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Institution

Infosys

CompanyBengaluru, India
About: Infosys is a company organization based out in Bengaluru, India. It is known for research contribution in the topics: Cloud computing & Business process. The organization has 1880 authors who have published 1840 publications receiving 20595 citations. The organization is also known as: Infy & Infosys Limitied.


Papers
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Book ChapterDOI
Wil M. P. van der Aalst1, Wil M. P. van der Aalst2, A Arya Adriansyah1, Ana Karla Alves de Medeiros3, Franco Arcieri4, Thomas Baier5, Tobias Blickle6, Jagadeesh Chandra Bose1, Peter van den Brand, Ronald Brandtjen, Joos C. A. M. Buijs1, Andrea Burattin7, Josep Carmona8, Malu Castellanos9, Jan Claes10, Jonathan Cook11, Nicola Costantini, Francisco Curbera12, Ernesto Damiani13, Massimiliano de Leoni1, Pavlos Delias, Boudewijn F. van Dongen1, Marlon Dumas14, Schahram Dustdar15, Dirk Fahland1, Diogo R. Ferreira16, Walid Gaaloul17, Frank van Geffen18, Sukriti Goel19, CW Christian Günther, Antonella Guzzo20, Paul Harmon, Arthur H. M. ter Hofstede1, Arthur H. M. ter Hofstede2, John Hoogland, Jon Espen Ingvaldsen, Koki Kato21, Rudolf Kuhn, Akhil Kumar22, Marcello La Rosa2, Fabrizio Maria Maggi1, Donato Malerba23, RS Ronny Mans1, Alberto Manuel, Martin McCreesh, Paola Mello24, Jan Mendling25, Marco Montali26, Hamid Reza Motahari-Nezhad9, Michael zur Muehlen27, Jorge Munoz-Gama8, Luigi Pontieri28, Joel Ribeiro1, A Anne Rozinat, Hugo Seguel Pérez, Ricardo Seguel Pérez, Marcos Sepúlveda29, Jim Sinur, Pnina Soffer30, Minseok Song31, Alessandro Sperduti7, Giovanni Stilo4, Casper Stoel, Keith D. Swenson21, Maurizio Talamo4, Wei Tan12, Christopher Turner32, Jan Vanthienen33, George Varvaressos, Eric Verbeek1, Marc Verdonk34, Roberto Vigo, Jianmin Wang35, Barbara Weber36, Matthias Weidlich37, Ton Weijters1, Lijie Wen35, Michael Westergaard1, Moe Thandar Wynn2 
01 Jan 2012
TL;DR: This manifesto hopes to serve as a guide for software developers, scientists, consultants, business managers, and end-users to increase the maturity of process mining as a new tool to improve the design, control, and support of operational business processes.
Abstract: Process mining techniques are able to extract knowledge from event logs commonly available in today’s information systems. These techniques provide new means to discover, monitor, and improve processes in a variety of application domains. There are two main drivers for the growing interest in process mining. On the one hand, more and more events are being recorded, thus, providing detailed information about the history of processes. On the other hand, there is a need to improve and support business processes in competitive and rapidly changing environments. This manifesto is created by the IEEE Task Force on Process Mining and aims to promote the topic of process mining. Moreover, by defining a set of guiding principles and listing important challenges, this manifesto hopes to serve as a guide for software developers, scientists, consultants, business managers, and end-users. The goal is to increase the maturity of process mining as a new tool to improve the (re)design, control, and support of operational business processes.

1,135 citations

Journal ArticleDOI
TL;DR: A new architecture for the implementation of IoT to store and process scalable sensor data (big data) for health care applications and uses MapReduce based prediction model to predict the heart diseases is proposed.

393 citations

Proceedings ArticleDOI
09 May 2005
TL;DR: This work identifies two families of resource allocation algorithms: task-based algorithms that greedily allocate tasks to resources, and workflow- based algorithms that search for an efficient allocation for the entire workflow.
Abstract: Grid applications require allocating a large number of heterogeneous tasks to distributed resources. A good allocation is critical for efficient execution. However, many existing grid toolkits use matchmaking strategies that do not consider overall efficiency for the set of tasks to be run. We identify two families of resource allocation algorithms: task-based algorithms, that greedily allocate tasks to resources, and workflow-based algorithms, that search for an efficient allocation for the entire workflow. We compare the behavior of workflow-based algorithms and task-based algorithms, using simulations of workflows drawn from a real application and with varying ratios of computation cost to data transfer cost. We observe that workflow-based approaches have a potential to work better for data-intensive applications even when estimates about future tasks are inaccurate.

382 citations

Journal ArticleDOI
TL;DR: Deep Learning-based models are used for predicting the number of novel coronavirus positive reported cases for 32 states and union territories of India and it is observed that the proposed method yields high accuracy for short term prediction with error less than 3 % for daily predictions and less than 8 % for weekly predictions.
Abstract: In this paper, Deep Learning-based models are used for predicting the number of novel coronavirus (COVID-19) positive reported cases for 32 states and union territories of India Recurrent neural network (RNN) based long-short term memory (LSTM) variants such as Deep LSTM, Convolutional LSTM and Bi-directional LSTM are applied on Indian dataset to predict the number of positive cases LSTM model with minimum error is chosen for predicting daily and weekly cases It is observed that the proposed method yields high accuracy for short term prediction with error less than 3% for daily predictions and less than 8% for weekly predictions Indian states are categorised into different zones based on the spread of positive cases and daily growth rate for easy identification of novel coronavirus hot-spots Preventive measures to reduce the spread in respective zones are also suggested A website is created where the state-wise predictions are updated using the proposed model for authorities,researchers and planners This study can be applied by other countries for predicting COVID-19 cases at the state or national level

231 citations

Journal ArticleDOI
TL;DR: A Point/Counterpoint department discusses whether global software development is indeed a business necessity, and presents five articles that cover various aspects of globalSoftware Development, including knowledge management strategies, distributed software development, requirements engineering, distributed requirements, and managing offshore collaboration.
Abstract: Global software development efforts have increased in recent years, and such development seems to have become a business necessity for various reasons, including cost, availability of resources, and the need to locate development closer to customers. However, there's still much to learn about global software development before the discipline becomes mature. This special issue aims to assess the gap between the state of the art and the state of the practice. It presents five articles that cover various aspects of global software development, including knowledge management strategies, distributed software development, requirements engineering, distributed requirements, and managing offshore collaboration. A Point/Counterpoint department discusses whether global software development is indeed a business necessity.This article is part of a special issue on Global Software Development.

230 citations


Authors

Showing all 1882 results

NameH-indexPapersCitations
Rajkumar Buyya133106695164
Abhijit Banerjee9333345992
Ken Kennedy7125317484
Engin Kirda6319916692
Avinash C. Kak5125425027
Arnab Banerjee442607535
Kamalakar Karlapalem312013414
Sanjoy Kumar Paul301602847
P.K. Suresh281492037
Pankaj Jalote271042938
Ashish Sureka261862490
Wasim Sadiq23602594
Santonu Sarkar221252048
Shubhashis Sengupta21961466
Ashutosh Saxena201222202
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20223
202138
202045
201935
201852
201755