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Although multi-temporal XML databases supporting schema versioning are used in several domains, like e-commerce, e-health, and e-government, existing database management systems and XML tools do not provide any support for managing (inserting, updating, and deleting) temporal XML data or temporal XML schema versioning. Besides, whereas much research work has focused in the last decade on schema versioning in temporal XML databases, any attention has been devoted to manipulating data in such databases. To fill this theoretical and practical gap, we propose in this paper a generic approach, named TempoX (Temporal XML), for data manipulation in multi-temporal and multi-schema-version XML databases. Indeed, we (i) define a new multi-temporal XML data model supporting temporal schema versioning, named TempoXDM (Temporal XML Data Model), (ii) introduce the principles on which our approach is based, and (iii) provide the specifications of the basic data manipulation operations: “insert”, “replace”, “evolve”, and “delete”. Moreover, to show the feasibility of TempoX, we use it to propose a temporal XML update language, named TempoXUF (Temporal XQuery Update Facility), as an extension of the W3C XQuery Update Facility language to temporal and versioning aspects. Furthermore, to validate our language proposal, we develop a system prototype, named TempoXUF-Manager, that supports TempoXUF.

This paper is situed at the interface between Lexicography (PORTO DAPENA, 2002; HARTMANN, 2016), Dialectology (CARDOSO, 2010; CHAMBERS; THUDGILL, 1994) and Computational Linguistics (HABERT, 2004; PÉREZ HERNÁNDEZ; MORENO ORTIZ, 2009; HAUSSER, 2014; KURDI, 2016). The objective is to discuss the proposal of building a database in XML (Extensible Markup Language), exploring the results obtained with NLP (Natural Language Processing). The XML file is also based on parameters of Dialectal Lexicography (ESQUERRA, 1997; NAVARRO CARRASCO, 1993) and is being fed with dialectal data from the project Atlas Linguístico do Brasil (ALiB) documented in the country's Northern region. Therefore, the jEdit software was used as a text editor and, to manage the database, the BaseX program. The linguistic information extraction was performed in the BaseX, from a sample of data and with the X-Query expressions support. Thus, the following data manipulations were performed: i) location of a specific lexical unit; ii) visualization of any microstructure data filtered by variables gender, age, education and location; iii) selection of information from one of the 14 semantic areas in which the questions of the ALiB semantic-lexical questionnaire were organized. In summary, it is understands that the construction of a XML database provides agility in concerning the information extraction and data compatibility to implement interfaces with another applications, for example, the development of a lexicographic product to be published in online support. Este artigo situa-se na interface entre a Lexicografia (PORTO DAPENA, 2002; HARTMANN, 2016), a Dialetologia (CARDOSO, 2010; CHAMBERS; THUDGILL, 1994) e a Linguística Computacional (HABERT, 2004; PÉREZ HERNÁNDEZ; MORENO ORTIZ, 2009; HAUSSER, 2014; KURDI, 2016). Objetiva-se discutir a proposta de construção de um banco de dados em XML (Extensible Markup Language), explorando os resultados obtidos com o PLN (Processamento Automático de Linguagem Natural). O arquivo XML também se fundamenta em parâmetros da Lexicografia Dialetal (EZQUERRA, 1997; NAVARRO CARRASCO, 1993) e está sendo alimentado com dados dialetais oriundos do Projeto Atlas Linguístico do Brasil (ALiB) documentados na região Norte do país. Para tanto, utilizou-se como editor de texto o software jEdit e, para gerenciar o banco de dados, o programa BaseX. A extração das informações linguísticas foi realizada, no BaseX, a partir de uma amostra de dados e com o auxílio de expressões X-Query. Assim, foram executadas as seguintes manipulações de dados: i) localização de uma unidade lexical específica; ii) visualização de qualquer dado da microestrutura filtrada pelas variáveis sexo, idade, escolaridade e localidade; iii) seleção de informações a partir de uma das 14 áreas semânticas em que as questões do questionário semântico-lexical do ALiB foram organizadas. Em síntese, entende-se que a construção do banco de dados em XML confere agilidade em relação à extração de informações e compatibilidade dos dados para executar interfaces com outras aplicações como, por exemplo, a elaboração de um produto lexicográfico a ser publicado em suporte on-line.

