XPath Queries Research Articles

AN IMPROVED INDEXING METHOD FOR QUERYING BIG XML FILES

The exponential growth of bioinformatics in the healthcare domain has revolutionized our understanding of DNA, proteins, and other biomolecular entities. This remarkable progress has generated an overwhelming volume of data, necessitating big data technologies for efficient storage and indexing. While big data technologies like Hadoop offer substantial support for big XML file storage, the challenges of indexing data sizes and XPath query performance persist. To enhance the efficiency of XPath queries and address the data size problem, a novel approach that is derived from the spatial indexing method of the R-tre family. The proposed method is to modify the structure of leaf nodes in the indexing tree to preserve XML-sibling connections. Then, new algorithms for constructing the new tree structure and processing sibling queries better are introduced. Experimental results demonstrate the superior efficiency of sibling XPath queries with reduced data sizes for indexing, while other XPath queries exhibit notable performance improvements. This research contributes to the development of more effective indexing methods for managing and querying large XML datasets in bioinformatics applications, ultimately advancing biomedical research and healthcare initiatives.

Web Scraping using Natural Language Processing: Exploiting Unstructured Text for Data Extraction and Analysis

In recent years, combining web scraping techniques with Natural Language Processing (NLP) has emerged as a powerful approach to unlock deeper insights from unstructured textual data. This research study presents a detailed exploration of web scraping using Natural Language Processing (NLP) techniques, demonstrating how these methodologies can be synergistically integrated to extract and analyze unstructured text from diverse web sources. This research study analyzes the challenges posed by unstructured data on the web and how NLP can play a pivotal role in converting this text into structured and actionable information. The first part of the paper covers an overview of web scraping methods, including rule-based parsing, XPath queries, and the use of web scraping libraries such as BeautifulSoup and Scrapy. The second part of this research work focuses on applying NLP techniques to process and analyze the extracted textual data. Further, the preprocessing steps such as tokenization, stemming, and stop word removal, are analyzed followed by more advanced techniques like Named Entity Recognition.

Schema-Based Automata Determinization

We propose an algorithm for schema-based determinization of finite automata on words and of step-wise hedge automata on nested words. The idea is to integrate schema-based cleaning directly into automata determinization. We prove the correctness of our new algorithm and show that it is alway smore efficient than standard determinization followed by schema-based cleaning. Our implementation permits to obtain a small deterministic automaton for an example of an XPath query, where standard determinization yields a huge stepwise hedge automaton for which schema-based cleaning runs out of memory.

Parallel XPath query based on cost optimization

Parallel XPath query based on cost optimization

SESCon: Secure Ethereum Smart Contracts by Vulnerable Patterns’ Detection

Ethereum smart contracts have been gaining popularity toward the automation of so many domains, i.e., FinTech, IoT, and supply chain, which are based on blockchain technology. The most critical domain, e.g., FinTech, has been targeted by so many successful attacks due to its financial worth of billions of dollars. In all attacks, the vulnerability in the source code of smart contracts is being exploited and causes the steal of millions of dollars. To find the vulnerability in the source code of smart contracts written in Solidity language, a state-of-the-art work provides a lot of solutions based on dynamic or static analysis. However, these tools have shown a lot of false positives/negatives against the smart contracts having complex logic. Furthermore, the output of these tools is not reported in a standard way with their actual vulnerability names as per standards defined by the Ethereum community. To solve these problems, we have introduced a static analysis tool, SESCon (secure Ethereum smart contract), applying the taint analysis techniques with XPath queries. Our tool outperforms other analyzers and detected up to 90% of the known vulnerability patterns. SESCon also reports the detected vulnerabilities with their titles, descriptions, and remediations as per defined standards by the Ethereum community. SESCon will serve as a foundation for the standardization of vulnerability detection.

Pipelined XPath Query Based on Cost Optimization

XPath query is the key part of XML data processing, and its performance is usually critical for XML applications. In the process of XPath query, there is inherent seriality between query steps, which makes it difficult to parallelize the query effectively as a whole. On the other hand, although XPath query has the characteristics of data stream processing and is suitable for pipeline processing, the data flow of each query step usually varies a lot, which results in limited performance under multithreading conditions. In this paper, we propose a pipelined XPath query method (PXQ) based on cost optimization. This method uses pipelined query primitives to process query steps based on relation index. During pipeline construction, a cost estimation model based on XML statistics is proposed to estimate the cost of the query primitive and provide guidance for the creation of a pipeline phase through the partition of query primitive sequence. The pipeline construction technique makes full use of available worker threads and optimizes the load balance between pipeline stages. The experimental results show that our method can adapt to the multithreaded environment and stream processing scenarios of XPath query, and its performance is better than the existing typical query methods based on data parallelism.

