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Temperature in various scientific and industrial applications requires accurate detection and measurement. Herein, a range of novel isostructural lanthanide‐based metal–organic frameworks (Ln‐MOFs), [EuxTb2−x(3,5‐pdc)3(phen)2(H2O)2]n (x = 0, Tb‐MOF; x = 2, Eu‐MOF; x = 0.002, 0.010, 0.020, 0.040, 0.080, EuxTb2−x‐MOF), were solvothermally synthesized using 3,5‐pyridinedicarboxylic acid (3,5‐H2pdc) and 1,10‐phenanthroline (phen) as ligands. Based on binuclear second building units, Ln‐MOFs exhibited 2D layered structure with sql topology. The luminescent properties of series Ln‐MOFs show systematic tuning shifted from green to red by adjusting the ratio of Eu3+ to Tb3+. The temperature sensing experiments showed that Eu0.020Tb1.980‐MOF exhibited self‐calibration temperature response in a range of 293–413 K, achieving maximum relative sensitivity (Sr) 2.46%·K−1 at 413 K, outscoring many state‐of‐the‐art temperature sensors reported in literature. This study poses great promise of the newly designed Eu0.020Tb1.980‐MOF for the ultra‐sensitive temperature sensing in diverse and large‐scale applications. [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]

Using the design science methodology, we develop an automated grading bot using robotic process automation (RPA) software. This grader demonstrates RPA's capability to enhance the efficiency and accuracy of grading structured query language (SQL) queries. Validation of the RPA grader demonstrates that it can grade assignments and/or exams in 30 to 60 minutes, a task that would take a human grader approximately 50 hours. In contrast to human graders, who misgraded 5 to 8 percent of queries, the RPA grader graded all queries correctly. Notably, it correctly evaluated 188 distinct correct solutions submitted by students to a single question. To facilitate broad adoption, we provide instructions and the necessary code for implementing the RPA grader. We validate that professors and teaching assistants can successfully implement the RPA grader even with no previous bot building experience. [ABSTRACT FROM AUTHOR]

The DHR architecture provides a revolutionary security defense structure for cyberspace. The multimode ruling in DHR is expected to alleviate the oracle problem, which still suffers from the existence of common model vulnerability. In this work, we design a test segmentation method to transform multimode ruling to a metamorphic testing problem. The text test input that causes inconsistency of heterogeneous executors is converted to a condition set, and we extract subsets of conditions based on its syntax tree. The original test can exploit a specific vulnerability, the follow‐up tests are composed by different subsets of conditions within the original test. We collect the execution matrix for the follow‐up tests to analyse the impact of each subset of conditions on ruling decision. Metamorphic relations are extracted based on the localization of independent condition, that is, the subsets of conditions that can impact ruling decision independently. The executors in an inconsistent ruling should be examined with metamorphic testing methods, rather than traditional majority voting mechanism. The proposed test segmentation and improved multimode ruling methods are evaluated on two DHR‐based cases, SQL injection in cyber‐range system and deserialization attack in super$$ super $$‐ xray$$ xray $$ project. The experimental results show that our test segmentation can help to locate malicious expressions and the metamorphic testing‐based multimode ruling can generate more correct results than majority voting mechanism with an average 15.8% performance loss. [ABSTRACT FROM AUTHOR]

Over the last decades, various cost-based optimizers have been proposed to generate optimal plans for SQL queries. These optimizers are key to achieving good performance in database systems and can speed up query execution. Still, they may need enormous expert efforts and perform poorly on complicated queries. Learning-based optimizers have been shown to achieve high-quality plans by learning from past experiences. However, these solutions treat each query separately and neglect the semantic equivalence among different queries. Intuitively, a high-quality plan may be obtained for a complicated query by discovering a simple equivalent query. Motivated by this, in this paper, we present Celo, a novel constraint-enhanced learned optimizer to directly integrate the equivalent information of queries into the learning-based model. We apply denial constraints to identify equivalent queries by replacing equivalent predicates. Given a query, we augment the query plans generated by the learning-based model with the high-quality plans of its equivalent queries. Then, a more potentially well-performed plan will be predicted among the augmented query plans. Extensive experiments using real-world datasets demonstrated that Celo outperforms the previous state-of-the-art (SOTA) results even with few constraints. [ABSTRACT FROM AUTHOR]

