International Business Machines Corporation

Accenture Global Solutions Limited

Kyndryl, Inc.

Sriram Lakshminarasimhan

Sameep Mehta

Himanshu Gupta

Suryanarayana K Rao

Srikanta Bedathur Jagannath

Prasanna Veeraraghavan

Sriram Raghavan

Nirav Sampat

Ramesh Chandra Pathak

Mohan Sekhar

Jayant Swamy

Stuart A Siegel

Narendran Sachindran

Raghavan Tinniyam Iyer

Saran Prasad

Animesh Nandi

Sumit Neelam

Manish Jain

Chandrashekhar Arun Deshpande

Bhavin Prafulchandra Mehta

Tanveer A Faruquie

Chandan Kumar Vishwakarma

Suja Jain

Vasu Gajendran

Deepali Jain

Sundar Sarangarajan

Suhas Vinod Sane

Saravanan Dakshnamoorthy

Senthil Nathan Gnanamani

Gnanam Badhmanaban Kothandaraman

Juhi A Gupta

Abigail Hart

Achal Srivastava

Sadanand Padmawar

Dharmesh Dhirajlal Barochia

Purnanga Prema Borah

A transaction is received. The transaction involves a data in a relational database management system. A page in the relational database management system is determined with the data. Whether the page has a hybrid lock enabled is determined using a hybrid lock hash table. Responsive to determining the page has the hybrid lock enabled, whether the page is above the hot page threshold is determined.

Hybrid locking mechanism in relational database management systems

A computer-implemented method of optimizing data rollback is disclosed. The method receives a request to perform a task on a disk storage. The method initiates the task by reading a plurality of data pages from the disk storage to a database buffer. Each of the plurality of data pages on the database buffer are modified to form a plurality of dirty pages. In response to reaching and/or exceeding a database buffer threshold, a portion of the plurality of dirty pages on the database buffer are externalized to a rollback buffer. In response to reaching and/or exceeding a rollback buffer threshold, a subset of the portion of the plurality of dirty pages on the rollback buffer are externalized to the disk storage. The method detects a task cancelling activity prior to completion of the task; and performs a rollback of the plurality of dirty pages to a pre-task state.

System and method of optimizing rollbacks

An example chatbot generation platform may receive a request to generate a chatbot; determine a chatbot template for the chatbot based on the request; obtain custom chatbot information according to the chatbot template; generate a chatbot corpus for the chatbot using the custom chatbot information and the chatbot template; generate a set of question and answer (QnA) pairs based on the chatbot corpus; configure a language analysis model for the chatbot; build the chatbot according to the set of QnA pairs and the language analysis model; and deploy the chatbot to a chatbot host platform for operation. The chatbot may be built to engage in an interaction with a user via the chatbot host platform, use the language analysis model to select one or more QnA pairs from the set of QnA pairs during the interaction, and train the language analysis model based on the interaction.

Chatbot generator platform

Archiving data in a relational database management system through a process in which an invisible index is created for each primary index of a data. When an input is received to initiate block copy of data to be archived; the data tables are locked and the identified blocks of data form the data table are block copied to an archive table. The invisible index is updated to correspond to the data table which had data archived. The primary index is then swapped with the invisible index and the data tables are unlocked. Data blocks from the main table which were copied into the archival table is deleted along with the old primary index.

Archival of data in a relational database management system using block level copy

A device may receive a feature submission identifying a candidate feature for a software application. The device may pre-process the feature submission to reformat data associated with the candidate feature to match a data format associated with stored information regarding past feature submissions. The device may perform natural language processing on the data associated with the candidate feature to determine a semantic meaning of the candidate feature. The device may compare the semantic meaning of the candidate feature with an index of past feature submissions. The device may provide a response indicating whether the candidate feature matches the past feature submissions wherein the response comprises an indication that the candidate feature does not match any past feature submission or information identifying one or more past feature submissions associated with a threshold semantic similarity to the candidate feature.

Feature submission de-duplication engine

Methods, systems, and computer program products for schema-free in-graph indexing are provided herein. A computer-implemented method includes creating multiple indexes directed to data within a knowledge graph; correlating two or more of the created indexes, thereby generating one or more multi-dimensional indexes; determining, based on a received query, one or more traversal paths within the data of the knowledge graph and the generated multi-dimensional indexes, wherein the traversal paths facilitate processing of the query; and outputting a response to the query based on the determined traversal paths.

Schema-free in-graph indexing

Methods, systems, and computer program products for multi-level colocation and analytical processing of spatial data on MapReduce are provided herein. A method includes correlating multiple items of spatial data and multiple items of attribute data within a file system to generate multiple blocks of correlated data; colocating each of the multiple blocks of correlated data on a given node within the file system based on a data block placement policy; and clustering multiple replicas generated for each of the multiple data blocks at multiple levels of spatial granularity within the file system.

Multi-level colocation and processing of spatial data on MapReduce

An approach is provided for managing a reorganization of a database object. Because a reorganization utility cannot acquire exclusive access to the object to complete the reorganization, determinations are made: an amount of transaction log records corresponding to active units of work using the database object is determined to be less than a first threshold; a sum of costs of SQL statements executed in the units of work is determined to be less than a second threshold; and a priority of the reorganization is determined to indicate a greatest priority among multiple priorities. Based on the determinations, (1) a reorganization lock providing exclusive access to the database object is acquired, (2) process(es) holding lock(s) on the database object experience a lockout error, a rollback of the process(es) is issued, and the lock(s) are released, and (3) the utility completes the reorganization based on the lock(s) being released.

Optimizing online schema processing for busy database objects

Methods, systems, and computer program products for historical state snapshot construction over temporally evolving data are provided herein. A computer-implemented method includes classifying each of multiple temporally evolving data entities into one of multiple categories based on one or more parameters; partitioning the multiple temporally evolving data entities into multiple partitions based at least on (i) said classifying and (ii) the update frequency of each of the multiple temporally evolving data entities; implementing multiple checkpoints at a distinct temporal interval for each of the multiple partitions; and creating a snapshot of the multiple temporally evolving data entities at a selected past point of time (i) based on said implementing and (ii) in response to a query pertaining to a historical state of one or more of the multiple temporally evolving data entities.

Historical state snapshot construction over temporally evolving data

Methods and arrangements for managing map-reduce jobs. There are identified intermediate data produced, in a current map-reduce cycle, by a plurality of nodes in the distributed network, the nodes being selected from the group consisting of: a plurality of map nodes, and a plurality of reducer nodes. There are identified a plurality of classes of data, for classifying the intermediate data. Discrete portions of the intermediate data are classified into respective ones of the classes of data, wherein at least one of the classes of data comprises intermediate data which are processed in a map-reduce cycle subsequent to the current map-reduce cycle. Other variants and embodiments are broadly contemplated herein.

Extending a map-reduce framework to improve efficiency of multi-cycle map-reduce jobs

Growing community of inventors

Chennai, India

Sriram Lakshminarasimhan