Agent data merging

Abstract The present article deals with data collection in a given field using the agent-based technologies from various information sources of the Internet with the aim to ob-tain reliable and up-to-date data. The agent-based approach is illustrated by the data collection on the nuclear power plants operating all over the world. Three open information sources have been selected for data extraction. The information sources concerned have been analyzed and the features of data provision structure identified. In the course of the present work the following tools for the develop-ment of the software agents have been described: the browser control for human behavior simulation, HTML markup analysis using the XPath query language and data extraction from PDF-documents using regular expressions. Above all, the article considers the software architecture and the database scheme. In the re-sult of the software operation, data regarding 789 nuclear power plants has been obtained.

Elements of XPath-like languages in problems of constructing semantic XML models of natural language texts

The subject of the research is the possibility of using XPath-like micro-languages of programming in the generation systems of programs of the PGEN ++ class for the selection and completion of XML-models describing the plan for solving the original problem, according to which the solver program is generated. It is supposed to build such models according to the description of the problem in natural language, thus, we are talking about elements of artificial intelligence technologies. XPath-like language works in the layer of regular-logical expressions (highlighting elements of the primary XML document), performing primary processing of the data obtained in the layer of grammatical parsing of the source text. In addition, XPath-like elements are used to render the final XML models. The standard natural language parsing libraries are used. Non-standard XPath query language extensions are used. For the first time, the idea of expanding the XPath query language to the level of an algorithmic language by introducing the minimum required number of syntactic elements is proposed. It is also proposed to use syntactic elements with an XPath-like structure as both generating and controlling weak constraints of the process of direct inference of final semantic XML models.

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.

Handling distributed XML queries over large XML data based on MapReduce framework

With the increase in available extensible markup language (XML) documents, numerous approaches to querying have been proposed in the literature. XPath queries and Twig pattern queries are the two basic approaches, directly affecting the efficiency of XML operations. Distributive manipulation of massive XML data is challenging. This paper aims to develop an efficient distributed XML query processing method using MapReduce, which simultaneously processes several queries on large volumes of XML data. First, we split up a large-scale XML data file into file-splits and put them in a distributed storage system. Then, we present an efficient algorithm to compute different fragments of the document tree using the MapReduce framework in parallel. In order to efficiently handle a large amount of XML data, we built a partition index and used a random access mechanism for specific queries. The experiment results show that our proposed approach is efficient with good scalability.

Depth-First Reasoning on Trees

The mu-calculus is an expressive modal logic with least and greatest fixed-point operators. This formalism encompasses many temporal, program and description logics, and it has been widely applied in a broad range of domains, such as, program verification, knowledge representation and concurrent pervasive systems. In this paper, we propose a satisfiability algorithm for the mu-calculus extended with converse modalities and interpreted on unranked trees. In contrast with known satisfiability algorithms, our proposal is based on a depth-first search. We prove the algorithm to be correct (sound and complete) and optimal. We also describe an implementation. The extension of the mu-calculus with converse modalities allows to efficiently characterize standard reasoning problems (emptiness, containment and equivalence) of XPath queries. We also describe several query reasoning experiments, which shows our proposal to be competitive in practice with known implementations.

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.

Design and Implementation of a Hardware Versatile Publish-Subscribe Architecture for the Internet of Things

A variety of contemporary technologies are being framed within the Internet of Things (IoT) architecture, including publish/subscribe (pub/sub) systems. In IoT, things, such as objects, machines, vehicles, and wireless sensors, have to communicate with other things or humans and exchange information based on Internet connectivity. With the integration of pub/sub mechanism with IoT, these things can “publish” their presence to a specific node, which can be named a broker or router, while things that “subscribe” to that node are able to receive information based on publishers’ content. In order to perceive a sound and efficient pub/sub structure in IoT, high performance processing and interoperability are valid requirements. This paper presents a versatile architecture of a broker, named X2CBBR, that can operate in IoT with different pub/sub systems. X2CBBR: 1) adopts XML-based publication data and XPath-based subscription data to yield interoperability; 2) processes both XML data and XPath queries in hardware (instead of software) to boost processing performance; 3) employs a hardware-based matching mechanism that exploits subscription commonalities; and 4) makes use of four different operation modes as a method for accepting or limiting acceptance of either publications or subscriptions. While maintaining the total decoupling feature between publishers and subscribers, the broker switches from an operation mode to another to keep traffic under control. Moreover, its content-based routing mechanism avoids redundant subscription and notification data. Finally, the broker can effectively operate in either centralized or distributed systems. The results obtained through a prototype hardware implementation targeting an FPGA demonstrate the high-efficiency of the broker/router in multiple scenarios.

On Regular Paths with Counting and Data Tests

Regular path expressions represent the navigation core of the XPath query language for semi-structured data (XML), and it has been characterized as the First Order Logic with Two Variables (FO2). Data tests refers to (dis)equality comparisons on data tree models, which are unranked trees with two kinds of labels, propositions from a finite alphabet, and data values from a possibly infinite alphabet. Node occurrences on tree models can be constrained by counting/arithmetic constructors. In this paper, we identify an EXPTIME extension of regular paths with data tests and counting operators. This extension is characterized in terms of a closed under negation Presburger tree logic. As a consequence, the EXPTIME bound also applies for standard query reasoning (emptiness, containment and equivalence).

