Abstract:
A method for pattern recognition performed by a physical computing system (100) includes, with the physical computing system (100), structuring data as a bi-partite graph (200), a set of instance nodes (214) within the graph (200) representing instances within the data and a set of property nodes (216) within the graph (200) representing properties of the instances, edges (218) between the instance nodes (214) and the property nodes (216) representing values of the properties, with the physical computing system (100), assigning a transition probability function (302, 402) to each of the instance nodes (214) and to each of the property nodes (216), and with the physical computing system (100), applying a random walker to the graph (200), the random walker utilizing the transition probability functions (302, 402).
Abstract:
Methods and systems of aligning annotation of fields of documents are provided. Training information that includes first measurement information pertaining to features of each of a plurality of fields associated with training clusters for documents of a document type is accessed. A first training cluster is annotated with a first name and the second training cluster is annotated with a second name. An electronic classification model is generated based on the training information. Second measurement information for features of fields associated with new clusters of a new document is accessed. Each of the new clusters is automatically annotated based on the second measurement information using the classification model. For example, a first new cluster that has fields of the first field type is annotated with the first name and a second new cluster that has fields of the second field type is annotated with the second name.
Abstract:
To determine a seasonal effect in temporal data, for each of a plurality of candidate seasons, the following is performed. An interval is defined for the corresponding candidate season. The interval is divided (108) into plural buckets containing respective sets of the temporal data, and statistical measures for corresponding buckets are computed. The computed statistical measures are used to compute (112) a score for the corresponding candidate season. Scores computed for corresponding candidate seasons are used for identifying which of the candidate seasons represents the seasonal effect of the temporal data.
Abstract:
In one example of the disclosure, data indicative of a word or phrase communicated during a meeting including a plurality of participants is obtained. For each participant, records electronically accessible to the participant are identified, and each record is associated with a tier from a hierarchy of record-relevancy tiers. A set of explanations for the communication and associated scores is identified, including for each participant, beginning with a most relevant tier, searching the records accessible to the participant tier by tier until an explanation is identified, and assigning a score to the explanation according to the tier associated with the record in which the explanation is found. A preferred explanation for the communication is determined based upon the scores, and a display of the preferred explanation is caused.
Abstract:
Determining suspected root causes of anomalous network behavior includes identifying anomalous components in a network exhibiting anomalous behavior from a plurality of network components, assigning a likelihood score to network components based on a scoring policy that considers recent change events affecting the anomalous components, and identifying a subset of the network components that are suspected to be root causes based on the likelihood score.
Abstract:
Methods performed by a physical computing system include automatically identifying, using at least one trained classifier, an action for responding to an anomaly in the execution of the application in a cloud computing system. The at least one trained classifier relates a metrics set to a result of performing an action for addressing an anomaly. Systems and computer readable media are also described herein.
Abstract:
Systems, methods, and machine-readable and executable instructions are provided for error developer association. Error developer association can include identifying a number of portions of the source code associated with a message, wherein the message is associated with an error. Error developer association can also include associating a developer with a portion of the source code of the number of portions of the source code. Error developer association can also include identifying a developer of the number of developers to resolve the error
Abstract:
A method and system comprise abstracting configuration items (CI) in at least a first anomaly and a second anomaly based on type of CI. Further, CIs are matched of a common type between the first and second anomalies based on a cost function. Additionally, a similarity score is computed for the first and second anomalies based, at least in part, on the cost function of the matched CI's and based on topology of the first and second anomalies.
Abstract:
Examples disclosed herein relate to generating a fingerprint representing a response of an application to a simulation of a fault of an external service. Examples include causing simulation of a fault of an external service in a simulation of the external service, and generating a testing application fingerprint representing a response of an application to the simulation of the fault of the external service during the testing of the application.
Abstract:
In one example of the disclosure, event notices are received, with each notice indicative of degradation of a configuration item. Configuration item past-preference pairings are accessed. Each pairing includes a count of operator-exhibited preferences for event notices associated with a first configuration item relative to event notices associated with a second configuration item. A prioritized ordering of the received event notices is created utilizing the past-preference pairings.