XML Query Research Articles

A NEW EFFECTIVE LABELLING SCHEME For EFFICIENT QUERYING And UPDATING XML DOCUMENTS

Nowadays Extensible Mark-up Language (XML) is a dominant technology for formatting and exchanging data across the Internet world. Updating and retrieving a massive amount of XML data is an interesting and active research area. In addition, indexing XML data is a significant task to improve the efficiency of XML queries. Labelling nodes is the used technique for indexing XML data efficiently. There are many labelling schemes that have been proposed. However, these schemes have many limitations and shortcomings. Therefore, this paper aims to propose a new XML labelling scheme that addresses the issue of efficiency of XML query performance. Thus, this paper developed a new XML labelling scheme. Consequently, four experiments were designed in order to evaluate this. The results of these experiments suggest that the proposed scheme achieved the target results and showed an improvement in the performance and the efficiency of labelling XML documents.

XML Query Optimization for Improvement of Data Warehouses Information Processing

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.

Metrics-Based Comparison of OWL and XML for Representing and Querying Cognitive Radio Capabilities

Collaborative spectrum access requires wireless devices to perform spectrum-related tasks (such as sensing) on request from other nodes. Thus, while joining the network, they need to inform neighboring devices and/or the central coordinator of their capabilities. During the operational phase, nodes may request other permissions from the the controller, like the opportunity to transmit according to the current policies and spectrum availability. To achieve such coordinated behavior, all associated devices within the network need a language for describing radio capabilities, requests, scenarios, policies, and spectrum availability. In this paper, we present a thorough comparison of the use of two candidate languages—Web Ontology Language (OWL) and eXtensible Markup Language (XML)—for such purposes. Towards this goal, we propose an evaluation method for automating quantitative comparisons with metrics such as precision, recall, device registration, and the query response time. The requests are expressed in both SPARQL Protocol and RDF Query Language (SPARQL) and XML Query Language (XQuery), whereas the device capabilities are expressed in both OWL and XML. The evaluation results demonstrate the advantages of using OWL semantics to improve the quality of matching results over XML. We also discuss how the evaluation method can be applicable to other scenarios where knowledge, datasets, and queries require richer expressiveness and semantics.

Count vectorizer model based web application vulnerability detection using artificial intelligence approach

A web application is a dynamic, intricate, and interactive program that provides end-users with information and services such as utility payments, online communication, e-learning, socializing, shopping, online banking, and income tax filing etc. Web applications have become a major target for attackers due to their accessibility, availability, and ubiquity. Web application vulnerabilities are hazardous for some reasons. Attackers can harm an organizations image and status. The implementation flaws in web application allow the invader to infuse user-input that violates the syntax-based assembly of the query or infuse malicious code etc. Among various types of injection flaws, SQL injection (SQLI) is more prominent than (XML) both are considered as common application-layer web attack, which allows the attacker to bypass the security mechanisms therefore; these two are ranked as the most common vulnerabilities. Hence, a methodology for detecting evaluating both SQLI & XML vulnerabilities in web applications are considered for research. This research work addresses the above mentioned flaws and proposed an Ensemble Method to classify the Structure Query Language injection vulnerabilities, we selected a benchmark dataset with 33,758 rows containing; various types of SQL and XML injection attacks. Raw data is preprocessed to remove artifacts, and then feature engineering is performed using Natural Language Processing techniques to clean the data and extract 6 types of features such as TF-IDF, Word-to-Vector, SkipGram, Count Vectorizer, Glove and Continuous Bag of words. Imbalance data is handled using sampling techniques, best features are selected using 4 types of validation techniques Significant Test, PCA, Variance Threshold and Sbest. Prepared data is provided to Ensemble Model having two stages; Stage-2 accepts URL from the user and detects presence of susceptibility in the sub domains and domains. Stage-1 having 9 different types of machine learning models Multinomial, Gaussian, Bernoulli Naive Bayes, Logistic Regression, Decision Tree, Random Forest, AdaBoost, SVC with, poly, rbf and linear kernel, these models are trained on additional vectors such as google news and glove to detect the new query either SQL or XML for presences or absence of vulnerability, using this proposed ensemble approach obtained the accuracy of 99%.