The distributed computing is huge gatherings of remote servers are organized to permit the incorporated information stockpiling. It has the access of computer services, resources and can be classified as public, private and reserved. In this study, we explored various types of Data bases used in cloud computing with respect to the category of Knowledge database, XML database, Online databases and Real-Time databases to improve the storage and data efficacy.

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.

In many real-world applications, information is often imprecise and uncertain. With the popularity of web-based applications, huge amounts of data are available on the web, and XML (eXtensible Markup Language) has become the de facto standard for data exchange over the internet. The XQuery is the language for querying XML data. However, XML and XQuery suffer from incapability of representing and manipulating imprecise and uncertain data. Consequently, this work represents fuzzy data in XML documents and extends XQuery language as providing a more flexible XQuery language by using the fuzzy set theory. In this thesis, an extension of the XQuery query, called Fuzzy XQuery is described. It allows users to define priority, threshold and fuzzy expressions in their queries. Users also can predefine linguistic terms to use them in querying. An algorithm for calculating the global constraint satisfaction degree using the Generalized Prioritized Fuzzy Constraint Satisfaction Problem (GPFCSP) is introduced. Furthermore, Fuzzy XQuery Interpreter (FXI) is implemented allowing execution of fuzzy XQuery queries based on open source technologies and native XML open- source database. Additionally, innovative methods for computing fuzzy set compatibility and introducing order over fuzzy sets have been implemented, which give serious improvements in computational performance compared to previous implementations.

The journey of the database started with a relational database followed by transactional, eXtensible Markup Language (XML)[10,11], Not only SQL (NoSQL), and Hybrid Transactional/Analytical Processing (HTAP) database[2]. Analytic functions in SQL are used to process analytical databases, XQuery is used to process XML databases and NewSQL queries are used to process Hybrid Transactional and Analytical Processing (HTAP) databases. In the current scenario, the research related to the XML database and analytics on the XML database is an important area of concern. Numerous enterprises use XML as a data exchange format. Considering all the benefits of XML and its use in the field of web applications, various works elaborate to adopt it as a data storage technology. In this work, an attempt has been made to perform analytical functions using XQuery on an XML data and compare its performance with Analytical SQL queries in terms of execution time. A sample set of queries has been performed on a sample dataset. According to the results, it is concluded that Analytic SQL is dominating XQuery to perform analytical functions. In experimentation, we explained the persistence of XML data (electronic health records dataset) in eXist-DB and BaseX and executing queries on that data. The finding is that the performance of XML data in a health care environment is better if we use BaseX instead of eXist-DB.

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.

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.

Very few research works have been done on XML security over relational databases despite that XML became the de facto standard for the data representation and exchange on the internet and a lot of XML documents are stored in RDBMS. In [14], the author proposed an access control model for schema-based storage of XML documents in relational storage and translating XML access control rules to relational access control rules. However, the proposed algorithms had performance drawbacks. In this paper, we will use the same access control model of [14] and try to overcome the drawbacks of [14] by proposing an efficient technique to store the XML access control rules in a relational storage of XML DTD. The mapping of the XML DTD to relational schema is proposed in [7]. We also propose an algorithm to translate XPath queries to SQL queries based on the mapping algorithm in [7].

PurposeAny XML schema definition can be organized according to one of the following design styles: “Russian Doll”, “Salami Slice”, “Venetian Blind” and “Garden of Eden” (with the additional “Bologna” style actually representing absence of style). Conversion from a design style to another can facilitate the reuse and exchange of schema specifications encoded using the XML schema language. Without any computer-aided engineering support, style conversions must be performed very carefully as they are difficult and error-prone operations. The purpose of this paper is to efficiently deal with such XML schema design style conversions.Design/methodology/approachA general approach, named StyleVolution, for automatic management of XML schema design style conversions, is proposed. StyleVolution is equipped with a suite of seven procedures: four for converting a valid XML schema from any other design style to the “Garden of Eden” style, which has been chosen as a normalized XML schema format, and three for converting from the “Garden of Eden” style to any of the other desired design styles.FindingsProcedures, algorithms and methods for XML schema design style conversions are presented. The feasibility of the approach has been shown through the encoding (using the XQuery language) and the testing (with the Altova XMLSpy 2019 tool) of a suite of seven ready-to-use procedures. Moreover, four test procedures are provided for checking the conformance of a given input XML schema to a schema design style.Originality/valueThe proposed approach implements a new technique for efficiently managing XML schema design style conversions, which can be used to make any given XML schema file to conform to a desired design style.

