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Global trade is plagued by slow and inefficient manual processes associated with physical documents. Firms are constantly looking for new ways to improve transparency and increase the resilience of their supply chains. This can be solved by the digitalisation of supply chains and the automation of document- and information-sharing processes. Blockchain is touted as a solution to these issues due to its unique combination of features, such as immutability, decentralisation and transparency. A lack of business cases that quantify the costs and benefits causes uncertainty regarding the truth of these claims. This paper explores how the costs and benefits of a blockchain-based solution for digitalising and automating documentation flows in cross-border supply chains compare to a conventional centralised relational database solution. The research described in this paper uses primary data collected through semi-structured interviews with industry experts, as well as secondary data from literature. Two models based on existing services were developed and the costs and benefits compared and then analysed using the Architecture Trade-off Analysis Method (ATAM) and the Analytic Network Process (ANP). Findings from the analysis show that a consortium blockchain solution like TradeLens is the favourable solution for digitalising and automating information flows in cross-border supply chains. [ABSTRACT FROM AUTHOR]

Databases are a fundamental element of contemporary software applications. The most widely used and recognized type in practice is the relational database, valued for its ability to store and organize data in tabular structures, its emphasis on data consistency and integrity, and its use of a standardized query language, SQL. However, with the rapid increase in both the volume and complexity of data, relational databases have recently encountered challenges in effectively modeling this expanding information. To address performance challenges, new database systems have emerged, offering alternative approaches to data modeling—these are known as NoSQL databases. In this paper, we present an indoor navigation application designed to operate on both a relational database, Microsoft SQL Server, and a graph-based NoSQL database, Neo4j. We describe the algorithms implemented for testing and the performance metrics analyzed to draw our conclusions. The results revealed Neo4j's strength in managing data with complex relationships but also exposed its limitations in handling concurrent access, where SQL Server demonstrated significantly greater stability. [ABSTRACT FROM AUTHOR]

In this paper, the new dynamic geophysical and geochemical MUltiparametric DAtabase (MUDA) is presented. MUDA is a new infrastructure of the National Institute of Geophysics and Volcanology (INGV), published online in December 2023, with the aim of archiving and disseminating multiparametric data collected by multidisciplinary monitoring networks. MUDA is a MySQL relational database with a web interface developed in PHP, aimed at investigating possible correlations between seismic phenomena and variations in endogenous and environmental parameters in quasi real time. At present, MUDA collects data from different types of sensors such as hydrogeochemical probes for physical–chemical parameters in waters, meteorological stations, detectors of air radon concentration, diffusive flux of carbon dioxide (CO2) and seismometers belonging both to the National Seismic Network of INGV and to temporary networks installed in the framework of multidisciplinary research projects. MUDA publishes data daily, updated to the previous day, and offers the chance to view and download multiparametric time series selected for different time periods. The resultant dataset provides broad perspectives in the framework of future high-frequency and continuous multiparametric monitoring as a starting point to identify possible seismic precursors for short-term earthquake forecasting. MUDA can be accessed at 10.13127/muda (Massa et al., 2023). [ABSTRACT FROM AUTHOR]

We review currently available Canadian and international lithostratigraphic and lithodemic schemes and find most of them inadequate for classifying intrusive and strongly metamorphosed rocks of Canada and beyond. A new system is proposed, one that unifies, complements, and extends components of the revised North American Stratigraphic Code, the British Geological Survey Rock Unit Classification System of 2021, and the International Stratigraphic Guide of 1999. This new Cooperative Lithodemic and Stratigraphic System (CLASS) is intended to serve as a practical guide to geoscientists who need to classify and report on lithodemic units in North America and has broad applicability to other jurisdictions. It is built with database management practices in mind and employs the concept of inheritance of root characteristics between seven formal rock unit ranks, which will allow incorporation of the scheme into compact object-oriented and relational databases designed for purpose. Broad application of CLASS, especially to historically problematic lithodemic units, should help to foster jurisdictional interoperability, data sharing, global compilations, and thematic studies. At the heart of this proposed system is a subdivision and accompanying nomenclature that allows classification of rocks into seven formal ranks. Following the well-established lithostratigraphic supergroup, group, subgroup, formation, member, submember, and bed approach, CLASS proposes three classes of lithodemic subdivisions for formally naming lithodemic rock units. [ABSTRACT FROM AUTHOR]

