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Replication in Data Grid reduces access latency and bandwidth consumption by creating multiple data copies. One of the challenges in data replication is to select the candidate sites where replicas should be placed, which is known as the allocation problem. One performance metric to determine the best place to host replicas is select for optimum average response time. We use the p-median model for the replica placement problem. The p-median model has been exploited in urban planning to find locations where new facilities should be built. In our problem, the p-median model finds the locations of p candidate sites to place a replica that optimize the aggregated response time. Motivated by the fact that the Grid environment is highly dynamic, we propose a dynamic replica maintenance algorithm that re-allocates replicas to new candidate sites when a performance metric degrades significantly. Simulation results demonstrate that the dynamic maintenance algorithm with static placement decisions performs best in dynamic environments like Data Grids. [ABSTRACT FROM AUTHOR]

This paper presents a clustering approach that integrates multi-objective optimization, weighted k-means and validity analysis in an iterative process to automatically estimate the number of clusters, and then partition the whole given data to produce the most natural clustering. The proposed approach has been tested on real-life dataset; results of both weighted and unweighed k-means are reported to demonstrate applicability and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Mining outliers from large datasets is like finding needles in a haystack. Even more challenging is sifting through the dynamic, unstructured, and ever-growing web data for outliers. This paper presents HyCOQ, which is a hybrid algorithm that draws from the power of n-gram-based and word-based systems. Experimental results obtained using embedded motifs without a dictionary show significant improvement over using a domain dictionary irrespective of the type of data used (words, n-grams, or hybrid). Also, there is remarkable improvement in recall with hybrid documents compared to using raw words and n-grams without a domain dictionary. [ABSTRACT FROM AUTHOR]

In this paper, we present a linked-list based encoding scheme for multiple objectives based genetic algorithm (GA) to identify clusters in a partition. Our approach obtains the optimal partitions for all the possible numbers of clusters in the Pareto Optimal set returned by a single genetic GA run. The performance of the proposed approach has been tested using two well-known data sets, namely Iris and Ruspini. The obtained results are promising and demonstrate the applicability and effectiveness of the proposed approach. Keywords: clustering, genetic algorithms, linkage-encoding, k-means, multi-objective optimization. [ABSTRACT FROM AUTHOR]

One of the challenges in data replication is to select the candidate sites to place replicas. We use the p-median and p-center models to address the replica placement problem. In our problem, the p-median model finds the locations of p candidate sites to place a replica to optimize the total (or average) response time. The p-center model hosts replicas to p candidate sites by minimizing the maximum response time among sites. A Grid environment is highly dynamic so placing a replica by considering one objective, i.e., optimize average response time or optimize maximum response time, may not be always a good choice. We propose a multi-objective model that considers the objectives of p-median and p-center simultaneously to select the candidate sites that will host replicas. Simulation results demonstrate that the multi-objective model outperforms single objective models in dynamic environments such as Data Grids. [ABSTRACT FROM AUTHOR]

This paper introduces the Emergent Thinker paradigm, an area-wide logical computing entity, named after a philosopher that continuously analyses information and has emergent computed solutions for new and/or existing requests. The Complex Adaptive System (CAS) emergent computation model and the CAS propagation model are proposed as mechanisms to achieve the Emergent Thinker. The Thinker is proposed as an alternative approach for new and existent design and implementation challenges in systems research. The Biologically Inspired Peer-to-Peer Distributed File System (BPD) is an instantiation of the Emergent Thinker paradigm and corroborates our hypothesis that CAS based computation is an alternative paradigm to provide scalable computation for large distributed systems. [ABSTRACT FROM AUTHOR]