An effective quality analysis of XML web data using clustering and classification approach is used in our proposed method. XML is turning into a standard in representation of data, it is attractive to support keyword search in XML database. A keyword search searches for words anyplace in record. It is developed as best worldview for finding data on web. The most imperative prerequisite for the keyword search is to rank the consequences of question so that the most pertinent outcomes show up. Here, we gather more XML documents. Followed by that, feature extraction occurs. Since the selected feature contains both relevant as well as irrelevant features it is essential to filter the irrelevant features. For the purpose of selecting, the relevant features probability-based feature selection method is used. Then for clustering the relevant features on the basis of keywords weighted fuzzy c means clustering algorithm is used. In order to assess the XML data quality, optimal neural network (ONN) classifier is utilized. In this ONN classifier in order to select the optimal weights, whale optimization algorithm is used. Thus, the web pages are effectively ranked. The efficiency of the proposed method is assessed using clustering and classification accuracy, RMSE, and search time. The proposed method is implemented in JAVA.

Lilith is a public Python library for constraining new physics from Higgs signal strength measurements. Version 2.0 of Lilith comes with an extensive XML database which includes the ATLAS and CMS Run 2 Higgs results for 36/fb, in addition the the Run 1 results. Both the code and the database were extended from the ordinary Gaussian approximation employed in Lilith-1.1 to using variable Gaussian and Poisson likelihoods. Moreover, Lilith-2 can make use of correlation matrices of arbitrary dimension. We will report on these novelties and ongoing developments. The importance of how correlations and uncertainties are treated will be demonstrated by means of detailed validations of the implemented experimental results. Moreover, we show the effects for global fits of reduced Higgs couplings, 2HDMs of Type I and Type II, and invisible Higgs decays. The program is publicly available on GitHub and can be used to constrain a wide class of new physics scenarios., Comment: 12 pages, 7 figures. Contribution to "Tools for High Energy Physics and Cosmology" (TOOLS2020), 2-6 Nov. 2020, IP2I Lyon, France

Although XML processing has been intensively studied in recent years, designing efficient implementations for evaluating XPath queries on XML documents remains a challenge in case XML documents are very large. In this study, we implemented a tree-shaped data structure called partial tree that is intrinsically suitable for large XML document processing with multiple computers. Our implementation uses two index sets to accelerate the evaluation of structural relationships among nodes, making it highly efficient for processing very large XML documents regarding three important classes of XPath queries: backward, order-aware and predicate-containing queries. Experiment results show that our implementation outperforms a start-of-the-art XML database BaseX in both absolute loading time and execution time for the target queries. The absolute execution time over 358 GB of XML data averagely is only seconds by using 32 EC2 instances.

The wireless communication network has a huge promotion effect on data processing. As an important standard for internet data transmission, XML markup language is currently widely used in the internet. The rapid development of XML has brought fresh blood to the research of database. This article is aimed at studying the application of wireless network communication in the storage of the XML metadata database of Wushu Historical Archives. One part uses data load-balancing algorithms and two-way path constraint algorithms to conduct research on the metadata storage of XML databases, such as document size, document loading, and document query. Combining wireless technology and comparing with SGML and HTML, it highlights the flexibility and importance of XML database. The other part analyzes the XML database of Wushu Historical Archives itself and uses questionnaire surveys combined with the experience of 30 users randomly checked to provide constructive suggestions for the construction of XML database, such as strengthening the operation page design, text, and graphics. The experimental results show that the time consumed by the XML database in the case of bidirectional path indexing is 0.37 s, and the time consumed in the case of unidirectional path indexing is 0.27 s. This data shows that the application of wireless network communication technology has played an important role in the easy update and flexibility of the XML database. The data load capacity presented by the two path indexes is inconsistent; the current experience of XML database mainly focuses on the copy of the database quantity.

The advancing COVID-19 pandemic caused by the novel coronavirus has taken the world by a storm due to its unprecedented nature. In order to increase the understanding of the disease and create countermeasures for the same, collecting and storing data in a proper and efficient format is of utmost importance. However, this tremendous amount of data is obtained from various heterogeneous sources and is usually dynamic in nature. Traditional RDBMS might not be the most efficient choice for the sporadic and ever-changing clinical data associated with COVID patients due to its highly rigid nature. This paper utilized a primary dataset acquired from COVID-19 patients as a premise to portray the inefficiencies of RDBMS and further proposes two new schemaless, unstructured databases, NoSQL and XML databases, as an offset to this drawback. The intention is to propose the two most efficient technologies and delineate the findings through a sample implementation.

