5155977
A Hybrid Keyword Search across Peer-to-Peer Federated Databases
The need for Keyword search in databases is suggested both by Web integration with legacy database management system and by dynamic Web publication. However, it sacrifices the inherent meaning of database schema. Web search engines provide clues for resource location on the Web, but have similar semantic problems. The Semantic Web suggests an ideal solution for the semantic problem on the Web. But due to the need for sophisticated domain definition and lack of unified definitions, many Web pages are not part of the Semantic Web. We define a hybrid search to be a search combining semantic metadata and keywords. A hybrid search on P2P based federated databases provides meaningful and scalable search on an overlay network across the Internet. This paper describes the design of the combined search for unstructured data with associated metadata, information retrieval from the repository, peer-to-peer based communication layer, and data integration hub.
3831285
Data Integration Hub for a Hybrid Paper Search
In this paper we describe the design of a hybrid search that combines simple metadata search with a traditional keyword search over unstructured context data. This paradigm provides the inquirer additional options to narrow the search with some semantic aspects through the XML metadata query. We demonstrate a paper search for a case study of the hybrid search, and describe a data integration hub to integrate those data dispersed on the Net.
2562587
Creating a Robust Desktop Grid using Peer-to-Peer Services
The goal of the work described in this paper is to design and build a scalable infrastructure for executing grid applications on a widely distributed set of resources. Such grid infrastructure must be decentralized, robust, highly available, and scalable, while efficiently mapping application instances to available resources in the system. However, current desktop grid computing platforms are typically based on a client-server architecture, which has inherent shortcomings with respect to robustness, reliability and scalability. Fortunately, these problems can be addressed through the capabilities promised by new techniques and approaches in peer-to-peer (P2P) systems. By employing P2P services, our system allows users to submit jobs to be run in the system and to run jobs submitted by other users on any resources available in the system, essentially allowing a group of users to form an ad-hoc set of shared resources. The initial target application areas for the desktop grid system are in astronomy and space science simulation and data analysis.
3103350
Random Matchings in Regular Graphs
d-regular graph G, let M be chosen uniformly at random from the set of all matchings of G, and for let be the probability that M does not cover x.We show that for large d, the 's and the mean μ and variance of are determined to within small tolerances just by d and (in the case of μ and ) :
Theorem. For any d-regular graph G,
(a), so that ,
(b),where the rates of convergence depend only on d.
3457933
On the Society of Genome: Social Affiliation Network Analysis of Microarray Data
To investigate the structure of the genomic interaction network built from yeast gene-expression compendium dataset of hundreds of systematic perturbations, social affiliation network analysis methodologies were applied through quantifying various density, closeness and centrality measures and exploring core-periphery structures. Genes affected by a larger number of perturbations were found to be involved in responses to various environmental challenges. Deletion of essential genes was suggested to cause larger number of genes to be significantly up or down regulated. We explored the network structure made up of several sub-networks using core-periphery models to find ancient pathways. Glycolysis and TCA cycle have relatively core positions in the energy-related processes of yeast.
6065142
On effective data clustering in bitemporal databases
Temporal databases provide built-in supports for efficient recording and querying of time-evolving data. In this paper, data clustering issues in temporal database environment are addressed. Data clustering is one of the most effective techniques that can improve performance of a database system. However, data clustering methods for conventional databases do not perform well in temporal databases because there exist crucial differences between their query patterns. We propose a data clustering measure, called Temporal Affinity, that can be used for the clustering of temporal data. The temporal affinity, which is based on the analysis of query patterns in temporal databases, reflects the closeness of temporal data objects in viewpoints of temporal query processing. We perform experiments to evaluate the proposed measure. The experimental results show that a data clustering method with the temporal affinity works better than other methods.
5105796
Two-stage normalization using background intensities in cDNA microarray data
BackgroundIn the microarray experiment, many undesirable systematic variations are commonly observed. Normalization is the process of removing such variation that affects the measured gene expression levels. Normalization plays an important role in the earlier stage of microarray data analysis. The subsequent analysis results are highly dependent on normalization. One major source of variation is the background intensities. Recently, some methods have been employed for correcting the background intensities. However, all these methods focus on defining signal intensities appropriately from foreground and background intensities in the image analysis. Although a number of normalization methods have been proposed, no systematic methods have been proposed using the background intensities in the normalization process.ResultsIn this paper, we propose a two-stage method adjusting for the effect of background intensities in the normalization process. The first stage fits a regression model to adjust for the effect of background intensities and the second stage applies the usual normalization method such as a nonlinear LOWESS method to the background-adjusted intensities. In order to carry out the two-stage normalization method, we consider nine different background measures and investigate their performances in normalization. The performance of two-stage normalization is compared to those of global median normalization as well as intensity dependent nonlinear LOWESS normalization. We use the variability among the replicated slides to compare performance of normalization methods.ConclusionsFor the selected background measures, the proposed two-stage normalization method performs better than global or intensity dependent nonlinear LOWESS normalization method. Especially, when there is a strong relationship between the background intensity and the signal intensity, the proposed method performs much better. Regardless of background correction methods used in the image analysis, the proposed two-stage normalization method can be applicable as long as both signal intensity and background intensity are available.