An Efficient Prefix-Based Labeling Scheme for XML Dynamic Updates Using Hexagonal Pattern

To improve XML query processing, it is necessary to label XML documents efficiently for the indexing process because it allows the structural relationships between the XML nodes to be preserved without having to access the original document. However, XML data on the Web is updated as time passes, which means that the dynamic updating of XML data is an issue that may need to be handled by a XML labeling scheme specifically designed for dynamic updates. Previous XML labeling schemes have limitations when updates take place. For example, a lot of node labels need to be relabeled, a lot of duplicate labels occur during this relabeling process, and the size and time costs of the updated labels are high. Therefore, this paper proposes an efficient prefix-based labeling scheme that uses a hexagonal pattern. The proposed labeling scheme has three main advantages: (i) it avoids the need for node relabeling when XML documents are updated at random locations, (ii) it avoids duplicated labels by creating a new label for every inserted node, and (iii) it reduces the size and time costs of the updated labels. The proposed scheme is evaluated against the three most recent prefix-based labeling schemes in terms of the size and time costs of the updated labels. In addition, the ability of the proposed labeling scheme to handle several updates (such as insertions) in XML documents is also evaluated. The evaluations show that the proposed labeling scheme outperforms previously developed prefix-based labeling schemes in terms of both size and time costs, particularly for large-scale XML datasets, resulting in improved query processing performance. Moreover, the proposed scheme efficiently supports frequent updates at arbitrary positions. The paper concludes with several suggestions for further research.

Considering Structural and Vocabulary Heterogeneity in XML Query: FPTPQ and Holistic Evaluation.

The integration of XML data sources which have different schemas/DTD can originate structural and vocabular heterogeneity. In this context, it is difficult to write satisfiable queries. As a solution, many Information Systems focus on building approximate evaluation techniques for exact queries. As a project, we build flexible and preference XML query languages and associated evaluation algorithms. In this paper, we propose the Flexible Preference Tree Pattern Query (FPTPQ), a new TPQ that allows multiple items/names (resp. paths) for the same node, in order to integrate (resp. to locate) all the different instances of the database nodes. The FPTPQ enable to have preference nodes and ordering operators among label items and paths. We also provide a holistic algorithm that evaluates the FPTPQ and capitalises the preferences to determine the best available solutions. Illustrations and experimentations are realized to show the effectiveness of our solutions.

Demythization of Structural XML Query Processing: Comparison of Holistic and Binary Approaches

XML queries can be modeled by twig pattern queries (TPQs) specifying predicates on XML nodes and XPath relationships satisfied between them. A lot of TPQ types have been proposed; this paper takes into account a TPQ model extended by a specification of output and non-output query nodes since it complies with the XQuery semantics and, in many cases, it leads to a more efficient query processing. In general, there are two types of approaches to process a TPQ: holistic joins and binary joins. Whereas the binary join approach builds a query plan as a tree of interconnected binary operators, the holistic join approach evaluates a whole query using one operator (i.e., using one complex algorithm). Surprisingly, a thorough analytical and experimental comparison is still missing despite an enormous research effort in this area. In this paper, we try to fill this gap; we analytically and experimentally show that the binary joins used in a fully-pipelined plan (i.e., the plan where each join operation does not wait for the complete result of the previous operation and no explicit sorting is used) can often outperform the holistic joins, especially for TPQs with a higher ratio of non-output query nodes. The main contributions of this paper can be summarized as follows: (i) we introduce several improvements of existing binary join approaches allowing to build a fully-pipelined plan for a TPQ considering non-output query nodes, (ii) we prove that for a certain class of TPQs such a plan has the linear time complexity with respect to the size of the input and output as well as the linear space complexity with respect to the XML document depth (i.e., the same complexity as the holistic join approaches), (iii) we show that our improved binary join approach outperforms the holistic join approaches in many situations, and (iv) we propose a simple combined approach that utilizes advantages of both types of approaches.