Mobile devices face SQL injection, malware, and web-based threats. Current solutions lack real-time detection. This paper introduces an Android app with advanced algorithms for real-time threat scanning. During testing, our application detected 94% of SQL injection attempts, outperforming the 86% average detection rate in similar studies. For malware analysis, it achieved a 97% detection accuracy on a dataset of infected files, higher than the industry standard of 93%. Additionally, our app can detect 85 malware variants and assign 15 attributes (Trojan.Gen.8, Worm.Autorun, Adware.Elex, Spyware.Zbot, Ransom.Cryptolocker, Rootkit.ZeroAccess, Exploit.CVE-2017-0143, Virus.MSIL.CoinMiner, Trojan.Emotet, Backdoor. DarkComet, PUP.Optional.Conduit, Adware.MyWebSearch, Virus.Win32.Sality, Trojan.Win32. Necurs, and Ransom.WannaCry) to some malwares, providing detailed analysis for better threat management. The application effectively scans both EXE and APK files, ensuring comprehensive protection. When assessing website links, the application identified security risks with 96% accuracy, demonstrating its capability in managing web-based threats. This app detects SQL injections, analyses malware, and assesses website security, bolstering cyber defence with user-friendly features and top-notch threat mitigation. [ABSTRACT FROM AUTHOR]

Web applications and databases continue to face grave danger from SQL injection attacks, which can result in unauthorized access, data modification, and system compromise. This report discusses the methods attackers use to exploit SQL injection vulnerabilities and emphasizes the dangers of successful attacks, such as data leaks and system compromise. This research proposes a comprehensive system for detecting SQL injection attacks using concatenated Input Validation and Parsing Method (CIVP). The site used as experimental material is the Multimedia and Journal Services Site. Based on the results of forensic analysis on the Journal Services Site, there were several attacks in cyberspace, including using SQLMAP and Python. The system created has successfully detected SQL injection attacks. Based on the test results, it was found that the use of the method proposed in this study succeeded in making processing time 15.2% more efficient. Experiments carried out with the method proposed in this study succeeded in increasing the attack detection accuracy from 96-97% to 99.5% with a p-value of 0.008446. [ABSTRACT FROM AUTHOR]

The Heisenberg uncertainty principle dictates that the position and momentum of an object cannot be simultaneously measured with arbitrary precision, giving rise to an apparent limitation known as the standard quantum limit (SQL). Gravitational-wave detectors use photons to continuously measure the positions of freely falling mirrors and so are affected by the SQL. We investigated the performance of the Laser Interferometer Gravitational-Wave Observatory (LIGO) after the experimental realization of frequency-dependent squeezing designed to surpass the SQL. For the LIGO Livingston detector, we found that the upgrade reduces quantum noise below the SQL by a maximum of three decibels between 35 and 75 hertz while achieving a broadband sensitivity improvement, increasing the overall detector sensitivity during astrophysical observations. [ABSTRACT FROM AUTHOR]

The rapid proliferation of network applications has led to a significant increase in network attacks. According to the OWASP Top 10 Projects report released in 2021, injection attacks rank among the top three vulnerabilities in software projects. This growing threat landscape has increased the complexity and workload of software testing, necessitating advanced tools to support agile development cycles. This paper introduces a novel test prioritization method for SQL injection vulnerabilities to enhance testing efficiency. By leveraging previous test outcomes, our method adjusts defense strength vectors for subsequent tests, optimizing the testing workflow and tailoring defense mechanisms to specific software needs. This approach aims to improve the effectiveness and efficiency of vulnerability detection and mitigation through a flexible framework that incorporates dynamic adjustments and considers the temporal aspects of vulnerability exposure. [ABSTRACT FROM AUTHOR]

