Database Storage Research Articles

Public Acceptance on the Benefits and Risks of DNA Profile Storage in Forensic DNA Databases: A Lesson from Malaysia

In December 2015, the Forensic DNA Databank of Malaysia (FDDM) was formally established to help in crime prevention and justice. Government activities are boosting public awareness, yet some misconceptions and concerns remain about the collection, storage, and use of Forensic DNA databases. The study's aim is to raise awareness about the benefits and risks of profile storage in forensic DNA data banks. A small number of public informants were selected using the qualitative technique. These included ten public informants and the study employs thematic analysis, and the results are somewhat unexpected. The findings of Focus Group Discussions (FGDs) show that informants' understanding of the benefits and risk of storing DNA profiles in forensic DNA data banks remains low. If the public is unclear and has limited understanding of the benefits and risks involved, it can have an impact on public health in general, and informants continue to blame practitioners in the relevant authorities for a lack of information. It raises the question of how the Malaysian forensic DNA database can be considered sustainable by the public. In recommendations, the government can collaborate with all key government agencies, including the judicial system, the attorney general's office, and the department of chemistry, to raise public acceptance of the benefits and risks of preserving DNA profiles in forensics DNA databanks. Future research is expected to involve additional stakeholders and use a variety of methodological approaches to cover a broader scope.

Comparison Performance Analysis of HDD, SSD and NVME for OLTP Database Server

Databases are an essential component of life in modern society. The database has become an online Transaction Processing (OLTP) operational support company. This study discusses the storage configuration in a virtual server database for OLTP Database with a benchmark against three storage configurations for four databases: SQL Server, MySQL, PostgreSQL, and MariaDB for Windows Server virtualization. Benchmarks using TPC-C to get the best performance from all three configurations have been tested. The results of this study show that the storage configuration of HDD as a virtual disk drive OS and NVME as a virtual disk drive database get better performance as online transaction processing (OLTP) compared to HDD as a virtual disk drive OS and HDD or SSD as a virtual disk drive database. Using the TPC-C benchmark, the study tested the performance of four databases (SQL Server, MySQL, PostgreSQL, and MariaDB) in various virtual storage configurations. The results of 36 experiments involving different numbers of virtual users (10, 50, and 100) showed that a hybrid configuration that used HDDs for the operating system and NVME for database storage provided the best performance. On SQL Server, SSD configurations show better results for smaller user loads. The results suggest that the combination of HDD and NVME is the optimal configuration for most databases in OLTP tasks, and it provides useful guidance in efficient storage investment decision-making. Further research is expected to be able to conduct tests.

Performance Analysis of Compression Algorithms on Matrix Data: Data Transfer Optimization in Microservices Architectures

With the rapid proliferation of microservices architectures these days, the efficient and fast transfer of large matrix data between services has become a significant challenge. This study presents an analysis aimed at finding solutions to this challenge. The analysis addresses the compression and decompression of large matrix data, focusing on lossless compression algorithms to optimize data transfer without data loss. The study is implemented on an example scenario. This scenario is taken from a project with a microservice architecture. In the example scenario, an image processing service developed in Python programming language generates 640x480 matrix data. After going through a compression algorithm, this data is periodically transferred to a backend service developed in C# programming language. This data is then stored in a database. In the final stage, decompression operations are performed so that this data can be used for reporting. The performance of various compression algorithms in the data compression, database storage and report generation stages is extensively tested. Within the scope of the study, tests were performed using five different compression algorithms (Gzip, Zlib, Deflate, Brotli and Bz2). The results are obtained through performance tests aimed at determining the most optimized end-to-end solution. Analyzing the performance of the compression algorithms on the example scenario, the Brotli algorithm gives the most optimal result in terms of both speed and compression size. This work makes an important contribution to data transfer optimization in microservice architectures and provides a reference for research in this area by presenting the performance analysis of various compression algorithms.

