J
Jayant R. Kalagnanam
Researcher at IBM
Publications - 156
Citations - 9714
Jayant R. Kalagnanam is an academic researcher from IBM. The author has contributed to research in topics: Common value auction & Computer science. The author has an hindex of 35, co-authored 148 publications receiving 9149 citations. Previous affiliations of Jayant R. Kalagnanam include Carnegie Mellon University.
Papers
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Journal ArticleDOI
QoS-aware middleware for Web services composition
Liangzhao Zeng,Boualem Benatallah,Anne H. H. Ngu,Marlon Dumas,Jayant R. Kalagnanam,Henry Chang +5 more
TL;DR: This paper presents a middleware platform which addresses the issue of selecting Web services for the purpose of their composition in a way that maximizes user satisfaction expressed as utility functions over QoS attributes, while satisfying the constraints set by the user and by the structure of the composite service.
Proceedings ArticleDOI
Quality driven web services composition
TL;DR: This paper proposes a global planning approach to optimally select component services during the execution of a composite service, and experimental results show that thisglobal planning approach outperforms approaches in which the component services are selected individually for each task in a Composite service.
Journal ArticleDOI
Foundations for smarter cities
Colin Harrison,Barbara Eckman,R. Hamilton,Perry G. Hartswick,Jayant R. Kalagnanam,Jurij R. Paraszczak,Peter Williams +6 more
TL;DR: The information technology foundation and principles for Smarter Cities™ are described, which enables the adaptation of city services to the behavior of the inhabitants, which permits the optimal use of the available physical infrastructure and resources.
Journal ArticleDOI
An efficient sampling technique for off-line quality control
TL;DR: A new sampling technique is presented that generates and inverts the Hammersley points to provide a representative sample for multivariate probability distributions and is compared to a sample obtained from a Latin hypercube design by propagating it through a set of nonlinear functions.
Journal ArticleDOI
Efficient sampling technique for optimization under uncertainty
TL;DR: In this paper, a sampling technique is presented that generates and inverts the Hammersley points (an optimal design for placing n points uniformly on a k-dimensional cube) to provide a representative sample for multivariate probability distributions.