XAncestor: An efficient mapping approach for storing and querying XML documents in relational database using path-based technique

XML has become a common language for data exchange on the Web, so it needs to be managed effectively. There are four central problems in XML data management: capture, storage, retrieval, and exchange. Even though numerous database systems are available, the relational database (RDB) is often used to store and query the content of XML documents. Therefore the processes of mapping from XML to RDB and vice versa occur frequently. Numerous researchers have proposed approaches to map hierarchically structured XML documents into the tabular format of a RDB. However, the previously developed approaches have faced problems in terms of storage and query response time. If the design of a RDB is inefficient, the number of join operations between tables increases when a query is executed, which affects the query response time. To overcome this limitation, this paper proposes a new mapping approach, known as XAncestor, which consists of two algorithms: an XML mapping algorithm (XtoDB) and a query mapping algorithm (XtoSQL). XtoDB maps XML documents to a fixed RDB with less storage space. XtoSQL translates XPath queries into corresponding SQL queries based on the constructed RDB in order to reduce the query response time i.e., the time taken to execute the translated SQL query. XAncestor is then developed as a prototype in order to test its effectiveness. The results of XAncestor are compared with those produced by five similar approaches. The comparison proves that XAncestor performs better than the previously developed approaches in terms of effectiveness and scalability. The correctness of XAncestor is also verified. The paper concludes with some recommendations for further work.

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.

Efficient message delivery models for XML-based publish/subscribe systems

XML-based publish/subscribe (pub/sub) systems have been receiving a great deal of attention from the academic community and the industry. This research focuses on efficient pub/sub systems and considers the devising of XML-based pub/sub systems from the perspective of subscription/query and publication message delivery. Existing research mainly focuses on the efficiency of XML publication message filtering algorithms. Not much research, however, has considered using the system or the communication model in the context of XML publication messages delivery. This paper presents innovative XML delivery techniques, the cross-layer model and the peer model; both techniques make use of publisher and customer edge brokers for efficient XML subscription aggregation and publication message delivery. The primary contribution of the proposed models is the reduction of the number of XML publication message filtering and XPath query aggregation operations performed in the conventional filter-based XML multicast model, which has a high computational overhead. The main idea is to store user subscriptions at customer and publisher edge brokers which are either directly connected or close to the subscribers and the publisher, respectively. We have performed a number of experiments within a controlled local area network (LAN) environment for demonstration of the basic concepts underlying the techniques and in the Amazon cloud environment that emulates the wide area network (WAN). Both the cross-layer and the peer models can reduce the end-to-end (E2E) delay in message delivery. For example, the results obtained from experiments in a LAN demonstrate several-fold performance improvement in E2E delay for both the cross-layer and the peer models compared to the conventional filter-based XML multicast model, and the results using the cloud show an improvement as high as 64% in E2E delay for the peer model over the multicast model.

Predicate enrichment of aligned XPaths for wrapper induction

Extracting data from various semi-structured sources is a topic that has received a lot of attention. Wrapper induction specifically has been studied extensively, where users annotate a couple of data sources with examples of the data they want, after which a procedure (wrapper) is constructed that can optimally extract similar data as well. In this paper a novel wrapper induction approach is proposed, exploiting the premise of the general applicability of the XPath query language, studied specifically within the context of web pages. After a user annotates a limited set of web pages with the required data, a generalised XPath is constructed that is capable of extracting the examples and, optimally, similar data as well. This generalised baseline XPath is then enriched with predicates, based on context and structure of the data sources, to optimise the precision/recall balance of the data extraction capability of the wrapper. Six variations of such limiting predicates are introduced and investigated. In this paper, it is shown that the baseline approach often generalises the samples too much, leading to a decreased precision. Enriching the baseline wrapper by the addition of predicates limits the generalisation power of the queries in an intelligent manner. Experimental results show that there is a significant improvement in the overall precision of the generalised query, without an excessive loss in recall. Documented tests and real world experience with a large amount of data show that the technique is flexible, easily understood and applicable in a broad range of applications. It is not only of interest in the fields of web information retrieval, but can also be used in the contexts of, e.g., reverse engineering of databases, ontology expansion and deep web data mining, as both simple lists of data and complex structures can be extracted.

A Partial-tree-based Approach for XPath Query on Large XML Trees

A Partial-tree-based Approach for XPath Query on Large XML Trees

面向XML流数据的树模式匹配方法

面向互联网时代XML流数据处理的高性能处理需求，充分考虑流数据查询需要处理持续到来的数据等特点，针对多数现有研究局限于XPath查询的缺陷，研究并实现了XML查询核心操作——树模式查询的流数据处理算法，通过实验说明该算法达到了XML流数据处理的性能要求。 For the needs of high-performance processing on XML stream data in the internet age, this paper fully considers that streaming data query needs to process the continuous arrival of data, etc., for the majority of the existing studies have been limited to XPath query defects, this paper studies and implements XML Query core operation—stream data processing algorithm on tree pattern matching through experiments show that the algorithm achieves the performance demand of XML stream data processing.