XML Query Research Articles

Association Policy for XML Query Answering By Mining Tree

As we consider any database, then to retrieve the information in the files is an important issue. Mostly the files are arranged in proper manner by using the trees and No SQL database. This approach of TBAS (Tree Based Association Rules) plays an important role for reducing the retrieval time of query. So that, if the user wants to retrieve the information about any particular subject then it can be provided by the relational database in very less time because the information is stored in XML files i.e. the information is stored by using tags. In this way the retrieval time is reduces. This Intentional Knowledge of mined information is beneficial, and this is an unique way to manipulate both the structure and the content of the XML document and, exact answers to the user queries whenever it required.

Comparative Study of Clustering Algorithms using Overall SimSUX Similarity Function for XML Documents

A huge amount of information is represented in XML format. Several tools have been developed to store, and query XML data. It becomes inevitable to develop high performance techniques for efficiently analysing extremely large collections of XML data. One of the methods that many researchers have focused on is clustering, which groups similar XML data, according to their content and structures. In previous work, there has been proposed the similarity function OverallSimSUX, that facilitates to capture the degree of similitude among the documents with a novel methodology for clustering XML documents using both structural and content features. Although this methodology shows good performance, endorsed by experiments with several corpus and statistical tests, on having had impliedly only one clustering algorithm, K-Star, we do not know the effect that it would suffer if we replaced this algorithm by other with dissimilar characteristics. Therefore to endorse completely the methodology, in this work we make a comparative study of the effects of applying the methodology for the OverallSimSUX similarity function calculation, using clustering algorithms of different classifications . Based on our analysis, we arrived to two important results: (1) The Fuzzy-SKWIC clustering algorithm works best both with methodology and without methodology, although there are not present significant differences respect to the K-Star clustering algorithm; (2) For each analysed algorithm when using the methodology, we obtain better results than when it is not taken into account.

Efficient XML Keyword Search Using H-Reduction Factor and Interactive Algorithm

XML is believed to be the most significant new standard for data model. The evolution of web search engines makes keyword search the most popular search model for both structured and semi-structured data. Keyword search on XML is considered to be an effective information retrieval method. It is suitable to underpin keyword search in XML database. It is not necessary for the user to understand the complex query languages and the database approach to query XML databases. Recent, work on effective Keyword Search in XML is based on concept such as XReal which resolved the keyword ambiguity problem and the Dynamic infer algorithm scheme was proposed to resolve the inconsistency and abnormality problems in XReal. In this paper, we propose H-reduction factor and Interactive algorithm to resolve the reduction factor issue, abnormality problem and the interactive approach to keyword search respectively. Experimental results and comparison shows the effectiveness of our approach.

Improved selectivity estimator for XML queries based on structural synopsis

With the increasing popularity of XML database applications, the use of efficient XML query optimizers is becoming very essential. The performance of an XML query optimizer depends heavily on the query selectivity estimators it uses to find the best possible query execution plan. In this work, we propose and evaluate a novel selectivity estimator, based on a structural synopsis, called SynopTech. The main idea of SynopTech is the generation of a summary tree by labeling the nodes of the source XML data tree using a fingerprint function and merging subtrees with similar structures. The generated summary tree is then used by SynopTech to estimate the selectivity of given queries. We experimented the proposed approach with four benchmark datasets of different structural characteristics and using different types of queries. Comparing with the Sampling algorithm, one of the state-of-the-art algorithms for selectivity estimations, SynopTech achieved lower selectivity estimation error rates, yet with very low memory budget. For example, for linear and existential queries, SynopTech had perfect estimations whereas the Sampling algorithm had an error rate of up to 70 %. For regular twig queries, SynopTech had a maximum error rate of 4.12 % whereas the Sampling algorithm had more than 55 %.

A Formal Description of XML Tree Pattern Query for XQuery Language

In order to express tree pattern query in query plan and take advantage of formal method to analyze its behavioral characteristics, this paper present a formal description of tree pattern query based on functional language and denotational semantics. This description major focuses on behavior of a tree pattern query on matching against an eXtensible Markup Language (XML) document tree. First, we introduce a formal definition for a kind of extended generalized tree pattern (GTP++). Then we present a functional tree pattern description language (XTPL) for GTP++ and give its complete denotational semantics based on a novel data structure, named WTree, which efficiently organizes this typical XML data query results and provides flexible data access method. In the end, we present the formal semantics of identifying tree pattern from path expressions. By using formal methods, the semantics of tree pattern query is consistent and analyzable. As the core operation of XML query, this formal description can provide an initial step for analyzing the correctness of XML queries, and improves the reliability and robustness of query processing methods.

