Accenture Global Solutions Limited

Accenture Global Services Gmbh

Massachusetts Institute of Technology

Kishore P Durg

Mahesh Venkataraman

Jothi Gouthaman

Mallika Fernandes

Sanjay Podder

Venugopal S Shenoy

Vikrant Shyamkant Kaulgud

Sunder Ranganathan Nochilur

Swati Sharma

Kulkarni Girish

Srinatha Sreedhara Mulugund

Adam Patten Burden

Vibhu Saujanya Sharma

Chinmaya Ranjan Jena

Radhika Golden

Priyavanshi Pathania

Joshy Ravindran

Sundeep Singh

Mainak Basu

Rajan Dilavar Mithani

Peter Michael Lacy

Akshay Kasera

Pavel Valerievich Ponomarev

Bhaskar Ghosh

Niraj Gupta

Sivasankar Ramalingam

Matthias Rasking

Sunder Nochilur Ranganathan

Kapil Singi

Neville Dubash

Anurag Dwarakanath

Mohan Sekhar

Ramkumar Kothandaraman

Samir Desai

Gordon Trujillo

Swapnajeet Gon Choudhury

Gopi Krishna Durbhaka

Bryan Michael McCart

Parag Pande

Srinivas Govindaiah

James Allen Glore

Ramchand R Bhambhani

William Alfred Jindrich, Jr

Damien X Panketh

David Petricca

Nobby Rajan

Syed Sibgathulla

Mahesh Venkata Raman

Surendra Sheshagiri

Alex Zorba

Lisa Kay DeMik

James Woodier

Raghavan Tinniyam Iyer

Puneet Kalra

Samarth Sikand

Sudeep Sharma

Jeffrey Steven Wilkinson

Manish Ahuja

Bhavna Butani

Raghotham M Rao

Arati Deo

Ravi Kant Gaur

Jagadeesh Chandra Bose Rantham Prabhakara

Rambhau Eknath Rote

Amit Bahree

Sriram Kannan

Dinesh Jaibhagwan Mittal

Praveen Subbnanjappa

Sundharraman Karthik Narain

John Francis Walsh

Kanchanjot Kaur Phokela

Teresa Escrig

Raghunandan Hemmige Keshava

Kalyan Kumar Veeramachaneni

Neha Patki

Shrikanth N C

Paul Sebastian

In some examples, policy-based application architecture generation may include generating, based on a knowledge model schema, data residency policies, and a data classification ontology, a knowledge model, and determining, based on the knowledge model, whether an application includes regulated data. Based on an analysis of application data and user data, user location and regulated data insights may be generated to determine location specific data residency policies. Location specific regulated data in-flow and data source hosted location insights may be analyzed to determine a location compliance assessment that includes an indication of whether a location associated with the application is compliant or not compliant with the location specific data residency policies. Based on an indication of non-compliance, a strategy may be generated for compliance of application architecture for the application with the location specific data residency policies, and re-architecting of the application architecture may be implemented.

Policy-based application architecture generation

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating customized recommendations for environmentally-conscious cloud computing frameworks for replacing computing resources of existing datacenters. One of the methods involves receiving, through a user interface presented on a display of a computing device, data regarding a user's existing datacenter deployment and the user's preferences for the new cloud computing framework, generating one or more recommendations for environmentally-conscious cloud computing frameworks based on the received data, and presenting such recommendations through the user interface for the user's review and consideration.

Green cloud computing recommendation system

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a win prediction for an opportunity. In some implementations, a server receives data representing an opportunity. The server obtains historical data from a data repository based on the received data representing the opportunity. The server generates standardized feature vectors based on non-standardized data, the non-standardized data comprising (i) the received data and (ii) the obtained historical data. The server filters the standardized feature vectors. The server generates a win percentage based on the filtered feature vectors using classifiers. In response to generating the win percentage for the opportunity, the server generates a plurality of data insights using the classifiers and the generated win percentage, wherein the plurality of data insights describe one or more data predictions for pursuing the opportunity. The server provides the win percentage and the plurality of data insights for output.

Enhanced data driven intelligent cloud advisor system

This document describes modeling and simulation techniques to select a cloud architecture profile based on correlations between application workloads and resource utilization. In some aspects, a method includes obtaining infrastructure data specifying utilization of computing resources of an existing computing system. Application workload data specifying tasks performed by one or more applications running on the existing computing system is obtained. One or more models are generated based on the infrastructure data and the application workload data. The model(s) define an impact on utilization of each computing resource in response to changes in workloads of the application(s). A workload is simulated, using the model(s), on a candidate cloud architecture profile that specifies a set of computing resources. A simulated utilization of each computing resource of the candidate cloud architecture profile is determined based on the simulation. An updated cloud architecture profile is generated based on the simulated utilization.

