[1] Probing techniques are useful to provide diagnostic evaluations of air quality models and to indicate the responses of model predictions to changes in emissions. Three probing tools are available in a three-dimensional air quality model, CAMx: the Decoupled Direct Method (DDM), the Ozone Source Attribution Technology (OSAT), and Process Analysis (PA). These tools are evaluated in terms of c...

In this chapter I begin by discussing Distributed Data Mining (DDM) for scaling up, beginning by asking what scaling up means, questioning whether it is necessary, and then presenting a brief survey of what has been done to date. I then provide motivation beyond scaling up, arguing that DDM is a more natural way to view data mining generally. DDM eliminates many difficulties encountered when co...

One of the services provided by the High Level Architecture (HLA) Run-time Infrastructure (RTI) is data distribution management (DDM), which aims to make data communication more efficient by sending data to only those federates requiring the data. Several DDM methods, notably region and grid-based, have been proposed and some of them have been implemented in RTI. This paper first briefly discus...

Distributed data mining (DDM) consists of mining data using distributed resources. DDM has emerged as a hot research area. Careful attention in the usage of distributed resources of data, computing, communication, storage and human factors in a near optimal fashion are paid by DDM. DDM is gaining attention in Peer-to-peer (P2P) systems which are emerging as a new distributed computing paradigm ...

Distributed Data Mining (DDM) has become one of the promising areas of Data Mining. DDM techniques include classifier approach and agent-approach. Classifier approach plays a vital role in mining distributed data, having homogeneous and heterogeneous approaches depend on data sites. Homogeneous classifier approach involves ensemble learning, distributed association rule mining, meta-learning an...

The increasing parallelism offered by the parallel architectures introduced by processor vendors, coupled with the need to extract more parallelism out of the applications, has led the community to examine more efficient programming and execution models. The Dataflow Multithreading model is known to be the model that can exploit the most parallelism out of a wide range of applications. The Data...

In this paper, we describe the Distributed Dispatcher Manager (DDM), a system for managing resource in very large-scale task and resource domains. In DDM, resources are modeled as cooperative mobile teams of agents and objects or tasks are assumed to be distributed over a virtual space. Each agent has direct access to only local and partial information about its immediate surroundings. DDM orga...

In the internet-based e-business environment, most business data are distributed, heterogeneous and private. To achieve true business intelligence, mining large amounts of distributed data is necessary. Through a thorough literature review, this paper identifies four main issues in distributed data mining (DDM) systems for e-business and classifies modern DDM systems into three classes with rep...

IBM's Customer Information Control System (CICS) is the leading product family in the on-line transaction processing (OLTP) market, OLTP systems are being used by many enterprises to implement their daily business processes and manage operational data such as accounts, inventories, and orders. CICS/Distributed Data Management (CICS/DDM) implements the distributed file function of Systems Applrc...

The decoupled direct method (DDM) has been implemented in a three-dimensional (3D) air quality model in order to calculate first-order sensitivities with respect to emissions and initial and boundary concentrations. This required deriving new equations for the sensitivities from the equations of the hybrid chemistry solver and the nonlinear advection algorithm in the model. The sensitivities fo...