Leonid Glimcher

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For a grid middleware to perform resource allocation, prediction models are needed, which can determine how long an application will take for completion on a particular platform or configuration. In this paper, we take the approach that by focusing on the characteristics of the class of applications a middleware is suited for, we can develop simple(More)
One important way in which sampling for approximate query processing in a database environment differs from traditional applications of sampling is that in a database, it is feasible to collect accurate summary statistics from the data in addition to the sample. This paper describes a set of sampling-based estimators for approximate query processing that(More)
Analysis of large geographically distributed scientific datasets, also referred to as distributed data-intensive science, has emerged as an important area in recent years. An application that processes data from a remote repository needs to be broken into several stages, including a data retrieval task at the data repository, a data movement task, and a(More)
Summary form only given. As scientific simulations are generating large amounts of data, analyzing this data to gain insights into scientific phenomenon is increasingly becoming a challenge. We present a case study on the use of a cluster middleware for rapidly creating a scalable and parallel implementation of a scientific data analysis application. Using(More)
This paper gives an overview of two middleware systems that have been developed over the last 6 years to address the challenges involved in developing parallel and distributed implementations of data mining algorithms. FREERIDE (FRamework for Rapid Implementation of Data mining Engines) focuses on data mining in a cluster environment. FREERIDE is based on(More)
In data-intensive computing, an important problem that has received relatively little attention is of transparent processing of data stored in remote data repositories. Interesting load balancing considerations arise for these scenarios. Particularly, based on where data is generated and how it is shared, a dataset of interest can be divided across multiple(More)
This paper presents a case study in creating a parallel and scalable implementation of a scientific data analysis application. We focus on a defect detection and categorization application which analyzes datasets produced by molecular dynamics (MD) simulations. In parallelizing this application, we had the following three goals. First, we obviously wanted(More)
In this paper, we consider the problem of developing service-oriented implementations of data-intensive applications that process data on remote servers. While the existing grid and web-service frameworks allow interoperability and flexible resource utilization, achieving efficiency and scalability remains a critical challenge. Similarly, the existing grid(More)
We have been developing a middleware which enables development, support, and deployment of services that can transparently access and process data from remote servers, are compatible with grid standards and frameworks, and yet are efficient and scalable. Our middleware is referred to as FREERIDE-G (FRamework for Rapid Implementation of Datamining Engines in(More)