Enhanced selection of cloud architecture profiles

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for test cycle optimization using contextual association mapping. In one aspect, a method includes obtaining an artifact that includes a collection of reference items, where each reference item includes a sequence of words, generating candidate tags from each of the reference items based on the sequences of words in the reference items, selecting a subset of the candidate tags as context tags based on an amount that the candidate tags appear in the reference items, obtaining a sample item that includes a sequence of words, identifying a subset of the context tags in the sequence of words in the sample item, and classifying a subset of the reference items as contextually similar to the sample item based the context tags that were identified.

Test cycle optimization using contextual association mapping

Implementations of the present disclosure include receiving, by the cloud migration platform, user input indicating a type of case for cloud migration planning, the type including one of an abstract case and a detailed case, in response to receiving the type of case, selectively initiating, by the cloud migration platform, a discovery process for generating discovery data representative of infrastructure assets and application assets of the enterprise network, processing the discovery data through a sub-set of engines of a set of engines of the cloud migration platform, the sub-set of engines providing output including cloud platform selection data, application disposition data, target architecture data, bill of materials (BOM) data, and application remediation data, and generating, by a cloud migration planning engine of the cloud migration platform and based on the output, a cloud migration plan including an application sequence plan for migrating applications to one or more cloud platforms.

Platform for migration planning of network infrastructures

In some examples, data classification and modelling based application compliance analysis may include generating, for application data for an application, and based on data laws, a knowledge graph schema that is used to generate an instantiated knowledge graph. Based on a plurality of data sources, domain knowledge and patterns, and the instantiated knowledge graph, classified data may be generated to generate an annotated knowledge graph. Based on the annotated knowledge graph, an indication of applicable sovereign laws and compliance related to the application data for the application may be generated. Based on the annotated knowledge graph and the applicable sovereign laws and compliance related to the application data for the application, a sovereign cloud strategy related to the application data for the application may be generated. Further, the sovereign cloud strategy may be applied to the application data for the application.

Data classification and modelling based application compliance analysis

A connected classroom system provides for local and remote control and display of media streams. An instructor may, for instance, direct audio/video streams of the instructor to any number of displays and speakers located within a classroom. The system may send and receive control instructions to and from remote classrooms as well, through a network interface. The control instructions may configure any set of presentation devices in any classroom to reproduce any content originating from any source within any classroom. Accordingly, multiple classrooms located in disparate locations may participate in media exchanges arising from any source, including instructors and individual student seating locations within the classrooms.

Connected classroom

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a touchless testing platform employed to, for example, create automated testing scripts, sequence test cases, and implement defect solutions. In one aspect, a method includes receiving a log file and testing results generated from a code base for an application; processing the log file through a pattern-mining algorithm to determine a usage pattern of code modules within the code base; clustering defects from the testing results based on a respective functionality of the application reported within each of the defects; generating testing prioritizations for test cases for the application by assigning weightages to the test cases based on the clusters of defects and the usage pattern of the code modules within the code base; sequencing a set of the test cases based on the test prioritizations; and transmitting the sequence to a test execution engine.

Test prioritization and dynamic test case sequencing

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for test cycle optimization using contextual association mapping. In one aspect, a method includes obtaining an artefact that includes a collection of reference items, where each reference item includes a sequence of words, generating candidate tags from each of the reference items based on the sequences of words in the reference items, selecting a subset of the candidate tags as context tags based on an amount that the candidate tags appear in the reference items, obtaining a sample item that includes a sequence of words, identifying a subset of the context tags in the sequence of words in the sample item, and classifying a subset of the reference items as contextually similar to the sample item based the context tags that were identified.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a touchless testing platform employed to, for example, create automated testing scripts, sequence test cases, and implement determine defect solutions. In one aspect, a method includes the actions of receiving requirements documentation for an application through a user interface (UI), analyzing the requirements documentation to extract terminologies based on an entity term corpus; categorizing the extracted terminologies based on a corpus of known terms; generating a semantic graph from standardized statements constructed from the categorized extracted terminologies; generating a process flow map for the application by identifying processes of the application and a respective relationship between each process from the semantic graph; generating a test scenario map of test scenarios for the application from the process flow map and the semantic graph; and providing the test scenario map to a tester through the UI.

Test scenario and knowledge graph extractor

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Bengaluru, India

Kishore P Durg