English

Providing efficient query to XML data for ebXML applications in e-commerce is crucial, as XML has become the most important technique to exchange data over the Internet. ebXML is a set of specification for companies to exchange their data in e-commerce. Following the ebXML specifications, companies have a standard method to exchange business messages, communicate data, and business rules in e-commerce. Due to its tree-structure paradigm, XML is superior for its capability of storing and querying complex data for ebXML applications. Therefore, discovering frequent XML query patterns has become an interesting topic for XML data management in ebXML applications. The study presents an efficient mining algorithm, namely ebX2Miner, to discover the frequent XML query patterns for ebXML applications. Unlike the existing algorithms, the study proposes a new idea by encoding the XML user queries and then storing these codes to generate the frequent XML user query patterns. Furthermore, the simulation results show that the ebX2Miner outperforms other algorithms in its execution time and used memory space.   Key words: XML query pattern mining, XML query, encoding scheme, ebXML, e-commerce.

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“Bidirectionalization for free” for monomorphic transformations

A bidirectional transformation is a pair of mappings between source and view data objects, one in each direction. When the view is modified, the source is updated accordingly with respect to some laws. Over the years, a lot of effort has been made to offer better language support for programming such transformations. In particular, a technique known as bidirectionalization is able to analyze and transform unidirectional programs written in general purpose languages, and “bidirectionalize” them.Among others, an approach termed semantic bidirectionalization proposed by Voigtländer stands out in terms of user-friendliness. A unidirectional program can be written using arbitrary language constructs, as long as the function it represents is polymorphic and the language constructs respect parametricity. The free theorems that follow from the polymorphic type of the program allow a kind of forensic examination of the transformation, determining its effect without examining its implementation. This is convenient, as the programmer is not restricted to using a particular syntax; but it does require the transformation to be polymorphic.In this paper, we lift this polymorphism requirement to improve the applicability of semantic bidirectionalization. Concretely, we provide a type class PackMγαμ, which intuitively reads “a concrete datatype γ is abstracted to a type α, and the ‘observations’ made by a transformation on values of type γ are recorded by a monad μ”. With PackM, we turn monomorphic transformations into polymorphic ones that are ready to be bidirectionalized. We demonstrate our technique with case studies of typical applications of bidirectional transformation, namely text processing, XML query and graph transformation, which were commonly considered beyond semantic bidirectionalization because of their monomorphic nature.

A Sliding-Window Method to Discover Recent Frequent Query Patterns from XML Query Streams

Providing efficient mining algorithm to discover recent frequent XML user query patterns is crucial, as many applications use XML to represent data in their disciplines over the Internet. These recent frequent XML user query patterns can be used to design an index mechanism or cached and thus enhance XML query performance. Several XML query pattern stream mining algorithms have been proposed to record user queries in the system and thus discover the recent frequent XML query patterns over a stream. By using these recent frequent XML query patterns, the query performance of XML data stream is improved. In this paper, user queries are modeled as a stream of XML queries and the recent frequent XML query patterns are thus mined over the stream. Data-stream mining differs from traditional data mining since its input of mining is data streams, while the latter focuses on mining static databases. To facilitate the one-pass mining process, novel schemes (i.e. XstreamCode and XstreamList) are devised in the mining algorithm (i.e. X2StreamMiner) in this paper. X2StreamMiner not only reduces the memory space, but also improves the mining performance. The simulation results also show that X2StreamMiner algorithm is both efficient and scalable. There are two major contributions in this paper. First, the novel schemes are proposed to encode and store the information of user queries in an XML query stream. Second, based on the two schemes, an efficient XML query stream mining algorithm, X2StreamMiner, is proposed to discover the recent frequent XML query patterns.

Static analysis of XML security views and query rewriting

In this paper, we revisit the view based security framework for XML without imposing any of the previously considered restrictions on the class of queries, the class of DTDs, and the type of annotations used to define the view. First, we study query rewriting with views when the classes used to define queries and views are Regular XPath and MSO. Next, we investigate problems of static analysis of security access specifications (SAS): we introduce the novel class of interval-bounded SAS and we define three different manners to compare views (i.e. queries) from a security point of view. We provide a systematic study of the complexity for deciding these three comparisons, when the depth of the XML documents is bounded, when the document may have an arbitrary depth but the queries defining the views are restricted to guarantee the interval-bounded property, and in the general setting without restriction on queries and document.

