LNV : A Storage Structure for Native XML

We propose an index structure and an algorithm to answer Descendant Tree queries, the subset of XPath queries limited to the descendant axis, in computational models where elements can be accessed in non-sequential order. The indexing scheme uses classical labeling techniques, but structurally represents the ancestor-descendant relationships between nodes of same type, in order to allow faster searches. The algorithm solves XPath location path queries along the descendant axis in a holistic way, looking at the whole query rather than just concentrating on individual components, as opposed to "set-at-a-time" strategies. Using an adaptive analysis, we prove tight bounds on the worst case complexity in the comparison model, and a reasonable upper bound on the worst case complexity in the hierarchical memory model, which accounts for the number of I/O transfers at any fixed level. Keywords: XPath location paths; comparison model; holistic; cache-oblivious; adaptive.

2004

Path expressions are ubiquitous in XML processing languages. Existing approaches evaluate a path expression by selecting nodes that satisfies the tag-name and value constraints and then joining them according to the structural constraints. In this paper, we propose a novel approach, next-of-kin (NoK) pattern matching, to speed up the nodeselection step, and to reduce the join size significantly in the second step. To efficiently perform NoK pattern matching, we also propose a succinct XML physical storage scheme that is adaptive to updates and streaming XML as well. Our performance results demonstrate that the proposed storage scheme and path evaluation algorithm is highly efficient and outperforms the other tested systems in most cases. * This research was done while the author was visiting the University of Waterloo.

2012

XML is recognized as a standard for data storage and exchange for web applications. This is because it has certain unique features like it is self describing, extensible and it is stored in the form of text document. In spite of all these unique features XML has an inherent limitation of verbosity. Because of the strong presence of XML in database technology and its inherent verbosity there is ever increasing need to design compact storage for XML which can be effectively utilized for efficient indexing and querying of XML. The proposed technique creates a structure index which is a compact summarization of the XML document and data index which groups and stores the contents of all similar paths at one place. Based on this compact storage a novel query algorithm is proposed which can answer xpath queries very efficiently. This approach dramatically reduces the storage requirement for XML coupled with efficient processing of xpath queries. The implementation of this technique and comparison with other techniques confirms our claim.

arXiv preprint arXiv:1203.6454, 2012

Storing XML documents in a relational database is a promising solution because relational databases are mature and scale very well and they have the advantages that in a relational database XML data and structured data can coexist making it possible to build application that involve both kinds of data with little extra effort . In this paper, we propose an algorithm schema named XRecursive that translates XML documents to relational database according to the proposed storing structure. The steps and algorithm are given in details to describe how to use the storing structure to storage and query XML documents in relational database. Then we report our experimental results on a real database to show the performance of our method in some features.

Indexing XML data to facilitate query processing has been a popular subject of study in recent years. Most of previous studies can be classified into three categories: path indexing, node indexing and sequence-based indexing. Many of them cannot answer both single-path and branching queries with various value predicates very efficiently. In this paper, we propose a novel compact tree (Ctree) structure, which provides not only concise path summaries but also detailed element relationships, and a configurable index scheme based on data statistics. We develop an efficient Ctree-based method for processing a tree structure query with various value constraints. Efficiency of our method is achieved by: (1) summarizing a large database into a condensed structure view to prune irrelevant search space; (2) evaluating a tree structure query directly without expensive join operations; (3) using Ctree properties such as trivial groups and bi-direction to reduce query processing time; (4) using ...

2005

XML has emerged as one of the popular data-representation formats for information storage and exchange. XML data today range from representing small files to encapsulating gigabytes of information. Large XML databases must be stored on mass storage devices for both persistence as well as costefficiency. For mass storage of data today, disk drives are the most cost-effective medium. Current approaches of mapping XML data to relational databases or simply using flat files incur a mismatch between the structure of XML data and the underlying storage device (disk drives). In this study, we investigate a new method to store XML data on disk drives that matches the characteristics of XML with those of disk drives. In particular, we present algorithms that, given an XML document and a disk drive, decide how to store the document on the drive, in a way that will later allow efficient execution of XML queries. We evaluate our proposed method using analytical modeling and by simulating the execution of benchmark XPath queries.

2014

Optimizing XML queries is an intensively studied problem in the field of databases of late. The topic has a host of applications, viz., web-scale XML and keyword search. In this paper, we address the problem of efficient execution of XML path queries (commonly known as XPath queries), branch queries and wild-card queries. Our index structure assists in fast identification of child-parent as well as ancestor-descendant relationship, thus increasing the efficiency of XPath query execution. Both XML data and queries possess an inherent tree structure and, thus, fast child-parent lookup is a necessity to improve performance. We propose a holistic hybrid index structure that combines the Extended Dewey labeling scheme with the CTree index structure to leverage advantages of both the mechanisms. Our index structure is capable of catering to all the queries (single path, branch and wild-card queries), with equal or better performance metrics when compared to the state-of-the-art.

XML is becoming prevalent in data presentation and data exchange on the internet. One important issue in the XML research community is how to query XML documents to extract and restructure information. Currently, XQuery based on XPath is the most promising standard. In this paper, we discuss limitations of XPath and XQuery, and propose a generalization of XPath called XTree that overcomes these limitations. Using XTree, multiple variable bindings can be instantiated in one expression; and XTree expressions, which represent a tree rather than a path, can be used in both the querying part and the result construction part of a query. Based on XTree, we develop an XTree query language, which is more compact and convenient to use than XQuery, and supports common query operations such as join, negation, grouping, and recursion in a direct way. We describe an algorithm that converts XTree query scripts to XQuery scripts. This algorithm provides not only a means of executing queries written in XTree query language but also highlights differences between the two query languages.

2010

AbstrAct As XML has evolved as a data model for semi-structured data and the de facto standard for data exchange (eg, Atom, RSS, and XBRL), XML data management has been the subject of extensive research and development in both academia and industry. Among the XML data management issues, storage and query processing are the most critical ones with respect to system performance. Different storage schemes have their own pros and cons.

Lecture Notes in Computer Science, 2002

With XML rapidly gaining popularity as the standard for data exchange on the World Wide Web, a variety of XML management systems (XMLMS) are becoming available. The choice of an XMLMS is made difficult by the significant difference in the expressive power of the queries and the performance shown by these XMLMS. Most XMLMS are legacy systems (mostly relational) extended to load, query, and publish data in XML format. A few are native XMLMS and capture all the characteristics of XML data representation. This paper looks at expressive power and efficiency of various XMLMS. The performance analysis relies on the testbed provided by XOO7, a benchmark derived from OO7 to capture both data and document characteristics of XML. We present efficiency results for two native XMLMS, an XML-enabled semi-structured data management system and an XML-enabled RDBMS, which emphasize the need for a delicate balance between the data-centric and document-centric aspects of XML query processing.