Garima Gupta

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Today, cloud computing is an emerging way of computing in computer science. Cloud computing is a set of resources and services that are offered by the network or internet. Cloud computing extends various computing techniques like grid computing, distributed computing. Today cloud computing is used in both industrial field and academic field. Cloud(More)
Cloud is a new trend in the computing. It is the combination of distributed, parallel and grid computing. It is a pay on demand computing. Basically cloud is a shared pool of resources. The resources are shared among users who are geographically distributed. As the area of cloud is increasing, several issues are also increasing. These issues are related to(More)
Probabilities and social behaviour are two common criteria used to route a message in disruption/delay tolerant network wherein there is only intermittent connectivity between the nodes. In this article we first discuss how the characteristics of these routing algorithms can be exploited by a malicious node to attract data packets and then dropping them to(More)
Very large grid applications that run and communicate simultaneously on different sites need reservation of start times and a minimum of resources. It becomes easier for each site's batch job scheduler to deal with the road blocks in the schedule that are created by such reservations if having some degree of freedom in how to meet the reservations. Thus,(More)
The evolution from traditional business intelligence to big data analytics has witnessed the emergence of `Data Lakes' in which data is ingested in raw form rather than into traditional data warehouses. With the increasing availability of many more pieces of information about each entity of interest, e.g., a customer, often from diverse sources(More)
Aggregate analysis, such as comparing country-wise sales versus global market share across product categories, is often complicated by the unavailability of common join attributes, e.g., category, across diverse datasets from different geographies or retail chains, even after disparate data is technically ingested into a common data lake. Sometimes this is(More)
Discovering causal models from observational and interventional data is an important first step preceding what-if analysis or counterfactual reasoning. As has been shown before[1], the direction of pairwise causal relations can, under certain conditions, be inferred from observational data via standard gradient-boosted classifiers (GBC) using carefully(More)
Neovascularization (NV) is a characteristic of the onset of sight-threatening stage of DR, called proliferative DR (PDR). Identification of PDR requires modeling of these unregulated ill-formed vessels, and other associated signs of PDR. We present an approach that models the micro-pattern of local variations (using texture based analysis) and quantifies(More)