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We demonstrate how to evaluate stepwise hedge automata (Shas) with subhedge projection while completely projecting irrelevant subhedges. Since this requires passing finite state information top-down, we introduce the notion of downward stepwise hedge automata. We use them to define in-memory and streaming evaluators with complete subhedge projection for Shas. We then tune the evaluators so that they can decide on membership at the earliest time point. We apply our algorithms to the problem of answering regular XPath queries on Xml streams. Our experiments show that complete subhedge projection of Shas can indeed speed up earliest query answering on Xml streams so that it becomes competitive with the best existing streaming tools for XPath queries. [ABSTRACT FROM AUTHOR]

This paper has presented a comprehensive literature study and discusses the primary problem of network congestion and bottlenecks in XML applications. The explosive growth of Web services has caused a significant problem in network traffic due to the largely repetitive XML textual structures. The challenge of XML message size reduction in web services has been examined, focusing on new approaches such as compression methods and message clustering for Aggregation and compression of XML messages efficiently. The goal is to examine how different strategies affect network traffic. This overview discusses twenty-five publications categorized into four: compression techniques, aggregation approaches, clustering for web services aggregation approaches, and other web services clustering models. In comparison to all models, evaluation has shown that web service clustering solutions efficiently reduce traffic. Technically, the web service clustering for aggregation models has achieved an excellent average compression ratio of up to (14.12). [ABSTRACT FROM AUTHOR]

The Extensible Markup Language (XML) files, widely used for storing and exchanging information on the web require efficient parsingmechanisms to improve the performance of the applications. With the existing Document Object Model (DOM) based parsing, the performance degrades due to sequential processing and large memory requirements, thereby requiring an efficient XML parser to mitigate these issues. In this paper, we propose a Parallel XML Tree Generator (PXTG) algorithm for accelerating the parsing of XML files and a Regression-based XML Parsing Framework (RXPF) that analyzes and predicts performance through profiling, regression, and code generation for efficient parsing. The PXTG algorithm is based on dividing the XML file into n parts and producing n trees in parallel. The profiling phase of the RXPF framework produces a dataset by measuring the performance of various parsing models including StAX, SAX, DOM, JDOM, and PXTG on different cores by using multiple file sizes. The regression phase produces the prediction model, based on which the final code for efficient parsing of XML files is produced through the code generation phase. The RXPF framework has shown a significant improvement in performance varying from 9.54% to 32.34% over other existing models used for parsing XML files. [ABSTRACT FROM AUTHOR]

We demonstrate how to evaluate stepwise hedge automata (Shas) with subhedge projection while completely projecting irrelevant subhedges. Since this requires passing finite state information top-down, we introduce the notion of downward stepwise hedge automata. We use them to define in-memory and streaming evaluators with complete subhedge projection for Shas. We then tune the evaluators so that they can decide on membership at the earliest time point. We apply our algorithms to the problem of answering regular XPath queries on Xml streams. Our experiments show that complete subhedge projection of Shas can indeed speed up earliest query answering on Xml streams so that it becomes competitive with the best existing streaming tools for XPath queries.

With the continuous expansion of spatiotemporal applications, spatiotemporal data has been widely used in various fields. Because XML (eXtensible Markup Language) has been the de-facto standard for representing and exchanging data on the Web, and fuzziness is an inherent feature of most spatiotemporal applications, researches on fuzzy spatiotemporal XML data have received increasing attention. Although XML has been employed to represent and query fuzzy spatiotemporal data, relatively little work has been carried out to integrate heterogeneous fuzzy spatiotemporal data. More importantly, current efforts do not take global and local cases into account when representing and querying heterogeneous fuzzy spatiotemporal XML data. In this paper, after presenting fuzzy spatiotemporal data based on XML, we propose an integration model for fuzzy spatiotemporal XML data. Then, feature expression and field mapping table are used to decompose the query, and the local data sources that do not exist in the query results are excluded to improve the query efficiency. Then, using the information in the feature expression, the XQuery query language is transformed into the query language corresponding to the local data source. After getting the results of each local data source, the semantic heterogeneity is eliminated by using field mapping table, and the query results are uniformly described as data source views by using XML Schema. Finally, the query results are screened and combined by using the query conditions stored in the query decomposition process. This query process shields the semantic heterogeneity and structural heterogeneity of the underlying data source, and ensures the transparency of user query. Finally, the presented instance show that our approach is correct, and the experimental results show the performance advantages of our approach. [ABSTRACT FROM AUTHOR]