Metadata enrichment through text mining techniques is becoming one of the most significant tasks in digital libraries. Due to the exponential increase of open access publications, several new challenges have emerged. Raw data are usually big, unstructured, and come from heterogeneous data sources. In this paper, we introduce a text analysis framework implemented in extended SQL that exploits the scalability characteristics of modern database management systems. The purpose of this framework is to provide the opportunity to build performant end-to-end text mining pipelines which include data harvesting, cleaning, processing, and text analysis at once. SQL is selected due to its declarative nature which offers fast experimentation and the ability to build APIs so that domain experts can edit text mining workflows via easy-to-use graphical interfaces. Our experimental analysis demonstrates that the proposed framework is very effective and achieves significant speedup, up to three times faster, in common use cases compared to other popular approaches. [ABSTRACT FROM AUTHOR]

Existing NL2SQL approaches do not fully utilize the information of data table columns, which plays an important role in the semantic understanding of the problem and the logical generation of SQL statements. This paper proposed an NL2SQL method that fused data table columns (FC-SQL) to improve the overall accuracy of SQL generation. Firstly, this method utilized BERT to merge the problem and database table columns for encoded representations. Secondly, it used multitask learning approach to construct a multi-task network by combining parallel and cascade to predict different sub-tasks. Finally, for the conditional value extraction sub-task, this method computed the similarity between the words in the problem and the table columns by fusing the information of the columns, and it used the similarity value as a weight to compute each word as the conditional value probability of each word as a conditional value, thus improving the accuracy of conditional value prediction. The logical form accuracy and SQL execution accuracy on the TableQA dataset reach 88.23% and 91.65%, respectively. This paper designed ablation experiments to verify the effect of table columns information on the model. The experimental results show that the incorporation of table columns improves the effectiveness of the conditional value extraction sub-task, which in turn improves the overall accuracy of the NL2SQL task and provides better SQL generation compared to the comparison model. [ABSTRACT FROM AUTHOR]

Numerous techniques, including problem-solving, seeking clarification, and creating questions, have been employed to utilize generative Artificial Intelligence (AI) in education. This study investigates the possibility of using Generate AI (GenAI) to grade Structured Query Language (SQL) queries automatically. Three models were used which are ChatGPT, Gemini, and Copilot. The study uses an experimental approach to assess how well the models perform in evaluating student responses by comparing the models' accuracy with those of human experts. The results showed that despite some inconsistencies, GenAI holds great promise for streamlining. Thus, further research is required in light of inconsistent GenAI performance. If these issues were resolved, GenAI can be utilized in education. However, human oversight and ethical issues must always come first. [ABSTRACT FROM AUTHOR]

Copyright of Smart Rail Transit is the property of Smart Rail Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Database-backed applications rely on the database access code to interact with the underlying database management systems (DBMSs). Although many prior studies aim at database access issues like SQL anti-patterns or SQL code smells, there is a lack of study of database access bugs during the maintenance of database-backed applications. In this paper, we empirically investigate 423 database access bugs collected from seven large-scale Java open-source applications that use relational DBMSs (e.g., MySQL or PostgreSQL). We study the characteristics (e.g., occurrence and root causes) of the bugs by manually examining the bug reports and commit histories. We find that the number of reported database and non-database access bugs share a similar trend but their modified files in bug fixing commits are different. Additionally, we generalize categories of the root causes of database access bugs, containing five main categories (SQL queries, Schema, API, Configuration, and SQL query result) and 25 unique root causes. We find that the bugs pertaining to SQL queries, Schema, and API cover 84.2% of database access bugs across all studied applications. In particular, SQL queries bug (54%) and API bug (38.7%) are the most frequent issues when using JDBC and Hibernate, respectively. Finally, we provide a discussion on the implications of our findings for developers and researchers. [ABSTRACT FROM AUTHOR]

This paper examines the application of the CHASING M2 PRO MAX underwater robot for the creation of databases for the technical inspection of the underwater components of water transport vehicles and their infrastructure. The paper defines and analyzes specific areas of application for the underwater robot, detailing the characteristics of its control systems that are essential for efficiently and accurately collecting data on water transport vehicles and underwater port infrastructure. Special attention is given to the operations of the technical inspection process of watercraft and port infrastructure. The study analyzes how this robot can improve the data collection process and inspection efficiency. Finally, the underwater inspection process is modeled, demonstrating how the CHASING M2 PRO MAX can be integrated into existing procedures to optimize data collection and analysis. This research opens new possibilities in the field of water transport safety and infrastructure maintenance, providing valuable insights into the application of underwater robot technologies.