The article is a response to a previous letter to the editor by Piney, discussing the evolution of work breakdown structures (WBS) and cost breakdown structures (CBS) in project management. It highlights the shift from flat file hierarchical structures to relational and object-oriented architectures, emphasizing the importance of standardized coding systems like NORZOK Z-014 and ISO Standard 19008:2016. The article also explores the use of multidimensional, standardized WBS/CBS coding systems in various industries, such as construction and government projects, and suggests incorporating both benefits and disbenefits in weighting factors for better decision-making. [Extracted from the article]

We study logical reduction (factorization) of relations into relations of lower arity by Boolean or relative products that come from applying conjunctions and existential quantifiers to predicates, i.e. by primitive positive formulas of predicate calculus. Our algebraic framework unifies natural joins and data dependencies of database theory and relational algebra of clone theory with the bond algebra of C.S. Peirce. We also offer new constructions of reductions, systematically study irreducible relations and reductions to them and introduce a new characteristic of relations, ternarity, that measures their 'complexity of relating' and allows to refine reduction results. In particular, we refine Peirce's controversial reduction thesis, and show that reducibility behaviour is dramatically different on finite and infinite domains. [ABSTRACT FROM AUTHOR]

Europe has set a target to become a decarbonised continent by 2050. To achieve this, intervention in buildings is crucial, as they serve as significant energy consumers and greenhouse gas emitters. This intervention encompasses two essential pathways: renovation and digitalisation. The combination of these two aspects gives rise to elements such as the Digital Building Logbook (DBL), a digital data repository expected to enhance the pace and quality of renovation efforts. This paper introduces, for the first time, a European DBL data model with a specific focus on building renovation purposes—the DBrL. It outlines its initial requirements, constituent entities, relationships, and attributes. While acknowledging the need to address issues related to data protection, integration with existing data sources, and connections with Building Information Modelling (BIM) and Geographic Information System (GIS) in subsequent design phases, the study's outcome represents a significant stride in defining this tool. [ABSTRACT FROM AUTHOR]

The Internet of Things massive adoption in many industrial areas in addition to the requirement of modern services is posing huge challenges to the field of data mining. Moreover, the semantic interoperability of systems and enterprises requires to operate between many different formats such as ontologies, knowledge graphs, or relational databases, as well as different contexts such as static, dynamic, or real time. Consequently, supporting this semantic interoperability requires a wide range of knowledge discovery methods with different capabilities that answer to the context of distributed architectures (DA). However, to the best of our knowledge there is no general review in recent time about the state of the art of Concept Analysis (CA) and multi-relational data mining (MRDM) methods regarding knowledge discovery in DA considering semantic interoperability. In this work, a systematic literature review on CA and MRDM is conducted, providing a discussion on the characteristics they have according to the papers reviewed, supported by a clusterization technique based on association rules. Moreover, the review allowed the identification of three research gaps toward a more scalable set of methods in the context of DA and heterogeneous sources. [ABSTRACT FROM AUTHOR]

This article elaborates a computationally enabled approach to the study of design methods in 1960s North America. This entails the construction, visualization, and analysis of a digital database built from entries of the Design Methods Group Newsletter, a periodical published monthly between 1966-71. The article proposes a workflow that combines methods such as topic modeling and network visualization to activate the Newsletter as a source of anecdotal and informal knowledge, and to enable histories of connectivity and transaction that may elude archival investigations on singular actors or institutions. In doing so, the article contributes arguments and techniques for the study of design methods as a complex social, technical, and intellectual meshwork. The meshwork brings discursive themes, techniques, actors, and institutions at the same level of investigation and allows for layered cartographies of the field that advanced the systematic study of design and ushered in the development of early computer applications. [ABSTRACT FROM AUTHOR]

