XML Reconstruction View Selection in XML Databases: Complexity Analysis and Approximation Scheme

Query evaluation in an XML database requires reconstructing XML subtrees rooted at nodes found by an XML query. Since XML subtree reconstruction can be expensive, one approach to improve query response time is to use reconstruction views - materialized XML subtrees of an XML document, whose nodes are frequently accessed by XML queries. For this approach to be efficient, the principal requirement is a framework for view selection. In this work, we are the first to formalize and study the problem of XML reconstruction view selection. The input is a tree $T$, in which every node $i$ has a size $c_i$ and profit $p_i$, and the size limitation $C$. The target is to find a subset of subtrees rooted at nodes $i_1,\cdots, i_k$ respectively such that $c_{i_1}+\cdots +c_{i_k}\le C$, and $p_{i_1}+\cdots +p_{i_k}$ is maximal. Furthermore, there is no overlap between any two subtrees selected in the solution. We prove that this problem is NP-hard and present a fully polynomial-time approximation scheme (FPTAS) as a solution

XML Vectorization: A Column-Based XML Storage Model

The usual method for storing tables in a relational database is to store each tuple contiguously in secondary storage. A simple alternative is to store the columns contiguously, so that a table is represented as a set of vectors all of the same length. It has been shown that such a representation performs well on queries requiring few columns. This paper reviews the shredding scheme used in XMill, an XML compressor, which represents the document structure by using a set of files, consisting of a file describing the structure, and files describing the character data to be found on designated paths (corresponding to the column data). We consider such a shredding as a storage model –- XML vectorization –- by presenting an indexing scheme and a physical algebra associated with a detailed cost model. We study query processing on the XML vectorization, in particular the XML join queries. XML join queries are often translated into a few relational join operations in the relational-based XML storage systems. The use of columns enables us to develop a fast join algorithm for vectorized XML based on two hashbased join algorithms. The important feature of the join algorithm is that the disk access of the algorithm is mostly sequential and the data not needed are not read from disk. Experimental results demonstrate the effectiveness of the join algorithm for vectorized XML

Archiving scientific data

We present an archiving technique for hierarchical data with key structure. Our approach is based on the notion of timestamps whereby an element appearing in multiple versions of the database is stored only once along with a compact description of versions in which it appears. The basic idea of timestamping was discovered by Driscoll et. al. in the context of persistent data structures where one wishes to track the sequences of changes made to a data structure. We extend this idea to develop an archiving tool for XML data that is capable of providing meaningful change descriptions and can also efficiently support a variety of basic functions concerning the evolution of data such as retrieval of any specific version from the archive and querying the temporal history of any element. This is in contrast to diff-based approaches where such operations may require undoing a large number of changes or significant reasoning with the deltas. Surprisingly, our archiving technique does not incur any significant space overhead when contrasted with other approaches. Our experimental results support this and also show that the compacted archive file interacts well with other compression techniques. Finally, another useful property of our approach is that the resulting archive is also in XML and hence can directly leverage existing XML tools

Anatomy of a Native XML Base Management System

Several alternatives to manage large XML document collections exist, ranging from file systems over relational or other database systems to specifically tailored XML repositories. In this paper we give a tour of Natix, a database management system designed from scratch for storing and processing XML data. Contrary to the common belief that management of XML data is just another application for traditional databases like relational systems, we illustrate how almost every component in a database system is affected in terms of adequacy and performance. We show how to design and optimize areas such as storage, transaction management comprising recovery and multi-user synchronisation as well as query processing for XML

Structural Summaries as a Core Technology for Efficient XML Retrieval

The Extensible Markup Language (XML) is extremely popular as a generic markup language for text documents with an explicit hierarchical structure. The different types of XML data found in today’s document repositories, digital libraries, intranets and on the web range from flat text with little meaningful structure to be queried, over truly semistructured data with a rich and often irregular structure, to rather rigidly structured documents with little text that would also fit a relational database system (RDBS). Not surprisingly, various ways of storing and retrieving XML data have been investigated, including native XML systems, relational engines based on RDBSs, and hybrid combinations thereof. Over the years a number of native XML indexing techniques have emerged, the most important ones being structure indices and labelling schemes. Structure indices represent the document schema (i.e., the hierarchy of nested tags that occur in the documents) in a compact central data structure so that structural query constraints (e.g., path or tree patterns) can be efficiently matched without accessing the documents. Labelling schemes specify ways to assign unique identifiers, or labels, to the document nodes so that specific relations (e.g., parent/child) between individual nodes can be inferred from their labels alone in a decentralized manner, again without accessing the documents themselves. Since both structure indices and labelling schemes provide compact approximate views on the document structure, we collectively refer to them as structural summaries. This work presents new structural summaries that enable highly efficient and scalable XML retrieval in native, relational and hybrid systems. The key contribution of our approach is threefold. (1) We introduce BIRD, a very efficient and expressive labelling scheme for XML, and the CADG, a combined text and structure index, and combine them as two complementary building blocks of the same XML retrieval system. (2) We propose a purely relational variant of BIRD and the CADG, called RCADG, that is extremely fast and scales up to large document collections. (3) We present the RCADG Cache, a hybrid system that enhances the RCADG with incremental query evaluation based on cached results of earlier queries. The RCADG Cache exploits schema information in the RCADG to detect cached query results that can supply some or all matches to a new query with little or no computational and I/O effort. A main-memory cache index ensures that reusable query results are quickly retrieved even in a huge cache. Our work shows that structural summaries significantly improve the efficiency and scalability of XML retrieval systems in several ways. Former relational approaches have largely ignored structural summaries. The RCADG shows that these native indexing techniques are equally effective for XML retrieval in RDBSs. BIRD, unlike some other labelling schemes, achieves high retrieval performance with a fairly modest storage overhead. To the best of our knowledge, the RCADG Cache is the only approach to take advantage of structural summaries for effectively detecting query containment or overlap. Moreover, no other XML cache we know of exploits intermediate results that are produced as a by-product during the evaluation from scratch. These are valuable cache contents that increase the effectiveness of the cache at no extra computational cost. Extensive experiments quantify the practical benefit of all of the proposed techniques, which amounts to a performance gain of several orders of magnitude compared to various other approaches

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

Automatic mapping of XML documents into relational database

Extensible Markup Language (XML) nowadays is one of the most important standard media used for exchanging and representing data through the Internet. Storing, updating and retrieving the huge amount of web services data such as XML is an attractive area of research for researchers and database vendors. In this thesis, we propose and develop a new mapping model, called MAXDOR, for storing, rebuilding, updating and querying XML documents using a relational database without making use of any XML schemas in the mapping process. The model addressed the problem of solving the structural hole between ordered hierarchical XML and unordered tabular relational database to enable us to use relational database systems for storing, updating and querying XML data. A multiple link list is used to maintain XML document structure, manage the process of updating document contents and retrieve document contents efficiently. Experiments are done to evaluate MAXDOR model. MAXDOR will be compared with other well-known models available in the literature(Tatarinov et al., 2002) and (Torsten et al., 2004) using total expected value of rebuilding XML document execution time and insertion of token execution time.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

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)