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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Aditi Singh Akash Shetty Abul Ehtesham Saket Kumar Tala Talaei Khoei Department of Computer Science Cleveland State University USA The Davey Tree Expert Company USA The Mathworks USA Khoury College of Computer Science Roux Institute at Northeastern University USA

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

Text-to-SQL systems facilitate smooth interaction with databases by translating natural language queries into Structured Query Language (SQL), bridging the gap between non-technical users and complex database management systems. This survey provides a comprehensive overview of the evolution of AI-driven text-to-SQL systems, highlighting their foundational components, advancements in large language model (LLM) architectures, and the critical role of datasets such as Spider, WikiSQL, and CoSQL in driving progress. We examine the applications of text-to-SQL in domains like healthcare, education, and finance, emphasizing their transformative potential for improving data accessibility. Additionally, we analyze persistent challenges, including domain generalization, query optimization, support for multi-turn conversational interactions, and the limited availability of datasets tailored for nosql databases and dynamic real-world scenarios. To address these challenges, we outline future research directions, such as extending text-to-SQL capabilities to support nosql databases, designing datasets for dynamic multi-turn interactions, and optimizing systems for real-world scalability and robustness. By surveying current advancements and identifying key gaps, this paper aims to guide the next generation of research and applications in LLM-based text-to-SQL systems.

关键词： Surveys Structured Query Language Translation nosql databases Scalability Query processing Medical services Benchmark testing Robustness Next generation networking

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Concept and Considerations for the Development of an Information Database for Efficient International Freight Transport

Concept and Considerations for the Development of an Informa...

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International Conference on Computational Cybernetics (ICCC)

作者： Petra Opitz Tamás Haidegger Enikö Nagy John von Neumann Faculty of Informatics Óbuda University Budapest Hungary University Research and Innovation Center (EKIK) Óbuda University Budapest Hungary School of Computing Queen's University Kingston ON John von Neumann Faculty of Informatics Óbuda University Antal Bejczy Center for Intelligent Robotics Budapest Hungary

ISBN: (数字)9798331502461

ISBN: (纸本)9798331502478

International freight transport is a growing market and a crucial component of the global economy, enabling the seamless flow of goods across regions and supporting global trade. However, the industry faces significant challenges, particularly in road transport orchestration, due to the increasing complexity of operations and the demand for real-time decision-making. This study sketched the algorithmic and programming framework to address these challenges by presenting an optimized database architecture designed to improve decision support systems for freight transport. The proposed system architecture may leverage the flexibility and scalability of nosql databases to manage large, semi-structured datasets in the future, while REST APIs enable seamless integration and real-time data manipulation. To enhance usability and transparency, the system incorporates advanced data visualization tools, allowing stakeholders to derive actionable insights and improve operational efficiency. By focusing on areas such as real-time tracking, sustainability, and process optimization, this work not only addresses current operational needs but also provides a robust foundation for future developments, including advanced analytics and mobile application integration. This contribution supports the logistics industry's shift toward more adaptive, efficient, and sustainable transport solutions.

关键词： Industries nosql databases Data visualization Systems architecture Real-time systems Visual databases Usability Sustainable development Optimization Logistics

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Comparative Analysis of Three IoT Data Storage System Architectures on AWS Cloud

Comparative Analysis of Three IoT Data Storage System Archit...

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IEEE International Conference on Software Architecture Workshops (ICSAW)

作者： Dominik Rohal Lucy Ellen Lwakatare Yusheng Wu Jesse Haataja Jukka K Nurminen Juha Kangasluoma Institute for Atmospheric and Earth System Research University of Helsinki Helsinki Finland Dept. of Computer Science University of Helsinki Helsinki Finland