XML becomes the most important technology for data exchange and storage with the emergence of the World Wide Web (WWW). Moreover, the size of the XML database has also increased considerably, which causes large size space for storage and a long time for data transfer on the web. The existing XML compression techniques show some drawbacks in dealing with these constraints. Therefore, in this paper, we propose a robust approach for the compression and decompression called: Non-redundancy Tags of SOAP messages (NTSOAP), which removes the redundancy of the tags and the indexed paths in the compressed format. The experimental results show that the proposed technique outperforms all other techniques.

Amazon DynamoDB is a next-gen NoSQL (Not only SQL), key-value and document database that delivers single-digit millisecond performance at any scale. As more complex web-based applications adopt DynamoDB, it is imperative to develop sound design strategies for DynamoDB schemas. Large-scale web-based applications often exhibit conflicting requirements. Hence, the research problem is to design the DynamoDB schemas of a given application with conflicting requirements such that the database must satisfy certain predetermined performance goals. In the era of relational databases, normal forms were developed to guide the schema design process. Past knowledge and experiences, we believe, are also applicable to DynamoDB schema design. Specifically, based upon Nested Normal Form and XML databases, this paper demonstrates the feasibility of a design strategy on DynamoDB schemas, particularly with the access patterns of the data in mind. Simulation further substantiates the feasibility of our approach.

This paper aims to devise a method for migrating a relational database (RDB) into an XML document. Database migration is concerned with the process of converting schema and data from a source RDB, as a one-time conversion, into a target XML database to be managed and handled in its new environment. The source schema is enriched semantically and translated into a target schema, and the data stored in the source database is converted into a target database based on the new schema. The semantic enrichment process is requisite to produce an enhanced metadata model from the source database and captures essential characteristics of target XML schema, and suitable for converting RDB data into an XML document. Algorithms are developed for producing the target database according to a set of migration rules to translate all constructs of an RDB into an XML Schema, based on which RDB data is then converted. A prototype system has been implemented and empirically evaluated by testing its results, looking at our achievements and reflecting on the results. From the evaluation of results, it is concluded that the proposed solution is efficient and correct.

This paper show how location named entity (LNE) extraction and annotation, which makes part of our named entity recognition (NER) systems, is an important task in managing the great amount of data. In this paper, we try to explain our linguistic approach in our rule-based LNE recognition and classification system based on syntactico-semantic patterns. To reach good results, we have taken into account morpho-syntactic information provided by morpho-syntactic analysis based on DIINAR database, and syntactico-semantic classification of both location name trigger words (TW) and extensions. Formally, different trigger word sense implies different syntactic entity structures. We also show the semantic data that our LNE recognition and classification system can provide to both information extraction (IE) and information retrieval(IR).The XML database output of the LNE system constituted an important resource for IE and IR. Future project will improve this processing output in order to exploit it in computerassisted Translation (CAT).

As scientific progress highly depends on the quality of research data, there are strict requirements for data quality coming from the scientific community. A major challenge in data quality assurance is to localise quality problems that are inherent to data. Due to the dynamic digitalisation in specific scientific fields, especially the humanities, different database technologies and data formats may be used in rather short terms to gain experiences. We present a model-driven approach to analyse the quality of research data. It allows abstracting from the underlying database technology. Based on the observation that many quality problems show anti-patterns, a data engineer formulates analysis patterns that are generic concerning the database format and technology. A domain expert chooses a pattern that has been adapted to a specific database technology and concretises it for a domain-specific database format. The resulting concrete patterns are used by data analysts to locate quality problems in their databases. As proof of concept, we implemented tool support that realises this approach for XML databases. We evaluated our approach concerning expressiveness and performance in the domain of cultural heritage based on a qualitative study on quality problems occurring in cultural heritage data., 28 pages. This paper is an extended version of a paper to be published in "ACM/IEEE 23rd International Conference on Model Driven Engineering Languages and Systems (MODELS '20)". Added subtitle

eXtensible Markup Language (XML) has been widely used as the standard for data exchange standard over the Internet. With the fast growing rate of data, especially with high updates, it is crucial to ensure that the XML is able to cope with frequent changes with very least effect on the existing structure. Therefore, in this paper, we investigate on the existing labeling schemes and mapping approaches to gauge a better understanding in terms of the robustness of the labeling schemes and the importance of the mapping schemes. Next, we propose ORD-GAP labeling schemes to identify the structural relationship among XML nodes and yet, it is persistent to re-labeling when new nodes are inserted. Subsequently, a mapping scheme is proposed to transform XML into Relational Database (RDB). Preliminary experimental evaluation demonstrated that the proposed approach achieve 66% better as compared to ORDPATH, and 56% better as compared to ME labeling in terms of data loading time.