The data produced by various services should be stored and managed in an appropriate format for gaining valuable knowledge conveniently. This leads to the emergence of various data models, including relational, semi-structured, and graph models, and so on. Considering the fact that the mature relational databases established on relational data models are still predominant in today’s market, it has fueled interest in storing and processing semi-structured data and graph data in relational databases so that mature and powerful relational databases’ capabilities can all be applied to these various data. In this survey, we review existing methods on mapping semi-structured data and graph data into relational tables, analyze their major features, and give a detailed classification of those methods. We also summarize the merits and demerits of each method, introduce open research challenges, and present future research directions. With this comprehensive investigation of existing methods and open problems, we hope this survey can motivate new mapping approaches through drawing lessons from each model’s mapping strategies, as well as a new research topic - mapping multi-model data into relational tables. [ABSTRACT FROM AUTHOR]

Seasonal adjustment (SA) is a crucial factor in the process of producing official macroeconomic statistics. The most important SA methods, X-13Arima-Seats and Tramo-Seats, are currently included into JDemetra+, a universal open-source environment, which is available on several platforms and operating systems, as a result of adoption of Java programming language for source codes, and Xml metalanguage for the definition of input specifications. This paper focuses on the potentials of RJDemetra, the R library developed for JDemetra+ suite. Its structure and functionalities will be illustrated with several examples, reporting the associated R scripts. In addition, a new operational practices will be suggested, exposing an alternative procedure to enhance interactive time-series updating in SA revision policies step, and also to ensure consistency checking in input system, in order to improve and to speed up the SA estimation process, providing greater security and efficiency. Finally, the interaction between two very different environments such as SAS-IML, and R will be displayed through a new SAS-R procedure available for estimating Quarterly Accounts SA series. [ABSTRACT FROM AUTHOR]

Lithium–ion batteries play a crucial role in clean transportation systems including EVs, aircraft, and electric micromobilities. The design of battery cells and their production process are as important as their characterisation, monitoring, and control techniques for improved energy delivery and sustainability of the industry. In recent decades, the data-driven approaches for addressing all mentioned aspects have developed massively with promising outcomes, especially through artificial intelligence and machine learning. This paper addresses the latest developments in explainable machine learning known as XML and its application to lithium–ion batteries. It includes a critical review of the XML in the manufacturing and production phase, and then later, when the battery is in use, for its state estimation and control. The former focuses on the XML for optimising the battery structure, characteristics, and manufacturing processes, while the latter considers the monitoring aspect related to the states of health, charge, and energy. This paper, through a comprehensive review of theoretical aspects of available techniques and discussing various case studies, is an attempt to inform the stack-holders of the area about the state-of-the-art XML methods and encourage those to move from the ML to XML in transition to a NetZero future. This work has also highlighted the research gaps and potential future research directions for the battery community. [ABSTRACT FROM AUTHOR]

In 2019, GeoScienceWorld was actively planning to bring a large content and data repository, that includes a significant proportion of highly valued Grey Literature, into our existing collection of 50+ peer-reviewed journals and over 2300 books in the geosciences. Due to various external situations, including the impacts of the COVID-19 pandemic, and an absence of communityaccepted standards for Grey Literature publishing, this project has stalled. GeoScienceWorld continues to investigate opportunities to bring original datasets, as well as other collections of Grey Literature, predominantly in the form of partner societies' conference proceedings and related conference materials, into our traditional research platform. We are also in the early stages of planning for a new research tool that will be truly content agnostic in bringing research and valuable insights to our primary end-user stakeholders, researchers, whether in academia or industry. As an organization, GeoScienceWorld is further implementing an Agile mindset and development philosophy to bring increasingly useful, and timely, resources to our stakeholder groups. A key ceremony of all truly Agile development processes is the Retrospective. In this paper, I review the initial aims of the project to incorporate a large grey dataset into our traditional scholarly literature platform and provide reflections on how both GeoScienceWorld and the wider Grey Literature community can move forward to bring such valuable datasets to audiences that both want and need, such content to advance their research. For each element of the initial project, I ask the following Agile Retrospective questions:  What did we do well?  What could we have done better?  What have we learned?  What are we still puzzled by? As a result of applying these questions to the initial project, I will present recommendations that both inform GeoScienceWorld's future integration and presentation of Grey Literature, as well as offer a clearer path toward greater Grey Literature acceptance within traditional scholarly platforms such as ours. [ABSTRACT FROM AUTHOR]

