Replica maintenance strategy for data grid

Data Grid is an infrastructure that manages huge amount of data files, and provides intensive computational resources across geographically distributed collaboration.Increasing the performance of such system can be achieved by improving the overall resource usage, which includes network and storage resources.Improving network resource usage is achieved by good utilization of network bandwidth that is considered as an important factor affecting job execution time.Meanwhile, improving storage resource usage is achieved by good utilization of storage space usage. Data replication is one of the methods used to improve the performance of data access in distributed systems by replicating multiple copies of data files in the distributed sites.Having distributed the replicas to various locations, they need to be monitored.As a result of dynamic changes in the data grid environment, some of the replicas need to be relocated.In this paper we proposed a maintenance replica placement strategy termed as Unwanted Replica Deletion Strategy (URDS) as a part of Replica maintenance service.The main purpose of the proposed strategy is to find the placement of unwanted replicas to be deleted.OptorSim is used to evaluate the performance of the proposed strategy. The simulation results show that URDS requires less execution time and consumes less network usage and has a best utilization of storage space usage compared to existing approaches

Data Replication-Based Scheduling in Cloud Computing Environment

Abstract— High-performance computing and vast storage are two key factors required for executing data-intensive applications. In comparison with traditional distributed systems like data grid, cloud computing provides these factors in a more affordable, scalable and elastic platform. Furthermore, accessing data files is critical for performing such applications. Sometimes accessing data becomes a bottleneck for the whole cloud workflow system and decreases the performance of the system dramatically. Job scheduling and data replication are two important techniques which can enhance the performance of data-intensive applications. It is wise to integrate these techniques into one framework for achieving a single objective. In this paper, we integrate data replication and job scheduling with the aim of reducing response time by reduction of data access time in cloud computing environment. This is called data replication-based scheduling (DRBS). Simulation results show the effectiveness of our algorithm in comparison with well-known algorithms such as random and round-robin

Aesthetic preference for art emerges from a weighted integration over hierarchically structured visual features in the brain

It is an open question whether preferences for visual art can be lawfully predicted from the basic constituent elements of a visual image. Moreover, little is known about how such preferences are actually constructed in the brain. Here we developed and tested a computational framework to gain an understanding of how the human brain constructs aesthetic value. We show that it is possible to explain human preferences for a piece of art based on an analysis of features present in the image. This was achieved by analyzing the visual properties of drawings and photographs by multiple means, ranging from image statistics extracted by computer vision tools, subjective human ratings about attributes, to a deep convolutional neural network. Crucially, it is possible to predict subjective value ratings not only within but also across individuals, speaking to the possibility that much of the variance in human visual preference is shared across individuals. Neuroimaging data revealed that preference computations occur in the brain by means of a graded hierarchical representation of lower and higher level features in the visual system. These features are in turn integrated to compute an overall subjective preference in the parietal and prefrontal cortex. Our findings suggest that rather than being idiosyncratic, human preferences for art can be explained at least in part as a product of a systematic neural integration over underlying visual features of an image. This work not only advances our understanding of the brain-wide computations underlying value construction but also brings new mechanistic insights to the study of visual aesthetics and art appreciation

STUDENT ACADEMIC PERFORMANCE IN SKILLS-BASED TECHNOLOGY COURSES DELIVERED THROUGH DIFFERENT SCHEDULING FORMATS

This descriptive study investigated student academic performance in skills-based word processing courses taught in two different scheduling formats at one small rural western United States university over the period of several years. One scheduling format followed a more traditional approach where courses were taken at the same time as at least one other course and in a time frame more resembling a typical semester. This distributed practice model, or cohort approach, required a prerequisite beginning level course or appropriate substitute course before enrolling in an advanced word processing course, thus spreading the instructional time over a longer timeframe. The other scheduling format allowed students to take only one course at a time, thus a massed practice model, in a compressed time format that presented the contents of the entire course in 18 instructional days. Student academic performance was measured by a subset of equivalent posttest questions that were common to both scheduling formats. Retention performance during the cohort approach was measured by a subset of equivalent questions common to the beginning and advanced cohort courses. The entire population of word processing students at this university was studied and thus there is no generalizability from this study to another population. Participants self-selected into groups by enrolling in course sections. Simple means were used to compute descriptive and comparative statistics. The distributed practice cohort group out-performed the massed practice group by an experimentally important five percent on the posttest. Results from the retention portion of the study indicate additional research is needed