XQStream++: Fast tuple extraction algorithm for streaming XML data

Tuple extraction from streaming XML should be cost effective for real-time query evaluation. Recently, StreamTX exhibits a good performance in terms of both running time and memory usage to support the tuple extraction queries for streaming XML. However, we empirically observe that StreamTX incurs computational overhead unnecessarily, since it builds on TwigStack, an XML query processing algorithm originally developed for stored XML. In this paper, we first design a non-recursive XQStream algorithm to handle inefficient recursive calls of StreamTX. Subsequently, we extend the basic XQStream by incorporating two novel schemes: (1) the relational pointer to efficiently and effectively evaluate the structural relationship of elements, and (2) the pattern reuse to reduce redundant path evaluations for pattern matching. The performance evaluation on various datasets provides new empirical findings. First, XQStream++, which incorporates the relational pointer and the pattern reuse scheme into XQStream, significantly outperforms the state-of-the-art algorithms in running time with a small, nearly constant memory usage. Second, the most recently released XQuery engines outperform StreamTX in running time.

Cost-based Query Optimization for Xpath

In this article, we address the issues which are related to the cost-based XML query optimization for XPath. Specially, we focus on the issue of how to determine the execution order for a given XPath expression according to the cost models. The main impact factor that dominates the execution order is the size of the intermediate results during the evaluation of queries. The hierarchy encoding scheme and the value-encoding histogram are introduced to support the size estimation of the path expressions. Two cost models are proposed to describe the costs of different types of join operations in the path expressions. A heuristic-based dynamic programming approach is proposed to determine the optimal execution tree. The primary experimental results demonstrate the validity of our approaches.

Unleashing XQuery for Data-Independent Programming

The XQuery language was initially developed as an SQL equivalent for XML data, but its roots in functional programming make it also a perfect choice for processing almost any kind of structured and semi-structured data. Apart from standard XML processing, however, advanced language features make it hard to efficiently implement the complete language for large data volumes. This work proposes a novel compilation strategy that provides both flexibility and efficiency to unleash XQuery’s potential as data programming language. It combines the simplicity and versatility of a storage-independent data abstraction with the scalability advantages of set-oriented processing. Expensive iterative sections in a query are unrolled to a pipeline of relational-style operators, which is open for optimized join processing, index use, and parallelization. The remaining aspects of the language are processed in a standard fashion, yet can be compiled anytime to more efficient native operations of the actual runtime environment. This hybrid compilation mechanism yields an efficient and highly flexible query engine that is able to drive any computation from simple XML transformation to complex data analysis, even on non-XML data. Experiments with our prototype and state-of-the-art competitors in classic XML query processing and business analytics over relational data attest the generality and efficiency of the design.

Efficiently Subtree Matching between XML and Probabilistic XML Documents

We explored the subtree matching problem of probabilistic XML documents: finding the matches of an XML query tree over a probabilistic XML document, using the canonical tree edit distance as a similarity measure between subtrees. Probabilistic XML is a probability distribution model capturing uncertainty of both value and structure. Query over probabilistic XML documents is difficult: an naivie algorithm has exponential complexity by directly compute the tree edit distance between the query tree and each certain XML tree represented by the probabilistic XML document. Based on the method of tree edit distance computation over certain XML subtrees, we defined a minimum-solution to the edit distance computation, which means the minimum cost to translate the query tree to the probabilistic XML tree. Furthermore, we developed an algorithm---ASM (Algorithm of Subtree Matching) to compute the minimum solution. Finally, we proved the complexity of ASM is linear in the size of the probabilistic XML document.

Mapping XML to a Wide Sparse Table

XML is commonly supported by SQL database systems. However, existing mappings of XML to tables can only deliver satisfactory query performance for limited use cases. In this paper, we propose a novel mapping of XML data into one wide table whose columns are sparsely populated. This mapping provides good performance for document types and queries that are observed in enterprise applications but are not supported efficiently by existing work. XML queries are evaluated by translating them into SQL queries over the wide sparsely-populated table. We show how to translate full XPath 1.0 into SQL. Based on the characteristics of the new mapping, we present rewriting optimizations that dramatically reduce the number of joins. Experiments demonstrate that query evaluation over the new mapping delivers considerable improvements over existing techniques for the target use cases.

