TwigStackPrime: A Novel Twig Join Algorithm Based on Prime Numbers

The growing number of XML documents leads to the need for appropriate XML querying algorithms which are able to utilize the specific characteristics of XML documents. A labelling scheme is fundamental to processing XML queries efficiently. They are used to determine structural relationships between elements corresponding to query nodes in twig pattern queries (TPQs). This article presents a design and implementation of a new indexing technique which exploits the property of prime numbers to identify Parent-Child (P-C) relationships in TPQs during query evaluation. The Child Prime Label (CPL, for short) approach can be efficiently incorporated within the existing labelling schemes. Here, we propose a novel twig matching algorithm based on the well known TwigStack algorithm [3], which applies the CPL approach and focuses on reducing the overhead of storing useless elements and performing unnecessary join operations. Our performance evaluation demonstrates that the new algorithm significantly outperforms the previous approaches

Solving the intractable problem: optimal performance for worst case scenarios in XML twig pattern matching

In the history of databases, eXtensible Markup Language (XML) has been thought of as the standard format to store and exchange semi-structured data. With the advent of IoT, XML technologies can play an important role in addressing the issue of processing a massive amount of data generated from heterogeneous devices. As the number and complexity of such datasets increases there is a need for algorithms which are able to index and retrieve XML data efficiently even for complex queries. In this context twig pattern matching , finding all occurrences of a twig pattern query (TPQ), is a core operation in XML query processing. Until now holistic joins have been considered the state-of-the-art TPQ processing algorithms, but they fail to guarantee an optimal evaluation except at the expense of excessive storage costs which limit their scope in large datasets. In this article, we introduce a new approach which significantly outperforms earlier methods in terms of both the size of the intermediate storage and query running time. The approach presented here uses Child Prime Labels (Alsubai & North, 2018) to improve the filtering phase of bottom-up twig matching algorithms and a novel algorithm which avoids the use of stacks, thus improving TPQs processing efficiency. Several experiments were conducted on common benchmarks such as DBLP, XMark and TreeBank datasets to study the performance of the new approach. Multiple analyses on a range of twig pattern queries are presented to demonstrate the statistical significance of the improvements

A survey on tree matching and XML retrieval

International audienceWith the increasing number of available XML documents, numerous approaches for retrieval have been proposed in the literature. They usually use the tree representation of documents and queries to process them, whether in an implicit or explicit way. Although retrieving XML documents can be considered as a tree matching problem between the query tree and the document trees, only a few approaches take advantage of the algorithms and methods proposed by the graph theory. In this paper, we aim at studying the theoretical approaches proposed in the literature for tree matching and at seeing how these approaches have been adapted to XML querying and retrieval, from both an exact and an approximate matching perspective. This study will allow us to highlight theoretical aspects of graph theory that have not been yet explored in XML retrieval

Optimizing cursor movement in holistic twig joins

Holistic twig join algorithms represent the state of the art for evaluating path expressions in XML queries. Using inverted in-dexes on XML elements, holistic twig joins move a set of index cursors in a coordinated way to quickly ¯nd structural matches. Because each cursor move can trigger I/O, the performance of a holistic twig join is largely determined by how many cursor moves it makes, yet, surprisingly, existing join algorithms have not been optimized along these lines. In this paper, we describe TwigOptimal, a new holistic twig join algorithm with optimal cur-sor movement. We sketch the proof of TwigOptimal's optimality, and describe how TwigOptimal can use information in the return clause of XQuery to boost its performance. Finally, experimen-tal results are presented, showing TwigOptimal's superiority over existing holistic twig join algorithms

Ranking clustered Keyword Search On Semi structured data

No Abstrac

Reasoning & Querying – State of the Art

Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF

Efficient processing of multiple XML twig queries

Master'sMASTER OF SCIENC

A Labeling DOM-Based Tree Walking Algorithm for Mapping XML Documents into Relational Databases

XML has emerged as the standard format for representing and exchanging data on the World Wide Web. For practical purposes, it is found to be critical to have efficient mechanisms to store and query XML data, as well as to exploit the full power of this new technology. Several researchers have proposed to use relational databases to store and query XML data. With the understanding the limitations of current approaches, this thesis aims to propose an algorithm for automatic mapping XML documents to RDBMS with XML-API as a database utility. The algorithm uses best fit auto mapping technique, and dynamic shredding, of a specified selected XML document type (datacentric, document-centric, and mixed documents).e. The propose algorithm use DOM(Data Object Model) as a warehouse and stack as a data structure to mapping the XML document into relational database and reconstructing the XML document from the relational database. The experiment study show that the algorithm mapping document and reconstructing it again well. Finally, the algorithm compare with other algorithms the result is good in time and efficiency, also the algorithm complexity is O(11n+2)