SUCXENT: An Efficient Path-Based Approach to Store and Query XML Documents

Because of increasing use of XML data on the internet, the need for an efficient method of storing and querying XML data is vital. So far, two major types of system for XML data management have been introduced: XML Enabled systems and XML native systems. The former uses relational database system for storing and querying XML data and the latter is a special XML database system which is based on XML data model. Since relational database systems are more mature than XML native systems, it seems that the use of abilities and efficiencies of relational systems is more economical. In this article, we have introduced a new relational schema named XLight for storing and processing XML data, and we have compared it with some similar methods.

As Extensible Markup Language (XML) [5] is emerging as the data format of the internet era, there are increasing needs to effciently store and query XML data. One way towards this goal is using relational database by transforming XML data into relational format. In this paper, we argue that existing transformation algorithms are not complete in the sense that they focus only on structural aspects and ignoring semantic aspects. We present the semantic knowledge that needs to be captured during the transformation to ensure a correct relational schema. Further, we show a simple algorithm that can 1) derive such semantic knowledge from the given XML Document Type Definition (DTD) and 2) preserve the knowledge by representing them in terms of semantic constraints in relational database terms. By combining the existing transformation algorithms and our constraints-preserving algorithm, one can transform XML DTD to relational schema where correct semantics and behaviors are guaranteed by the preserved constraints. Experimental results are also presented.

XML data is likely to be widely used as a data exchange format but users also need to store and query XML data. The purpose of this panel is to explore whether and how to best provide this functionality.

Recently, ontology-based data access is more and more employed in the context of data integration. That is, to the usual data layer of an information system we superimpose a conceptual layer to be exposed to the client. In this paper, we discuss a way to query XML data over conceptual layer using Description Logics. Given a XML data file, we first map the XML DTD to a corresponding relational schema preserving the original semantics of the XML DTD, and at the meantime, we store the XML data into the relational database. After that, we create an ontology described in Description Logics with respect to the relational schema. When a Description Logic query is posted to the ontology TBox, we map it to SQL query with respect to the relational database. In this way, we can query XML data using Description Logics. The main contribution of this thesis can be seen as the following two parts: 1. We proposed a mapping from XML DTD to relational schema with constraints preserving the original XML DTD semantics. 2. We described a framework of querying XML data using Description Logics, more precisely, the DL-LiteA logic.

The MIX mediator system, MIX m , is developed as part of the MIX Project at the San Diego Supercomputer Center, and the University of California, San Diego. 1 MIX m uses XML as the common model for data exchange. Mediator views are expressed in XMAS ( XML Matching And Structuring Language ), a declarative XML query language. To facilitate user-friendly query formulation and for optimization purposes, MIX m employs XML DTDs as a structural description (in effect, a “schema”) of the exchanged data. The novel features of the system include: Data exchange and integration solely relies on XML, i.e., instance and schema information is represented by XML documents and XML DTDs, respectively. XML queries are denoted in XMAS, which builds upon ideas of languages like XML-QL, MSL, Yat, and UnQL. Additionally, XMAS features powerful grouping and order constructs for generating new integrated XML “objects” from existing ones. The graphical user interface BBQ ( Blended Browsing and Querying ) is driven by the mediator view DTD and integrates browsing and querying of XML data. Complex queries can be constructed in an intuitive way, resembling QBE. Due to the nested nature of XML data and DTDs, BBQ provides graphical means to specify the nesting and grouping of query results. Query evaluation can be demand-driven, i.e., by the user's navigation into the mediated view.

The exploitation of large volume of XML (eXtensible Markup Language) data with a limited storage space implies the development of a special and reliable treatment to compress data and query them. This work studies and treats these processes in order to combine them via a mediator while facilitating querying compressed XML data without recourse to the decompression process. We propose a new technique to compress, re-index and query XML data while improving XMill and B+Tree algorithms. We show the reliability and the speed up of the proposed querying system towards response time and answers' exactitude.