ISBN: (数字)9798331533366

ISBN: (纸本)9798331533373

Internet of Things (IoT) devices generate large amounts of data, creating the challenge of designing efficient IoT cloud storage solutions. This study focuses on an IoT application managing air quality measurement data, which requires frequent retrieval of recent data for near-real-time monitoring and access to long segments of time-series data. The study performs a comparative analysis of three IoT storage system architecture options on Amazon Web Services (AWS) cloud. In contrast to existing literature, the options take into account data access and retrieval patterns specific to the use cases of the IoT application under study, where only data produced by a particular device is accessed at a time. The first option utilizes a nosql database, the second employs data streams and an object storage, and the third uses a time-series database. Each option was tested by performing retrievals of different lengths of time segments, focusing on retrieval times and query capabilities. Results show that a time-series database is suitable for applications benefiting from query options or time-series functions, such as interpolation. A Key-Value nosql database is suitable for applications retrieving short time segments without extensive queries. Conversely, an object storage excels in storing and retrieving large volumes of raw data, while data streams are unsuitable as a direct source of data for near-real-time applications. The study provides a framework for comparing alternative architectures to analyze their suitability, guiding decisions in selecting the most appropriate storage solution based on specific IoT application requirements.

关键词： Performance evaluation Cloud computing databases nosql databases Buffer storage Memory Computer architecture Real-time systems Internet of Things Streams

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Mitigating nosql Injection Vulnerabilities: Techniques, Examples, and Best Practices

Mitigating NoSQL Injection Vulnerabilities: Techniques, Exam...

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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： Thamizhiniyan C S Chiranjeevi Sai Kapuluru Balakiruthiga B Department of Networking and Communications SRMIST Kattankulathur Chennai India

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

nosql injection is a security vulnerability that allows attackers to interfere with an application’s queries to a nosql database. Such attacks can result in bypassing authentication mechanisms, extracting or altering data, causing denial of service, or even executing code on the server. Unlike traditional SQL databases, nosql databases use varied query languages and data structures, which results in different types of injection vulnerabilities. The two primary forms of nosql injection are syntax injection, where attackers manipulate query syntax similar to SQL injection techniques, and operator injection, where attackers leverage query operators to manipulate queries. This paper focuses on a practical example of operator injection and outlines effective prevention methods, including input sanitization, parameterized queries, access control, encryption, network security, and compliance with security standards. These strategies aim to mitigate the risks associated with nosql injection and enhance the overall security of applications using nosql databases.

关键词： Access control databases nosql databases Prevention and mitigation Authentication Syntactics Security Servers Best practices Standards

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Machine Learning-Driven Web System for Automated Sequential-to-Parallel Code Transformation

Machine Learning-Driven Web System for Automated Sequential-...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： I. V. Viktorov R. F. Gibadullin Department of Computer Systems Kazan National Research Technical University named after A. N. Tupolev – KAI Kazan Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

This paper presents the design and implementation of a machine learning-driven web system for automating the transformation of sequential code into parallel code. The proposed system leverages a shared memory programming model and is built on a microservices architecture to ensure scalability and modularity. It incorporates advanced tools for constructing and analyzing Abstract Syntax Trees to generate parallel programs that adhere to Bernstein's conditions. A supervised learning mechanism, using Word2Vec for vector representation and TF-IDF for significance evaluation, enables the system to adapt and refine parallelization rules based on programmer input. Additionally, the system integrates an AMQP-based message broker to handle high-load processing efficiently and supports data storage through relational or nosql databases. The solution provides a robust and flexible platform for enhancing computational performance and reducing development complexity in parallel programming. The results demonstrate the feasibility and effectiveness of this approach in addressing the challenges of automating code parallelization in modern software systems.

