the set of incidents to the set of changes to identify a root cause probability, where the probability is formed by from a Galois linkage chain between the two sets, and developing, from the cause analysis application, a root cause probability value of the first incident, and executing, using a parameter management application, a mitigation process.

Incident root cause analysis using Galois connections

A method and associated systems for using direct sums and invariance groups to optimize the testing of partially symmetric quantum-logic circuits is disclosed. A test system receives information that describes the architecture of a quantum-logic circuit to be tested. The system uses this information to organize the circuit's inputs into two or more mutually exclusive subsets of inputs. The system computes a direct sum of a set of groups associated with the subsets in order to generate an invariance group that contains one or more invariant permutations of the circuit's inputs. These invariant permutations can be used to reduce the number of tests required to fully verify the circuit for all possible input vectors. Once one specific input vector has been verified, there is no need to test other vectors that can be generated by performing any one of the invariant permutations upon the previously verified vector.

Optimized testing of quantum-logic circuits

A method and associated systems for predicting a degree of risk associated with a planned change to a computer server or other electronic component. A computerized change-management system receives Probability and Impact inputs derived from user-derived input, from which it determines a Baseline risk of change failure. The system processes mined data to determine an historic change-failure rate as a function of a type of change, and computes a predictive incident probability based on a predictive analytics engine's forecast of whether a particular type of server will be problematic. The system then computes a final Change Risk by adjusting the Baseline risk as a function of the historic change-failure rate, the predictive incident-probability, and a Baseline-specific weighting factor. If the resulting Change Risk is judged to be elevated, the system initiates collateral actions and notifications intended to reduce the probability and impact of a change failure.

Mitigation of likelihood and impact of a server-reconfiguration failure

Mitigating risk and impact of server-change failures

Reducing complexity when testing quantum-logic circuits

Estimating server-change risk by corroborating historic failure rates, predictive analytics, and user projections

Using direct sums and invariance groups to test partially symmetric quantum-logic circuits

A method and associated systems for using wreath products and invariance groups to test a partially symmetric quantum-logic circuits. A test system receives information that describes the architecture of a quantum-logic circuit to be tested. The system uses this information to hierarchically organize the circuit's inputs into non-overlapping blocks. The system creates set of groups associated with the blocks, and then generates an invariance group that contains one or more invariant permutations of the inputs by computing a wreath product of the set of groups. These invariant permutations identify a minimal number of tests required to verify the circuit for all possible input vectors. The system then directs a test apparatus to perform the resulting optimized test sequence upon the circuit.

Optimized testing of a partially symmetric quantum-logic circuit

Systems and methods for optimized testing of partially symmetric quantum-logic circuits. A test system receives information that describes the architecture of a quantum-logic circuit to be tested. The system uses this information to organize the circuit's inputs into two or more mutually exclusive blocks of inputs. The system computes a wreath product of a set of groups associated with the blocks in order to generate an invariance group that contains one or more invariant permutations of the circuit's inputs. These invariant permutations can be used to reduce the number of tests required to fully verify the circuit for all possible input vectors. Once one specific input vector has been verified, there is no need to test other vectors that can be generated by performing any one of the invariant permutations upon the previously verified vector.

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Wroclaw, Poland

Pawel Jasionowski