Structured Query Language Research Articles

P-270 Analysis of patient response rates to automated cryomanagement platform communications by length of cryostorage and tissue type

Abstract Study question Does the length of storage time of cryopreserved samples impact patient response rate and is this influenced by the tissue type stored. Summary answer Patient response rates decline as storage time increases. The decline is faster and higher with sperm and testicular tissue, compared to oocytes and embryos What is known already Clinics are experiencing increasing inventories of cryopreserved samples. While clinics provide storage consents and contracts, an increasing issue has emerged of abandoned/unclaimed samples, even with provision of multiple disposition options. Cryomanagement is labour intensive, both in terms of physical and administrative maintenance, the latter further increasing when communication and payment from the patient inadvertently or deliberately ceases. Full cryomanagement automation including patient billing, education and engagement with disposition decisions reduces the workload for the embryologist and increases consistency. It may also provide data insights allowing clinics to identify issues and implement strategies to improve patient engagement Study design, size, duration Data were retrospectively analysed from an automated cryomanagement platform. In total, 418,895 patient accounts from 122 clinics based in the USA over the period 2015 to 2024 Participants/materials, setting, methods Data was extracted from the automated cryomanagement platform database - a live feed of clinic information – and encrypted. Logic was created to develop calculations utilising the coding language, Structured Query Language (SQL). Revenue collected was compared with collectable charges to generate the collection/response rate. Data was analysed by collection rate by tissue type to identify trends and differences Main results and the role of chance An inverse relationship was identified between storage duration and response rates. Collection rates fell from 97.44% to 68.44% in relation to oocytes, 94.87% to 45.08% for embryos, 87.01% to 29.41% for sperm, and 95.67% to 29.75% for testicular tissue. Patients with stored oocytes are least likely to abandon cryopreserved samples, with 68.44% of storage payments continuing to be received beyond 14yrs. Patients with stored sperm or testicular tissue are the most likely to abandon cryopreserved samples. Collection rates fall below 50% at 6-8yrs and 10-12yrs respectively, and beyond 14yrs storage have fallen to 29.41% and 29.75% Limitations, reasons for caution The storage duration does not necessarily reflect the duration of patient interaction with the automated cryomanagement platform, where freeze date preceeded platform implementation. Further research is necessary to identify if patients exclusively interacting with the platform from freeze date have altered response rates Wider implications of the findings Data held by automated cryomanagement systems allows accurate review and analysis of cryostorage and cryomanagement metrics. Mined data could be used to develop policies based on real-life data. Including patient treatment information and patient engagement with the system from freeze date may help identify and address reasons for abandonment Trial registration number Not Applicable

P-537 Linking genetic variation to infertility and embryonic genomic instability. Lessons from a database of whole genomes from targeted populations: challenges, and future directions

Abstract Study question Can novel genomic analysis methods be developed to identify genetic variants that predispose to specific types of infertility and genomic instability in embryos? Summary answer Whole genome analysis and the creation of a genomic variation database of highly targeted populations yielded several genomic links to human infertility and genomic instability. What is known already Infertility is a phenotypic manifestation that originates from highly variable causes, like repeated miscarriage (RM), repeated implantation failure (RIF) or various male and female factors. Specific genetic variations can predispose carriers to distinct types of infertility however quantifiable genetic risks are difficult to be established clinically due to the high amount of variables that exist in any relevant infertility studies. In addition, the combination of the maternal and paternal genetics further complicates the interpretation of genomics results especially when genetic instability in embryos are studied. Several studies have indicated multi[le risk variants however few have managed to establish causal relationships. Study design, size, duration The causes of infertility are extremely variable, which presents a challenge in the design and analysis of genome sequencing studies that involve large populations of participants with similar characteristics. In this study a targeted approach highly selected populations allows more detailed examination of the whole genome of the participants and the different subgroups that exist among them. The participants were recruited using specific selection criteria and samples were collected over a period of 5 years. Participants/materials, setting, methods Couples presenting with infertility and went through preimplantation genetic testing for aneuploidy (PGT- A) were invited to participate. Selection criteria were applied: i) referred for PGT-A after >3 assisted reproduction failed cycles, ii) Normal karyotypes, iii) Female age less than 37, iv) Embryonic instability in their subsequent PGT-A cycles in more than 40% of the produced embryos. After whole genome sequencing (WGS) and variant calling, a bespoke genomic SQL database was constructed for genomic analysis. Main results and the role of chance From 118 individuals that consented to participate 28 individuals with repeated implantation failure (RIF) and 8 individuals RIF/RM passed the selection criteria and their DNA was processed with WGS using a short read platform. In addition, WGS Validation was performed using genomes with known mutations. After the WGS quality control and validation. In total 50 genomes were processed and produced variant results. A novel MySQL database was successfully constructed and validated. Variant z scores were calculated for each participant and the significantly flagged variants were pulled and analysed and comparison with other bespoke and publicly available databases was performed. Ethnicity bias in novel variations detected and corrected. Several smaller cohorts were identified with similar genomic characteristics and accumulation of specific sets of polymorphisms, that were associated with certain types of infertility and preimplantation embryo abnormalities. Of particular interest were variants in synaptonemal complex genes associated with gamete failure, meiotic errors and instability in embryos. Structural variations were significant for male factor infertility. The WGS approach also revealed novel intronic and extragenic variants that would not be usually picked up by other methods like exome sequencing but can play a vital role in gene expression. Limitations, reasons for caution As with all genomic studies relating to infertility, association does not mean causation. Further functional analysis would be required to establish definitive causal pathways based on significant risk variants. In addition, although our study is highly targeted more genomic data can enhance to validation of these results. Wider implications of the findings This highly targeted study using a personalised approach to genome analysis combined with WGS can lead to protocols that determine the cumulative clinical risk for infertility, by revelling the accumulation of specific sets of polymorphisms, intronic and exonic, can be associated with certain types of infertility and preimplantation embryo abnormalities. Trial registration number not applicable