Fast Computational Mining Technique for XML Query Answering Support

database research field has focused on the Extensible Mark-up Language (XML) because of its adaptable progressive nature which can use to represent to huge amount of data, likewise it doesn't have absolute and fixed schema, yet having possibly spasmodic and deficient structure. Quite hard undertaking to concentrate data from semi organized documents and is set to wind up more challenging as the measure of computerized data accessible on the Internet develops. Really, the data set returned as response to a query may be so enormous it is not possible pass on interpretable information, as documents are regularly so extensive. A methodology based on Tree- Based Association Rules (TARs), which furnish rough, intentionaldata about the structure and the contents of XML documents both, and additionally it might be saved in XML format. This mined information is utilized to give, a brief thought of both the structure and the content of the XML archive and snappy, inexact replies to queries at whatever point needed.

Semantic-based Structural and Content indexing for the efficient retrieval of queries over large XML data repositories

The emergence of XML adoption as semi-structured data representation in multi-disciplinary domains has highlighted the need to support the optimization of complex data retrieval processing. In a Big Data environment, the need to speed up data retrieval processes has further grown significantly. In this paper, we have adopted an optimization approach that takes into consideration the semantics of the dataset in order to deal with the complexity of multi-disciplinary domains in Big Data, in particular when the data is represented as XML documents. Our method particularly addresses a twig XML query (or a branched path query), as it is one of the most costly query tasks due to the complexity of the join operation between multiple paths. Our work focuses on optimizing the structural and the content part of XML queries by presenting a method for indexing and processing XML data based on the concept of objects that is formed from the semantic connectivity between XML data nodes. Our method performs object-based data partitioning, which aims at leveraging the notion of frequently-accessed data subsets and putting these subsets together into adjacent partitions. Then, it evaluates branched queries through two essential components: (i) Structural and Content indexing, which use an object-based connection to construct indices i.e. Schema Index, Data Index and Value Index; and (ii) query processing to produce the final results in optimal time. At the end of this paper, a set of experimental results for the proposed approach on a range of real and synthetic XML data, as well as a comparative study with other related work in the area, are presented to demonstrate the effectiveness of our proposed method in terms of CPU cost, matching and merging cost, scalability (size and number of branches) and total number of scanned elements. Our evaluation demonstrates the benefit of the proposed index in terms of performance speed as well as scalability which is critical in a large data repository.

A XML Pattern Match Algorithm of Structural Index

XML query processing needs to search an XML document and return results that satisfy a request. In this paper, we proposed an effective pattern match algorithm based on structural index. This algorithm uses a suitable labeling scheme and an efficient structural index by identifying parent-child, ancestor-descendant and sibling relationships among nodes. Due to the algorithm avoids invalid immoderate results to be pushed stacks, So, It reduces the amount of joint and improves the performance of Query. Experiment results proved that our algorithm, TwigM performs about 15% better compared to TwigStack [ and 10% better than TwigINLAB[ for all types of queries.

MLTwig: A Multi-Layer Tree Pattern Matching Approach for XQuery

Twig query, also known as tree pattern query (TPQ), is considered as the core operation of XML data queries. However, a complex XML query described by XQuery often cannot be represented by a single basic TPQ. Aiming at XQuery language, this paper presents a multi-layer tree pattern representation method, named MTP, and a matching algorithm MLTwig, which can effectively use intermediate results of outer query to match inner tree pattern in MTP based on a novel data structure, WTree, thereby saving time and space. Experimental results show that this algorithm can improve the processing efficiency for XQuery expression which can be represented by MTP.

English

XML data format is widely used across devices to exchange data due to its main feature of supporting platform independency. Existing or current system used to find given keyword in XML data, a user need to know XML query language to create keyword query, submit the query for processing and get the results. In this approach user having limited knowledge about XML data, often feels complex and difficult to find require keyword in XML data and sometimes they has to use try and see approach to get relevant information. In this paper we will study different keyword based searching methods focusing on fuzzy search approach to find keyword in XML data. This searching techniques allows user explore data based on keyword as they typed even with minor mistakes in their keyword. Our survey paper helps to understand keyword based alternative method for searching XML data. Using this techniques user is able to find require sort of information from XML based file, without knowing XML query language and XML file content. Our survey paper focuses on techniques based on keyword search to retrieve top-k results, top-k algorithm to achieve high result quality and search efficiency and effective ranking method, early algorithm termination techniques while identifying top-k results and effective index structures.