Improving the support for XML dynamic updates using a hybridization labeling scheme (ORD-GAP)

Background: As the standard for the exchange of data over the World Wide Web, it is important to ensure that the eXtensible Markup Language (XML) database is capable of supporting not only efficient query processing but also capable of enduring frequent data update operations over the dynamic changes of Web content. Most of the existing XML annotation is based on a labeling scheme to identify each hierarchical position of the XML nodes. This computation is costly as any updates will cause the whole XML tree to be re-labelled. This impact can be observed on large datasets. Therefore, a robust labeling scheme that avoids re-labeling is crucial. Method: Here, we present ORD-GAP (named after Order Gap), a robust and persistent XML labeling scheme that supports dynamic updates. ORD-GAP assigns unique identifiers with gaps in-between XML nodes, which could easily identify the level, Parent-Child (P-C), Ancestor-Descendant (A-D) and sibling relationship. ORD-GAP adopts the OrdPath labeling scheme for any future insertion. Results: We demonstrate that ORD-GAP is robust enough for dynamic updates, and have implemented it in three use cases: (i) left-most, (ii) in-between and (iii) right-most insertion. Experimental evaluations on DBLP dataset demonstrated that ORD-GAP outperformed existing approaches such as ORDPath and ME Labeling concerning database storage size, data loading time and query retrieval. On average, ORD-GAP has the best storing and query retrieval time. Conclusion: The main contributions of this paper are: (i) A robust labeling scheme named ORD-GAP that assigns certain gap between each node to support future insertion, and (ii) An efficient mapping scheme, which built upon ORD-GAP labeling scheme to transform XML into RDB effectively.

A CSP-Based Approach for Managing the Dynamic Reconfiguration of Software Architecture

Software applications are composed of a set of interconnected software components running on different machines. Most of these applications have a dynamic nature and need to reconfigure structure and behavior at run-time. The dynamic reconfiguration of software is a problem that must be dealt with. Reconfiguring this kind of applications is a complicated task and risks to take software at an undesirable situation. In this paper, the authors present a solution whose objective is to provide a complete support for reconfiguring and formally verifying consistency of dynamic updates of software before performing them. The aim is to provide highly available systems with the ability to safely modify their structure and behavior at run-time. The proposed approach is based mainly on the use of the meta-model concept for reconfiguration structural checking, and the CSP language, refinement technique, and the FDR model checking tool for the verification of reconfiguration behavioral consistency. The authors have also developed a tool prototype that validates and implements their proposals.

Classifying a Lending Portfolio of Loans with Dynamic Updates via a Machine Learning Technique

Bankruptcy prediction has been broadly investigated using financial ratios methodologies. One involved factor is the quality of the portfolio of loans which is given. Hence, having a model to classify/predict position of each loan candidate based on several features is important. In this work, an application of machine learning approach in mathematical finance and banking is discussed. It is shown how we can classify some lending portfolios of banks under several features such as rating categories and various maturities. Dynamic updates of the portfolio are also given along with the top probabilities showing how the financial data of this type can be classified. The discussions and results reveal that a good algorithm for doing such a classification on large economic data of such type is the k-nearest neighbors (KNN) with k=1 along with parallelization even over the support vector machine, random forest, and artificial neural network techniques to save as much as possible on computational time.

ORD-GAP: a hybrid-based labeling schemes to support XML dynamic updates

<span>eXtensible Markup Language (XML) has been widely used as the standard for data exchange standard over the Internet. With the fast growing rate of data, especially with high updates, it is crucial to ensure that the XML is able to cope with frequent changes with very least effect on the existing structure. Therefore, in this paper, we investigate on the existing labeling schemes and mapping approaches to gauge a better understanding in terms of the robustness of the labeling schemes and the importance of the mapping schemes. Next, we propose ORD-GAP labeling schemes to identify the structural relationship among XML nodes and yet, it is persistent to re-labeling when new nodes are inserted. Subsequently, a mapping scheme is proposed to transform XML into Relational Database (RDB). Preliminary experimental evaluation demonstrated that the proposed approach achieve 66% better as compared to ORDPATH, and 56% better as compared to ME labeling in terms of data loading time. </span>