Enhancing Data Query Flexibility with GraphQL: Implementation and Best Practices

Query optimization is a sophisticated process whose resource consumption and quality of the best execution plan depends on the query complexity, available resources of the RDBMS server, and the current instance of the database. For a self-managing RDBMS such as SAP SQL Anywhere, the query optimizer must adapt each optimization process to the current resources as these servers are run in highly diverse environments with no DBA for tuning, running mixed workloads with little correlation between query complexity, its execution time, or database size. The main goal of the work presented in this paper is to be able to dynamically choose a join enumeration algorithm among the algorithms available to the query optimizer based on the known characteristics of the algorithms, the query complexity and its expensiveness, and the available resources for the optimization process. First, the paper discusses the experimental results of the optimization time breakdown and the memory consumption for a set of join enumeration algorithms ranging from highly heuristics algorithms to dynamic programming algorithms with exhaustive bushy trees enumeration. Next, we present a novel technique to predict the optimization time and memory consumption for a current query by using similarity distances between its query graph and a set of etalon queries with known performance. The new technique can be used by any query optimizer for choosing among join enumeration algorithms, or optimization levels, based on such characteristics as query complexity and available resources.

With the increasing adoption of RESTful APIs as the backbone of modern web and mobile applications, ensuring their security has become a critical concern. Traditional security mechanisms such as rule-based firewalls and static rate-limiting policies are often ineffective against sophisticated, evolving threats like zero-day attacks, automated bot traffic, and API abuse patterns. This research proposes an intelligent, machine learning-based framework to detect and prevent malicious activity in REST API traffic. The approach involves collecting and preprocessing real-time API request logs to extract behavioural and contextual features. Supervised and unsupervised machine learning models such as isolation forests, LSTM-based anomaly detectors, and decision trees are evaluated for their effectiveness in detecting anomalies, injection attacks, and abnormal usage behavior. Furthermore, the study incorporates reinforcement learning to dynamically adjust security policies (e.g., rate limits, IP bans) in response to detected threats without impacting legitimate users. A proof-of-concept prototype will be developed and deployed in a controlled environment to evaluate performance in terms of detection accuracy, false positive rates, and system latency. The outcome of this research aims to advance the state-of-the-art in API security by introducing adaptive, self-learning mechanisms capable of protecting APIs from modern security threats while maintaining usability and performance.

Information-Centric Networking (ICN) is widely accepted as the network architecture for the next generation Internet. Caching strategy is one of the most important issues of ICN and has received much attention in recent years. In this paper we focus on caching strategy for Scalable Video Coding (SVC) in ICN. We first develop the mathematical models of user behavior, video popularity, user experience and caching efficiency. Then we propose a new caching strategy named PoProb, which determines the caching probability of a video according to its popularity. Simulation of PoProb is conducted using AMuSt simulator based on the ndnSIM simulation platform. Simulation results show that compared with existing caching strategies, PoProb can improve user experience and cache efficiency to a certain extent.

Customer support systems handle a vast number of queries daily, often containing multiple intents within a single request. Traditional intent detection models primarily focus on single-intent classification, limiting their effectiveness in realworld scenarios where users express multiple concerns simultaneously. This research proposes a novel multi-intent detection model leveraging artificial intelligence (AI) and natural language processing (NLP) to raise automated customer support. Our approach integrates a transformer-based deep learning model to accurately identify and classify multiple intents within a single query. We utilize a labeled dataset of customer service interactions, implementing advanced tokenization, semantic embedding, and attention mechanisms to improve detection accuracy. The proposed model is evaluated against traditional machine learning classifiers and state-of-theart deep learning architectures, exhibit superior performance in terms of precision, recall, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{F 1}$</tex>-score. Experimental results show a significant improvement in intent classification accuracy, reducing misinterpretation and enhancing the efficiency of automated support systems. This research contributes to the advancement of AI-driven customer support, enabling more effective query resolution and improving user experience.