关键词： Codes Parallel programming Scalability nosql databases Supervised learning Microservice architectures Machine learning Syntactics Software systems Vectors

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Improving the energy efficiency of relational and nosql databasesvia via query optimizations

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2019年 22卷 120-133页

作者： Mahajan, Divya Blakeney, Cody Zong, Ziliang Texas State Univ Dept Comp Sci San Marcos TX 78666 USA

As big data becomes the norm of various industrial applications, the complexity of database workloads and database system design has increased significantly. To address these challenges, conventional relational databases have been constantly improved and nosql databases such as MongoDB and Cassandra have been proposed and implemented to compete with SQL databases. In addition to traditional metrics such as response time, throughput, and capacity, modern database systems are posing higher requirements on energy efficiency due to the large volume of data that need to be stored, queried, updated, and analyzed. While decades of research in the database and data processing communities has produced a wealth of literature that optimize for performance, research on optimizations for energy efficiency has been historically overlooked and only a few studies have investigated the energy efficiency of database systems. To the best our knowledge, there are currently no comprehensive studies that analyze the impact of query optimizations on performance and energy efficiency across both relational and nosql databases. In fact, the energy behavior of many basic database operations (e.g. insertion, deletion, searching, update, indexing, etc) remains largely unknown due to the lack of accurate power measurement methodologies for various databases and queries. In this paper, we investigate a series of query optimization techniques for improving the energy-efficiency of relational databases and nosql databases. We use both widely acceptable benchmarks (e.g. Yahoo! Cloud Server Benchmark) and customized datasets (converted from (1) over tilde 00GB of Twitter data) in our experiments to evaluate the effectiveness of various optimization techniques. We conduct cross database analysis on relational database (MySQL) and nosql based databases (MongoDB and Cassandra) to compare their performance and energy efficiency. Additionally, we study a variety of optimization techniques that can improv

关键词： Energy-efficiency Relational databases nosql databases MySQL MongoDB Cassandra DVFS

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Digitization of Medical Data: LLM-Based Data Entry, Cleansing, and Visualization Solutions

Digitization of Medical Data: LLM-Based Data Entry, Cleansin...

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Learning and Technology Conference (L&T)

作者： Mahmoud Amgad Bahaa Shamoon Hagar Mekhemar Rodeen Mostafa Toqa Tamer Habiba Mohamed Sahar Fawzi Center for Informatics Science (CIS) School of Information Technology and Computer Science Nile University Sheikh Zayed City Giza Egypt

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

Healthcare data management faces several challenges, including errors from manual data entry, variations in data quality, and an increasing need for advanced tools to efficiently handle large datasets. This paper introduces Data-Care, an innovative application designed to automate medical record digitization by leveraging advanced technologies such as Large Language Models (LLMs), Optical Character Recognition (OCR), and nosql databases. Together, these technologies significantly reduce the need for manual input, minimizing errors and enhancing data accuracy. A key feature of Data-Care is its data cleansing module, which resolves issues like data inconsistencies and missing values, ensuring the integrity and accuracy of the dataset. Additionally, the application provides a powerful offline visualization tool, enabling healthcare professionals to generate comprehensive reports that support informed decision-making and streamline workflows across various healthcare processes. Extensive performance tests demonstrated that the integration of LLMs and OCR in Data-Care achieved accuracy rates between 76% and 93%, depending on the quality of the input data. These results underscore Data-Care's capacity to improve both the speed and accuracy of data entry, offering a robust solution for the digitization and management of healthcare data.

关键词： Accuracy Data integrity nosql databases Large language models Optical character recognition Decision making Data visualization Medical services Manuals Usability

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Development of a Responsive Web Application for Human Resource Management Using a nosql Database

Development of a Responsive Web Application for Human Resour...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Gorana Mišković Dragoljub Krneta Dwelt d.o.o Banja Luka BiH

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

The topic of this paper is the development of a responsive web application for human resource management using a nosql database. The technical goal of the paper is to demonstrate the possibility of developing a modern three-tier web application with the option of using MongoDB, a nosql database management system. From an economic perspective, the goal is to support business processes and assist in most activities related to modern human resource management in companies or organizations, thus creating conditions for faster and higher-quality decision-making by management concerning human resource management.