Causal Consistency has been proven to be the strongest type of consistency that can be achieved in a fault-tolerant, distributed system. This paper describes an implementation of D-Thespis, which is an approach that employs the actor mathematical model of concurrent computation to establish a distributed middleware that enforces causal consistency on a widely used relational database management system (RDBMS). D-Thespis prioritises developer experience by encapsulating the intricacies of causal consistency behind an interface that is accessible over standard REST protocol. Here, we discuss several novel results. Firstly, we define a method that builds a causally consistent DBMS supporting elastic horizontal scalability. Secondly, we deliver a cloud-native implementation of the middleware and provide results and insights on 6804 benchmark configurations executed on our implementation while running on a public cloud infrastructure across several data centres. The evaluation concerns transaction processing performance, an evaluation of our implementation's update visibility latency, and a memory profiling exercise. The results of our evaluation show that under a transactional workload, a single-node installation of our implementation of D-Thespis is 1.5 times faster than a relational DBMS running serialisable transaction processing, while the performance of the middleware can improve by more than three times when scaled horizontally within the same data centre. Our study of the memory profile of the D-Thespis implementation shows that the system distributes its memory requirements evenly across all the available machines, as it is scaled horizontally. Finally, we also illustrate how our middleware propagates data changes across geographically-distributed infrastructures in a timely manner: our tests show that most of the effects of data change operations in one data centre are available in a remote data centre within less than 300 ms over and above the network round trip latency between the two data centres. [ABSTRACT FROM AUTHOR]

The use of eXtensible Business Reporting Language (XBRL) technology in the context of financial reporting on the internet is evident, whether due to its advantages and benefits or governmental impositions. However, the data to be transported by this language is mostly stored in defined structures such as relational databases, JSON files or CSV files. It is therefore essential for organizations to integrate XBRL technology with other data storage technologies. This article presents an Extract, Transform and Load (ETL) solution for extracting data from different storage formats and generating XBRL instances, called XBRL Processor. This tool includes different types of data source and generates the XBRL instance. In addition, parameterizations were implemented to meet the delivery of the Accounting Balances Matrix to the Accounting and Financial Information System of Brazil's National Treasury Secretariat (SICONFI) and a case study was carried out in order to validate the XBRL Processor tool. [ABSTRACT FROM AUTHOR]

In the relational database realm, there has been a shift towards novel hybrid database architectures combining the properties of transaction processing (OLTP) and analytical processing (OLAP). OLTP workloads are made up by read and write operations on a small number of rows and are typically addressed by indexes such as B+trees. On the other side, OLAP workloads consists of big read operations that scan larger parts of the dataset. To address both workloads some databases introduced an architecture using a buffer or delta partition. Precisely, changes are accumulated in a write-optimized delta partition while the rest of the data is compressed in the read-optimized main partition. Periodically, the delta storage is merged in the main partition. In this paper we investigate for the first time how this architecture can be implemented and behaves for RDF graphs. We describe in detail the indexing-structures one can use for each partition, the merge process as well as the transactional management. We study the performances of our triple store, which we call qEndpoint , over two popular benchmarks, the Berlin SPARQL Benchmark (BSBM) and the recent Wikidata Benchmark (WDBench). We are also studying how it compares against other public Wikidata endpoints. This allows us to study the behavior of the triple store for different workloads, as well as the scalability over large RDF graphs. The results show that, compared to the baselines, our triple store allows for improved indexing times, better response time for some queries, higher insert and delete rates, and low disk and memory footprints, making it ideal to store and serve large Knowledge Graphs. [ABSTRACT FROM AUTHOR]

Key–value stores (KV stores) are becoming popular in both academia and industry due to their high performance and simplicity in data management. Unlike traditional database systems such as relational databases, KV stores manage data in key–value pairs and do not support relationships between the data. This simplicity enables KV stores to offer higher performance. To further improve the performance of KV stores, high-performance storage devices such as solid-state drives (SSDs) and non-volatile memory express (NVMe) SSDs have been widely adopted. These devices are intended to expedite data processing and storage. However, our studies indicate that, due to a lack of multi-thread-oriented programming, the performance of KV stores is far below the raw performance of high-performance storage devices. In this paper, we analyze the performance of existing KV stores utilizing high-performance storage devices. Our analysis reveals that the actual performance of KV stores is below the potential performance that these storage devices could offer. According to the profiling results, we argue that this performance gap is due to the coarse-grained locking mechanisms of existing KV stores. To alleviate this issue, we propose a multi-threaded compaction operation that leverages idle threads to participate in I/O operations. Our experimental results demonstrate that our scheme can improve the performance of KV stores by up to 16% by increasing the number of threads involved in I/O operations. [ABSTRACT FROM AUTHOR]