Abstract— Educational platforms are usually spread across systems for delivering courses, managing hackathons, listing internships and processing payments. These systems often don't have security and strong ways to check transactions. This paper talks about EduN- exus, a scalable educational ecosystem built using the MERN stack, which includes MongoDB, Express.js, React.js and Node.js. EduN- exus brings together student and admin login systems with role-based access control to ensure system management is controlled. Login is done using JSON Web Tokens and RESTful APIs make backend communication structured. Financial transactions are secure through Razorpay integration [16] with HMAC-SHA256, signature verification on the server-side to prevent payment tampering and unauthor- ized confirmations. The platform allows for course enrollment, registration, browsing internships, managing content and interacting with a chatbot in one unified system. Security considerations include handling credentials role-based authorization protecting API routes and managing secrets based on the environment. Tests show that the system has fast/optimal API response times, reliable payment validation secure multi-role authentication and stable performance under moderate concurrent usage. EduNexus offers an extensible and security-focused foundation, for future educational platforms. Index Terms— MERN Stack, Educational Platform, Secure Payment Gateway, REST API Architecture, HMAC Verification

Aims: The study delves into the machine learning (ML) paradigm shift in enhancing mobile application testing processes for higher accuracy, efficiency, and overall user experience, with a particular focus on Decision Tree and Random Forest models. Study Design: Experimental Research Design. Methodology: The research applies an experimental A/B testing framework using real-world datasets and cloud-based testing environments (e.g., Firebase Test Lab) to compare ML-driven and traditional testing approaches. Techniques include automated UI defect detection through convolutional neural networks, reinforcement learning for intelligent test case prioritization, natural language processing for extracting UX-related insights from user feedback, as well as a structured user survey involving 20 participants to evaluate perceived improvements in usability and stability. MATLAB R2024b was used for model development and evaluation. Results: Experimental results demonstrate that ML-based testing significantly outperforms traditional approaches, achieving 15–20% higher defect detection rates, 30–35% greater test coverage, and 40–50% faster execution times, alongside a notable reduction in false positives. Decision Tree and Random Forest models showed superior performance in identifying usability and performance issues. Additionally, the integration of ML into CI/CD pipelines facilitated faster bug resolution with minimal manual intervention. User survey results further confirmed improvements in user experience, with over 70% of respondents reporting enhanced application stability and responsiveness. Conclusion: Despite its promise, deploying ML in real-world testing presents challenges, including dataset bias, variability across device environments, and limited interpretability of some model decisions. To address these, the study recommends developing robust ML-based testing frameworks, ensuring access to representative and high-quality training data, and designing hybrid models that integrate supervised learning with unsupervised anomaly detection techniques.

With the rapid development of technology, especially in the industrial sector and the widespread use of the internet in Indonesia, effective data storage media is needed. Like MySQL, database management systems (DBMS) have become an effective solution for managing and processing data. As a type of DBMS, MySQL has demonstrated the ability to support application development by both experienced and novice users. On the other hand, in the ever-evolving digital era, web and mobile applications have become important, and Node.js and REST APIs have become the basis for creating effective web applications. Technological advances have also produced an alternative to relationless data storage, known as NoSQL. MongoDB, for example, offers flexibility and fast performance when storing data in JSON document format. Load testing performance analysis between MySQL and MongoDB on Node.js applications is very important because they have different data models and workload types. This is an important technique for measuring and evaluating application performance. Search speed, data storage, and query complexity are influenced by the MySQL and MongoDB NoSQL relational data models. The goal of this load testing performance analysis is to gain an understanding of how MySQL and MongoDB perform when faced with different workloads, particularly in the context of Node.js applications. The results of this analysis are expected to provide a clear picture of the advantages and disadvantages of each database when used with Node.js applications, so that developers and system architects can make wise, data-driven decisions when they use Node.js applications.