Land administration systems are being modernised to streamline the cadastral data lodgement. However, in many jurisdictions, cadastral data are still stored as a flat file. This method of data storage has significant limitations in terms of effective access, management, query, and analysis of cadastral data. Therefore, this study elicited the requirements and proposed an approach to automate the cadastral data storage. The proposed approach was successfully implemented within the land registry organisation in Victoria, Australia and the database management system was rigorously tested. The outcomes can potentially contribute to the implementation of a similar data storage infrastructure in other jurisdictions. [ABSTRACT FROM AUTHOR]

Implementing embedded critical system tools using general-purpose programming languages usually challenges conformance to relevant standards. This letter proposes implementing tools that manipulate a system model expressed in extensive markup language using the extensive stylesheet language transformations (XSLT) and related technologies playing specific roles in a particular sequence developed to ease verification, validation, testing, and certification efforts. XSLT is a data-centric, domain-specific language that produces a more straightforward tool implementation, thus reducing human errors. We will introduce the proposal by reviewing, as an example, the reimplementation of a tool initially written in Python that automatically generates the VHDL source code of an embedded critical system. Finally, we will compare both implementations, noting compelling results. [ABSTRACT FROM AUTHOR]

Frequent queries on semi-structured hierarchical data are Content-and-Structure (CAS) queries that filter data items based on their location in the hierarchical structure and their value for some attribute. We propose the Robust and Scalable Content-and-Structure (RSCAS) index to efficiently answer CAS queries on big semi-structured data. To get an index that is robust against queries with varying selectivities, we introduce a novel dynamic interleaving that merges the path and value dimensions of composite keys in a balanced manner. We store interleaved keys in our trie-based RSCAS index, which efficiently supports a wide range of CAS queries, including queries with wildcards and descendant axes. We implement RSCAS as a log-structured merge tree to scale it to data-intensive applications with a high insertion rate. We illustrate RSCAS's robustness and scalability by indexing data from the Software Heritage (SWH) archive, which is the world's largest, publicly available source code archive. [ABSTRACT FROM AUTHOR]

Native mobile applications have been the first choice for companies and independent developers to build software that utilizes device hardware and functionality. For Android development, this means using Java/Kotlin for the program logic and XML files for the user interfaces. Recently, Jetpack Compose has gained significant attention in building user interfaces for Android applications. This paper aims to analyze the benefits and limitations of these two technologies. An intensive literature review is conducted and presented to identify the benefits and limitations of XML and Jetpack Compose. In order to compare their development processes and code complexity, we also developed the same application using both technologies. The analysis of the code used in each technology provides an insight into how they uniquely solve the same problem, helping us to identify which one is better suited from a developer's perspective. The results of the study indicate that both XML and Jetpack Compose have their respective strengths and weaknesses. XML provides a structured approach to UI development, fully respecting the separation of concerns between the view (what is being displayed on the screen) and the controller (which data is being sent to the view). On the other hand, Jetpack Compose simplifies the UI development process by offering a declarative approach, which leads to a more readable and maintainable code. This study identified the advantages and disadvantages of using XML and Jetpack Compose in the development of native Android applications and recommended criteria for choosing one of these tools for new users, as well as switching to a new tool for experienced users. [ABSTRACT FROM AUTHOR]

The current scene of Digital Scholarly Editions, particularly those concerning modern manuscripts, registers a trend towards documentary and diplomatic editions. This “bédierian” tendence leads to editions focused mainly on the diplomatic transcription of the source, lacking in consideration about the genetic process. Lying under this situation we could figure out the existence of digital tools and languages more adequate to this documentary approach. In addition to that, this article is aimed at showing the concrete problems in representing the diachronic elaboration of the text in a digital form, through methodological considerations and examples of realisation. Despite the widespread trend and the difficulty in choosing a different way, it seems possible to reflect on genetic digital editions from an optimistic perspective. In fact, these editions are not only necessary, at least for certain textual situations, but also feasible, as shown by some ongoing projects and experiments (Saba 2021, VaSto 2022 and Il Conte di Carmagnola), presented at the end of the contribution.

This paper presents how we enhanced the accessibility and utility of historical linguistic data in the project Bullinger Digital. The project involved the transformation of 3,100 letters, primarily available as scanned PDFs, into a dynamic, fully digital format. The expanded digital collection now includes 12,000 letters, 3,100 edited, 5,400 transcribed, and 3,500 represented through detailed metadata and results from handwritten text recognition. Central to our discussion is the innovative workflow developed for this multilingual corpus. This includes strategies for text normalisation, machine translation, and handwritten text recognition, particularly focusing on the challenges of code-switching within historical documents. The resulting digital platform features an advanced search system, offering users various filtering options such as correspondent names, time periods, languages, and locations. It also incorporates fuzzy and exact search capabilities, with the ability to focus searches within specific text parts, like summaries or footnotes. Beyond detailing the technical process, this paper underscores the project’s contribution to historical research and digital humanities. While the Bullinger Digital platform serves as a model for similar projects, the corpus behind it demonstrates the vast potential for data reuse in historical linguistics. The project exemplifies how digital humanities methodologies can revitalise historical text collections, offering researchers access to and interaction with historical data. This paper aims to provide readers with a comprehensive understanding of our project’s scope and broader implications for the field of digital humanities, highlighting the transformative potential of such digital endeavours in historical linguistic research.

