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This book explain the XML technology in detail .

Here is Introduction to XML from Keystone Learning Systems In the intro to XML course, you will be taught the XML basics like what it is, what markup language is, the features of XML and its differences to HTML and XML applications, what XML is used for, XML vs EDI and XML references. You will also be introduced to creating XML documents by learning the anatomy, the terminology of XML, case sensitivity, comments, and XML document structure Your Instructor David Harding will guide you through the introduction of XML

Here is Introduction to XML from Keystone Learning Systems In the intro to XML course, you will be taught the XML basics like what it is, what markup language is, the features of XML and its differences to HTML and XML applications, what XML is used for, XML vs EDI and XML references. You will also be introduced to creating XML documents by learning the anatomy, the terminology of XML, case sensitivity, comments, and XML document structure Your Instructor David Harding will guide you through the introduction of XML

Arguably the biggest "buzz word" of the current year has been "learning or knowledge object". To understand the learning object and why it should be such a highly desirable commodity, it is necessary to unpack not only this concept but more importantly revisit some contributing concepts and constructs (more buzz words) that support the building of truly pedagogically informed reusable objects (Boyle & Cook 2001). The words and relationships explored in this paper are: learning or knowledge objects--the desirable construct in today's E-learning environment; ontologies and ways of expressing them through topic maps as they allow users to define and describe the components of an entity; metadata and XML used to create categorize, label and communicate the value of these objects; and hermeneutics and phenomenology as they refer to the interpretation of experience and events and evaluation of learning events. (Contains 23 references.) (Author)

Inferring an appropriate DTD or XML Schema Definition (XSD) for a given collection of XML documents essentially reduces to learning deterministic regular expressions from sets of positive example words. Unfortunately, there is no algorithm capable of learning the complete class of deterministic regular expressions from positive examples only, as we will show. The regular expressions occurring in practical DTDs and XSDs, however, are such that every alphabet symbol occurs only a small number of times. As such, in practice it suffices to learn the subclass of deterministic regular expressions in which each alphabet symbol occurs at most k times, for some small k. We refer to such expressions as k-occurrence regular expressions (k-OREs for short). Motivated by this observation, we provide a probabilistic algorithm that learns k-OREs for increasing values of k, and selects the deterministic one that best describes the sample based on a Minimum Description Length argument. The effectiveness of the method is empirically validated both on real world and synthetic data. Furthermore, the method is shown to be conservative over the simpler classes of expressions considered in previous work.

Inferring an appropriate DTD or XML Schema Definition (XSD) for a given collection of XML documents essentially reduces to learning deterministic regular expressions from sets of positive example words. Unfortunately, there is no algorithm capable of learning the complete class of deterministic regular expressions from positive examples only, as we will show. The regular expressions occurring in practical DTDs and XSDs, however, are such that every alphabet symbol occurs only a small number of times. As such, in practice it suffices to learn the subclass of deterministic regular expressions in which each alphabet symbol occurs at most k times, for some small k. We refer to such expressions as k-occurrence regular expressions (k-OREs for short). Motivated by this observation, we provide a probabilistic algorithm that learns k-OREs for increasing values of k, and selects the deterministic one that best describes the sample based on a Minimum Description Length argument. The effectiveness of the method is empirically validated both on real world and synthetic data. Furthermore, the method is shown to be conservative over the simpler classes of expressions considered in previous work.

In the digital world, classifying real sensed data in huge volumes derived from numerical problems is a challenging task due to the computational complexity of the metaheuristic searching process. The deep learning approach includes convolutional neural network (CNN), long short-term memory (LSTM), and Bidirectional (BI)-LSTM, suitable for an optimistic processing time of analyzing XML datasets (i.e., social media, trade center, and surveillance data exchanged in the internet world). However, it faces process deviation when datasets extend their range beyond the expected volume. This paper proposes a novel deep learning formwork referred to as archimed improved numerical optimization deep learning (AINODL) to improve the classification of XML datasets. The proposed AINODL framework first extracts feature from XML documents using the vector space model. Secondly, it classifies the XML data using the inbuilt function of the AINODL framework. The experiments demonstrate that the performance parameters accuracy (90%), sensitivity (93%), and specificity (94%) of the proposed AINODL framework are significantly enhanced compared with the existing approaches CNN, LSTM, and BI-LSTM.

