International Business Machines Corporation

Ramkumar Ramalingam

Devaprasad Khandurao Nadgir

Dinakaran B Joseph

Michael Beisiegel

David Elliot Shepard

Lohith Ravi

Anket Jain

S VenkataKrishnan

Hima Patel

Matu Agarwal

Manjunatha D

Nagarjuna Surabathina

Balaji Sankar

Venumadhav Vanagondi

Jothiponsundar Radhakrishnan

Subhojeet Pramanik

Saptarshi Misra

Shramona Chakraborty

Nitin Gupta

An embodiment computes a plurality of similarity scores, each similarity score in the plurality of similarity scores measuring a similarity of task data of a first data transformation task to data of a stored data transformation in a plurality of stored data transformations, wherein each of the plurality of stored data transformations comprises a stored data transformation program, each stored data transformation program comprising a data transformation from a first data format to a second data format. An embodiment generates, from a first stored data transformation program in the plurality of stored data transformations, a generated data transformation program. An embodiment performs, by modifying a plurality of records described by the first data transformation task into a second plurality of records according to the generated data transformation program, the first data transformation task.

Semantically similar historical example based data transformation

Methods, computer program products, and/or systems are provided that perform the following operations: obtaining source schema metadata, wherein the source schema metadata is associated with fields of a source schema; obtaining target schema metadata with a target schema, wherein the target schema metadata is associated with fields of a target schema; determining, for each field of the source schema and each field of the target schema, a representation for each field based, at least in part, on the source schema metadata or the target schema metadata associated with each field; and providing the representation for each field of the source schema and each field of the target schema for use in generating data mappings between the source schema and the target schema.

Metadata based mapping assist

A high availability environment of resource adapters implements processes to manage and to distribute work among the adapters or adapter instances. An input resource, such as a file, is received and tasks are created to distribute the content to the different instances of the adapters that are configured in the cluster. A resource adapter instance switches to manage the creation of the task based on task-definitions of the adapter. The task-definitions are rules specified in the adapter on chunks of data. The tasks are created such that chunks of data are independently locked and processed without duplication. In order to distribute the work, the tasks are persisted into a table/xml on a persistent disk. The remaining instances interact with the table to access the tasks specified by the entries in the table, thus executing the tasks.

Partial reading of input files to process business objects

A computer-implemented method for error standardization in a cloud based hybrid application integration environment. The method includes receiving at least one error schema definition for an error type from a software application, and mapping the received at least one error schema definition with a corresponding error schema definition of at least one architecture style. The method includes generating an error mapping object based on the mapping of the received at least one error schema definition and the corresponding error schema definition of the at least one architecture style. The method further includes receiving an error object from the software application, and generating a resolved error object, for the received error object, based on the generated error mapping object.

Error handling in a cloud based hybrid application integration

In an approach to contextual search of electronic records, one or more computer processors receive a first search request from a user. The one or more computer processors send a plurality of first search results associated with the first search request to the user. The one or more computer processors receive one or more selected policies from the user, based, at least in part, on the plurality of search results. Responsive to receiving the one or more selected policies, the one or more computer processors apply the one or more selected policies to the plurality of first search results. The one or more computer processors generate a second search request based, at least in part, on the one or more applied selected policies. The one or more computer processors send a plurality of second search results associated with the second search request to the user.

Policy driven contextual search

Parallel access of partially locked content of input file

A data processing system assigns stored documents within a distributed storage system (DSS) to various document categories to enable a target number of documents to be deleted. An intelligent storage management (ISM) utility identifies a data storage threshold value used to control data storage within the DSS. If a current storage usage exceeds the data storage threshold value, the ISM utility calculates, based on the current storage usage, a target number of documents that can be deleted from the DSS. The ISM utility utilizes a recursive process, which includes assigning stored documents to groups including a set of document categories based on data characteristics of the stored documents. The ISM utility further utilizes the recursive process to delete, based on an established ordering of the groups, all of the stored documents assigned to a subset of the groups in order to remove the target number of stored documents.

Intelligently categorizing data to delete specified amounts of data based on selected data characteristics

A method assigns stored documents within a distributed storage system (DSS) to various document categories to enable a target number of documents to be deleted. An intelligent storage management (ISM) utility identifies a data storage threshold value used to control data storage within the DSS. If a current storage usage exceeds the data storage threshold value, the ISM utility calculates, based on the current storage usage, a target number of documents that can be deleted from the DSS. The ISM utility utilizes a recursive process, which includes assigning stored documents to groups including a set of document categories based on data characteristics of the stored documents. The ISM utility further utilizes the recursive process to delete, based on an established ordering of the groups, all of the stored documents assigned to a subset of the groups in order to remove the target number of stored documents.

Determination and indication of operational performance of deployments in an expert integrated system that has multiple deployments, with each deployment having multiple patterns. The determination is based upon pipe analytics, with the pipe analytics being performed over a common subnet that includes both the analytics platform and the management nodes of each deployment of the multiple deployments.

Analytics platform spanning a subset using pipeline analytics

Analytics platform spanning subset using pipeline analytics

A method assigns stored documents within a distributed storage system (DSS) to various document categories to enable a target number of documents to be deleted. An intelligent storage management (ISM) utility identifies a data storage threshold value used to control data storage within the DSS. If a current storage usage exceeds the data storage threshold value, the ISM utility calculates, based on the current storage usage, a target number of documents that can be deleted from the DSS. The ISM utility utilizes a recursive process which includes assigning stored documents to groups including a set of document categories based on data characteristics of the stored documents. The ISM utility further utilizes the recursive process to delete, based on an established ordering of the groups, all of the stored documents assigned to a subset of the groups in order to remove the target number of stored documents.

Method for intelligently categorizing data to delete specified amounts of data based on selected data characteristics

A data processing system (DPS) and a computer program product assigns stored documents within a distributed storage system (DSS) to various document categories to enable a target number of documents to be deleted. An intelligent storage management (ISM) utility identifies a data storage threshold value used to control data storage within the DSS. If a current storage usage exceeds the data storage threshold value, the ISM utility calculates, based on the current storage usage, a target number of documents that can be deleted from the DSS. The ISM utility utilizes a recursive process which includes assigning stored documents to groups including a set of document categories based on data characteristics of the stored documents. The ISM utility further utilizes the recursive process to delete, based on an established ordering of the groups, all of the stored documents assigned to a subset of the groups in order to remove the target number of stored documents.

Growing community of inventors

Theni, India

Ramkumar Ramalingam