As XML becomes the standard of digital data exchange format we need to manage a lot of XML data effectively. Unlike tables in relational model XML documents are not structural. That makes it difficult to store XML documents as tables in relational model. To solve these problems there have been significant researches in relational database systems. There are two kinds of approaches: 1) One way is to decompose XML documents so that elements of XML match fields of relational tables. 2) The other one stores a whole XML document as a field of relational table. In this paper we adopted the second approach to store XML documents because sometimes it is not easy for us to decompose XML documents and in some cases their element order in documents are very meaningful. We suggest an efficient table schema to store only inverted index as tables to retrieve required data from XML data fields of relational tables and shows SQL translations that correspond to XML full-text retrievals. The functionalities of XML retrieval are based on the W3C XQuery which includes full-text retrievals. In this paper we show the superiority of our method by comparing the performances in terms of a response time and a space to store inverted index. Experiments show our approach uses less space and shows faster response times.

XML is rapidly becoming one of the most adopted standard for information representation and interchange over the Internet. With the proliferation of mobile devices of communication such as palmtop computers in recent years, there has been growing numbers of web applications that generate tremendous amount of XML data transmitted via the Internet. We therefore need to investigate an effective means to handle such ever-growing XML data in various merging activities such as aggregation, accumulation or updating, in addition to storing and querying XML data. Previously, we recognized that FDs are an important and effective means to achieve consistent XML data merging, which we restricted data consistency for leaf nodes in an XML data tree. In this paper we further extend FDs to be satisfied in an XML document by comparing subtrees in a specified context of an XML tree. Given an XML tree T and a set of FDs F defined over a set of given path expressions, called targeted functional path expressions, we tackle the problem of repairing the inconsistency with respect to F in the most concise merged format of T.

The ability to summarize information from many XML data sources is becoming crucial. Problems that need to be addressed are related to different representations of data and different levels of generality/specificity of data. In this paper we propose to solve these problems by creating a methodology for efficient XML schema diagram transformations in static and dynamic situations. We focus on a graphical approach for XML schema diagram transformations and XML data querying. This approach allows us to design XML processing system for diverse XML documents. In the paper we classify schema diagram changes based on adding/removing/modifying element attributes, and changing the structure of the elements.

Although the native (tree-like) storage of XML data becomes more and more important there will be an enduring demand to manage XML data in its textual representation, for instance in relational structures or file systems. XML data has to be wellformed by definition and additionally, in many cases, it has to be valid according to a given XML schema. Because the XML column types are often derived from text types (e.g. CLOBs) guaranteeing well-formedness as well as validity is not trivial. And even worse, for frequently modified data it is usually too expensive to re-validate the whole XML data after each update - but waiving re-validation may lead to inconsistencies and malfunctions of applications. In this paper we present a schema-aware pushdown automaton (i.e. a stack machine) that validates an XML string/stream. Using an element/state-index, the pushdown automaton is able to re-validate local modifications of the data while guaranteeing overall validity. Update operations (e.g. SQLXML, XQuery updates) are validated before executing them.

Due to the rapid growth of XML representation for information exchange, XML databases have been widely adopted in a variety of applications. This paper presents two layers of optimisation for dealing with large XML databases: (1) OXDP (Object-Based Methodology for XML Data Partitioning) which has been developed to partition XML data efficiently and (2) OXiP (Object-Based XML Indexing for Partitions) which is an indexing and linking mechanism for partitioned data. OXDP provides optimal XML data partitioning based on an object’s semantic features which improves XML data query performance. The OXiP method tokenises all rooted label paths and preserves the pathways within each XML object partition. The semanticbased data partition ultimately enhances the notion of a frequently accessed data subset which is an advantageous feature in our proposed methods to decrease the time to answer queries. Experimentally, OXDP and OXiP can achieve an order of magnitude performance improvement for querying XML data.

