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XML has emerged as the standard for information representation over the Internet. However, most enterprises today have long secured the use of relational databases. Thus, it is crucial to map XML data into relational data to provide seamless integration between these database infrastructures. Many mapping techniques have been proposed, yet, none has provides a unified view on these techniques. Ultimately, understanding how these techniques work is important especially if one needs to decide which technique to adopt in their organization. This paper (i) reviews on some existing model-based mapping schemes focusing on how the mapping technique works, the advantages and the disadvantages, (ii) present the simulation engine to evaluate the performance of selected mapping schemes, (iii) highlight the future direction of the related area.

Water vapor is an important constituent of any gas and in many applications is regarded as a contaminant that needs to be monitored and controlled. AIN-CuO composites (2 % ≤ CuO ≤ 50 % by weight) have been studied to exploit them as novel humidity sensors over wide ranges of moisture levels and temperature. Development of benign microstructure with open porosity has been attempted by varying the composition and firing conditions. The impedance data acquired on the composites over the frequency range 5 Hz to 13 MHz, revealed a bulk response in terms of a single semicircular relaxation in the complex Z*-plane. A systematic variation of electrical conductivity with CuO content in the composites has been explained in the light of percolation theory.

XML has emerged as the standard for information representation over the Internet. It is critical to store and query XML data to exploit the full power of the new technology. However, most enterprises today have long secured the use of relational databases. Thus, simply replacing relational databases with a pure XML database is not a good choice. It is thus crucial to map XML data into relational data. This paper studies the performance evaluation of storing XML documents into relational databases and identifies which mapping approach is best suited for which business environment. The performance results for all approaches are presented and a number of interesting results obtained from these evaluations are highlighted.

With the rapid emergence of XML as a data exchange and data transfer medium over the Web, querying XML data has become a major concern. Labeling schemes have been developed to optimize query retrieval, since they provide a quick way to determine the type of relationships that are present among the nodes. In this paper, we analyze how each approach works, as well as its advantages and disadvantages. In addition, we discuss some of the current trends in labeling methods, which indicate a clear shift towards hybrid approaches. Hybrid systems open the possibility of balancing one technology’s weakness with another technology’s strengths.

XML (extensible mark-up language) has emerged as one of the popular data representation standards for information storage and exchange. In this paper, we propose an extended INLAB architecture, INLAB2, focusing on preprocessing the XML document for fast native storage and accurate query retrieval. Firstly, we propose our xParse parser to check the well-formedness of an XML document. Next, we use a ( self-end) labeling scheme to encode each element in the XML database, by its positional information, to establish parent-child (P-C) or ancestor-descendant (A-D) relationships between nodes. Subsequently, our TwigINLAB2 algorithm is used to optimize query retrieval. TwigINLAB2 is a generalization of TwigStack, the stack-based algorithm for matching twig query. However, the TwigStack algorithm is efficient for A-D relationship queries only. Thus, in order to overcome this limitation, we enhance query retrieval by utilizing indices to speed up the matching and merging phases. Experimental results indicat...