Caching at network edges has attracted more and more research interests recently for the purpose of alleviating the network traffic pressure especially in backhaul links and improving user experience. We study Heterogeneous Cellular Caching Networks (HCCNs) consisting of macro cells in which <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> small cell base stations (SBSs) equipped with cache memory operate in conjunction with the macro cell base station (MBS). We provide closed-form expressions of the MBS and SBSs utilization factors and average user-experienced-delay in HCCNs with overlapping coverage regions, considering general traffic models for the request arrivals based on the Independent Reference Model (IRM) and renewal traffic models. Moreover, we propose a novel caching scheme in HCCNs, namely Cooperative Most Popular Caching (CMPC), which outperforms the existing schemes in terms of delay. Subsequently, we present the bandwidth assignment problem aiming to minimize the average user-experienced-delay under stability and cache size constraints in HCCNs with overlapping coverage regions and stochastic request arrivals. Finally, the analytic results are validated through numerical results and real trace-driven experiments.

Emotion recognition using artificial intelligence (AI) has become an increasingly vital area of research, offering transformative applications across healthcare, human-computer interaction, marketing, education, and entertainment. This paper provides a comprehensive review of the techniques and applications of AI in emotion recognition. Key methodologies, including facial expression analysis, speech emotion recognition, text-based emotion detection, physiological signal analysis, and multimodal approaches, are explored in detail. Each technique's underlying algorithms, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and natural language processing (NLP) methods, are examined for their efficacy in identifying human emotions. This paper also delves into practical applications, showcasing how emotion recognition enhances mental health monitoring, improves user experience, optimizes customer service, personalizes education, and enriches entertainment media. Moreover, this study addresses the ethical and technical challenges associated with emotion recognition, such as data privacy concerns, potential biases in AI models, and the need for accuracy and reliability in emotion detection systems. The discussion extends to future directions, emphasizing the integration of AI with wearable devices, advancements in multimodal systems, and the expansion of emotion recognition technology into new domains. By presenting a thorough analysis of current techniques and applications, this paper aims to highlight the significant impact and potential of AI-driven emotion recognition, paving the way for future innovations and ethical considerations in this dynamic field. The proliferation of Internet of Things (IoT) technology has enabled the development of smart home systems that can enhance home security, energy efficiency, and convenience. This paper presents the design and implementation of an IoT-based smart home security system that utilizes a variety of sensors and actuators to monitor and control various aspects of home security. The proposed system integrates multiple components, including motion detectors, door/window sensors, cameras, smart locks, and a central control unit, all interconnected through a wireless network. The system leverages cloud computing and machine learning techniques to process sensor data, detect potential threats, and initiate appropriate responses. A user-friendly mobile application allows homeowners to monitor and control the security system remotely. The paper discusses the system architecture, hardware and software components, communication protocols, and security considerations. The implemented system demonstrates the feasibility and potential benefits of IoT-based smart home security solutions.

SummaryThe history of science gateway development has, in many ways, been a story of the ‘Haves’ vs. the ‘Have‐nots’. Large infrastructure projects led the way, building thick client portals to provide coherent interfaces to an incoherent environment. Contrast this with the way the modern Web is designed using light, front end components, and outsourcing much of the heavy lifting to a mash‐up of REST application programming interfaces, and it is easy to see why modern web applications can be prototyped and refined into stable products in the time it previously took thick client portals to do an initial release. This paper argues that a ‘build for today’ philosophy can lead to the rapid development of science gateways to serve the ‘Have‐nots’. With this philosophy in mind, we are presenting Gateway DNA, a set of responsive front end components built on top of the iPlant Agave application programming interfaces. This toolkit provides the boilerplate for rapid development of lightweight science gateways using only HTML, JavaScript, and CSS. Using Gateway DNA, developers can easily stand up new gateways or quickly add new functionality to existing ones. Copyright © 2014 John Wiley &amp; Sons, Ltd.

HTML5 represents an important milestone for the modern web. It offers a standardized way of developing feature-rich Internet applications in a platform-independent manner. This paper presents a software platform for deploying purely web-based agents. It provides the necessary infrastructure for utilizing many techniques of the multiagent technology, such as agent interaction and mobility, in modern web applications.