Software vulnerabilities, also known as flaws, bugs or weaknesses, are common in modern information systems, putting critical data of organizations and individuals at cyber risk. Due to the scarcity of resources, initial risk assessment is becoming a necessary step to prioritize vulnerabilities and make better decisions on remediation, mitigation, and patching. Datasets containing historical vulnerability information are crucial digital assets to enable AI-based risk assessments. However, existing datasets focus on collecting information on individual vulnerabilities while simply storing them in relational databases, disregarding their structural connections. This article constructs a compact vulnerability knowledge graph, VulKG, containing over 276 K nodes and 1 M relationships to represent the connections between vulnerabilities, exploits, affected products, vendors, referred domain names, and more. We provide a detailed analysis of VulKG modeling and construction, demonstrating VulKG-based query and reasoning, and providing a use case of applying VulKG to a vulnerability risk assessment task, i.e., co-exploitation behavior discovery. Experimental results demonstrate the value of graph connections in vulnerability risk assessment tasks. VulKG offers exciting opportunities for more novel and significant research in areas related to vulnerability risk assessment. The data and codes of this article are available at https://github.com/happyResearcher/VulKG.git. [ABSTRACT FROM AUTHOR]

In ethnographic research, data collected through surveys, interviews, or questionnaires in the fields of sociology and anthropology often appear in diverse forms and languages. Building a powerful database system to store and process such data, as well as making good and efficient queries, is very challenging. This paper extensively investigates modern database technology to find out what the best technologies to store these varied and heterogeneous datasets are. The study examines several database categories: traditional relational databases, the NoSQL family of key-value databases, graph databases, document databases, object-oriented databases and vector databases, crucial for the latest artificial intelligence solutions. The research proves that when it comes to field data, the NoSQL lineup is the most appropriate, especially document and graph databases. Simplicity and flexibility found in document databases and advanced ability to deal with complex queries and rich data relationships attainable with graph databases make these two types of NoSQL databases the ideal choice if a large amount of data has to be processed. Advancements in vector databases that embed custom metadata offer new possibilities for detailed analysis and retrieval. However, converting contents into vector data remains challenging, especially in regions with unique oral traditions and languages. Constructing such databases is labor-intensive and requires domain experts to define metadata and relationships, posing a significant burden for research teams with extensive data collections. To this end, this paper proposes using Generative AI (GenAI) to help in the data-transformation process, a recommendation that is supported by testing where GenAI has proven itself a strong supplement to document and graph databases. It also discusses two methods of vector database support that are currently viable, although each has drawbacks and benefits. [ABSTRACT FROM AUTHOR]

The article explores the integration of fundamental database concepts into a Science, Technology, Engineering, and Mathematics (STEM) Success course, emphasizing hands-on projects and ethical considerations. Topics discussed include the pedagogical approach of using database design and Structured Query Language (SQL) queries, the importance of data ethics, and the practical application of relational databases in scientific data management.

Spatial data management is crucial for applications like urban planning and environmental monitoring. While traditional relational databases are commonly used, they struggle with large and complex spatial data. NoSQL databases provide support for unstructured data and scalability. This article compares the performance and disk space usage of SQL Server (a relational database) and MongoDB (NoSQL database) using an open-source library. Experiments conducted with the OpenStreetMap dataset from Central America show that the MongoDB database outperformed the relational SQL Server database in most cases, offering practical advantages for spatial data management in Geographic Information System applications. [ABSTRACT FROM AUTHOR]

