We consider the problem of moving a large amount of data from several source hosts to a destination host over a wide-area network, i.e., a large-scale data collection problem. This problem is important since improvements in data collection times are crucial to performance of many applications, such as wide-area uploads, high-performance computing, and data mining. Existing approaches to the lar...

In this paper, we propose a new, effective, and unified scoring method for local feature-based image retrieval. The proposed scoring method is derived by solving the large-scale image retrieval problem as a classification problem with a large number of classes. The resulting proposed score is based on the ratio of the probability density function of an object model to that of a background model...

The last few years have led to a series of discoveries that uncovered statistical properties, which are common to a variety of diverse real-world social, information, biological and technological networks. The goal of the present paper is to investigate, for the first time, the statistical properties of networks of people engaged in distributed problem solving and discuss their significance. We...

Intrepid has a very-large, production GPFS storage system consisting of 128 file servers, 32 storage controllers, 1152 disk arrays, and 11,520 total disks. In such a large system, performance problems are both inevitable and difficult to troubleshoot. We present our experiences, of taking an automated problem diagnosis approach from proof-of-concept on a 12-server test-bench parallel-filesystem...

In this work, we investigate a multi-period Home Health Care Scheduling Problem (HHCSP) under stochastic service and travel times. We first model the deterministic problem as an integer linear programming model that incorporates real-world requirements, such as time windows, continuity of care, workload fairness, inter-visit temporal dependencies. We then extend the model to cope with uncertain...

The much-publicized Netflix competition has put the spotlight on the application domain of collaborative filtering and has sparked interest in machine learning algorithms that can be applied to this sort of problem. The demanding nature of the Netflix data has lead to some interesting and ingenious modifications to standard learning methods in the name of efficiency and speed. There are three b...

In this paper, we are interested in routing vehicles to service large-scale emergencies, such as natural disasters and terrorist attacks. We describe the specifics of routing vehicles in a large-scale emergency and decompose the problem into two stages: a planning stage and an operational stage. In the planning stage, we aim to generate the routes well in advance of any emergencies; in the oper...

The last few years have led to a series of discoveries that uncovered statistical properties that are common to a variety of diverse real-world social, information, biological, and technological networks. The goal of the present paper is to investigate the statistical properties of networks of people engaged in distributed problem solving and discuss their significance. We show that problem-sol...

This paper introduces a three-phase heuristic approach for a large-scale energy management and maintenance scheduling problem. The problem is concerned with scheduling maintenance and refueling for nuclear power plants up to five years into the future, while handling a number of scenarios for future demand and prices. The goal is to minimize the expected total production costs. The first phase ...

The Quadratic Assignment Problem (QAP) consists of assigning n facilities to n locations so as to minimize the total weighted cost of interactions between facilities. The QAP arises in many diverse settings, is known to be NP-hard, and can be solved to optimality only for fairly small size instances (typically, n ≤ 25). Neighborhood search algorithms are the most popular heuristic algorithms to...