Соціотопографія Вільнюса в XVII–XVIII ст.: основні особливості та напрямки розвитку

The article summarises the characteristics of the development of the Vilnius community in the Grand Duchy of Lithuania, regarding the context of historical events between the 17th and 18th centuries. The summary is part of a scientific project carried out between 2018 and 2021, based on a research of real estate registers and lists of taxes collected by the magistrate. The research used a statistical method, a spatial relational database and the structured query language (SQL) as well as Python programming language libraries NumPy and Pandas for managing and analysing big data. A detailed description of the study and its results can be found in the collective monograph published in 2021. Based on these sources, the value of real estate in different districts of Vilnius, spatial distribution, size and function of the buildings as well as the distribution of their owners in the city between first half of the 17th and the end of the 18th century were analysed. It was determined that frequent large fires were causing the biggest changes to the spatial structure of Vilnius. The greatest changes in demography and sociotopography were recorded after the occupation of Vilnius by the Moscow army between 1655 and 1661 and plague outbreaks in 1657 and between 1709 and 1710. During these times, the city has lost between 30 and 50 percent of its population. As a result, the mediaeval spatial structure of the old city gradually disappeared and its current appearance was formed with large, almost completely built-up plots of land, elite residences and dominant brick architecture of the Baroque style. The social structure has been partially changed. Poor townspeople and some artisans moved from the city centre to the suburbs. In the fortified centre, most real estate passed from the hands of ordinary citizens to wealthy individuals and organisations, who often acquired entire groups of empty land areas. However, some characteristics of the sociotopography of the 16th and first half of the 17th centuries remained unchanged, including the locations of some areas where social groups were living.

Deep learning for sea surface temperature applications: A comprehensive bibliometric analysis and methodological approach

AbstractThis study explored the potential application of deep learning techniques in sea surface temperature (SST) investigations using a mixed method, bibliometric analysis and methodological approach. CiteSpace software was utilized for a bibliometric study on 137 academic publications from 2018 to 2023. Various databases were employed for methodological analysis, which involved examining publications based on models, methodologies, applications and research areas. The data were manually organized in a relational framework of an SQL database. The analysis underscored China's prominence as a leader in the extensive research devoted to this field. The United States of America and the United Kingdom played pivotal roles in providing the essential data that served as the foundation for these studies. Moreover, the long short‐term memory (LSTM) algorithm was the predominant computational deep learning algorithm extensively used in this specific context. The analysis highlighted significant knowledge gaps in areas such as SST forecasting, modelling, satellite remote sensing, extreme events and data reconstruction. Future scientists need to show more interest in these and related subjects, while Chinese and American scientists should prioritize paper quality over quantity. Additionally, fostering stronger collaborations between universities and institutions is vital for further advancements. Ultimately, this study offers valuable insights into hotspot research areas and development processes, establishing the foundation for research and suggesting possible avenues for future development. The results of this evaluation serve as an essential guide for researchers and modellers involved in prediction initiatives using deep learning.

