XML Tree Research Articles

Strategies and Approaches for Generating Identical Extensive XML Tree Instances

In recent years, XML has become the de facto internet wire language. Data may be organized and given context with the use of XML. A well-organized document facilitates the transformation of raw data into actionable intelligence. In B2B1 applications, the XML data is sent and created. This implies the need for fast query processing on XML data. The processing of XML tree sample queries (XTPQ) that provide an efficient response (also known as sample matching) is a topic of active study in the XML database field.DOM (Parser) may be used to transform an XML document into a tree representation. Extensible Markup Language (XML) query languages like XPath and XQuery use tree samples (twigs) to express query results.XML query processing focuses mostly on effectively locating all instances of twig 1 samples inside an XML database. Numerous techniques for matching such tree samples have been presented in recent years. In this study, we survey recent developments in XTPQ processing. This summary will begin by introducing several algorithms for twig sample matching and then go on to provide some background on holistic techniques to process XTPQ.

Semantics to the rescue of document‐based XML diff: A JATS case study

ABSTRACTThe writing of digital text documents has become a longer process that usually goes through revision rounds. Document comparison is important for the human reader interested in changes made by the authors. These documents contain structural data using text‐centric XML as one of their main storage systems. Current XML diff algorithms are able to represent differences with a limited number of edit operations: insert, delete, move and update. This approach does not fit the scope of digital text document comparison where the human reader needs to understand actual modifications made by the author. With JATS being a text‐centric XML vocabulary, we propose within this paper a new XML diff algorithm called jats‐diff, able to support bijection between higher‐level modifications made by the authors, such as structural changes and restyling, and the changes detected between XML documents. In addition, jats‐diff provides similarity information between different nodes in order to measure the impact of the text changes on the XML tree.

Improving the support for XML dynamic updates using a hybridization labeling scheme (ORD-GAP)

Background : As the standard for the exchange of data over the World Wide Web, it is important to ensure that the eXtensible Markup Language (XML) database is capable of supporting not only efficient query processing but also capable of enduring frequent data update operations over the dynamic changes of Web content. Most of the existing XML annotation is based on a labeling scheme to identify each hierarchical position of the XML nodes. This computation is costly as any updates will cause the whole XML tree to be re-labelled. This impact can be observed on large datasets. Therefore, a robust labeling scheme that avoids re-labeling is crucial. Method: Here, we present ORD-GAP (named after Order Gap), a robust and persistent XML labeling scheme that supports dynamic updates. ORD-GAP assigns unique identifiers with gaps in-between XML nodes, which could easily identify the level, Parent-Child (P-C), Ancestor-Descendant (A-D) and sibling relationship. ORD-GAP adopts the OrdPath labeling scheme for any future insertion. Results: We demonstrate that ORD-GAP is robust enough for dynamic updates, and have implemented it in three use cases: (i) left-most, (ii) in-between and (iii) right-most insertion. Experimental evaluations on DBLP dataset demonstrated that ORD-GAP outperformed existing approaches such as ORDPath and ME Labeling concerning database storage size, data loading time and query retrieval. On average, ORD-GAP has the best storing and query retrieval time. Conclusion: The main contributions of this paper are: (i) A robust labeling scheme named ORD-GAP that assigns certain gap between each node to support future insertion, and (ii) An efficient mapping scheme, which built upon ORD-GAP labeling scheme to transform XML into RDB effectively.

Improving the support for XML dynamic updates using a hybridization labeling scheme (ORD-GAP).

Background: As the standard for the exchange of data over the World Wide Web, it is important to ensure that the eXtensible Markup Language (XML) database is capable of supporting not only efficient query processing but also capable of enduring frequent data update operations over the dynamic changes of Web content. Most of the existing XML annotation is based on a labeling scheme to identify each hierarchical position of the XML nodes. This computation is costly as any updates will cause the whole XML tree to be re-labelled. This impact can be observed on large datasets. Therefore, a robust labeling scheme that avoids re-labeling is crucial. Method: Here, we present ORD-GAP (named after Order Gap), a robust and persistent XML labeling scheme that supports dynamic updates. ORD-GAP assigns unique identifiers with gaps in-between XML nodes, which could easily identify the level, Parent-Child (P-C), Ancestor-Descendant (A-D) and sibling relationship. ORD-GAP adopts the OrdPath labeling scheme for any future insertion. Results: We demonstrate that ORD-GAP is robust enough for dynamic updates, and have implemented it in three use cases: (i) left-most, (ii) in-between and (iii) right-most insertion. Experimental evaluations on DBLP dataset demonstrated that ORD-GAP outperformed existing approaches such as ORDPath and ME Labeling concerning database storage size, data loading time and query retrieval. On average, ORD-GAP has the best storing and query retrieval time. Conclusion: The main contributions of this paper are: (i) A robust labeling scheme named ORD-GAP that assigns certain gap between each node to support future insertion, and (ii) An efficient mapping scheme, which built upon ORD-GAP labeling scheme to transform XML into RDB effectively.