Researchers are interested in improving the XML database management system using an indexing system by labelling XML nodes. They proposed many labelling schemes to be equivalent to the indexing system of the relational database management system. There are many algorithms that have been suggested for efficiently assigning labels to XML documents in a small amount of storage space. But, the majority of the models concentrated on assessing the effectiveness of proposed labelling schemes. The pertinence of a labelling technology with a certain pattern of XML database has not been adequately inspected. For analysing this point, several experiments were carried out have been executed to label different XML data structures using Containment and Dewey labelling schemes. The requirements of time and space have been taken into account to measure the relevance of the labelling scheme to a specific structure of XML document. It was discovered that both schemes are relatively efficient when allocating labels for a shallow structure of an XML tree. Both schemes are efficient, however, the former for short tree and the latter for wide tree. [ABSTRACT FROM AUTHOR]

This study implemented the measurement results and administrative information obtained from the hole plate into the Digital Calibration Certificate (DCC). The DCC comprises three parts: Norms and Standards, Hierarchical Structure, and XML as Exchange Format. DCCs play a significant role in the field of metrology and statistics by ensuring data interoperability, correctness, and traceability during the conversion and transmission process. The hole plate is a length standard used for two-dimensional geometric error measurements. We evaluated the accuracy of the high-precision coordinate measuring machine (CMM) in measuring a hole plate and compared the measurement error results obtained from the hole plate with those of the laser interferometer, autocollimator, and angle square. The results show that the maximum difference in linear error is −0.30 μm, the maximum difference in angle error is −0.78″, and the maximum difference in squareness error is 4.54″. The XML is designed for machine-readability and is modeled and edited using the XMLSpy 2022 software, which is based on information published by PTB. The administrative management and measurement results tasks are presented in PDF format, which is designed for human-readability and ease of use. Overall, we implemented the measurement results and information obtained from the hole plate into the DCC. [ABSTRACT FROM AUTHOR]

As an increasing volume of data objects in the real world have spatial features and temporal features, spatiotemporal data emerge in more and more spatiotemporal applications. Meanwhile, uncertainty of spatiotemporal data cannot be ignored in such spatiotemporal applications. On the other hand, since there is a growing interest in XML (eXtensible Markup Language), researches on uncertain spatiotemporal XML data have received increasing attention. Although keyword query has been studied on spatiotemporal data in traditional database, relatively little work has been carried out to investigate keyword query of uncertain spatiotemporal data. In this paper, we study uncertain spatiotemporal data representation based on XML. On the basis of it, we further propose a keyword query approach for uncertain spatiotemporal XML data, including data structure of dynamic keyword data warehouse, probability calculation, and a keyword query algorithm. Finally, the experimental results demonstrate the performance advantages of our approach. Such approach optimizes the structure of spatiotemporal XML data, which facilitates its keyword query. [ABSTRACT FROM AUTHOR]

Many efforts have been deployed by the IR community to extend freetext query processing toward semi-structured XML search. Most methods rely on the concept of Lowest Comment Ancestor (LCA) between two or multiple structural nodes to identify the most specific XML elements containing query keywords posted by the user. Yet, few of the existing approaches consider XML semantics, and the methods that process semantics generally rely on computationally expensive word sense disambiguation (WSD) techniques, or apply semantic analysis in one stage only: performing query relaxation/refinement over the bag of words retrieval model, to reduce processing time. In this paper, we describe a new approach for XML keyword search aiming to solve the limitations mentioned above. Our solution first transforms the XML document collection (offline) and the keyword query (on-the-fly) into meaningful semantic representations using context-based and global disambiguation methods, specially designed to allow almost linear computation efficiency. We use a semantic-aware inverted index to allow semantic-aware search, result selection, and result ranking functionality. The semantically augmented XML data tree is processed for structural node clustering, based on semantic query concepts (i.e., key-concepts), in order to identify and rank candidate answer sub-trees containing related occurrences of query key-concepts. Dedicated weighting functions and various search algorithms have been developed for that purpose and will be presented here. Experimental results highlight the quality and potential of our approach. [ABSTRACT FROM AUTHOR]