Improved Centralized XML Query Processing Using Distributed Query Workload

eXtensible Markup Language (XML) is used widely to transfer data among a wide variety of systems. Due to an increase in query workloads and management of larger datasets, centralized processing is no longer feasible for XML query processing. To address this issue, we propose a technique that improves XML query processing through query workload distribution. An effective distributed XML query processing can be affected by several criteria such as indexing, fragmentation, distribution strategy, and well as the query handling in the distributed servers. However, we believe that an efficient labeling mechanism and an inexpensive centralized query processors or a pruning method at dedicated servers contribute greatly to the overall performance of a distributed query processor. In this paper, we present an effective centralized pruning technique that is adopted into our proposed distributed XML query processing technique to process XML queries robustly. Experimental evaluations showed that the proposed distributed query processor superseded the performance of centralized query processor.

Query processing optimization in broadcasting XML data in mobile communications

Todays, XML as a de facto standard is used to broadcast data over mobile wireless networks. In these networks, mobile clients send their XML queries over a wireless broadcast channel and recieve their desired XML data from the channel. However, downloading the whole XML data by a mobile device is a challenge since the mobile devices used by clients are small battery powered devices with limited resources. To meet this challenge, the XML data should be indexed in such a way that the desired XML data can be found easily and only such data can be downloaded instead of the whole XML data by the mobile clients. Several indexing methods are proposed to selectively access the XML data over an XML stream. However, the existing indexing methods cause an increase in the size of XML stream by including some extra information over the XML stream. In this paper, a new XML stream structure is proposed to disseminate the XML data over a broadcast channel by grouping and summarizing the structural information of XML nodes. By summarizing such information, the size of XML stream can be reduced and therefore, the latency of retrieving the desired XML data over a wirless broadcast channel can be reduced. The proposed XML stream structure also contains indexes in order to skip from the irrelevant parts over the XML stream. It therefore can reduce the energy consumption of mobile devices in downloading the results of XML queries. In addition, our proposed XML stream structure can process different types of XML queries and experimental results showed that it improves the performace of XML query processing over the XML data stream compared to the existing research works in terms of access and tuning times.

Enabling Massive XML-Based Biological Data Management in HBase.

Publishing biological data in XML formats is attractive for organizations who would like to provide their bioinformatics resources in an extensible and machine-readable format. In the era of big data, massive XML-based biological data management is emerged as a challengeable issue. With the continuous growth of the XML-based biological data sets, it is usually frustrating to use traditional declarative query languages to provide efficient query capabilities in terms of processing speed and scale. In this study, we report a novel platform to store and query massive XML-based biological data collections. A prototype tool for constructing HBase tables from XML-based biological data collections is first developed, and then a formal approach to transform the XML query model into the MapReduce query model is proposed. Finally, an evaluation of the query performance of the proposed approach on the existing XML-based biological databases is presented, showing that the performance advantages of the proposed solution. The source code of the massive XML-based biological data management platform is freely available at https://github.com/lyotvincent/X2H.

Pentagonal scheme for dynamic XML prefix labelling

In XML databases, the indexing process is based on a labelling or numbering scheme and generally used to label an XML document to perform an XML query using the path node information. Moreover, a labelling scheme helps to capture the structural relationships during query processing without the need to access the physical document. Two of the main problems for labelling XML schemes are duplicated labels, and cost efficiency regarding labelling time and size. This research presents a novel dynamic XML labelling scheme, called the Pentagonal Scheme, in which data are represented as ordered XML nodes with relationships between them. The update of these nodes from large-scale XML documents has been widely investigated and represents a challenging research problem, as it means relabelling a whole tree. Our algorithms provide an efficient dynamic XML labelling scheme to support data updates without duplicating labels or relabelling old nodes. Our work evaluates the labelling process in terms of size and time, and evaluates the labelling scheme’s ability to handle several insertions in XML documents. The findings indicate that the Pentagonal scheme shows a better initial labelling time performance than compared schemes. Particularly when using large size XML datasets. Moreover, it efficiently supports random skewed updates, has fast calculations and uncomplicated implementations to thus handle updates efficiently. In addition, the comparable evaluation of the query response time and relationships in Pentagonal scheme can be efficiently performed without presenting any extra cost. It was for this reason that our labelling scheme achieved the goal of this research.