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.

SQL injection (SQLI) black-box detection, which simulates external attack scenarios, is crucial for assessing vulnerabilities in real-world web applications. However, existing black-box detection methods rely on predefined rules to cover the most common SQLI cases, lacking diversity in vulnerability detection scheduling and payload, suffering from limited efficiency and accuracy. Large Language Models (LLMs) have shown significant advancements in several domains, so we developed SqliGPT, an LLM-powered SQLI black-box scanner that leverages the advanced contextual understanding and reasoning abilities of LLMs. Our approach introduces the Strategy Selection Module to improve detection efficiency and the Defense Bypass Module to address insufficient defense mechanisms. We evaluated SqliGPT against six state-of-the-art scanners using our SqliMicroBenchmark. Our evaluation results indicate that SqliGPT successfully detected all 45 targets, outperforming other scanners, particularly on targets with insufficient defenses. Additionally, SqliGPT demonstrated excellent efficiency in executing detection tasks, slightly underperforming Arachni and SQIRL on 27 targets but besting them on the other 18 targets. This study highlights the potential of LLMs in SQLI black-box detection and demonstrates the feasibility and effectiveness of LLMs in enhancing detection efficiency and accuracy. [ABSTRACT FROM AUTHOR]

Constructing three dimensional (3D) covalent organic frameworks (COFs) through the entanglement of two dimensional (2D) nets is a promising but underdeveloped strategy. Herein, we report the design and synthesis of a fluorine functionalized 3D COF (3D-An-COF-F) formed by entangled 2D sql nets. The structure of 3D-An-COF-F was determined by the combination of continuous rotation electron diffraction technique and modelling based on the chemical information from real space. Interestingly, compared to the isostructural 3D-An-COF without F atoms, 3D-An-COF-F showed an improved CO2 sorption ability and higher CO2/N2 selectivity. Our study not only demonstrated the generality of constructing 3D COFs with entangled 2D nets by introducing bulky groups vertically in planar building blocks, but also will expand the diversity of 3D COFs for various applications. [ABSTRACT FROM AUTHOR]

Four lanthanoid anilate coordination polymers (CPs) were synthesised using fluoranilate and chloranilate ligands with La(III), Eu(III) and Dy(III), and PPh4+ as the countercation. The presence of the PPh4+ countercation had a structure directing effect on the CP topology, with a 3D diamondoid (dia) network obtained with La(III) whilst 2D square grid (sql) networks were obtained with Eu(III) and Dy(III). This work represents the first use of fluoranilate in lanthanoid anilate CPs. The additional hydrogen bonding sites afforded by the fluorine substituent influences the packing of the crystal structure whilst the variation in lanthanoid ion size helps determine the topology of the CP. [ABSTRACT FROM AUTHOR]

Structured query language (SQL) injection threats pose severe risks to web applications, necessitating robust detection measures. This study introduced DSQLIA, employing ensemble learning algorithms-Bagging, Stacking, and AdaBoost classifiers-for SQL injection detection. Results unveiled the bagging classifier's 84% accuracy with perfect precision (100%) but moderate recall (68%). The stacking classifier achieved 85% accuracy, exceptional precision (99%), and balanced memory (72%), yielding an 83% F1-Score. Remarkably, the AdaBoost classifier outperformed, achieving 99% accuracy, high precision (98%), and outstanding recall (99%), leading to a remarkable 99% F1-Score. These findings highlight AdaBoost's superior ability to identify malicious queries with minimal false positives accurately. Overall, this research underscores the potential of ensemble learning in fortifying web application security against SQL injection attacks, emphasizing the AdaBoost classifier's exceptional performance in achieving precise and comprehensive detection. [ABSTRACT FROM AUTHOR]