The CDISC Standard for Exchange of Nonclinical Data (SEND) data standard has created new opportunities for collaborative development of open-source software solutions to facilitate cross-study analyses of toxicology study data. A public-private partnership between BioCelerate and the FDA/Center for Drug Evaluation and Research (CDER) was established in part to develop and publicize novel methods to facilitate cross-study analysis of SEND datasets. As part of this work in collaboration with the Pharmaceutical Users Software Exchange (PHUSE), an R package sendigR has been developed to enable users to construct a relational database from a collection of SEND datasets and then query that database to perform cross-study analyses. The sendigR package also includes an integrated Python package, xptcleaner, which can be used to harmonize the terminology used in SEND datasets by mapping to CDISC controlled terminologies. The sendigR R package is freely available on the comprehensive R Archive Network (CRAN) and at https://github.com/phuseorg/sendigR. An R Shiny web application was included in the R package to enable toxicologists with no coding experience to perform historical control analyses. Experienced R programmers will be able to integrate the package functions into their own custom scripts/packages and potentially contribute improvements to the functionality of sendigR. [ABSTRACT FROM AUTHOR]

With the distributed and decentralized nature of blockchain, and with its sequential data access, query processing emerges as a challenging issue in the blockchain systems. These features hinder efficient query processing and make it difficult to guarantee the validity and privacy-preserving of query results. Several solutions have been proposed to tackle the efficiency, reliability, and privacy challenges of query processing in blockchain systems. There has been rarely a comprehensive solution addressing all of these issues. In addition, the existing solutions often assume that the blockchain nodes are homogeneous in terms of their capabilities and available resources, while the blockchain nodes can have heterogeneous computational, communication, and storage resources, and can also contribute to the blockchain network in different manners. This work, considering the heterogeneity of network nodes, introduces a multi-level and score-based sharding solution for query processing where the nodes are organized into a hierarchical tree-like structure based on their score and store a proportion of transaction data in a DAG-based data structure resulting in an efficient query time. Additionally, the nodes reach a consensus over the query results from the bottom to the top of the hierarchical structure enabling reliable and fast query processing. The experiments conducted during the evaluation show that the efficiency of the proposed work is near that of relational databases in terms of query response time. It also provides a high validity rate taking advantage of its hierarchical consensus mechanism and preserves the privacy of query results using a delegation-based integration method where the final query result is integrated by the client's representative. [ABSTRACT FROM AUTHOR]

This study examines the potential of microgrids (MG), which utilize renewable energy sources to provide sustainable power solutions. To conduct the analysis, we examined load and photovoltaic (PV) data, calculated minimum and maximum averages, and visualized the correlation using big data tools. We cleaned the data by removing unnecessary rows, merged the tables, converted them into CSV format, and uploaded them to the Databricks file distribution system (DBFS). Subsequently, we processed the data by creating a pipeline and using ETL (extract, transform, load) processes. We analyzed and visualized the data using tools such as Power BI and Tableau. The analysis identified the maximum and minimum PV production, assessed the impact of weather patterns on production, and measured the energy shortage between load demand and PV generation. Our research demonstrates the steps involved in handling and analyzing data, uploading it to the Hadoop ecosystem, transforming it into different file formats, connecting it to a relational database management system (RDBMS), and visualizing it using BI tools. In this study, we utilized cloud infrastructure to perform analytical tasks, including the use of business intelligence (BI) tools. [ABSTRACT FROM AUTHOR]

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This article introduces FiGaRo, an algorithm for computing the upper-triangular matrix in the QR decomposition of the matrix defined by the natural join over relational data. FiGaRo 's main novelty is that it pushes the QR decomposition past the join. This leads to several desirable properties. For acyclic joins, it takes time linear in the database size and independent of the join size. Its execution is equivalent to the application of a sequence of Givens rotations proportional to the join size. Its number of rounding errors relative to the classical QR decomposition algorithms is on par with the database size relative to the join output size. The QR decomposition lies at the core of many linear algebra computations including the singular value decomposition (SVD) and the principal component analysis (PCA). We show how FiGaRo can be used to compute the orthogonal matrix in the QR decomposition, the SVD and the PCA of the join output without the need to materialize the join output. A suite of experiments validate that FiGaRo can outperform both in runtime performance and numerical accuracy the LAPACK library Intel MKL by a factor proportional to the gap between the sizes of the join output and input. [ABSTRACT FROM AUTHOR]