Discovering XML Conditional Dependencies for Data Quality Issues

Extensible Markup Language (XML) is emerging as the primary standard for representing and exchanging data, with more than 60% of the total; XML considered the most dominant document type over the web; nevertheless, their quality is not as expected. XML integrity constraint especially XFD plays an important role in keeping the XML dataset as consistent as possible, but their ability to solve data quality issues is still intangible. The main reason is that old-fashioned data dependencies were basically introduced to maintain the consistency of the schema rather than that of the data. The purpose of this study is to introduce a method for discovering pattern tableaus for XML conditional dependencies to be used for enhancing XML document consistency as a part of data quality improvement phases. The notations of the conditional dependencies as new rules are designed mainly for improving data instance and extended traditional XML dependencies by enforcing pattern tableaus of semantically related constants. Subsequent to this, a set of minimal approximate conditional dependencies (XCFD, XCIND) is discovered and learned from the XML tree using a set of mining algorithms. The discovered patterns can be used as a Master data in order to detect inconsistencies that don’t respect the majority of the dataset.

CBSL − A Compressed Binary String Labeling Scheme for Dynamic Update of XML Documents

The growing volume of XML documents over the Web has increased the need for an efficient mechanism to facilitate query processing. Many labeling schemes have been introduced to optimize data retrieval and query processing on XML database documents. As it is known, labels hold information about XML tree nodes such as their position, their relationship with other nodes and their order, which helps in query processing. Most of the existing labeling schemes support query processing over static XML documents. However, they need to re-label during dynamic update. In this paper a compressed binary string labeling scheme is proposed which supports dynamic update of XML documents without re-labeling existing nodes. Analytical results show that the proposed labeling scheme takes less label size in comparison with other labeling schemes. Also, an experiment has been conducted to evaluate the label generation time as well as update processing.

Heterogeneous fuzzy XML data integration based on structural and semantic similarities

Web data integration has become a crucial requirement for Web data management. A considerable number of approaches for integrating Extensible Markup Language (XML) data from heterogeneous data sources have been proposed. Yet these existing approaches are not fit for integration of fuzzy XML data because of their fuzzy characteristics. In this article, we provide a framework to deal with fuzzy XML document integration. Firstly, we propose a new fuzzy XML tree model. Secondly, we present an effective algorithm based on the tree edit distance to identify the structural and semantic similarities between the fuzzy documents represented in the proposed fuzzy XML tree model. Thirdly, we propose an integration strategy that is applied to integrate the fuzzy documents from different data sources. Finally, we conduct experiments to demonstrate that our approach can efficiently integrate fuzzy XML documents.

Synthesis of Programs from Multimodal Datasets

We describe MultiSynth, a framework for synthesizing domain-specific programs from a multimodal dataset of examples. Given a domain-specific language (DSL), a dataset is multimodal if there is no single program in the DSL that generalizes over all the examples. Further, even if the examples in the dataset were generalized in terms of a set of programs, the domains of these programs may not be disjoint, thereby leading to ambiguity in synthesis. MultiSynth is a framework that incorporates concepts of synthesizing programs with minimum generality, while addressing the need of accurate prediction. We show how these can be achieved through (i) transformation driven partitioning of the dataset, (ii) least general generalization, for a generalized specification of the input and the output, and (iii) learning to rank, for estimating feature weights in order to map an input to the most appropriate mode in case of ambiguity. We show the effectiveness of our framework in two domains: in the first case, we extend an existing approach for synthesizing programs for XML tree transformations to ambiguous multimodal datasets. In the second case, MultiSynth is used to preorder words for machine translation, by learning permutations of productions in the parse trees of the source side sentences. Our evaluations reflect the effectiveness of our approach.