RESULTING APPROXIMATE QUERIES USING XML DATA

Daunting for users to formulate precise queries and search correct answers. Therefore, approximate matching is introduced to trot out the problem in respondent users’ queries, and this matching might be addressed by 1st reposeful the structure and content of a given question, so longing for answers that match the relaxed queries. Ranking and returning the foremost relevant results of a question became the foremost widespread paradigm in XML query process. However, the present proposals don't adequately take structures into consideration and those them so lack the strength to elegantly mix structures with contents to answer the relaxed queries. To handle this drawback, we have a tendency to 1st propose a classy framework of question relaxations for supporting approximate queries over XML information. The answers underlying this framework aren't compelled to strictly satisfy the given question formulation, instead they will be based on properties inferable from the initial question. we have a tendency to then develop a unique top-k retrieval approach.

Systematic Partitioning and Labeling XML Subtrees for Efficient Processing of XML Queries in IoT Environments

With the advent of IoT, storing, indexing and querying XML data efficiently is critical. To minimize the cost of querying XML data, researchers have proposed many indexing techniques. Nearly all the techniques, partition the XML data into a number of data-streams. To evaluate a query, existing twig pattern matching algorithms process a subset of the data-streams simultaneously. Processing many data-streams simultaneously results in some or all of the following four problems, namely, the accessing of many data nodes which don’t appear in the final solution of a given query, the generation of duplicate results, the generation of huge number of intermediate results, and the cost of merging the generated intermediate results. To the best of our knowledge, all the existing twig pattern matching algorithms suffer from some or all of the above mentioned problems. This paper proposes a new twig pattern matching algorithm called MatchQTP which processes one data-stream at a time and avoids all the above mentioned four problems. It also proposes a new indexing technique called RLP-Index and a new XML node labeling scheme called RLP-Scheme, both of which are used by MatchQTP. Unlike the existing indexing techniques, RLP-Index stores a subset of the data nodes. The rest of the data nodes can be generated efficiently. This minimizes storage space utilization and query processing time and makes RLP-Index the first of its kind. Many experiments were conducted to study the performance of MatchQTP. The results show that MatchQTP is very efficient and highly scalable. It was also compared with four algorithms, three of which are used frequently in the literature to compare the performance of new algorithms and the fourth algorithm is the state-of-the-art algorithm. MatchQTP significantly and consistently outperformed all of them

Solving the Intractable Problem: Optimal Performance for Worst Case Scenarios in XML Twig Pattern Matching

In the history of databases, eXtensible Markup Language (XML) has been thought of as the standard format to store and exchange semi-structured data. With the advent of IoT, XML technologies can play an important role in addressing the issue of processing a massive amount of data generated from heterogeneous devices. As the number and complexity of such datasets increases there is a need for algorithms which are able to index and retrieve XML data efficiently even for complex queries. In this context twig pattern matching, finding all occurrences of a twig pattern query (TPQ), is a core operation in XML query processing. Until now holistic joins have been considered the state-of-the-art TPQ processing algorithms, but they fail to guarantee an optimal evaluation except at the expense of excessive storage costs which limit their scope in large datasets. In this article, we introduce a new approach which significantly outperforms earlier methods in terms of both the size of the intermediate storage and query running time. The approach presented here uses Child Prime Labels (Alsubai & North, 2018) to improve the filtering phase of bottom-up twig matching algorithms and a novel algorithm which avoids the use of stacks, thus improving TPQs processing efficiency. Several experiments were conducted on common benchmarks such as DBLP, XMark and TreeBank datasets to study the performance of the new approach. Multiple analyses on a range of twig pattern queries are presented to demonstrate the statistical significance of the improvements.

A Narrative Review of Storing and Querying XML Documents Using Relational Database

Extensible Markup Language (XML) has become a common language for data interchange and data representation in the Web. The evolution of the big data environment and the large volume of data which is being represented by XML on the Web increase the challenges in effectively managing such data in terms of storing and querying. Numerous solutions have been introduced to store and query XML data, including the file systems, Object-Oriented Database (OODB), Native XML Database (NXD), and Relational Database (RDB). Previous research attempts indicate that RDB is the most powerful technology for managing XML data to date. Because of the structure variations of XML and RDB, the need to map XML data to an RDB scheme is increased. This growth has prompted numerous researchers and database vendors to propose different approaches to map XML documents to an RDB, translating different types of XPath queries to SQL queries and returning the results to an XML format. This paper aims to comprehensively review most cited and latest mapping approaches and database vendors that use RDB solution to store and query XML documents, in a narrative manner. The advantages and the drawbacks of each approach is discussed, particularly in terms of storing and querying. The paper also provides some insight into managing XML documents using RDB solution in terms of storing and querying and contributes to the XML community.

