Simulation data as data streams

Data Streams: Models and Algorithms Data Streams: Models and Algorithms

In recent years, advances in hardware technology have facilitated new ways of collecting data continuously. In many applications such as network monitoring, the volume of such data is so large that it may be impossible to store the data on disk. Furthermore, even when the data can be stored, the volume of the incoming data may be so large that it may be impossible to process any particular reco...

Management of Data Streams for a Real Time Flood Simulation

We examine the challenge of coupling applications to real-time data sources in a Grid environment. Such problems are important to the emerging field of Grid-based emergency planning and crisis response, in which realtime data sources must be coupled to modeling applications to provide timely forecasting to emergency planners and responders. In this paper we present a Grid-based scripting workfl...

Estimating Missing Data in Data Streams

Networks of thousands of sensors present a feasible and economic solution to some of our most challenging problems, such as real-time traffic modeling, military sensing and tracking. Many research projects have been conducted by different organizations regarding wireless sensor networks; however, few of them discuss how to estimate missing sensor data. In this research we present a novel data e...

Mining Data Bases and Data Streams

Data mining represents an emerging technology area of great importance to homeland security. Data mining enables knowledge discovery on databases by identifying patterns that are novel, useful, and actionable. It has proven successful in many domains, such as banking, ecommerce, genomic, investment, telecom, web analysis, link analysis, and security applications. In this chapter, we will survey...

Clustering Parallel Data Streams