Transformation of fuzzy spatiotemporal data from XML to object-oriented database

The rapid development of the Internet promotes that XML is increasingly accepted as a medium for integrating and exchanging data. Meanwhile, object-oriented database with many excellent properties has been applied more and more widely. In particular, considering the need of processing the fuzzy spatiotemporal data existing in practical applications, we study the methodology of building fuzzy spatiotemporal data model and transforming fuzzy spatiotemporal data from XML to object-oriented database as well. To establish a suitable model, we devise a fuzzy spatiotemporal data model in XML to capture the semantics of fuzzy spatiotemporal features. In addition, we take the object-oriented schema that best describes the exciting fuzzy XML Schema and the characteristics of the XML tree structure into account to develop a series of rules to map spatiotemporal data from XML to object-oriented database. Such mapping rules could provide a significant consolidation of the interoperability of fuzzy spatiotemporal data from XML to object-oriented databases.

Automata Approach to XML Data Indexing

The internal structure of XML documents can be viewed as a tree. Trees are among the fundamental and well-studied data structures in computer science. They express a hierarchical structure and are widely used in many applications. This paper focuses on the problem of processing tree data structures; particularly, it studies the XML index problem. Although there exist many state-of-the-art methods, the XML index problem still belongs to the active research areas. However, existing methods usually lack clear references to a systematic approach to the standard theory of formal languages and automata. Therefore, we present some new methods solving the XML index problem using the automata theory. These methods are simple and allow one to efficiently process a small subset of XPath. Thus, having an XML data structure, our methods can be used efficiently as auxiliary data structures that enable answering a particular set of queries, e.g., XPath queries using any combination of the child and descendant-or-self axes. Given an XML tree model with n nodes, the searching phase uses the index, reads an input query of size m, finds the answer in time O ( m ) and does not depend on the size of the original XML document.

Rank and select: Another lesson learned

Rank and select queries on bitmaps are essential building bricks of many compressed data structures, including text indexes, membership and range supporting spatial data structures, compressed graphs, and more. Theoretically considered yet in 1980s, these primitives have also been a subject of vivid research concerning their practical incarnations in the last decade. We present a few novel rank/select variants, focusing mostly on speed, obtaining competitive space-time results in the compressed setting. Our findings can be summarized as follows: (i) no single rank/select solution works best on any kind of data (ours are optimized for bit arrays obtained from wavelet trees for real text datasets, but also work well for LOUDS-encoded XML tree layouts), (ii) it pays to efficiently handle blocks consisting of all 0 or all 1 bits, (iii) compressed select does not have to be significantly slower than compressed rank at a comparable memory use.

Pushing similarity joins down to the storage layer in XML databases

PurposeThis article aims to explore how to incorporate similarity joins into XML database management systems (XDBMSs). The authors aim to provide seamless and efficient integration of similarity joins on tree-structured data into an XDBMS architecture.Design/methodology/approachThe authors exploit XDBMS-specific features to efficiently generate XML tree representations for similarity matching. In particular, the authors push down a large part of the structural similarity evaluation close to the storage layer.FindingsEmpirical experiments were conducted to measure and compare accuracy, performance and scalability of the tree similarity join using different similarity functions and on the top of different storage models. The results show that the authors’ proposal delivers performance and scalability without hurting the accuracy.Originality/valueSimilarity join is a fundamental operation for data integration. Unfortunately, none of the XDBMS architectures proposed so far provides an efficient support for this operation. Evaluating similarity joins on XML is challenging, because it requires similarity matching on the text and structure. In this work, the authors integrate similarity joins into an XDBMS. To the best of the authors’ knowledge, this work is the first to leverage the storage scheme of an XDBMS to support XML similarity join processing.

Efficient XML query and update processing using a novel prime-based middle fraction labeling scheme

XML data can be represented by a tree or graph and the query processing for XML data requires the structural information among nodes. Designing an efficient labeling scheme for the nodes of Order-Sensitive XML trees is one of the important methods to obtain the excellent management of XML data. Previous labeling schemes such as region and prefix often sacrifice updating performance and suffer increasing labeling space when inserting new nodes. To overcome these limitations, in this paper we propose a new labeling idea of separating structure from order. According to the proposed idea, a novel Prime-based Middle Fraction Labeling Scheme (PMFLS) is designed accordingly, in which a series of algorithms are proposed to obtain the structural relationships among nodes and to support updates. PMFLS combines the advantages of both prefix and region schemes in which the structural information and sequential information are separately expressed. PMFLS also supports Order-Sensitive updates without relabeling or recalculation, and its labeling space is stable. Experiments and analysis on several benchmarks are conducted and the results show that PMFLS is efficient in handling updates and also significantly improves the performance of the query processing with good scalability.