A privacy protection approach for XML-based archives management in a cloud environment

PurposeThe security of archival privacy data in the cloud has become the main obstacle to the application of cloud computing in archives management. To this end, aiming at XML archives, this paper aims to present a privacy protection approach that can ensure the security of privacy data in the untrusted cloud, without compromising the system availability.Design/methodology/approachThe basic idea of the approach is as follows. First, the privacy data before being submitted to the cloud should be strictly encrypted on a trusted client to ensure the security. Then, to query the encrypted data efficiently, the approach constructs some key feature data for the encrypted data, so that each XML query defined on the privacy data can be executed correctly in the cloud.FindingsFinally, both theoretical analysis and experimental evaluation demonstrate the overall performance of the approach in terms of security, efficiency and accuracy.Originality/valueThis paper presents a valuable study attempting to protect privacy for the management of XML archives in a cloud environment, so it has a positive significance to promote the application of cloud computing in a digital archive system.

An efficient stream structure for broadcasting the encrypted XML data in mobile wireless broadcast channels

In mobile wireless broadcast networks, XML data is encrypted before it is sent over the broadcast channel in order to ensure the confidentiality of XML data. In these networks, mobile clients must not have access to all the XML data; rather they should have access to some parts of the XML data that are relevant to them and to which they are authorized to have access. In this paper, a new encrypted XML data stream structure is proposed which supports the confidentiality of XML data over the broadcast channel. In our proposed stream structure, the size of encrypted XML data stream is reduced by grouping the paths, XML nodes, texts, and attributes together. The proposed structure includes several indexes to skip from irrelevant data over the broadcast channel. The experimental results demonstrate that the use of our proposed stream structure efficiently disseminates XML data in mobile wireless broadcast networks in a secure manner and the indexes in our proposed stream structure improve the performance of XML query processing over the encrypted XML data stream.

Querying XML documents using Prolog engines: When is this a good idea?

XML has become a universal standard for information exchange over the Web due to features such as simple syntax and extensibility. Processing queries over these documents has been the focus of several research groups. In fact, there is broad literature in efficient XML query processing which explore indexes, fragmentation techniques, etc. However, for answering complex queries, existing approaches mainly analyze information that is explicitly defined in the XML document. A few work investigate the use of Prolog to increase the query possibilities, allowing inference over the data content. This can cause a significant increase in the query possibilities and expressive power, allowing access to non-obvious information. However, this requires translating the XML documents into Prolog facts. But for regular queries (which do not require inference), is this a good alternative? What kind of queries could benefit from the Prolog translation? Can we always use Prolog engines to execute XML queries in an efficient way? There are many questions involved in adopting an alternative approach to run XML queries. In this work, we investigate this matter by translating XML queries into Prolog queries and comparing the query processing times using Prolog and native XML engines. Our work contributes by providing a set of heuristics that helps users to decide when to use Prolog engines to process a given XML query. In summary, our results show that queries that search elements by a key value or by its position (simple search) are more efficient when run in Prolog than in native XML engines. Also, queries over large datasets, or that searches for substrings perform better when run by native XML engines.

Effective Evaluation of XML Queries in a Visual Environment

Effective Evaluation of XML Queries in a Visual Environment

An Algebra for Fuzzy Spatiotemporal Data in XML

A formal algebra is essential for applying standard database-style query optimization to XML queries. We develop such algebra for manipulating fuzzy spatiotemporal XML data. We also introduce several formal characterizations of fuzzy spatiotemporal algebraic equivalences and study how XQuery expressions can be transformed into algebraic expressions. It shows that the algebra can lay a firm foundation for managing fuzzy spatiotemporal XML data.