This paper presents a migration from relational databases (RDB) to NoSQL databases, leveraging a data model that utilizes concepts such as objects, semantic enrichment, and metadata. It addresses the limitations of previous approaches and extends them to overcome issues encountered in the relational model, allowing for simpler and faster handling of large datasets. This approach eliminates problems related to joins, incorporates the concepts of references and embedded documents, and includes a comparative study between our approach and other methods for migrating from RDB to NoSQL. This comparison covers aspects such as structural changes, insertion rates, selection, and deletion. Automated migration uses document-oriented nested types to store related data in one database record. This simplifies the design phase and reduces queries and updates, speeding up user responses. This research provides a comprehensive summary of the achievement of migrating from a relational database to a document-oriented NoSQL database. The essence of this migration lies in its automatic nature, aimed at transforming the pre-existing relational model into an object-oriented model. This transition aims to align the database structure with MongoDB's documentoriented paradigm, ensuring optimal integration and efficient utilization of the technology's features. This research significantly distinguishes itself from previous work, primarily due to its ability to facilitate automatic database migration. It accomplishes this task by autonomously identifying object concepts, including those related to inheritance. This functionality is unique and represents a significant advancement, as no other migration method has thus far been capable of performing this automatic detection of object concepts, especially inheritance. The result of the migration provides several advantages in terms of reducing record count, particularly as we deal with massive data sets, and in terms of the time required for data deletion and insertion. A prototype shows how effective this automated approach is. Maintain that the migration process is automated. [ABSTRACT FROM AUTHOR]

The field of Advanced Air Mobility (AAM) is witnessing a transformation with innovations such as electric aircraft and increasingly automated airspace operations. Within AAM, the Urban Air Mobility (UAM) concept focuses on providing air‐taxi services in densely populated urban areas. This research introduces the utilization of Large Language Models (LLMs), such as OpenAI's GPT‐4, to enhance the UAM Requirement discovery process. This study explores two distinct approaches to leverage LLMs in the context of UAM Requirement discovery. The first approach evaluates the LLM's ability to provide responses without relying on additional outside systems, such as a relational or graph database. Instead, a vector store provides relevant information to the LLM based on the user's question, a process known as Retrieval Augmented Generation (RAG). The second approach integrates the LLM with a graph database. The LLM acts as an intermediary between the user and the graph database, translating user questions into cypher queries for the database and database responses into human‐readable answers for the user. Our team implemented and tested both solutions to analyze requirements within a UAM dataset. This paper will talk about our approaches, implementations, and findings related to both approaches. [ABSTRACT FROM AUTHOR]

Hive can store massive data through extended clusters, far exceeding the expansion and storage capabilities of traditional databases, and has become a major tool for building data warehouses in the era of big data. Based on Hive technology, this paper studies the enterprise-level data warehouse architecture composed of data storage layer, Hive data warehouse layer and application layer. Then, the paper studies data warehouse tools, uses HDFS for underlying storage and MapReduce for computing engine, and compares Hive data warehouse with relational database. Then, the ETL process is studied. The data goes through three stages: extraction, conversion and loading, and finally the data flows from the source to the target end. Finally, the system function and test are studied, the subsystems including data processing, data management, data migration and data analysis are constructed, and the core functions are tested. The test results were consistent with the expected results and met the delivery standards. [ABSTRACT FROM AUTHOR]

The term data double denotes information generated by and collected from users of networked communications to construct relational databases in marketing and other domains. Each subject (user) of this surveillance-panoptical system inevitably informs and objectifies all other subjects. Social media experiences are based on our continuously "tracked" engagement. Surveillance functions across written and spoken language, biometrics, geolocation, and visual and behavioral patterns. This text is primarily concerned with visual media and its production and (re)circulation as the accumulation of data through uploading, viewing, liking, commenting, remixing, and sharing. The article explores how selected media artists reflect upon the potential of recirculating information to reveal our data doubles and the surveillance-panoptical system. [ABSTRACT FROM AUTHOR]