Pro Spring MVC provides in-depth coverage of Spring MVC and Spring Web Flow, two highly customizable and powerful web frameworks brought to you by the developers and community of the Spring Framework. Spring MVC is a modern web application framework built upon the Spring Framework, and Spring Web Flow is a project that complements Spring MVC for building reusable web controller modules that encapsulate rich page navigation rules. Along with detailed analysis of the code and functionality, plus the first published coverage of Spring Web Flow 2.x, this book includes numerous tips and tricks to help you get the most out of Spring MVC, Spring Web Flow, and web development in general. Spring MVC and Spring Web Flow have been upgraded in the new Spring Framework 3.1 and are engineered with important considerations for design patterns and expert object-oriented programming techniques. This book explains not only the design decisions of the frameworks, but also how you can apply similar designs and techniques to your own code. This book takes great care in covering every inch of Spring MVC and Spring Web Flow to give you the complete picture. Along with all the best known features of these frameworks, youll discover some new hidden treasures. Youll also learn how to correctly and safely extend the frameworks to create customized solutions. This bookis for anyone who wishes to write robust, modern, and useful web applications with the Spring Framework. What youll learn Key Spring Framework fundamentals How to use the Spring MVC architecture How to develop with the DispatcherServlet How to write Controllers How to work with Resolving and Implementing Views What are the supported View Types How to customize your website What isand how to use the Spring Web Flow framework How to test your Spring MVC applications How to implement Spring Security Who this book is for This book is for Spring or Java EE application developers who want to learn and fully leverage Spring's Web-tier creator and framework, Spring MVC. This book is also for experienced enterprise and Java programmers who are new to the Spring Framework. Table of Contents Configuring a Spring Development Environment Spring Framework Fundamentals Web Application Architecture Spring MVC Architecture Implementing Controllers Implementing Controllers Advanced REST and AJAX Resolving and Implementing Views Testing Spring MVC Applications Spring Web Flow Building Applications with Spring Web Flow Advanced Spring Web Flow Spring Security Cloud Foundry - Deploying to the Cloud

Expert Spring MVC and Web Flow provides in-depth coverage of Spring MVC and Spring Web Flow, two highly customizable and powerful web frameworks brought to you by the developers and community of the Spring Framework. Spring MVC is a modern web application framework built upon the Spring Framework, and Spring Web Flow is a new project that compliments Spring MVC for building reusable web controller modules that encapsulate rich page navigation rules. Along with detailed analysis of the code and functionality, plus the first-published coverage of Spring Web Flow, this book includes numerous tips and tricks to help you get the most out of Spring MVC, Spring Web Flow, and web development in general. Spring MVC and Spring Web Flow are engineered with an important consideration for design patterns and expert object oriented programming techniques. This book explains not only the design decisions of the frameworks, but also how you can apply similar designs and techniques to your own code. This book takes great care in covering every inch of Spring MVC and Spring Web Flow to give you the complete picture. Along with all the best known features of these frameworks, youll discover some new hidden treasures. Youll also learn how to correctly and safely extend the frameworks to create customized solutions. From beginner to expert, this book is for anyone who wishes to write robust, modern, and useful web applications with the Spring Framework. Table of Contents Introduction Spring Fundamentals Spring MVC Application Architecture Jump into Spring MVC The Processing Pipeline The Controller Menagerie The View Layer Supported View Types Validation Testing Spring MVC Applications Introduction to Spring Web Flow Advanced Spring Web Flow

The paper discusses the drawbacks associated with modern web developers' shortage of expertise in working with both unstructured and structured data. While data structures design is generally unnecessary in smaller, highly dynamic and visual presentation-oriented IT projects, it becomes crucial in complex enterprise intranet systems development. Currently, there is a “generation gap” in Russian IT industry, so that younger practitioners are often unaware of long-established patterns in structured data. At the same time, older specialists lack both authority, due to low involvement in real projects, and accessible language to convey their experience. In an attempt to alleviate this problem, the paper provides classification of modern web applications and data design tools, considers relevant problems in information systems development, and proposes some recommendations.

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