International Business Machines Corporation

Pei Ni Liu

Jing Min Xu

Fan Jing Meng

Zi Xiao Zhu

Jia Qi Li

Yaoping Ruan

Xiao Xi Liu

Yu Gu

Hui Lei

Jian Min Jiang

Wen Sun

Tian Wu

Daniel Dean

Wei Li

Hao Wang

Bo Yang

Ke Fei Wang

Anca Sailer

Harigovind Venkatraj Ramasamy

Dong Sheng Li

Elliot Karl Kolodner

Yong Qin

Guo Tong Xie

Matt Richard Hogstrom

Zhe Xiang

Junmei Qu

Jian Huang

Yuan Ni

Michael C Smith

Songfang Huang

Dingcheng Li

Sandhya Narayan

Checed A Rodgers

Ruo Yi Liu

Bradley W Crawford

Dingcheng Lil

A method and system for operation objects discovery from operation data includes performing pattern matching of operation data with patterns in a database. Fields in the operation data are identified as having matching patterns with the database as first potential objects. Data profiling is performed on unmatched fields of the operation data to generate data profiles. The data profiles are field classified and second potential objects are generated. The first potential objects and the second potential objects are de-duplicated, and operation objects are generated.

Method and system for operation objects discovery from operation data

Embodiments of the present invention relate to methods, systems, and computer program products for event management. In a method, a plurality of notes that are comprised in a plurality of event records are obtained in a computer system. A plurality of paragraphs that are comprised in the plurality of notes are classified into a plurality of content types based on a content analysis of the plurality of paragraphs. The plurality of notes are classified into a plurality of semantic types based on the plurality of content types and a syntactic parsing to the plurality of notes. A knowledge item is generated for managing an event in the computer system based on a group of notes in the plurality of notes that are classified into the plurality of semantic types. With these embodiments, knowledge items for managing events may be obtained in an easier and more effective way.

Event management in computer system

Methods and systems for secure data analysis include determining that analysis provider access rules and data provider access rules are compatible. Analysis software is received from an analysis provider and a dataset is received from a data provider. The analysis software is executed on the dataset to generate an analysis output, with access to data in the dataset being constrained by the analysis provider access rules and the data provider access rules. An output of the analysis is sent to the analysis provider.

Secure data analysis

A computer-implemented method, system, and non-transitory machine readable medium for a graph-based analysis for an Information Technology (IT) operations includes generating a temporal graph by extracting one or more of operation objects, relations and attributes from operation data of workloads distributed across a plurality of levels of the IT operation within a predetermined time window. Anomalies are detected from the extracted operation data and annotating corresponding objects in the graph. A directional impact between corresponding objects on the temporal graph is determined, and the temporal graph is refined based on the determined directional impact. Accessible paths in the temporal graph indicating error propagation are searched, and potential causes for the detected anomalies in the temporal graph are identified. A list of the potential causes of the anomalies is generated, and a root cause ranked for each of the corresponding objects in the temporal graph.

Method and system for graph-based problem diagnosis and root cause analysis for IT operation

Performing an operation comprising transforming an input dataset to a predefined format, extracting, from the transformed dataset, a plurality of features describing the transformed dataset, and generating, by a machine learning (ML) algorithm executing on a processor and based on an ML model, a plurality of rules for modifying the transformed dataset to conform with a first data model.

Cognitive data discovery and mapping for data onboarding

Methods and systems for processing records include extracting feature vectors from words in an unstructured portion of a record. The feature vectors are weighted based similarity to a topic vector from a structured portion of the record associated with the unstructured portion. The weighted feature vectors are classified using a machine learning model to determine respective probability vectors that assign a probability to each of a set of possible relations for each feature vector. Relations between entities are determined within the record based on the probability vectors. An action is performed responsive to the determined relations.

Extracting entity relations from semi-structured information

A method and system for relation discovery from operation data includes classifying categories of extracted entities from operation data into three or more classes identified in a knowledge base. A log affiliation of the extracted entities is determined, and relations of the extracted entities are identified according to a log affiliation. The identified relations information of the extracted entities is associated with operation objects of the operation data.

Method and system for relation discovery from operation data

Embodiments generally relate transforming data for a target schema. In some embodiments, a method includes receiving input data, where the input data includes a plurality of segments, and where the segments include a plurality of source fields containing target data. The method further includes characterizing the input data based at least in part on a plurality of predetermined metrics, where the predetermined metrics determine a structure of the input data. The method further includes mapping the target data in the source fields of the segments to a plurality of target fields of a target schema based at least in part on the characterizing. The method further includes populating the target fields of the target schema with the target data from the source fields based at least in part on the mapping.

Transforming data for a target schema

Embodiments of the present disclosure relate to a method for processing alerts. According to an embodiment of the present disclosure, a set of alerts matching a metric template are identified from received alerts during a period of time. A plurality of variable values are acquired from the set of alerts based on the metric template. The plurality of variable values are normalized according to a normalization rule of the metric template. A severity level for the set of alerts is determined based on the normalized variable values. In response to the severity level exceeding a certain threshold, an abstract alert including information related to the set of alerts is generated.

Alerts reduction based on severity level using metric template matching and normalization rule

Systems, computer-implemented methods and/or computer program products that facilitate automatically mapping different data types are provided. In one embodiment, a computer-implemented method comprises: constructing, by a system operatively coupled to a processor, an index from one or more classifier models for one or more data types; scoring and ranking, by the system, one or more candidate pairs for the one or more data types based on confidence score; and analyzing, by the system, how the one or more candidate pairs are scored and automatically generating the one or more classifier models used to construct the index.

Facilitating data-driven mapping discovery

A method is provided for data matching between a set of source data structures and a set of target data structures. The method includes obtaining, using a processor device configured to perform machine learning, source to target matching results with matching scores, based on the sets of source and target data structures. The method further includes calculating, by the processor device, context information for data structure pairs based on a structure similarity and an ontology similarity between constituent data structures thereof. Each of data structure pairs include as the constituent data structures a respective source data structure and a respective target data structure from the sets of source and target data structures. The method also includes updating, by the processor device, the matching scores based on the context information. The method additionally includes controlling, by the processor device, a hardware device responsive to at least one updated matching score.

Data matching accuracy based on context features

An application server disk image generating apparatus and method comprising a basic disk image generating component for generating basic disk images for basic programs used by the server; an incremental disk image generating component for generating incremental disk images including heritage relationship for other respective applications based on the basic programs used by the server; and an image profile generating means for generating an image profile for each of the basic disk images and the incremental disk images. By using the apparatus, the storage consumption can be decreased greatly and the storage efficiency can be improved. The invention also discloses an application server disk image management and distribution system to which the application server disk image generating apparatus is applied, and a system for provisioning the application server using disk images.

Application server provisioning system and method based on disk image profile

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Beijing, China

Pei Ni Liu