Shilong Ma

A fuzzy TOPSIS model via chi-square test for information source selection

The Information Source (IS) selection involves various aspects with different requirements under indeterminate conditions. It is such a complicated process pertaining to seeking for the most appropriate solution that how to resolve the constraint resources needs to be congruously considered. This paper proposes a Multi-Criteria Group Decision Making (MCGDM) model, which uniforms the quantitative and qualitative factual value of different attributes with trapezoidal fuzzy numbers. Analytic Hierarchy Process (AHP) and Entropy Weights (EW) are integrated to alleviate the conflicts by experts intuitions and provide the accurate weight vector in this model. Besides, the Euclidean Distance (ED) is substituted by the Value of Chi-Square Test (VCST) to refine the Relative Closeness (RC), which theoretically excluded the potential bias arising from relative importance of the two types of distances, in a revised Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). The optimal recommendation compromises in a social decision making way. Finally, the software named Evaluator, which is based on the presented model, is illustrated to show how it can be practically used for IS selection with comparative analysis.

Missing data analyses: a hybrid multiple imputation algorithm using Gray System Theory and entropy based on clustering

Researchers and practitioners who use databases usually feel that it is cumbersome in knowledge discovery or application development due to the issue of missing data. Though some approaches can work with a certain rate of incomplete data, a large portion of them demands high data quality with completeness. Therefore, a great number of strategies have been designed to process missingness particularly in the way of imputation. Single imputation methods initially succeeded in predicting the missing values for specific types of distributions. Yet, the multiple imputation algorithms have maintained prevalent because of the further promotion of validity by minimizing the bias iteratively and less requirement on prior knowledge to the distributions.This article carefully reviews the state of the art and proposes a hybrid missing data completion method named Multiple Imputation using Gray-system-theory and Entropy based on Clustering (MIGEC). Firstly, the non-missing data instances are separated into several clusters. Then, the imputed value is obtained after multiple calculations by utilizing the information entropy of the proximal category for each incomplete instance in terms of the similarity metric based on Gray System Theory (GST).Experimental results on University of California Irvine (UCI) datasets illustrate the superiority of MIGEC to other current achievements on accuracy for either numeric or categorical attributes under different missing mechanisms. Further discussion on real aerospace datasets states MIGEC is also applicable for the specific area with both more precise inference and faster convergence than other multiple imputation methods in general.

Combinational backfilling for parallel job scheduling

FCFS is the most simple, basic and commonly used method of job scheduling in clusters. Backfilling scheduling that small jobs are moved ahead in the schedule can fill the resources gap that is generated by FCFS. However, existing backfilling scheduling algorithms are available for a queued job backfilled to schedule. The resources gap cant be fully utilized. A method of combinational backfilling for parallel job scheduling in clusters is proposed. It can select multiple jobs combined from the waiting job queue to backfill to maximize the use of idle resources. A comparison with FCFS, EASY backfilling algorithm is given. The results of experiments show that the algorithm proposed can attain the higher utilization of resources in the system than FCFS, EASY backfilling.

Gird or cloud? Survey on scientific computing infrastructure

Recently, scientific computing not only brings benefits for science but also brings new challenges for scientists. Therefore, scientists have to face headache “4 how” problems, which are how to know, organize, use and guarantee resources, and as the solution, scientific computing infrastructure should coordinate and organize kinds of capabilities effectively, so as to provide on-demand and safe computing environment for scientists. So, this paper concludes the related research about scientific computing infrastructure in recent years, and base on that systematically analyze the primary demands of infrastructure, and moreover, we propose Capability Meta Model-CMM as the foundation model for kinds of infrastructure; and then, based on CMM we compare kinds of different infrastructure, including grid and cloud computing, from resources, knowledge, quality and security these four aspects, finally, we summarize the problems and challenges the current infrastructure is facing.

Clustering-Based Multiple Imputation via Gray Relational Analysis for Missing Data and Its Application to Aerospace Field

A large number of scientific researches and industrial applications commonly suffer from missing data. Some inappropriate techniques of missing value treatment compromise data quality, which detrimentally influences the knowledge discovery. In this paper, we propose a missing data completion method named CBGMI. Firstly, it separates the nonmissing data instances into several clusters by excluding the missing-valued entries. Then, it utilizes the entropy of the proximal category for each incomplete instance in terms of the similarity metric based on gray relational analysis. Experiments on UCI datasets and aerospace datasets demonstrate that the superiority of our algorithm to other approaches on validity.

An Attributes-Based Access Control Architecture within Large-Scale Device Collaboration Systems Using XACML

Containing multiple domains and a large number of heterogeneous distributed devices, large-scale device collaboration systems require a fine-grained, flexible and secure mechanism for device access control. This chapter presents and evaluates a distributed device access control architecture Multiple Policies supported Attribute-Based Access Control (MPABAC) to support device authentication and authorization among multiple domains. Based on eXtensible Access Control Markup Language (XACML) standard and Attribute-Based Access Control (ABAC) model, this architecture supports cross-domain authentication and authorization, hierarchical policy combination and enforcement, unified device access control and fine-grained attributes-based privilege description. Experiments show that the performance of this implementation is acceptable within the production environment.

A Scalable Data Acquisition Architecture in Web-Based IOT

The data acquisition system in Web-based IOT needs to consider not only the requirements of the daily routine analysis of the data, but also the requirements from the real-time reaction of an emergency, which presents a dilemma for system design. This paper presents scalable distributed data acquisition system architecture SCADA, which can extend the system capability temporally using extra resources to meet the demands. Based on the traditional Master-Worker model, the system makes uses of the executors for task execution and resource allocation and can execute the tasks efficiently. The experiment proves the scalability of SCADA.

Research on Awareness Driven Schedule for Sensors in Web-Based IOT

In smart IOT, sensor resources are organized to monitor events in real world for all time that may produce huge number of data. Due to difference of resources’ awareness of event, the valuable data may be flooded by dull data if task processes without discrimination. This paper introduced an approach, called Awareness Driven Schedule (ADS), which enables involved resources that provide differentiated data service on their capability of awareness. Upon ADS, a middleware, called SSRDSs is built for CAE’s SPON. As dull data could be banned out, applications in IOT may be more efficient.

An association-driven approach to composite services discovery

Nowadays, how to find composite Web services that meet users needs with high efficiency and accuracy has been the key issue in the service composition area. To solve this problem, we adopt service association mechanism. First, services with the same function and heterogeneous quality are gathered into one group. Each group has some associations with others according to their service function and the past execution experiences. With these associations stored in a association depository, we design an approach to not only target related service group quickly, which results in high performance, but also guarantee the success ratio. Experimental results show the effectiveness of the approach.

A meta-scheduling model with multi-agent coordination mechanism in multi-cluster

A multi-cluster computing environment is composed of many clusters which are distributed over different sites in an organization or an enterprise. All the clusters can be resource shared, autonomous, load balanced, interconnected and collaborative. However, there is still a problem that the uncertainty of the cluster resources caused difficulty in scheduling jobs effectively. To address this problem, a meta-scheduling model with multi-agent coordination mechanism in multi-cluster is presented. Agents are deployed in each cluster and communicated with coordinator. Coordinator can provide global job scheduling service, can access and monitor the state of all the clusters resources, do prediction and evaluation the performance of the resources. It can schedule the job to the fittest cluster according to the scheduling strategy and the evaluation results of the performance of the clusters. The model is applied to the national seismic network computing application system in china. The national seismic network computing platform is constructed by using the meta-scheduling model with multi-agent coordination. An example of seismic scientific computing application is given. The multi-agent coordination meta-scheduling model has been applied in practice and verified actually.