Computational Differential Privacy for Encrypted Databases Supporting Linear Queries

Differential privacy is a fundamental concept for protecting individual privacy in databases while enabling data analysis. Conceptually, it is assumed that the adversary has no direct access to the database, and therefore, encryption is not necessary. However, with the emergence of cloud computing and the << on-cloud >> storage of vast databases potentially contributed by multiple parties, it is becoming increasingly necessary to consider the possibility of the adversary having (at least partial) access to sensitive databases. A consequence is that, to protect the on-line database, it is now necessary to employ encryption. At PoPETs'19, it was the first time that the notion of differential privacy was considered for encrypted databases, but only for a limited type of query, namely histograms. Subsequently, a new type of query, summation, was considered at CODASPY'22. These works achieve statistical differential privacy, by still assuming that the adversary has no access to the encrypted database. In this paper, we take an essential step further by assuming that the adversary can eventually access the encrypted data, making it impossible to achieve statistical differential privacy because the security of encryption (beyond the one-time pad) relies on computational assumptions. Therefore, the appropriate privacy notion for encrypted databases that we target is computational differential privacy, which was introduced by Beimel et al. at CRYPTO '08. In our work, we focus on the case of functional encryption, which is an extensively studied primitive permitting some authorized computation over encrypted data. Technically, we show that any randomized functional encryption scheme that satisfies simulation-based security and differential privacy of the output can achieve computational differential privacy for multiple queries to one database. Our work also extends the summation query to a much broader range of queries, specifically linear queries, by utilizing inner-product functional encryption. Hence, we provide an instantiation for inner-product functionalities by proving its simulation soundness and present a concrete randomized inner-product functional encryption with computational differential privacy against multiple queries. In terms of efficiency, our protocol is almost as practical as the underlying inner product functional encryption scheme. As evidence, we provide a full benchmark, based on our concrete implementation for databases with up to 1 000 000 entries. Our work can be considered as a step towards achieving privacy-preserving encrypted databases for a wide range of query types and considering the involvement of multiple database owners.

Analysis on addressing the threats to cloud computing on the basis of security safeguards for sap cloud services

Cloud computing has become an integral part of combination/multi-cloud infrastructures due to its many benefits, including quick deployment, flexibility, cheap costs, and scalability, which have attracted businesses of all sizes. New possibilities and threats in terms of security flaws are cropping up in this field, even if cloud storage provides substantial advantages and inexpensive choices for IT administration and growth. When we talk about safeguarding cloud infrastructure systems, we're referring to a set of rules, regulations, and systematic procedures. This is also known as cloud computing security. The purpose of implementing these security measures is to safeguard cloud-based data, facilitate the enforcement of legislation, ensure the privacy of consumer information, and set standards for encryption in specific devices and applications. Modern cloud computing and security concerns throughout the cloud's many layers are addressed in this study. Within this context, the current study would make a substantial contribution to lowering the frequency of security breaches in cloud computing, paving the way for the continued provision of safe and reliable services. Storage of databases on the cloud is expanding rapidly in the IT industry, including SAP networks. Because enterprises are ultimately responsible for the security of their data, migrating databases to cloud computing environments brings with it a plethora of security risks. To guarantee data integrity, availability, and secrecy, appropriate security measures should be put in place before a company migrates its sensitive data to the cloud. When data is at rest, it is most vulnerable to unauthorized access; when it is in transit, it must be subject to strict supervision.

DEVELOPMENT AND IMPLEMENTATION OF A MOBILE APPLICATION FOR STUDENT ATTENDANCE MANAGEMENT

The article deals with the process of development and implementation of a mobile application, designed to automate the attendance management of students of Astana IT University. The application is implemented using React Native technologies for cross-platform development, which allows to work effectively on devices. cross-platform development, which allows it to work effectively on devices with different operating systems, such as Android and iOS. with different operating systems, such as Android and iOS. For database management uses the Firebase cloud platform, which provides reliable storage and fast processing of databases. reliable storage and fast data processing. One of the important factors of the application is its scalability. To realize the flexibility and scalability of the application, as well as communication with the learning management system, it was It was decided to use the REST API technology.The stages of architecture design, technology selection, data processing algorithms, visualization and security are described. Factors such as usability, speed of development, and data security were considered when selecting technologies. Special attention was paid to data processing algorithms and data visualization to present users with an intuitive interface for tracking attendance.The application significantly improves the interaction between students and faculty with the learning system, simplifying the process of attendance tracking, and providing transparency and automation of the attendance process. The developed application also reduces manual labor and reduces the possibility of human error.