XML (Extensible Markup Language) has emerged as the standard data-exchange format for Internet-based business applications. These applications introduce a new set of data management requirements involving XML. However, for the foreseeable future, a significant amount of business data will continue to be stored in relational database systems. Thus, a bridge is needed to satisfy the requirements of these new XML-based applications while still using relational database technology. This paper describes the design and implementation of the XTABLES middleware system, which we believe achieves this goal. In particular, XTABLES provides a general framework to create XML views of relational data, query XML views, and store and query XML documents using a relational database system. Some of the novel features of the XTABLES architecture are that it 1) provides users with a single XML query language for creating and querying XML views of relational data, 2) executes queries efficiently by pushing most computation down to the relational database engine, c) allows users to query seamlessly over relational data and meta-data, and 4) allows users to write queries that span XML documents and XML views of relational data.

XML data warehouses give decision-support systems a chance to use complicated data by giving them a place to start. But native-XML database management systems are slow right now, so it is important to look into ways to make them faster. This study presents two strategies to think about. To start, we suggest using a link index that was made with the fact that XML warehouses have a lot of dimensions in mind. The join procedures are taken out, but the data from the first warehouse stays the same. Second, we show how to choose XML materialized views by grouping the query load to show how this can be done. To prove that these ideas work, we built a set of decision support XQuery tools and ran them against an XML data warehouse. We compared the results obtained with and without our optimization methods. Our tests show that it works, even though the queries themselves are a little hard to understand and the datasets themselves are very big. XML has emerged as the widely accepted standard for transmitting data over mobile wireless networks. In these kinds of networks, mobile clients can use a wireless broadcast channel to send queries to get the XML data they need. Because mobile devices are so small and have so little storage space and a short battery life, it may be hard for customers to download the whole XML data set on one of these devices. To solve this problem, you need to index XML data so that mobile clients only have to download the parts of the file they need. Users who want to access only certain parts of the XML content in an XML stream could use one of several indexing methods. Still, the indexing methods that are used now add more data to an XML stream that is already very large. This research comes up with a new XML stream structure for broadcasting XML data by compressing and summarizing the information about how XML nodes are put together. This study was conducted in the United Kingdom. When data is summed up before being sent, the time it takes to get it in XML format over a wireless broadcast channel can be cut down. The recommended XML stream structure also has indexes that will help you skip over any data that is not important. So, it could make it less likely that XML query results will drain the batteries of mobile devices when they are being processed. We also found that our suggested XML stream design was better than its predecessors in terms of access and tuning times for processing XML queries over the XML data stream. So, our architecture can be used to answer a wide range of XML questions.

The XML data is a typical kind of tree-data. However, the XML research community has given little attention to the traditional Relational database Management System(RDBMS) based encoding schemes for tree-data. In this paper, we will investigate one of the traditional RDBMS-based encoding schemes, called Nested Interval, for storage and retrieval of XML data. Especially, our approach is very robust for updating XML data, including insertion of new nodes. In fact, the existing RDBMS-based XML storage and indexing techniques work very poorly against XML data updates because they should be rebuilt from the scratch when any update occurs in XML data. In contract, our scheme does not require re-encoding. In this respect, our approach is a viable option for storing and querying update-intensive XML applications.

As XML data storage and interchange become ubiquitous, analysts and data engineers increasingly need tools to model their data and map it to XML schemas and to reverse engineer XML documents and schemas in support of evolution and integration activities. For effective data management, model transformations require guarantees of properties of interest including guarantees of information and constraintpreservation, redundancy-free and compactness guarantees, and assurances about readability and maintainability. In this paper, we make foundational observations about XML data management, including conceptual modeling for XML data, transformations to and from XML Schema and XML data models, and transformation guarantees concerning properties of interest, and we provide resolutions for conceptual mismatches between XML data management and more traditional data management. Our implemented prototype tools show that these observations and insights can provide a strong foundation for XML data engineering.