With the rapid acceleration of urbanization and the widespread adoption of social networks, A vast amount of spatiotemporal check-in data from social media users has been generated. Traditional relational databases have limitations in handling massive spatiotemporal data, whereas graph databases, with their efficiency in data querying and processing, offer a novel solution for the analysis of spatiotemporal data. The paper introduces a method for constructing a user check-in spatiotemporal knowledge graph based on the Neo4j graph database, utilizing a designed graph model to create a visual knowledge graph. By incorporating temporal and spatial characteristics into entities, we realize efficient management and complex query of spatiotemporal data of social media user check-ins. By adding sentiment attributes to relationships through sentiment analysis of check-in text, the application of check-in text is realized. Ultimately, employing Cypher to achieve knowledge querying, we analyze the hot spots of urban activities and categories. [ABSTRACT FROM AUTHOR]

In recent decades, the "big microdata revolution" has transformed access to transcribed historical census data for social science research. However, the population records of the Ottoman Empire, spanning Southeastern Europe, Western Asia, and Northern Africa, remained inaccessible to the big microdata ecosystem due to their prolonged unavailability. This publication marks the inaugural release of complete population data for an Ottoman urban center, Bursa, derived from the 1839 population registers. The dataset presents originally non-tabulated register data in a tabular format integrated into a relational Microsoft Access database. Thus, we showcase the extensive and diverse data found in the Ottoman population registers, demonstrating a level of quality and sophistication akin to the censuses conducted worldwide in the nineteenth century. This valuable resource, whose potential has been massively underexploited, is now presented in an accessible format compatible with global microdata repositories. Our aim with this dataset is to enable historical demographic studies for the Ottoman realm and beyond, while also broadening access to the datasets constructed by our large research team. [ABSTRACT FROM AUTHOR]

For more than 80 years, the scientific community has extensively used International Centre for Diffraction Data's (ICDD®) Powder Diffraction File (PDF®) for material characterization, including powder X-ray diffraction analysis. Historically, PDF was made available for two major material types: one for inorganic analysis and the other for organic analysis. In the early years of the PDF, this two-material approach was implemented due to limited computer capabilities. With Release 2024, ICDD provides a comprehensive database consisting of the entire PDF in one database called PDF-5+, comprised of more than one million entries (1,061,898). The PDF-5+ with a relational database (RDB) construct houses extensive chemical, physical, bibliographic, and crystallographic data, including atomic coordinates and raw data, enabling qualitative and quantitative phase analysis. This wealth of information in one database is advantageous for phase identification, materials characterization, and several data mining applications in materials science. A database of this size needs rigorous data curation and structural and chemical classifications to optimize pattern search/match and characterization methods. Each entry in the PDF has an editorially assigned quality mark. An editorial comment will describe the reason if an entry does not meet the top-quality mark. The editorial processes of ICDD's quality management system are unique in that they are ISO 9001:2015 certified. Among several classifications implemented in PDF-5+, subfiles (such as Bioactive, Pharmaceuticals, Minerals, etc.) directly impact the search/match in minimizing false positives. Scientists with specific field expertise continuously review these subfiles to maintain their quality. This paper describes the features of PDF with an emphasis on the newly released PDF-5+. [ABSTRACT FROM AUTHOR]

With the continuous development of the power grid, its structure is becoming increasingly complex. The occurrence of faults in transmission lines may lead to cascading failures in the power grid, ultimately resulting in widespread power outages. The transmission of equipment information and the sending of fault reports in the power grid rely on the power communication network. This network is crucial for ensuring the safe, stable, and economical operation of the power grid. As the number of devices in the power grid increases and sensor technology becomes more widespread, the volume of data generated by both the power grid and the power communication network has increased sharply. However, relational databases have limited scalability and struggle to meet the growing volume of data and user demands. This paper proposes a graph mapping method based on the power grid and communication network, utilizing data from both networks to construct a unified data plane in a graph database. Taking power transfer operations as an example, a unified standard data model and monitoring indicator system are established for both networks, enabling faster response and power restoration to blackout areas in the event of power grid faults. Simulation results demonstrate that compared to traditional relational databases, graph databases exhibit significantly improved efficiency in handling large-scale, highly connected data, making them more suitable for future power grids. [ABSTRACT FROM AUTHOR]