Quantifying Vulnerabilities: A Systematic Review of the State-of-the-Art Web-Based Systems

Web-based Systems Vulnerabilities (WSVs) have been existing over a long time in all Open System Interconnection (OSI) layers. WSV tends to affect online business operations by letting attackers to gain unauthorized access. Different researchers have been publishing common WSVs regularly. From the published vulnerabilities, it can be noted that the ranking of vulnerabilities is not static. Prevalence of common vulnerabilities tends to vary with time. Moreover, ranking of vulnerabilities from various practitioners, such as OWASP and CWE, at a particular point in time tends to be different because of different approaches and sources. This work sought to come up with an objective way of establishing the latest ranking of common WSV by conducting a Systematic Literature Review from scholarly sources. This study extracted 127 publications from Scholarly Databases: Association of Computing Machineries, ScienceDirect, Springer, IEEE, and Google scholar. After the review, only 62 articles were considered based on five inclusion and exclusion criteria. The review reveals that cross site script, structured query language injection, broken authentication and session management, operating system command injection and file inclusion are the most common WSV.

Data-Driven Quality Improvement for Sustainability in Automotive Packaging

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.

AI-Augmented LangChain: Facilitating Natural Language SQL Queries for Non-Technical Users

This study explores the potential of LangChain, a framework for constructing applications with advanced language models, to translate natural language queries into executable SQL code. Study propose an innovative LangChain-based architecture that receives a natural language query, analyzes it with a language model, and generates the corresponding SQL statement for database querying. This approach aims to empower non-technical users, facilitate inter-team collaboration, and enable data-informed decision-making. However, challenges persist, including managing complex queries and grasping domain-specific terminology. This research investigates the methodology and system design of our proposed natural language interface for databases, leveraging LangChain and extensive language models. Study also explore the possibilities and potential applications of this system, as well as future research avenues for enhancing its functionalities and addressing current constraints. By integrating advanced natural language processing with database technologies, this research aims to enable inclusive and powerful data querying experiences.

Advanced White-Box Heuristics for Search-Based Fuzzing of REST APIs

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.

MedT5SQL: a transformers-based large language model for text-to-SQL conversion in the healthcare domain.

In response to the increasing prevalence of electronic medical records (EMRs) stored in databases, healthcare staff are encountering difficulties retrieving these records due to their limited technical expertise in database operations. As these records are crucial for delivering appropriate medical care, there is a need for an accessible method for healthcare staff to access EMRs. To address this, natural language processing (NLP) for Text-to-SQL has emerged as a solution, enabling non-technical users to generate SQL queries using natural language text. This research assesses existing work on Text-to-SQL conversion and proposes the MedT5SQL model specifically designed for EMR retrieval. The proposed model utilizes the Text-to-Text Transfer Transformer (T5) model, a Large Language Model (LLM) commonly used in various text-based NLP tasks. The model is fine-tuned on the MIMICSQL dataset, the first Text-to-SQL dataset for the healthcare domain. Performance evaluation involves benchmarking the MedT5SQL model on two optimizers, varying numbers of training epochs, and using two datasets, MIMICSQL and WikiSQL. For MIMICSQL dataset, the model demonstrates considerable effectiveness in generating question-SQL pairs achieving accuracy of 80.63%, 98.937%, and 90% for exact match accuracy matrix, approximate string-matching, and manual evaluation, respectively. When testing the performance of the model on WikiSQL dataset, the model demonstrates efficiency in generating SQL queries, with an accuracy of 44.2% on WikiSQL and 94.26% for approximate string-matching. Results indicate improved performance with increased training epochs. This work highlights the potential of fine-tuned T5 model to convert medical-related questions written in natural language to Structured Query Language (SQL) in healthcare domain, providing a foundation for future research in this area.

OPTIMIZING SQL DATABASES FORBIG DATA WORKLOADS: TECHNIQUES AND BEST PRACTICES

In the era of big data, SQL databases face significant challenges in handling vast volumes of data efficiently. This article explores optimization techniques and best practices for enhancing the performance and scalability of SQL databases in handling big data workloads. The study addresses the significant challenges faced by traditional SQL databases, including scalability issues, performance bottlenecks, resource constraints, and data integration complexities. Through a comprehensive methodology involving literature review, case studies, expert interviews, and performance testing, the research identifies effective strategies such as indexing, partitioning, sharding, and caching. Findings from case studies in e-commerce and financial services sectors demonstrate substantial improvements in query performance and resource utilization, validating the practical benefits of these optimization techniques. The study underscores the importance of a multifaceted approach to database optimization, integrating both theoretical and practical insights to address the complexities of big data environments. By staying informed and adopting the latest optimization strategies, database administrators and IT professionals can ensure their SQL databases remain efficient, scalable, and capable of managing the increasing demands of large-scale data processing, ultimately enabling organizations to derive valuable insights from their data.