Automatic mapping of XML documents into relational database

Extensible Markup Language (XML) nowadays is one of the most important standard media used for exchanging and representing data through the Internet. Storing, updating and retrieving the huge amount of web services data such as XML is an attractive area of research for researchers and database vendors. In this thesis, we propose and develop a new mapping model, called MAXDOR, for storing, rebuilding, updating and querying XML documents using a relational database without making use of any XML schemas in the mapping process. The model addressed the problem of solving the structural hole between ordered hierarchical XML and unordered tabular relational database to enable us to use relational database systems for storing, updating and querying XML data. A multiple link list is used to maintain XML document structure, manage the process of updating document contents and retrieve document contents efficiently. Experiments are done to evaluate MAXDOR model. MAXDOR will be compared with other well-known models available in the literature(Tatarinov et al., 2002) and (Torsten et al., 2004) using total expected value of rebuilding XML document execution time and insertion of token execution time.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

31. međunarodna konferencija Very Large Data Bases

Dana je vijest o održanoj 31. međunarodnoj konferenciji Very Large Data Bases

31. međunarodna konferencija Very Large Data Bases

Dana je vijest o održanoj 31. međunarodnoj konferenciji Very Large Data Bases

High-Density Diffuse Optical Tomography During Passive Movie Viewing: A Platform for Naturalistic Functional Brain Mapping

Human neuroimaging techniques enable researchers and clinicians to non-invasively study brain function across the lifespan in both healthy and clinical populations. However, functional brain imaging methods such as functional magnetic resonance imaging (fMRI) are expensive, resource-intensive, and require dedicated facilities, making these powerful imaging tools generally unavailable for assessing brain function in settings demanding open, unconstrained, and portable neuroimaging assessments. Tools such as functional near-infrared spectroscopy (fNIRS) afford greater portability and wearability, but at the expense of cortical field-of-view and spatial resolution. High-Density Diffuse Optical Tomography (HD-DOT) is an optical neuroimaging modality directly addresses the image quality limitations associated with traditional fNIRS techniques through densely overlapping optical measurements. This thesis aims to establish the feasibility of using HD-DOT in a novel application demanding exceptional portability and flexibility: mapping disrupted cortical activity in chronically malnourished children. I first motivate the need for dense optical measurements of brain tissue to achieve fMRI-comparable localization of brain function (Chapter 2). Then, I present imaging work completed in Cali, Colombia, where a cohort of chronically malnourished children were imaged using a custom HD-DOT instrument to establish feasibility of performing field-based neuroimaging in this population (Chapter 3). Finally, in order to meet the need for age appropriate imaging paradigms in this population, I develop passive movie viewing paradigms for use in optical neuroimaging, a flexible and rich stimulation paradigm that is suitable for both adults and children (Chapter 4)

Mining a Small Medical Data Set by Integrating the Decision Tree and t-test

[[abstract]]Although several researchers have used statistical methods to prove that aspiration followed by the injection of 95% ethanol left in situ (retention) is an effective treatment for ovarian endometriomas, very few discuss the different conditions that could generate different recovery rates for the patients. Therefore, this study adopts the statistical method and decision tree techniques together to analyze the postoperative status of ovarian endometriosis patients under different conditions. Since our collected data set is small, containing only 212 records, we use all of these data as the training data. Therefore, instead of using a resultant tree to generate rules directly, we use the value of each node as a cut point to generate all possible rules from the tree first. Then, using t-test, we verify the rules to discover some useful description rules after all possible rules from the tree have been generated. Experimental results show that our approach can find some new interesting knowledge about recurrent ovarian endometriomas under different conditions.[[journaltype]]國外[[incitationindex]]EI[[booktype]]紙本[[countrycodes]]FI