Data Mining - Mehmed Kantardzic [206]
that are able to be transacted through e-services are not restricted to real entities such as electronics, furniture, or plane tickets. The Internet and the WWW evolve to include also resources such as software, computation abilities, or useful data sets. These new resources are potentially able to be sold or rented to clients as services for Internet users. Data mining is emerging as intuitively suitable for being delivered as an e-service because the approach reduces the high cost of setting up and maintaining infrastructure of supporting technologies. To efficiently and effectively deliver data mining as a service in the WWW, Web-service technologies are introduced to provide layers of abstractions and standards above existing software systems. These layers are capable of bridging any operating system, hardware platform, or programming language, just as the Web does. The natural extension for these services is grid computing. The grid is a distributed computing infrastructure that enables coordinated resource sharing within dynamic organizations consisting of individuals, institutions, and resources. The main aim of grid computing is to give organizations and application developers the ability to create distributed computing environments that can utilize computing resources on demand. Grid computing can leverage the computing power of a large numbers of server computers, desktop PCs, clusters, and other kinds of hardware. Therefore, it can help increase efficiencies and reduce the cost of computing networks by decreasing data processing time and optimizing resources and distributing workloads. Grid allows users to achieve much faster results on large operations and at lower costs. Recent development and applications show that the grid technology represents a critical infrastructure for high-performance DDM and knowledge discovery. This technology is particularly suitable for applications that typically deal with very a large amount of distributed data such as retail transactions, scientific simulation, or telecommunication data that cannot be analyzed on traditional machines in acceptable times. As the grid is becoming a well-accepted computing infrastructure in science and industry, it provides more general data-mining services, algorithms, and applications. This framework helps analysts, scientists, organizations, and professionals to leverage grid capacity in supporting high-performance distributed computing for solving their data-mining problem in a distributed way. The creation of the so-called Knowledge Grids on top of data and computational grids is the condition for meeting the challenges posed by the increasing demand for power and abstractions coming from complex data-mining scenarios in business, science, and engineering.
It is not only that DDM infrastructure is changing by offering new approaches through Web services together with the grid technology. Basic data-mining algorithms also need changes in a distributed environment. Most off-the-shelf data-mining systems are designed to work as a monolithic centralized application. They normally download the relevant data to a centralized location and then perform the data-mining operations. This centralized approach does not work well in many of the emerging distributed, ubiquitous, possibly privacy-sensitive data-mining applications. A primary goal of DDM algorithms is to achieve the same or similar data-mining result as a centralized solution without moving data from their original locations. The distributed approach assumes that local computation is done on each of the sites, and either a central site communicates with each distributed site to compute the global model, or a peer-to-peer architecture is used. In the latter case, individual nodes perform most of the tasks by communicating with neighboring nodes by message passing over an asynchronous network. Illustrative examples are networks of independent and intelligent sensors that are connected to each other in an ad hoc fashion. Some features of a distributed mining scenario are as follows:
The system