50 Years of Queries

This paper discusses the evolution of the database industry over the last half century. It focuses on the relational database concepts introduced by E. F. Codd, and offers some explanations for why these concepts have proven to be so resilient over several decades. It describes some early relational systems and the context in which they were developed. It describes the origin of the SQL database language, how it became an international standard, and how it has evolved to meet new requirements. It also briefly describes some “post-relational” research directions inspired by the need for massively scalable web applications. Much of the paper is written from a personal perspective, based on the author’s own experiences.

ADVANCED QUERY OPTIMIZATION IN SQL DATABASES FOR REAL-TIME BIG DATA ANALYTICS

This study investigates the effectiveness of advanced query optimization techniques in SQL databases, focusing on multi-level indexing, query rewriting, and dynamic query execution plans. The research employs a qualitative approach, gathering data from a variety of SQL databases characterized by large datasets typical of big data environments. Through structured interviews, focus groups, and observational methods, insights from database administrators highlight the practical benefits and challenges associated with implementing these techniques. The findings reveal significant improvements in query performance, with multi-level indexing reducing data retrieval times by approximately 40%, query rewriting decreasing execution times by 35%, and dynamic query execution plans enhancing resource utilization efficiency by 25%. These techniques were also praised for their ease of use, adaptability to different data types and query complexities, and overall reliability. This study contributes to the existing body of knowledge by providing a comprehensive analysis of the practical applications and performance enhancements offered by advanced query optimization methods in SQL databases, underscoring their value in managing large-scale, dynamic data environments.

From traditional teaching to flipped classroom: Impact on learning in engineering degrees

Computer engineering students should develop competences related to the contents of databases design and SQL queries. For this purpose, the recommendations on the convenience of changing the traditional teaching methodology to the flipped classroom are followed. In this article we present a quantitative study in which we compare the potential for the development of engineering students' competences in the design and use of databases of the flipped classroom methodology and the traditional teaching methodology. The results obtained in the evaluation of the subject in two different courses are compared. In the first course, traditional teaching methodology was used. In the second one, flipped classroom was used when its implementation had already been tried and tested. The results of the content blocks of database design and SQL queries are compared, according to the type of learning promoted. In this article we show evidence that the implementation of the flipped classroom teaching methodology provides different results depending on the learning promoted and on the specific contents in the subject of relational databases. The benefits of implementing the flipped classroom depend on the content and type of learning promoted. We have found evidence that they improve theoretical learning outcomes for database design and in the resolution of non-reproductive activities in the SQL queries block. No conclusive benefits are reported for students’ competency development for either of the two content blocks.

A market intelligence model for the health care business system

Health in Brazil is an old problem that has worsened with the Covid-19 pandemic and resources have become scarce. Studying market behavior and growth trends has become fundamental. This research aims to build a Market Intelligence Model, based on information obtained from official secondary data sources, using the Fuzzy logic methodology. The concepts of Data Analytics and Decision Making applied in the Health Area are addressed. The model was developed in RSdudio software, stored in SQL Database, and presented in Power BI and QGis. The model was validated in private health units in the southeast and northeast regions of Brazil for the treatment of chronic diseases, mainly in the areas of nephrology and oncology. The results were obtained, and it can be concluded that the proposed model has become very effective in estimating the growth of chronic diseases.

A Clustering and PL/SQL-Based Method for Assessing MLP-Kmeans Modeling

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: hk→H=SSE(C),wherek>0and∃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.

PHREESQL: A toolkit to efficiently compute and store geochemical speciation calculation

Both abiotic and biotic natural spheres benefit from the high reactivity of natural waters, which are ubiquitous on planet Earth. The use of speciation-solubility codes like PHREEQC can provide a deeper understanding of aqueous equilibria and water-rock interactions. A significant number of newly derived variables are produced by these computations, which may significantly increase compared to input data. It is crucial to process vast amounts of data efficiently, particularly when dealing with datasets that contain thousands of water analyses.To tackle this problem, we present PHREESQL, a software package designed to efficiently store and manage extensive data generated by geochemical speciation computations performed by PHREEQC. High efficiency in data extraction and filtering of the entire output from a single run can be achieved through a well-designed relational SQL database structure. The PHREESQL can be used as a stand-alone package or embedded in third-party applications. Thanks to the SQL structure, it is possible to create links with unstructured meshes by developers and experts in reaction-transport problems. Real-time data management from multiparameter devices in field and laboratory settings is made possible and efficient by parallel computation options and software integration. The toolkit encompasses both a C++ library and a command-line interface, facilitating its use by geochemists with limited programming skills.