关键词： Economics Training Web services nosql databases Soft sensors Decision making Companies Software systems Resource management Human resource management

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Persistence of RDF Data into nosql: A Survey and a Reference Architecture

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第3期34卷 1370-1389页

作者： Zambom Santana, Luiz Henrique Mello, Ronaldo dos Santos Univ Fed Santa Catarina Dept Informat & Estat BR-88040900 Florianopolis SC Brazil

RDF is being increasingly considered in a broad range of information management scenarios. Governments, large corporations, startups, and other organizations around the world are using RDF as a data model to represent and share knowledge. However, there is still a long evolutionary track with multiple challenges for RDF reaching the scale of the most recent Big Data intensive applications (e.g., Smart Cities, Sensor Networks, eHealth, Internet of Things). In this survey, we review the usage of nosql databases to the storage of large RDF graphs by rehearsing the latest surveys and expanding their findings by updating proposals and bringing light to aspects such as model mapping between RDF and nosql, triple indexing and partitioning, graph fragmentation and data caching. Moreover, we explain how the surveyed works extended the RDF capabilities so the datasets can benefit of the characteristics of scalability, schemaless data, and better overall performance of nosql databases. The survey summarizes the current state of art, discusses open problems, and proposes a Reference Architecture (RA). For the best of our knowledge, this is the first survey where the focus is solely on papers that use one or more nosql systems for the RDF persistence.

关键词： Resource description framework nosql databases Data models Indexing Benchmark testing Scalability nosql RDF SPARQL and Semantic Web

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nosql Data Model for Semi-automatic Integration of Ethnomedicinal Plant Data from Multiple Sources

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PHYTOCHEMICAL ANALYSIS 2014年第6期25卷 495-507页

作者： Ningthoujam, Sanjoy Singh Choudhury, Manabendra Dutta Potsangbam, Kumar Singh Chetia, Pankaj Nahar, Lutfun Sarker, Satyajit D. Basar, Norazah Talukdar, Anupam Das Ghanapriya Womens Coll Dept Bot Imphal Manipur India Assam Univ Bioinformat Ctr Silchar Assam India Manipur Univ Dept Life Sci Imphal Manipur India Liverpool John Moores Univ Sch Pharm & Biomol Sci Med Chem & Nat Prod Res Grp Fac Sci Liverpool L3 3AF Merseyside England UTM Dept Chem Fac Sci Skudai 81310 Johor Malaysia Assam Univ Dept Life Sci & Bioinformat Silchar Assam India

IntroductionSharing traditional knowledge with the scientific community could refine scientific approaches to phytochemical investigation and conservation of ethnomedicinal plants. As such, integration of traditional knowledge with scientific data using a single platform for sharing is greatly needed. However, ethnomedicinal data are available in heterogeneous formats, which depend on cultural aspects, survey methodology and focus of the study. Phytochemical and bioassay data are also available from many open sources in various standards and customised formats. ObjectiveTo design a flexible data model that could integrate both primary and curated ethnomedicinal plant data from multiple sources. Materials and methodsThe current model is based on MongoDB, one of the Not only Structured Query Language (nosql) databases. Although it does not contain schema, modifications were made so that the model could incorporate both standard and customised ethnomedicinal plant data format from different sources. ResultsThe model presented can integrate both primary and secondary data related to ethnomedicinal plants. Accommodation of disparate data was accomplished by a feature of this database that supported a different set of fields for each document. It also allowed storage of similar data having different properties. ConclusionThe model presented is scalable to a highly complex level with continuing maturation of the database, and is applicable for storing, retrieving and sharing ethnomedicinal plant data. It can also serve as a flexible alternative to a relational and normalised database. Copyright (c) 2014 John Wiley & Sons, Ltd. Scientific approaches to phytochemical investigation and conservation of ethnomedicinal plants could be refined by sharing traditional knowledge with scientific community, by integrating traditional knowledge with scientific data in a single platform. To address this issue, a platform based on MongoDB, one of the nosql databases, has been proposed. T

关键词： MongoDB nosql databases ethnomedicinal plants

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