Cost-effectiveness of protein-rich meals and snacks for increasing protein intake in older adults

ObjectivesTo investigate the cost-effectiveness of protein-rich meals and snacks for increasing protein intake in home-dwelling older adults. DesignCost effectiveness analysis from a randomized controlled trial, the Power Meals study. SettingParticipants were randomized into one of three groups for eight weeks: a protein-rich meal, snack and bread (Protein), a regular meal (Normal) and a control group without meal service (Control). ParticipantsHome-dwelling home care clients, caregivers and care recipients aged ≥ 65 years (n = 65). MeasurementsProtein intake was assessed by a three-day food diary at the end of the study. Cost for the daily diet was estimated by using Finnish grocery store databases and the prices of the food service. The cost-effectiveness was assessed by an incremental cost-effectiveness ratio (ICER). ResultsCosts for the daily diet in the Protein (8.35 €/d) and the Normal (7.94 €/d) groups were significantly higher than in the control group (5.65 €/d) (p < 0.001). Incremental cost-effectiveness analysis showed that increasing protein intake was cost-effective in the Protein group as incremental cost-effectiveness ratio was 8.11 in the Protein, 8.72 in the Normal and 6.45 in the Control group. ConclusionsIncluding protein rich meals and snacks in a diet increases protein intake in home-dwelling older adults cost-effectively.

Study on penetration depth in laser welding: A process information database-based control strategy and OCT measuring verification

Penetration depth acts as a crucial indicator reflecting laser welding quality, thus the control of its stability and the perception of its fluctuation state are increasingly garnering attention. This paper proposes a process information database-based control strategy for penetration depth, and the control validity is verified through penetration depth detection utilizing optical coherence tomography (OCT). The process information database stores diverse expected penetration depth knowledge formed by a substantial quantity of varying welding speeds with fixed other process parameters under undisturbed welding conditions. In the database, the stable average values inside the standard penetration depth information and the corresponding heat input (HI) values are connected and mapped via an artificial neural network (ANN). In response to abnormal variations in the penetration depth curve caused by interferences during welding, according to the HI gap predicted by the trained ANN from the penetration depth gap arising from the curve deviation, the control unit can calculate the new welding speed required to feed the penetration depth curve back to within the steady fluctuation range. Based on OCT, the keyhole depth signal is acquired, and a deep belief network is built to predict the penetration depth curve via the correlation between the reconstructed keyhole depth obtained by ensemble empirical mode decomposition and the penetration depth. This detection method demonstrates that the penetration depth curve can be controlled accurately. Finally, a closed-loop real-time feedback control system for penetration depth is established.

Using Blockchain Evidence in China’s Digital Copyright Legislation to Enhance the Sustainability of Legal Systems

To achieve sustainable development of social systems, it is necessary to modernize the legal system, which is the foundation of any society, to increase the efficiency of resources and simultaneously optimize the performance of the environment and society. The immutable and timestamped features of blockchain offer a robust solution for tracking and authenticating digital copyright evidence, thereby enhancing the integrity and transparency of judicial systems. This ensures that the integration of blockchain into legal systems not only advances technological efficiency but also promotes environmental consciousness. Through comprehensive analyses that integrate questionnaires, interviews, case studies and legislative assessments, this research reveals that there are still problems in the application of blockchain evidence in China’s judicial practice, such as insufficient and stable credibility, inadequate database storage, deficient original rights mechanisms, and the imperfect application of rules of evidence. These problems can be solved by enhancing correspondence legal systems, such as establishing an officially trusted copyright certificate blockchain, creating a blockchain copyright certificate technology supervision system and formulating specific laws and regulations on the application and identification of blockchain evidence. As such, our study contributes to aligning blockchain with judicial records, supporting the sustainable development goals of social systems, fostering institutional justice and social progress.

