A comprehensive investigation was carried out to minimize the cost price of the rock fragmentation process including: drilling, blasting, secondary blasting and separating the large boulders (oversize rocks) while loading, until the optimized fragmentation, powder factor and ratio of burden (B) to blasthole diameter (?h) were achieved by doing several stages of drilling and blasting practically...

The objective of this paper is to present cost models for estimating the response time for the distributed data mining (DDM) process. These cost models form the basis for developing a hybrid approach to distributed data mining which integrates the client-server and mobile agent paradigms. The underlying objective of the hybrid model is to optimise the DDM process.

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...

Variational phase-field models of fracture are widely used to simulate nucleation and propagation cracks in brittle materials. They based on the approximation solutions a free-discontinuity energy by two smooth function: displacement damage field. Their numerical implementation is typically discretization both fields nodal $$\mathbb {P}^1$$ Lagrange finite elements. In this article, we propose ...

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...