Learning the SQL language for working with relational databases is a fundamental subject for future computer engineers. However, in distance learning contexts or unexpected situations like the COVID-19 pandemic, where students had to follow lectures remotely, they may find it hard to learn. Chatbots are software applications that aim to have conversations with people to help them solve problems or provide support in a specific domain. This paper proposes a chatbot-based learning platform to assist students in learning SQL. A case study has been conducted to evaluate the proposal, with undergraduate computer engineering students using the learning platform to perform SQL queries while being assisted by the chatbot. The results show evidence that students who used the chatbot performed better on the final SQL exam than those who did not. In addition, the research shows positive evidence of the benefits of using such learning platforms to support SQL teaching and learning for both students and lecturers: students use a platform that helps them self-regulate their learning process, while lecturers get interesting metrics on student performance. [ABSTRACT FROM AUTHOR]

Query optimization involves identifying and implementing the most effective and efficient methods and strategies to enhance the performance of queries. This is achieved by intelligently utilizing system resources and considering various performance metrics. Table joining optimization involves optimizing the process of combining two or more tables within a database. Structured query language (SQL) optimization is the progress of utilizing SQL queries in the possible way to achieve fast and accurate database results. SQL optimization is critical to decreasing the no of queries in research description framework (RDF) and the time for processing a huge number of relatable data. In this paper, four new algorithms are proposed such as hash-join, sort-merge, rademacher averages and mapreduce for the progress of SQL query join optimization. The proposed model is evaluated and tested using waterloo sparql diversity test suite (WatDiv) and lehigh university benchmark (LUBM) benchmark datasets in terms of time execution. The results represented that the proposed method achieved an enhanced performance of less execution time for various queries such as Q3 of 5362, Q8 of 5921, Q9 of 5854 and Q10 of 5691 milliseconds. The proposed gives better performance than other existing methods like hybrid database-map reduction system (AQUA+) and join query processing (JQPro). [ABSTRACT FROM AUTHOR]

PURPOSE: Although the International Neuroblastoma Risk Group Data Commons (INRGdc) has enabled seminal large cohort studies, the research is limited by the lack of real-world, electronic health record (EHR) treatment data. To address this limitation, we evaluated the feasibility of extracting treatment data directly from EHRs using the REDCap Clinical Data Interoperability Services (CDIS) module for future submission to the INRGdc. METHODS: Patients enrolled on the Children's Oncology Group neuroblastoma biology study ANBL00B1 (ClinicalTrials.gov identifier: NCT00904241) who received care at the University of Chicago (UChicago) or the Vanderbilt University Medical Center (VUMC) after the go-live dates for the Fast Healthcare Interoperability Resources (FHIR)–compliant EHRs were identified. Antineoplastic drug orders were extracted using the CDIS module. To validate the CDIS output, antineoplastic agents extracted through FHIR were compared with those queried through EHR relational databases (UChicago's Clinical Research Data Warehouse and VUMC's Epic Clarity database) and manual chart review. RESULTS: The analytic cohort consisted of 41 patients at UChicago and 32 VUMC patients. Antineoplastic drug orders were identified in the extracted EHR records of 39 (95.1%) UChicago patients and 26 (81.3%) VUMC patients. Manual chart review confirmed that patients with missing (n = 8) or discontinued (n = 1) orders in the CDIS output did not receive antineoplastic agents during the timeframe of the study. More than 99% of the antineoplastic drug orders in the EHR relational databases were identified in the corresponding CDIS output. CONCLUSION: Our results demonstrate the feasibility of extracting EHR treatment data with high fidelity using HL7-FHIR via REDCap CDIS for future submission to the INRGdc. A path to unlocking high-quality EHR treatment data for neuroblastoma research using REDCap CDIS. [ABSTRACT FROM AUTHOR]