Tata Consultancy Services Limited

Maitreya Natu

Vaishali Paithankar Sadaphal

Vikrant Vikas Shimpi

Harrick Mayank Vin

Deepa Vaidyanathan

Neha Behl

Vaishali Shashank Kulkarni

Rahul Ramesh Kelkar

Satya Narayana Samudrala

Parag Agrawal

Sachin Sadashiv Jadhav

Priyadarshi Rai

Monika Bhave

Praveen Venkateswaran

Siddartha Kshirsagar

Data inaccuracy and insufficiency are critical aspects to be analyzed to improve batch predictions, specifically in context of SLA jobs as they are foremost in affecting deliverables. Embodiments of the present disclosure provide a method and system for enhancing batch predictions by localizing jobs contributing to time deviation and generating fix recommendations by fixing data inaccuracy and insufficiency. The term fix recommendation refers to recommending a list of plausible fixes to identified causes that reduce batch prediction errors enhancing accuracy of predictions. The localization is performed by bottom-up traversing of a batch graph representing a batch process, if the batch process has a Service level Agreement (SLA) job, by narrowing down to the SLA job that has end time inaccuracies. The localization enables identifying the origin or real contributors and root cause analysis is performed for the localized jobs to generate effective fix recommendations by fixing data inaccuracy and insufficiency.

Enhancing batch predictions by localizing jobs contributing to time deviation and generating fix recommendations

This disclosure relates generally to method and system for predicting batch processes. Conventional batch schedulers provide a single point of control for defining and monitoring background executions in a distributed network. The method of the present disclosure obtains a set of batch jobs from one or more users to generate a set of batch graphs by deriving a metadata. Further, a set of batch models is generated for the set of batch graphs. The set of batch models includes at least one of a forecasting model, a job-job regression model, and a job-workload regression model. Further, a batch job schedule is generated for the set of batch graphs to predict a revised batch job schedule with a real time feed and the set of batch models. Additionally, a proactive notification is sent to each user alarming one or more unexpected delays indicating the revised batch job schedule.

Method and system for predicting batch processes

A trouble ticket may contain various details of an observed issue and information of the actual issue which is hidden in the trouble ticket description along with other information. Embodiments herein describe an algorithm of algorithms (AoA) layer approach for extracting issues from trouble ticket descriptions. The method identifies the type of trouble ticket whether it is machine generated or user generated trouble tickets and accordingly selects the appropriate algorithm for issue extraction. It selects a relevant algorithm for the machine generated trouble tickets, and user generated trouble tickets based on trouble ticket data properties, historical performance, and a user feedback. It identifies right mix of columns from a service catalog to be used for matching based on presence of weighted keywords in the input data and identifies high-weighted keywords from input data descriptions and compares them with selected columns from catalog to generate accurate matches with their similarity score.

System and method for extracting issues based on trouble ticket mining

A system for smart alerts in a batch system for an IT enterprise. The method includes alert configuration by identifying recent steady state of a batch job and deriving schedules for the steady state. The normal behaviour is then computed within the schedules. The method further includes aggregating the one or more alerts by identifying correlated group of alerts by pruning of one or more jobs and alerts, detecting correlations between the two or more alerts and deriving causality of the grouped alerts. The method finally includes predicting of future alerts of a batch job.

System and method for smart alerts

A method for managing alerts generated in an enterprise environment is described. The method comprises discarding, by a processor, one or more duplicate alerts based on one or more predetermined rules, wherein a duplicate alert is an instance of an alert occurring within a duplicate alert arrival time associated with the alert. The method further comprises obtaining an alert correlation signature associated with the alert from a database, wherein the alert correlation signature comprises information associated with one or more alerts correlated to the alert. The method further comprises, obtaining, from the database, a temporal signature associated with the alert, wherein the temporal signature indicates a probable time of occurrence of the alert. Further, the alert, the alert correlation signature, and the temporal signature are transmitted to a computing device of a user of the enterprise environment.

Alert management system for enterprises

Systems and methods for generating a blueprint for an enterprise is provided. The system obtains information pertaining to an enterprise from data sources, extracts entities from the information, and identifies relationships across the entities, performs conflict resolution check on the information received from the data sources based on pre-defined rules, identifies one or more missing links in the information. The system further computes a completeness score for the information based on at least one of the conflict resolution check and the missing links, and generates a blueprint of the information and the one or more entities for the enterprise. Authentic data sources are identified from the data sources based on at least one of assigned weight, and one or more instances of, and contribution by, the data sources in the information. The completeness score is computed based on the instances and contribution of the sources in the information.

Systems and methods for generating blueprints for enterprises

Described herein are a method and a system for extracting issues based on ticket mining. In one implementation, a plurality of tickets comprising descriptions of the issues in computing systems are received. The received descriptions are then cleaned by removing unwanted details. Upon cleaning, the clean descriptions are mapped with descriptions stored in service catalog data to obtain unmapped clean descriptions. In an example, the unmapped clean descriptions include one of user-generated descriptions, system-generated descriptions, and both the user-generated descriptions and the system-generated descriptions. For the user-generated descriptions; the issues are extracted by pre-processing the user-generated descriptions, determining keywords from the processed unmapped clean descriptions, constructing n-grams of keywords from the extracted keywords, and extracting the n-grams of keywords as the issues present in the computing systems.

Issue extraction based on ticket mining

Systems and Methods for detection and localization of performance faults in data centers are described. In one embodiment, a method comprises identifying a performance fault in a data center upon detection of the performance fault at any of a plurality of monitors in the data center, wherein the plurality of monitors are placed at monitor nodes, amongst a plurality of nodes, in the data center. Further, the method comprises evaluating a fault vector for the data center upon identification of the performance fault, wherein the fault vector is evaluated based on a fault indicator corresponding to each of the plurality of monitors. Based on the comparison of the fault vector with signatures of each of the plurality of nodes, one or more faulty nodes, amongst the plurality of nodes in the data center, are determined as likely root cause of the performance fault.

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Pune, India

Maitreya Natu