Application of Secret Sharing in XML Protection Mechanism

In order to better protect the data in XML document, a novel method is presented for the XML (eXtensible Markup language) tree structure, which is based on Shamir's secret sharing about polynomial interpolation. In this paper, according to purpose-based access control we establish different structure views for different users, and then pick out the address of leaf nodes’ content in structure view as the coefficient in polynomial. The modified theory of secret sharing is adopted to accomplish the security protection for XML. To evaluate this model, experiments were conducted to measure its performance.

Efficient Identification of Structural Relationships for XML Queries using Secure Labeling Schemes

XML emerged as a de-facto standard for data representation and information exchange over the World Wide Web. By utilizing document object model (DOM), XML document can be viewed as XML DOM tree. Nodes of an XML tree are labeled to uniquely identify every node by following a labeling scheme. This paper proposes a method to efficiently identify the two structural relationships namely document order (DO) and sibling relationship that exist between the XML nodes using two secure labeling schemes specifically enhanced Dewey coding (EDC) and secure Dewey coding (SDC). These structural relationships influence the performance of XML queries so they need to be identified in efficient time. This paper implements the method to identify DO and sibling relationship using EDC and SDC labels for various real-time XML documents. Experiment results show the identification of DO and sibling relationship using SDC labels performs better than EDC labels for processing XML queries.

An Improved Indexing Method for Xpath Queries

Today, the XML is used as data storage for complex data models like bioinformatics information. A bioinformatics system deals with large data sets and complex queries. Thus, it is necessary to have accessing methods for XML data. XPath is a method to quickly locate any information that we need in an XML (tree) data starting from the context node in a root node to subtrees. In this paper, we propose a system model to store XML data more efficiently and also an improved indexing method to support Xpath queries. In the system model, we integrated big data model with relational data model in order to get benefit from both of them. The new indexing method is an improvement of R-tree that helps Xpath queries run more efficiently in some axes. Our experiments showed that the proposed method gains better results for node queries compared to the R-tree in transformed XML data. Our method is intended to apply to phylogenetic queries of Treefam databases.

A Queue based Reconstruction Algorithm for Reconstructing XML Tree from Relational Storage

Storage of XML documents in relational databases is a wide area of research for researches now days. Reconstruction of an XML tree from storage of relational databases is an important topic in this area. In this paper, we propose a Queue Based reconstruction algorithm to reconstruct an XML tree from relational storage of XML documents. This Algorithm uses queue Based approach to reconstruct an XML tree.

Building semantic trees from XML documents

The distributed nature of the Web, as a decentralized system exchanging information between heterogeneous sources, has underlined the need to manage interoperability, i.e., the ability to automatically interpret information in Web documents exchanged between different sources, necessary for efficient information management and search applications. In this context, XML was introduced as a data representation standard that simplifies the tasks of interoperation and integration among heterogeneous data sources, allowing to represent data in (semi-) structured documents consisting of hierarchically nested elements and atomic attributes. However, while XML was shown most effective in exchanging data, i.e., in syntactic interoperability, it has been proven limited when it comes to handling semantics, i.e., semantic interoperability, since it only specifies the syntactic and structural properties of the data without any further semantic meaning. As a result, XML semantic-aware processing has become a motivating challenge in Web data management, requiring dedicated semantic analysis and disambiguation methods to assign well-defined meaning to XML elements and attributes. In this context, most existing approaches: (i) ignore the problem of identifying ambiguous XML elements/nodes, (ii) only partially consider their structural relationships/context, (iii) use syntactic information in processing XML data regardless of the semantics involved, and (iv) are static in adopting fixed disambiguation constraints thus limiting user involvement. In this paper, we provide a new XML Semantic Disambiguation Framework titled XSDFdesigned to address each of the above limitations, taking as input: an XML document, and then producing as output a semantically augmented XML tree made of unambiguous semantic concepts extracted from a reference machine-readable semantic network. XSDF consists of four main modules for: (i) linguistic pre-processing of simple/compound XML node labels and values, (ii) selecting ambiguous XML nodes as targets for disambiguation, (iii) representing target nodes as special sphere neighborhood vectors including all XML structural relationships within a (user-chosen) range, and (iv) running context vectors through a hybrid disambiguation process, combining two approaches: concept-basedand context-based disambiguation, allowing the user to tune disambiguation parameters following her needs. Conducted experiments demonstrate the effectiveness and efficiency of our approach in comparison with alternative methods. We also discuss some practical applications of our method, ranging over semantic-aware query rewriting, semantic document clustering and classification, Mobile and Web services search and discovery, as well as blog analysis and event detection in social networks and tweets.

