Investigation into Indexing XML Data Techniques

The rapid development of XML technology improves the WWW, since the XML data has many advantages and has become a common technology for transferring data cross the internet. Therefore, the objective of this research is to investigate and study the XML indexing techniques in terms of their structures. The main goal of this investigation is to identify the main limitations of these techniques and any other open issues. Furthermore, this research considers most common XML indexing techniques and performs a comparison between them. Subsequently, this work makes an argument to find out these limitations. To conclude, the main problem of all the XML indexing techniques is the trade-off between the size and the efficiency of the indexes. So, all the indexes become large in order to perform well, and none of them is suitable for all users’ requirements. However, each one of these techniques has some advantages in somehow

Intuitionistic fuzzy XML query matching and rewriting

With the emergence of XML as a standard for data representation, particularly on the web, the need for intelligent query languages that can operate on XML documents with structural heterogeneity has recently gained a lot of popularity. Traditional Information Retrieval and Database approaches have limitations when dealing with such scenarios. Therefore, fuzzy (flexible) approaches have become the predominant. In this thesis, we propose a new approach for approximate XML query matching and rewriting which aims at achieving soft matching of XML queries with XML data sources following different schemas. Unlike traditional querying approaches, which require exact matching, the proposed approach makes use of Intuitionistic Fuzzy Trees to achieve approximate (soft) query matching. Through this new approach, not only the exact answer of a query, but also approximate answers are retrieved. Furthermore, partial results can be obtained from multiple data sources and merged together to produce a single answer to a query. The proposed approach introduced a new tree similarity measure that considers the minimum and maximum degrees of similarity/inclusion of trees that are based on arc matching. New techniques for soft node and arc matching were presented for matching queries against data sources with highly varied structures. A prototype was developed to test the proposed ideas and it proved the ability to achieve approximate matching for pattern queries with a number of XML schemas and rewrite the original query so that it obtain results from the underlying data sources. This has been achieved through several novel algorithms which were tested and proved efficiency and low CPU/Memory cost even for big number of data sources

Efficient processing of multiple XML twig queries

Master'sMASTER OF SCIENC

New Path Based Index Structure for Processing CAS Queries over XML Database

Querying nested data has become one of the most challenging issues for retrieving desired information from the Web. Today diverse applications generate a tremendous amount of data in different formats. These data and information exchanged on the Web are commonly expressed as nested representation such as XML, JSON, etc. Unlike the traditional database system, they don\u27t have a rigid schema. In general, the nested data is managed by storing data and its structures separately which significantly reduces the performance of data retrieving. Ensuring efficiency of processing queries which locates the exact positions of the elements has become a big challenging issue. There are different indexing structures which have been proposed in the literature to improve the performance of the query processing on the nested structure. Most of the past researches on nested structure concentrate on the structure alone. This paper proposes new index structure which combines siblings of the terminal nodes as one path which efficiently processes twig queries with less number of lookups and joins. The proposed approach is compared with some of the existing approaches. The results also show that they are processed with better performance compared to the existing ones

Child Prime Label Approaches to Evaluate XML Structured Queries

The adoption of the eXtensible Markup Language (XML) as the standard format to store and exchange semi-structure data has been gaining momentum. The growing number of XML documents leads to the need for appropriate XML querying algorithms which are able to retrieve XML data efficiently. Due to the importance of twig pattern matching in XML retrieval systems, finding all matching occurrences of a tree pattern query in an XML document is often considered as a specific task for XML databases as well as a core operation in XML query processing. This thesis presents a design and implementation of a new indexing technique, called the Child Prime Label (CPL) which exploits the property of prime numbers to identify Parent-Child (P-C) edges in twig pattern queries (TPQs) during query evaluation. The CPL approach can be incorporated efficiently within the existing labelling schemes. The major contributions of this thesis can be seen as a set of novel twig matching algorithms which apply the CPL approach and focus on reducing the overhead of storing useless elements and performing unnecessary computations during the output enumeration. The research presented here is the first to provide an efficient and general solution for TPQs containing ordering constraints and positional predicates specified by the XML query languages. To evaluate the CPL approaches, the holistic model was implemented as an experimental prototype in which the approaches proposed are compared against state-of-the-art holistic twig algorithms. Extensive performance studies on various real-world and artificial datasets were conducted to demonstrate the significant improvement of the CPL approaches over the previous indexing and querying methods. The experimental results demonstrate the validity and improvements of the new algorithms over other related methods on common various subclasses of TPQs. Moreover, the scalability tests reveal that the new algorithms are more suitable for processing large XML datasets

