Most data mining approaches assume that the data can be provided from a single source. If
data was produced from many physically distributed locations like Wal-Mart, these methods …

Data mining (DM) is increasingly used in the analysis of data generated in life sciences,
including biological data produced in several disciplines such as genomics and proteomics …

The problem addressed in this paper is the challenge of automated construction of
knowledge discovery workflows, given the types of inputs and the required outputs of the …

Novel data-mining tasks in e-science involve mining of distributed, highly heterogeneous
data and knowledge sources. However, standard data mining platforms, such as Weka and …

Achieving low costs and high efficiency in wastewater treatment plants (WWTPs) is a
common challenge in develo** countries, although many optimizing tools on process …

Deploying lightweight tasks individually on grid resources would lead to a situation where
communication overhead dominates the overall application processing time. The …

Data analysis techniques and services are needed to mine the massive amount of data
available and to extract useful knowledge from it. e service-oriented architecture (SOA) is …

Link prediction is a central task in the field of dynamic complex network analysis. A major
trend in this area consists of applying a dyadic topological approach. Most of existing …

Data mining techniques are used in many application areas to extract useful knowledge
from large datasets. Very often, parameter swee** is used in data mining applications to …

As a new distributed heterogeneous computing platform, grid aims at achieving Internet-
wide resource sharing and collaborative computing. Grid task scheduling (GTS) is the key …