Bridging XML and Relational Databases: An Effective Mapping Scheme based on Persistent

XML has emerged as the leading medium for data transfer over the World Wide Web. At the present days, relational database is still widely used as the back-end database in most organizations. Since there is mismatch in these two structures, an effective mapping scheme is definitely essential that provides seamless integration with relational databases. On the other hand, an immutable labeling scheme is certainly significant to dentify the XML nodes uniquely as well as supports dynamic update without having the existing labels to be re-labeled when there is an occurance of dynamic update. As such, in this paper, we propose s-XML by adopting the Persistent Labeling scheme as the annotation scheme to ensure seamless integration with relational database and able to support updates without the need to re-construct the existing labels. We conduct experiments to show that s-XML performs better in terms of mapping the XML nodes to relational databases, query retrieval and dynamic update compared to the existing approaches.DOI:http://dx.doi.org/10.11591/ijece.v2i2.21

INTEGRATED SOLUTION SCHEME FOR HANDOVER LATENCY DIMINUTION IN PROXY MOBILE IPV6

ABSTRAC

The HARX-GJR-GARCH skewed-t multipower realized volatility modelling for S&P 500

The heterogeneous autoregressive (HAR) models are used in modeling high frequency multipower realized volatility of the S&P 500 index. Extended from the standard realized volatility, the multipower realized volatility representations have the advantage of handling the possible abrupt jumps by smoothing the consecutive volatility. In order to accommodate clustering volatility and asymmetric of multipower realized volatility, the HAR model is extended by the threshold autoregressive conditional heteroscedastic (GJR-GARCH) component. In addition, the innovations of the multipower realized volatility are characterized by the skewed student-t distributions. The extended model provides the best performing in-sample and out-of-sample forecast evaluations

Exploration of Road Traffic Tweets for Congestion Monitoring

Online social network services such as Twitter and Facebook have gained popularity in recent years with continuous increase of users. This is especially true for Twitter, a popular micro-blogging service that enables users to send tweets which contain valuable data in real-time. Real-time tweets information can be used in many areas and one of the least explored areas is crowdsourcing of road traffic conditions. We have found that not many people tweet about traffic conditions; however, there are formal sources that keep their accounts updated with the latest traffic info. In this paper, we present an analysis of tweets that are related to the traffic conditions in Malaysia. Detailed analysis was conducted to understand the structures and the nature of the traffic tweets. Based on our analysis, we found that the real-time nature of the tweets is useful in reporting road traffic conditions and such information will be useful to the road-user

Differencing techniques in semi-parametric panel data varying coefficient models with fixed effects: a Monte Carlo study.

Recently, some new techniques have been proposed for the estimation of semi-parametric fixed effects varying coefficient panel data models. These new techniques fall within the class of the so-called differencing estimators. In particular, we consider first-differences and within local linear regression estimators. Analyzing their asymptotic properties it turns out that, keeping the same order of magnitude for the bias term, these estimators exhibit different asymptotic bounds for the variance. In both cases, the consequences are suboptimal non-parametric rates of convergence. In order to solve this problem, by exploiting the additive structure of this model, a one-step backfitting algorithm is proposed. Under fairly general conditions, it turns out that the resulting estimators show optimal rates of convergence and exhibit the oracle efficiency property. Since both estimators are asymptotically equivalent, it is of interest to analyze their behavior in small sample sizes. In a fully parametric context, it is well-known that, under strict exogeneity assumptions the performance of both first-differences and within estimators is going to depend on the stochastic structure of the idiosyncratic random errors. However, in the non-parametric setting, apart from the previous issues other factors such as dimensionality or sample size are of great interest. In particular, we would be interested in learning about their relative average mean square error under different scenarios. The simulation results basically confirm the theoretical findings for both local linear regression and one-step backfitting estimators. However, we have found out that within estimators are rather sensitive to the size of number of time observations

Simulated evaluation of the impact on inter-domain and intra-domain handover performance

With the rapid developments and convergence in the communication and personal computing technology fields, the overall quality of ubiquitous communication has improved significantly. This is especially true with invention of portable mobile devices that can be connected almost everywhere at any time. However, the recent explosion on the usage of mobile devices has also generated several issues in terms of performance and quality of service. Nowadays, mobile users demand high quality performance, best quality of services and seamless connections that support real-time application such as audio and video streaming. The aim of this paper is to study the impact and evaluate the inter-domain and intra-domain protocols on network layer handover performance. We conducted simulations to analyze the relationship between the network performances with the moving speed of mobile host over mobility protocols. From our simulation results, we presented and analyzed the results of mobility protocols under intra-domain and inter-domain traffics