In the digital world, classifying real sensed data in huge volumes derived from numerical problems is a challenging task due to the computational complexity of the metaheuristic searching process. The deep learning approach includes convolutional neural network (CNN), long short-term memory (LSTM), and Bidirectional (BI)-LSTM, suitable for an optimistic processing time of analyzing XML datasets (i.e., social media, trade center, and surveillance data exchanged in the internet world). However, it faces process deviation when datasets extend their range beyond the expected volume. This paper proposes a novel deep learning formwork referred to as archimed improved numerical optimization deep learning (AINODL) to improve the classification of XML datasets. The proposed AINODL framework first extracts feature from XML documents using the vector space model. Secondly, it classifies the XML data using the inbuilt function of the AINODL framework. The experiments demonstrate that the performance parameters accuracy (90%), sensitivity (93%), and specificity (94%) of the proposed AINODL framework are significantly enhanced compared with the existing approaches CNN, LSTM, and BI-LSTM.

In the digital world, classifying real sensed data in huge volumes derived from numerical problems is a challenging task due to the computational complexity of the metaheuristic searching process. The deep learning approach includes convolutional neural network (CNN), long short-term memory (LSTM), and Bidirectional (BI)-LSTM, suitable for an optimistic processing time of analyzing XML datasets (i.e., social media, trade center, and surveillance data exchanged in the internet world). However, it faces process deviation when datasets extend their range beyond the expected volume. This paper proposes a novel deep learning formwork referred to as archimed improved numerical optimization deep learning (AINODL) to improve the classification of XML datasets. The proposed AINODL framework first extracts feature from XML documents using the vector space model. Secondly, it classifies the XML data using the inbuilt function of the AINODL framework. The experiments demonstrate that the performance parameters accuracy (90%), sensitivity (93%), and specificity (94%) of the proposed AINODL framework are significantly enhanced compared with the existing approaches CNN, LSTM, and BI-LSTM.

here is XML Entities and Basic Scripting from Keystone Learning Systems In this cd you will be introduced to Entities such as parameter entities, module DTDs, conditional sections, attribute entities, external parsed entity, external unparsed entity and notations, declare attributes, and processing entities. You will also be introduced to XML and HTML that covers browser support, internet, scripting with XML, DOM support, declare data island and accessing XML elements

Here is XML Markup and Content from Keystone Learning Systems In this CD, you will learn about XML markup like element and attribute markup, you will also learn about predefined entries, character reference, multi-language support, entity references, declare internal entities, CDATA section, processing instruction and namespaces, You will also learn about defining structure like XML freeform, databases and structured data, document type definitions, element declarations, occurence indicator character content and element choices David Harding will guide you through this course on Markup and Content

Here is XML Markup and Content from Keystone Learning Systems In this CD, you will learn about XML markup like element and attribute markup, you will also learn about predefined entries, character reference, multi-language support, entity references, declare internal entities, CDATA section, processing instruction and namespaces, You will also learn about defining structure like XML freeform, databases and structured data, document type definitions, element declarations, occurence indicator character content and element choices David Harding will guide you through this course on Markup and Content

Here is XML More on Content Modeling from Keystone Learning Systems In this CD we will introduce you to special element types such as empty elements, unrestricted elements, mixed content elements, attributes, and do we use just elements or attributes. You will also be introduced to entities such as external entities, declaring, identifiers, using external entities, when to use it, and tips for good content modeling. You will learn about well formed and valid documents and checking for well formedness, parsers and validity. Finally you will leran about DTDs featuring standalone documents and external DTD's

We investigate the problem of learning XML queries, path queries and tree pattern queries, from examples given by the user. A learning algorithm takes on the input a set of XML documents with nodes annotated by the user and returns a query that selects the nodes in a manner consistent with the annotation. We study two learning settings that differ with the types of annotations. In the first setting the user may only indicate required nodes that the query must return. In the second, more general, setting, the user may also indicate forbidden nodes that the query must not return. The query may or may not return any node with no annotation. We formalize what it means for a class of queries to be \emph{learnable}. One requirement is the existence of a learning algorithm that is sound i.e., always returns a query consistent with the examples given by the user. Furthermore, the learning algorithm should be complete i.e., able to produce every query with a sufficiently rich example. Other requirements involve tractability of learning and its robustness to nonessential examples. We show that the classes of simple path queries and path-subsumption-free tree queries are learnable from positive examples. The learnability of the full class of tree pattern queries (and the full class of path queries) remains an open question. We show also that adding negative examples to the picture renders the learning unfeasible. Published in ICDT 2012, Berlin.

Here is Keystone Learning XML Dynamic Web Sites and Documents In This course, we will be teaching you Dynamic HTML, the enchanced user interaction, how DHTML works. you will learn about XML Data islands, DOM, and DHTML. the web applications and XML. You will also get an introduction to Dynamic Documents such as XML vs Databases