A Schema Extraction Method using Elements Information in XML Documents

This chapter discusses existing approaches to evaluate and measure structural similarity in sources of XML documents. A relevant peculiarity of XML documents, indeed, is that information on the document structure is available in the document itself. In the chapter we present different approaches aiming at evaluating structural similarity at three different levels: among documents, between a document and a schema, and among schemas. The most relevant applications of such measures are for document classification and schema extraction, and for document and schema structural clustering, though other interesting applications such as document change detection and structural querying can be devised, and will be discussed throughout the chapter.

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XML Data Integration

Advanced Applications and Structures in XML Processing ◽

10.4018/978-1-61520-727-5.ch014 ◽

2010 ◽

pp. 308-332

Author(s):

Huiping Cao ◽

Yan Qi ◽

K. Selçuk Candan ◽

Maria Luisa Sapino

Keyword(s):

Data Integration ◽

Data Exchange ◽

Markup Language ◽

Schema Mapping ◽

Xml Data ◽

Xml Documents ◽

Data Schema ◽

Schema Extraction ◽

Extensible Markup ◽

Xml Data Integration

Many applications require exchange and integration of data from multiple, heterogeneous sources. eXtensible Markup Language (XML) is a standard developed to satisfy the convenient data exchange needs of these applications. However, XML by itself does not address the data integration requirements. This chapter discusses the challenges and techniques in XML Data Integration. It first presents a four step outline, illustrating the steps involved in the integration of XML data. This chapter, then, focuses on the first two of these steps: schema extraction and data/schema mapping. More specifically, schema extraction presents techniques to extract tree summaries, DTDs, or XML Schemas from XML documents. The discussion on data/schema mapping focuses on techniques for aligning XML data and schemas.

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XStruct: Efficient Schema Extraction from Multiple and Large XML Documents

22nd International Conference on Data Engineering Workshops (ICDEW'06) ◽

10.1109/icdew.2006.166 ◽

2006 ◽

Cited By ~ 26

Author(s):

J. Hegewald ◽

F. Naumann ◽

M. Weis

Keyword(s):

Xml Documents ◽

Schema Extraction

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Efficient schema extraction from large XML documents

2012 5th International Conference on BioMedical Engineering and Informatics ◽

10.1109/bmei.2012.6513057 ◽

2012 ◽

Author(s):

Yin Zhang ◽

Hua Zhou ◽

Junhui Liu ◽

Zhihong Liang ◽

Peng Duan

Keyword(s):

Xml Documents ◽

Schema Extraction

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Evaluation of the dark field method for large association of spread DNA

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100081814 ◽

1978 ◽

Vol 36 (2) ◽

pp. 190-191

Author(s):

Douglas C. Barker

Keyword(s):

Electron Microscopy ◽

Molecular Weight ◽

Mitochondrial Dna ◽

Extraction Method ◽

Field Method ◽

Dark Field ◽

Kinetoplast Dna ◽

Field Electron ◽

Field Electron Microscopy ◽

Dark Field Electron

A number of satisfactory methods are available for the electron microscopy of nicleic acids. These methods concentrated on fragments of nuclear, viral and mitochondrial DNA less than 50 megadaltons, on denaturation and heteroduplex mapping (Davies et al 1971) or on the interaction between proteins and DNA (Brack and Delain 1975). Less attention has been paid to the experimental criteria necessary for spreading and visualisation by dark field electron microscopy of large intact issociations of DNA. This communication will report on those criteria in relation to the ultrastructure of the (approx. 1 x 10-14g) DNA component of the kinetoplast from Trypanosomes. An extraction method has been developed to eliminate native endonucleases and nuclear contamination and to isolate the kinetoplast DNA (KDNA) as a compact network of high molecular weight. In collaboration with Dr. Ch. Brack (Basel [nstitute of Immunology), we studied the conditions necessary to prepare this KDNA Tor dark field electron microscopy using the microdrop spreading technique.

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