Xiaojie Yuan

A Storage Scheme of Native XML Database Supporting Efficient Updates

XML storage schemes have become an important research topic in the XML data management field. Several alternatives to manage XML documents exist, ranging from file systems over relational or other database systems to native XML database systems. This paper presents a novel storage scheme, which directly stores XML nodes as records into a paged file to build up the persistent document object model.The model retains the original tree structure of XML data, and then the updating algorithm is proposed for dynamic maintenance of the physical persistent storage. Finally several query experimental results are reported to demonstrate the superiority of our storage scheme.

Query XML Data in RDBMS

With the rapid growing popularity of XML to represent data, how to make good use of XML data in relational databases is worthy of study. Storing XML data as text in relational databases is a traditional strategy which cannot reflect the feature of XML format. In this paper, a mechanism for XML data storage and query in relational databases is proposed. XML data can be stored in relational tables and XQuery expressions can be evaluated as a part of SQL for XML data query. XQuery grammar tree and Query tree model for XML data query in rela-tional databases is presented to gain more efficient performance while querying XML data. Appropriative algorithm for evaluating XPath is also presented in this paper by which XQuery can be evaluated rapidly and efficiency. Finally, experiments invalidate the strategy of XML storage and run the algorithm on real XML datasets to show the efficiency compared with other mechanisms.

Effective keyword search for candidate fragments of XML documents

In this paper, we focus on the problem of effectively and efficiently answering XML keyword search. We first show the weakness of existing SLCA (Smallest Lowest Common Ancestor) based solutions, and then we propose the concept of Candidate Fragment. A Candidate Fragment is a meaningful sub tree in the XML document tree, which has the appropriate granularity. To efficiently compute Candidate Fragments as the answers of XML keyword search, we design Node Match Algorithm and Path Match algorithm. Finally, we conduct extensive experiments to show that our approach is both effective and efficient.

XML Retrieval with Structural Context Relaxation

In this paper, we propose an approach to retrieval XML data with structural context relaxation. We process user query and XML documents as structural term set. Context resemblance is computed based on level weight of element in context, level similarity between elements in longest matched subsequence and other factors. We extend Vector Space Model to answer XML content and structure search. Experiments show that our method has good performance on XML content and structure search.

XML micro benchmark framework based on algebra analysis

XML query processing has taken on considerable importance; researchers are proposing various storage models, indexing structures and query processing and optimization techniques for efficient XML query answering and are constructing XML database management systems on a variety of platforms. To assist the database designers to analyze and refine their engineering design, we propose to (1) study existing query processing techniques; (2) investigate the important characteristics of XML queries, at both semantic and instance levels, that impacts the performance of query evaluation; (3) design a configuration scheme for systems designers to specify query (sets) that suits their engineering demands; and (4) develop efficient algorithms to construct query sets desired.

User relationship index based on social network community analysis

Online social networking community is an important type of internet service. Large scale social network can be built upon the large quantities of user data kept by social networking community. We model the large scale social network on the live online community and evaluate the user relationship index from the model. We further use the user relationship index in analyzing the user relation data and optimize the humanity services of networking engine in social product.

XPC: A novel method for retrieving massive smallscale XML documents via path constraints

This paper proposes a novel method for searching massive and small-scale XML documents via path constraints, referred to as XPC, to overcome drawbacks of conventional approaches. Firstly, we propose employing keywords with simple path constraints to retrieve XML data, which provides a user-friendly way without need of understanding complex knowledge and could express user demand accurately. This paper further proposes a novel method for computing term weight in documents via path constraints, called rtf-idf. It measures the similarity of path constraints by N-Gram and other factors according to the structure of the XML documents. Then we rank the relevant documents by an extension of the vector space model. The experimental results show that XPC indeed outperforms the baseline methods such as VSM in plain text and JuruXML.

DRC:An efficient dynamic region code for XML

It is an important part to determine the structural relationship of nodes in XML queries. With a numbering scheme for XML data, the structural relationships of nodes can be quickly determined. Most of the numbering schemas for XML data are static and they cannot number XML data which are updated frequently. Some numbering schemas that are dynamic consume more storage space. So it is a challenging research area to design an efficient dynamic numbering schema. This paper presents an efficient dynamic region code, called DRC, for XML tree numbering based on decimal. The experimental results show that DRC consumes less storage space and is more steady than other dynamic numbering schemes.

A Native XML Storage Scheme Seamlessly Integrated with Relational Databases

With the promotion and application of XML standards, an increasing number of organizations and institutions are using XML in their information management and data exchange. The classic problem of data management has emerged, namely, how to effectively store, retrieve, index, query and update the XML documents. This paper presents a native XML storage scheme seamlessly integrated with relational database, whose central idea is to take advantage of effective XML node numbering scheme to support XML data model over relational table. Firstly, this relational table of XML nodes is a non-destructive mapping of XML data model; Secondly, the storage scheme maximize the reuse of the underlying storage mechanism existing in relational database, which guarantees the transaction processing, log and recovery management, concurrent control of XML data. Using a variety of data sets, we conduct experiments on this scheme and previous XML storage schemes to compare the storage space, storage time and publication time between them. Experimental results show that our storage scheme is a high-performance XML storage scheme.

A XML Update Pattern Based on XQuery Update Facility and Seamlessly Integrated with Relational Databases

With the promotion and application of XML standards, The research hotspot in database domain has emerged, namely, how to effectively manage the large-scale XML data. XQuery Update Facility, which proposed by W3C in 2008 has become the grammar and semanteme standard for the realization of XML data updating. Based on this standard, this paper presents a XML update pattern which seamlessly integrated with relational database. Firstly, according to W3Cs XDM model, define the XML node operation model and propose the grammar format of Insert, delete, rename and replace operations; secondly, present a mixture update schema for XML type data and relational type data. In the schema, the structure to restore the XML type data is defined and the function of XML data update is implemented. The experiments on this scheme show that XML update grammar fully supports W3C update specification, has the natural grammar format and apts to seamlessly integrate with relational database.