Fuzz testing technology is an important approach to detecting SQL injection vulnerabilities. Among them, coverage-guided gray-box fuzz testing technology is the current research focus, and has been proved to be an effective method. However, for SQL injection vulnerability, coverage-guided gray-box fuzz testing as a detection method has the problems of low efficiency and high false positives. In order to solve the above problems, we propose a potentially vulnerable code-guided gray-box fuzz testing technology. Firstly, taint analysis technology is used to locate all the taint propagation paths containing potential vulnerabilities as potentially vulnerable codes. Then, the source code of the application program is instrumented according to the location of the potentially vulnerable code. Finally, the feedback of seeds during the run is used to guide seed selection and seed mutation, and a large number of test cases are generated. Based on the above techniques, we implement the sqlFuzz prototype system, and use this system to analyze eight modern PHP applications. The experimental results show that sqlFuzz can not only detect more SQL injection vulnerabilities than the existing coverage-guided gray box fuzz testing technology, but also significantly improve the efficiency, in terms of time efficiency increased by 80 percent. [ABSTRACT FROM AUTHOR]

This case offers learning experiences for (1) following a business process diagram for car dealers' recording of sales and sales returns of new cars and the car manufacturer's subsequent recording of the same transactions and (2) querying a database to determine why dashboards maintained by the dealers and the manufacturer do not always agree. Scaffolding resources (videos and script with screenshots) enable learners with no querying experience to work the case as they develop basic query skills. For learners with query skills, the case (without scaffolding resources) can be used as a data analysis project covering all major query operations. The data can be queried in any structured query language (SQL) platform. The case offers practice and quiz questions for assessing business process skill and the query skills of joining tables, aggregating data, setting criteria, using built-in functions, developing expressions, and verifying query correctness. [ABSTRACT FROM AUTHOR]

As a crucial task in natural language processing, table question answering has garnered significant attention from both the academic and industrial communities. It enables intelligent querying and question answering over structured data by translating natural language into corresponding SQL statements. Recently, there have been notable advancements in the general domain table question answering task, achieved through prompt learning with large language models. However, in specific domains, where tables often have a higher number of columns and questions tend to be more complex, large language models are prone to generating invalid SQL or NoSQL statements. To address the above issue, this paper proposes a novel few-shot table prompt question answering approach. Specifically, we design a prompt template construction strategy for structured SQL generation. It utilizes prompt templates to restructure the input for each test data and standardizes the model output, which can enhance the integrity and validity of generated SQL. Furthermore, this paper introduces a contrastive exemplar selection approach based on the question patterns and formats in domain-specific contexts. This enables the model to quickly retrieve the relevant exemplars and learn characteristics about given question. Experimental results on the two datasets in the domains of electric energy and structural inspection show that the proposed approach outperforms the baseline models across all comparison settings. [ABSTRACT FROM AUTHOR]

Text-to-SQL, a computational linguistics task, seeks to facilitate the conversion of natural language queries into SQL queries. Recent methodologies have leveraged the concept of slot-filling in conjunction with predetermined SQL templates to effectively bridge the semantic gap between natural language questions and structured database queries, achieving commendable performance by harnessing the power of multi-task learning. However, employing identical features across diverse tasks is an ill-suited practice, fraught with inherent drawbacks. Firstly, based on our observation, there are clear boundaries in the natural language corresponding to SELECT and WHERE clauses. Secondly, the exclusive features integral to each subtask are inadequately emphasized and underutilized, thereby hampering the acquisition of discriminative features for each specific subtask. In an endeavor to rectify these issues, the present work introduces an innovative approach: the hierarchical feature decoupling model for SQL query generation from natural language. This novel approach involves the deliberate separation of features pertaining to subtasks within both SELECT and WHERE clauses, further dissociating these features at the subtask level to foster better model performance. Empirical results derived from experiments conducted on the WikiSQL benchmark dataset reveal the superiority of the proposed approach over several state-of-the-art baseline methods in the context of text-to-SQL query generation. [ABSTRACT FROM AUTHOR]