An Optimal Ancestry Labeling Scheme with Applications to XML Trees and Universal Posets

In this article, we solve the ancestry -labeling scheme problem, which aims at assigning the shortest possible labels (bit strings) to nodes of rooted trees, so ancestry queries between any two nodes can be answered by inspecting their assigned labels only. This problem was introduced more than 20 years ago by Kannan et al. [1988] and is among the most well-studied problems in the field of informative labeling schemes. We construct an ancestry-labeling scheme for n -node trees with label size log 2 n + O (log log n ) bits, thus matching the log 2 n + Ω(log log n ) bits lower bound given by Alstrup et al. [2003]. Our scheme is based on a simplified ancestry scheme that operates extremely well on a restricted set of trees. In particular, for the set of n -node trees with a depth of at most d , the simplified ancestry scheme enjoys label size of log 2 n + 2log 2 d + O (1) bits. Since the depth of most XML trees is at most some small constant, such an ancestry scheme may be of practical use. In addition, we also obtain an adjacency -labeling scheme that labels n -node trees of depth d with labels of size log 2 n + 3log 2 d + O (1) bits. All our schemes assign the labels in linear time, and guarantee that any query can be answered in constant time. Finally, our ancestry scheme finds applications to the construction of small universal partially ordered sets (posets). Specifically, for any fixed integer k , it enables the construction of a universal poset of size Õ ( n k ) for the family of n -element posets with a tree dimension of at most k . Up to lower-order terms, this bound is tight thanks to a lower bound of n k − o (1) by to Alon and Scheinerman [1988].

Building Semantic Trees from XML Documents

The distributed nature of the Web, as a decentralized system exchanging information between heterogeneous sources, has underlined the need to manage interoperability, i.e., the ability to automatically interpret information in Web documents exchanged between different sources, necessary for efficient information management and search applications. In this context, XML was introduced as a data representation standard that simplifies the tasks of interoperation and integration among heterogeneous data sources, allowing to represent data in (semi-) structured documents consisting of hierarchically nested elements and atomic attributes. However, while XML was shown most effective in exchanging data, i.e., in syntactic interoperability, it has been proven limited when it comes to handling semantics, i.e., semantic interoperability, since it only specifies the syntactic and structural properties of the data without any further semantic meaning. As a result, XML semantic-aware processing has become a motivating challenge in Web data management, requiring dedicated semantic analysis and disambiguation methods to assign well-defined meaning to XML elements and attributes. In this context, most existing approaches: (i) ignore the problem of identifying ambiguous XML elements/nodes, (ii) only partially consider their structural relationships/context, (iii) use syntactic information in processing XML data regardless of the semantics involved, and (iv) are static in adopting fixed disambiguation constraints thus limiting user involvement. In this paper, we provide a new XML Semantic Disambiguation Framework titled XSDF designed to address each of the above limitations, taking as input: an XML document, and then producing as output a semantically augmented XML tree made of unambiguous semantic concepts extracted from a reference machine-readable semantic network. SDF consists of four main modules for: (i) linguistic pre-processing of simple/compound XML node labels and values, (ii) selecting ambiguous XML nodes as targets for disambiguation, (iii) representing target nodes as special sphere neighborhood vectors including all XML structural relationships within a (user-chosen) range, and (iv) running context vectors through a hybrid disambiguation process, combining two approaches: concept-based and context-based disambiguation, allowing the user to tune disambiguation parameters following her needs. Conducted experiments demonstrate the effectiveness and efficiency of our approach in comparison with alternative methods. We also discuss some practical applications of our method, ranging over semantic-aware query rewriting, semantic document clustering and classification, Mobile and Web services search and discovery, as well as blog analysis and event detection in social networks and tweets.