An exact query is a query in which the user specifies precisely what to retrieve from a database (XML or relational database). For these queries only data that strictly respect all user’s conditions is returned. XML documents are generally semi-structured. Due to the non-existence or lack of knowledge of the model of the document being queried, when exact queries are used, there is a high risk of obtaining an empty result (in the case of too specific queries) or too large (in the case of too vague queries). In contrast to exact queries, requests with preferences aim to return only the most relevant results in order to avoid empty or too important results as much as possible. To achieve this goal, requests with preferences generally consist of two parts: the first part is used to express strict constraints and the second part to express preferences or wishes. The satisfaction of both parts increases the relevance of the corresponding results. This paper presents XQuery preference, an extension of the XQuery language, that allows to express requests with preferences relating to both the values and the structure of an XML document. A representation model of such requests based on the Generalized Tree Pattern (GTP) model is also proposed in order to allow an evaluation of these requests through a tree pattern matching process. Integration of the proposed language in open source implementations of XQuery like BaseX, Berkeley DB XML, eXist-db, Galax and much more, will allow users to get much more relevant responses to their concerns.

Data mining is the process of identifying patterns in data sets by applying appropriate methods with cluster of machine learning techniques. In recent decades, high utility itemset (HUI) mining has become the emerging research area, which focuses on frequency and also on utility related with the itemsets. Each itemset has a value like profit or user’s interest, called as the utility of that itemset. HUIs are present in a customer transaction database which yield a high profit. The target of HUI is to discover the itemsets that have utility value higher than the threshold value. The issues faced in HUIs are dealing with negative item values and number of database scans, mining in XML database, candidate sets and distributed computing network. This paper presents a survey of various algorithms and their restrictions in mining HUIs and the performance analysis of the surveyed algorithms.

In this paper the idea of temporal reachability in fuzzy directed graph is introduced. Temporality in fuzzy graphs is a generalization of a fuzzy graph on temporal relations, where the incidence property of graph vertices is changed in the discrete time. Reachability in such temporal graph structures refers to the ability to get from one vertex to another within different time moments and the reachability relation on fuzzy directed graph is the transitive closure on the edge set. Efficient processing queries and graph structured information under uncertainty is an important research topic especially in XML databases, geographical information systems (GIS), web mining and etc. Fuzzy temporal graph models are usually applied in geographical information systems to formalize relations between objects and to check connections between them. A method for searching temporal reachability in fuzzy graph structure is proposed, an example of finding a fuzzy reachability set in temporal graph is considered as well.

Many organizations today store their critical business information permanently in XML format. XML data can be managed using: XML-Enabled Database (XED) systems which convert and store XML files in traditional database systems; Native XML Database (NXD) systems which store XML data natively using three main storage technologies – text-based, model-based, and schema-based techniques; and Hybrid Database systems which are comprised of both XML-Enabled and Native XML database systems. NXDs are faster than other database technologies because there is no need to convert the format of the data prior to storage. No performance evaluation has been carried out to compare all three storage strategies, hence, this paper reports on the first attempt to evaluate all three storage strategies by using open source products to measure the response time taken for each of the database basic tasks such as database creation, dataset insertion, and data manipulation. The results of the evaluation show that the schema-based storage strategy: performs 3.5 times faster than the other two storage techniques in data insertion; shows very good performance in query processing on small and large datasets; performs 10.33 times faster than text-based, and 7.5 times faster than model-based storage techniques in query processing of large datasets.

This paper presents a way to query databases of different models, namely XML and the relational model. As they possess some robust features in data exchange and extraction. Also, the tremendous amount of research to query data presented in these two models makes integrating relational and XML databases very appealing and shows the necessity of their existence side by side. At this point, we attempt to define a system able to retrieve data regardless of the nature of their models. Users are not expected to know several query languages at once, they can extract data represented in the two aforementioned models by a single query language, that is not necessarily the query language associated with the model in question. Thus, we will be able to query data regardless of their models.