Featured Application: The methodology described in this paper was implemented in an automotive manufacturing facility that operates under a just-in-time manufacturing scheme. The methodology greatly improved the efficiency of day-to-day operations at the facility. This study addresses sustainability challenges in automotive packaging by introducing a novel data-driven approach to audit and improve packaging quality. Traditional manual auditing processes are both time-consuming and prone to errors. To counter these inefficiencies, we developed an automated system utilizing Microsoft Power Apps and SQL databases to streamline data collection and processing. The automated tools facilitate real-time data capture and visualization through Microsoft Power BI, enabling precise tracking and management of returnable and expendable packaging materials. Our results show that our approach offers a significant enhancement in the accuracy and speed of data analysis, leading to more informed decision-making. Our methodology supports sustainability by reducing waste and reliance on expendable packaging and improving the economic and operational efficiency of automotive packaging systems. [ABSTRACT FROM AUTHOR]

This research analyzes and designs an evaluation benchmark for SQL and NoSQLdatabase systems, specifically tailored for higher institutions in Zamfara State. By benchmarking various database operations, including insert, select, update, delete, and stored procedures, we identify performance differences between the two systems. MongoDB demonstrates superior performance in handling insert, select, update, and delete operations, making it ideal for applications with high transactional demands. Conversely, MySQL excels in executing stored procedures due to its native support, which is crucial for complex procedural logic. Considering that insert, select, update, and delete operations are more common, MongoDB is generally recommended. However, for applications heavily reliant on stored procedures, MySQL remains the preferred choice. Additionally, the complexity of converting an existing MySQL database to MongoDB should be factored into the decision-making process. This study provides valuable insights and recommendations to help institutions in Zamfara State choose the most suitable database system based on their specific operational needs and performance requirements. [ABSTRACT FROM AUTHOR]

(Source) code search is widely concerned by software engineering researchers because it can improve the productivity and quality of software development. Given a functionality requirement usually described in a natural language sentence, a code search system can retrieve code snippets that satisfy the requirement from a large-scale code corpus, e.g., GitHub. To realize effective and efficient code search, many techniques have been proposed successively. These techniques improve code search performance mainly by optimizing three core components, including query understanding component, code understanding component, and query-code matching component. In this article, we provide a 3-dimensional perspective survey for code search. Specifically, we categorize existing code search studies into query-end optimization techniques, code-end optimization techniques, and match-end optimization techniques according to the specific components they optimize. These optimization techniques are proposed to enhance the performance of specific components, and thus the overall performance of code search. Considering that each end can be optimized independently and contributes to the code search performance, we treat each end as a dimension. Therefore, this survey is 3-dimensional in nature, and it provides a comprehensive summary of each dimension in detail. To understand the research trends of the three dimensions in existing code search studies, we systematically review 68 relevant literatures. Different from existing code search surveys that only focus on the query end or code end or introduce various aspects shallowly (including codebase, evaluation metrics, modeling technique, etc.), our survey provides a more nuanced analysis and review of the evolution and development of the underlying techniques used in the three ends. Based on a systematic review and summary of existing work, we outline several open challenges and opportunities at the three ends that remain to be addressed in future work. [ABSTRACT FROM AUTHOR]

Due to its importance and widespread use in industry, automated testing of REST APIs has attracted major interest from the research community in the last few years. However, most of the work in the literature has been focused on black-box fuzzing. Although existing fuzzers have been used to automatically find many faults in existing APIs, there are still several open research challenges that hinder the achievement of better results (e.g., in terms of code coverage and fault finding). For example, under-specified schemas are a major issue for black-box fuzzers. Currently, EvoMaster is the only existing tool that supports white-box fuzzing of REST APIs. In this paper, we provide a series of novel white-box heuristics, including for example how to deal with under-specified constrains in API schemas, as well as under-specified schemas in SQL databases. Our novel techniques are implemented as an extension to our open-source, search-based fuzzer EvoMaster. An empirical study on 14 APIs from the EMB corpus, plus one industrial API, shows clear improvements of the results in some of these APIs. [ABSTRACT FROM AUTHOR]