IoT-based Machine Learning Comparative Models of Stream Water Parameters Forecasting for Freshwater Lobster

Water quality parameters such as dissolved oxygen, pH, and mineral content are important factors for aquaculture. Predictive analytics can predict water conditions in aquaculture and significantly reduce the mortality probability of aquaculture products. This paper applied stream predictive analytics to the freshwater lobster farming dataset where its real-time data supplied by End Node Unit (ENU) which integrated with dissolved oxygen (DO), potential hydrogen (pH), electrical conductivity (EC), and total dissolved solids (TDS). The real-time data of ENU in Structured Query Language (SQL) is normally displayed for remote monitoring and the analytics will only be done after in different processing platform called batch analytics. Instead of batch, this paper demonstrates capability of stream analytics where the real-time data query from ENU streaming through Structured Query Language (SQL) right into R Studio and Autoregressive Integrated Moving Average (ARIMA) predictions executed on the query table simultaneously on the same processing platform. Previously, ARIMA, Neural Network Autoregressive (NNETAR), and Naïve Bayes, were run and evaluated in R Studio to identify the best algorithm for stream analytics. Prediction procedure in R studio start with importing real-time data stored in SQL database and stream into R Studio using command of “dbGetQuery(con,sql)”. These three models evaluated the performance of freshwater lobster water conditions, dissolved oxygen (DO), potential hydrogen (pH), electrical conductivity (EC), and total dissolved solids (TDS). The data was collected for six months, and 70% was used as training data and 30% as test data. Compared to NNETAR and Naïve Bayes, ARIMA fits the entire data set well for 7 days; the ARIMA model exhibited lower absolute errors for pH and electrical conductivity, with errors ranging from 0.04 to 1.7 across days, while the NNETAR model had generally lower errors for TDS, with errors ranging from 0.3 to 0.7; however, the Naïve Bayes model's performance varied, with the lowest error for DO on day (5) 0.15 but higher errors for other parameters and days, including the highest error for electrical conductivity on day (6) 6.2. In conclusion, the average absolute errors for DO, pH, EC, and TDS are 0.163, 0.064, 0.705, and 0.498, respectively. Our findings underscore the efficacy of ARIMA for comprehensive water quality via stream prediction while highlighting the nuanced strengths and weaknesses of each model in forecasting specific parameters. This study contributes to the aquaculture literature by providing a nuanced comparative analysis of predictive models tailored to freshwater lobster farming, emphasizing the imperative role of stream predictive modelling. It enables real-time monitoring of water quality parameters, ensuring prompt interventions to maintain optimal conditions, thereby minimizing risks, enhancing aquaculture productivity, and ultimately contributing to sustainable and efficient freshwater lobster farming practices.

Structured query language query join optimization by using rademacher averages and mapreduce algorithms

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).

Enhancing data retrieval efficiency in large-scale JavaScript object notation datasets by using indexing techniques

<div class="translate-tooltip-mtz green sm-root translate hidden_translate"><div class="header-wrapper"><div class="header-controls"><span>The use of JavaScript Object Notation (JSON) format as a Not only Structured Query Language (NoSQL) storage solution has grown in popularity, but has presented technical challenges, particularly in indexing large-scale JSON files. This has resulted in slow data retrieval, especially for larger datasets. In this study, we propose the use of JSON datasets to preserve data in resource survey processes. We conducted experiments on a 32-gigabyte dataset containing 1,000,000 transactions in JSON format and implemented two indexing methods, dense and sparse, to improve retrieval efficiency. Additionally, we determined the optimal range of segment sizes for the indexing methods. Our findings revealed that adopting dense indexing reduced data retrieval time from 15,635 milliseconds to 55 milliseconds in one-to-one data retrieval, and from 38,300 milliseconds to 1 millisecond in the absence of keywords. In contrast, using sparse indexing reduced data retrieval time from 33,726 milliseconds to 36 milliseconds in one-to-many data retrieval and from 47,203 milliseconds to 0.17 milliseconds when keywords were not found. Furthermore, we discovered that the optimal segment size range was between 20,000 and 200,000 transactions for both dense and sparse indexing.</span></div></div></div>

Intelligent Trash Level Detection for Sustainable Urban Waste Management

Abstract This paper introduces an advanced trash bin fill ratio detection system, addressing the challenges of municipal solid waste management. Utilising the ESP32 CAM module, the system captures and transmits bin images via the ESP MESH protocol, enhancing waste management efficiency. The hardware relies on sustainable energy sources, including a photovoltaic cell and LiPo battery, ensuring continuous operation. On the software front, a client-server architecture is employed. The client-side captures images, while the server-side processes these images using a Python script and SQL database to determine bin fill levels. This innovative approach offers a scalable, cost-effective solution for improving urban waste collection and management.