Efficient processing of XML twig pattern matching

Ph.DDOCTOR OF PHILOSOPH

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)

Teak: A Novel Computational And Gui Software Pipeline For Reconstructing Biological Networks, Detecting Activated Biological Subnetworks, And Querying Biological Networks.

As high-throughput gene expression data becomes cheaper and cheaper, researchers are faced with a deluge of data from which biological insights need to be extracted and mined since the rate of data accumulation far exceeds the rate of data analysis. There is a need for computational frameworks to bridge the gap and assist researchers in their tasks. The Topology Enrichment Analysis frameworK (TEAK) is an open source GUI and software pipeline that seeks to be one of many tools that fills in this gap and consists of three major modules. The first module, the Gene Set Cultural Algorithm, de novo infers biological networks from gene sets using the KEGG pathways as prior knowledge. The second and third modules query against the KEGG pathways using molecular profiling data and query graphs, respectively. In particular, the second module, also called TEAK, is a network partitioning module that partitions the KEGG pathways into both linear and nonlinear subpathways. In conjunction with molecular profiling data, the subpathways are ranked and displayed to the user within the TEAK GUI. Using a public microarray yeast data set, previously unreported fitness defects for dpl1 delta and lag1 delta mutants under conditions of nitrogen limitation were found using TEAK. Finally, the third module, the Query Structure Enrichment Analysis framework, is a network query module that allows researchers to query their biological hypotheses in the form of Directed Acyclic Graphs against the KEGG pathways

Algorithms for XML filtering

In a publish-subscribe system based on XML filtering, the subscriber profiles are usually specified by filters written in the XPath language. The system processes the stream of XML documents and delivers to subscribers a notification or the content of those documents that match the filters. The number of interested subscribers and their stored profiles can be very large, thousands or even millions. In this case, the scalability of the system is critical. In this thesis, we develop several algorithms for XML filtering with linear XPath expressions. The algorithms are based on a backtracking Aho-Corasick pattern-matching automaton (PMA) built from "keywords" extracted from the filters, where a keyword is a maximal substring consisting only of XML element names. The output function of the PMA indicates which keyword occurrences of which filter are recognized at a given state. Our best results have been obtained by using a dynamically changing output function, which is dynamically updated during the processing of the input document. We have conducted an extensive performance study in which we compared our filtering algorithms with YFilter and the lazy DFA, two well-known automata-based filtering methods. With a non-recursive XML data set, PMA-based filtering is tens of times more efficient than YFilter and also significantly more efficient than the lazy DFA. With a slightly recursive data set PMA-based filtering has the same performance as the lazy DFA and it is significantly more efficient than YFilter. We have also developed an optimization method called filter pruning. This method improves the performance of filtering by utilizing knowledge about the XML document type definition (DTD) to simplify the filters. The optimization algorithm takes as input a DTD and a set of linear XPath filters and produces a set of pruned linear XPath filters that contain as few wildcards and descendant operators as possible. With a non-recursive data set and with a slightly recursive data set the filter-pruning method yielded a tenfold increase in the filtering speed of the PMA-based algorithms and a hundredfold increase with YFilter and the lazy DFA. Filter pruning can also increase the filtering speed in the case of highly recursive data sets