The deep‐learning based code search model mainly takes accuracy as the only target for judging the performance of the model, ignoring the efficiency of code search. This article proposes a clustering‐based code search model (C‐DCS). C‐DCS uses the K‐Means to divide the code vector base into K clusters and obtains the center vectors of K clusters. While searching, C‐DCS first matches the query vector with the K center vectors to get the best matching center vector. After matching the center vector, C‐DCS matches the query vector with code vectors in the cluster corresponding to the best matching center vector one by one and then gets the best matching code snippet vector. To verify the efficiency of C‐DCS in the code search task, experimental analysis was built on a large dataset. The experimental results showed that C‐DCS saves 92.2% of the search time compared to the baseline model while remaining the accuracy. In the experimental evaluation section, we optimized the K‐Means algorithm to improve the code search efficiency of C‐DCS further, reducing the search time to 93.8% of the baseline model. Hence, C‐DCS reduces the code search time greatly with not affecting the accuracy, improving the efficiency of software development. [ABSTRACT FROM AUTHOR]

Document-oriented databases, a type of Not Only SQL (NoSQL) database, are gaining popularity owing to their flexibility in data handling and performance for large-scale data. MongoDB, a typical document-oriented database, is a database that stores data in the JSON format, where the upper field involves lower fields and fields with the same related parent. One feature of this document-oriented database is that data are dynamically stored in an arbitrary location without explicitly defining a schema in advance. This flexibility violates the above property and causes difficulties for application program readability and database maintenance. To address these issues, we propose a reconstruction support method for document structures in MongoDB. The method uses the strength of the Has-A relationship between the parent and child fields, as well as the similarity of field names in the MongoDB documents in natural language processing, to reconstruct the data structure in MongoDB. As a result, the method transforms the parent and child fields into more coherent data structures. We evaluated our methods using real-world data and demonstrated their effectiveness. [ABSTRACT FROM AUTHOR]

With new high-performance server technology in data centers and bunkers, optimizing search engines to process time and resource consumption efficiently is necessary. The database query system, upheld by the standard SQL language, has maintained the same functional design since the advent of PL/SQL. This situation is caused by recent research focused on computer resource management, encryption, and security rather than improving data mining based on AI tools, machine learning (ML), and artificial neural networks (ANNs). This work presents a projected methodology integrating a multilayer perceptron (MLP) with Kmeans. This methodology is compared with traditional PL/SQL tools and aims to improve the database response time while outlining future advantages for ML and Kmeans in data processing. We propose a new corollary: h k → H = S S E (C) , w h e r e k > 0 a n d ∃ X , executed on application software querying data collections with more than 306 thousand records. This study produced a comparative table between PL/SQL and MLP-Kmeans based on three hypotheses: line query, group query, and total query. The results show that line query increased to 9 ms, group query increased from 88 to 2460 ms, and total query from 13 to 279 ms. Testing one methodology against the other not only shows the incremental fatigue and time consumption that training brings to database query but also that the complexity of the use of a neural network is capable of producing more precision results than the simple use of PL/SQL instructions, and this will be more important in the future for domain-specific problems. [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]

Copyright of International Journal of Engineering Research & Development (IJERAD) is the property of International Journal of Engineering Research & Development and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In distributed and cloud database system, large amount of resources are used by complex queries that increase in payment overheads of cloud users. These resource needs can be minimised by evaluating common join operations and caching their results so that they could be applied to the execution of additional queries. In this research, the Query Optimization Technique Using Materialization (QOTUM) system is designed and developed that uses the mechanism of materialized views during query execution at distributed database in cloud environment. Extensive experiments are conducted with the help of standard benchmarks datasets and query workloads in cloud environment. The performance of QOTUM system is studied and evaluated against conventional SQL system based on various performance parameters. [ABSTRACT FROM AUTHOR]