Optimized Intrusion Detection for IoMT Networks with Tree-Based Machine Learning and Filter-Based Feature Selection.

The Internet of Medical Things (IoMTs) is a network of connected medical equipment such as pacemakers, prosthetics, and smartwatches. Utilizing the IoMT-based system, a huge amount of data is generated, offering experts a valuable resource for tasks such as prediction, real-time monitoring, and diagnosis. To do so, the patient's health data must be transferred to database storage for processing because of the limitations of the storage and computation capabilities of IoMT devices. Consequently, concerns regarding security and privacy can arise due to the limited control over the transmitted information and reliance on wireless transmission, which leaves the network vulnerable to several kinds of attacks. Motivated by this, in this study, we aim to build and improve an efficient intrusion detection system (IDS) for IoMT networks. The proposed IDS leverages tree-based machine learning classifiers combined with filter-based feature selection techniques to enhance detection accuracy and efficiency. The proposed model is used for monitoring and identifying unauthorized or malicious activities within medical devices and networks. To optimize performance and minimize computation costs, we utilize Mutual Information (MI) and XGBoost as filter-based feature selection methods. Then, to reduce the number of the chosen features selected, we apply a mathematical set (intersection) to extract the common features. The proposed method can detect intruders while data are being transferred, allowing for the accurate and efficient analysis of healthcare data at the network's edge. The system's performance is assessed using the CICIDS2017 dataset. We evaluate the proposed model in terms of accuracy, F1 score, recall, precision, true positive rate, and false positive rate. The proposed model achieves 98.79% accuracy and a low false alarm rate 0.007 FAR on the CICIDS2017 dataset according to the experimental results. While this study focuses on binary classification for intrusion detection, we are planning to build a multi-classification approach for future work which will be able to not only detect the attacks but also categorize them. Additionally, we will consider using our proposed feature selection technique for different ML classifiers and evaluate the model's performance empirically in real-world IoMT scenarios.

Matching and Rewriting Rules in Object-Oriented Databases

Graph query languages such as Cypher are widely adopted to match and retrieve data in a graph representation, due to their ability to retrieve and transform information. Even though the most natural way to match and transform information is through rewriting rules, those are scarcely or partially adopted in graph query languages. Their inability to do so has a major impact on the subsequent way the information is structured, as it might then appear more natural to provide major constraints over the data representation to fix the way the information should be represented. On the other hand, recent works are starting to move towards the opposite direction, as the provision of a truly general semistructured model (GSM) allows to both represent all the available data formats (Network-Based, Relational, and Semistructured) as well as support a holistic query language expressing all major queries in such languages. In this paper, we show that the usage of GSM enables the definition of a general rewriting mechanism which can be expressed in current graph query languages only at the cost of adhering the query to the specificity of the underlying data representation. We formalise the proposed query language in terms declarative graph rewriting mechanisms described as a set of production rules L→R while both providing restriction to the characterisation of L, and extending it to support structural graph nesting operations, useful to aggregate similar information around an entry-point of interest. We further achieve our declarative requirements by determining the order in which the data should be rewritten and multiple rules should be applied while ensuring the application of such updates on the GSM database is persisted in subsequent rewriting calls. We discuss how GSM, by fully supporting index-based data representation, allows for a better physical model implementation leveraging the benefits of columnar database storage. Preliminary benchmarks show the scalability of this proposed implementation in comparison with state-of-the-art implementations.

Technology-Enabled Database Education: Challenges andOpportunities

Motivation: User-friendly systems and tools are paramount for facilitating learning of database systems. In many universities around the world, database systems courses are supplemented with the use of industrial-strength relational database management systems (RDBMS). Although there exist a few efforts on mini RDBMS for learners to practice implementing a database kernel, e.g., Minibase [6], unfortunately, there is a lack of learner-friendly technological support to facilitate the learning of various core components of a database systems course, such as database storage and indexing, relational query processing and optimization, and transaction management.

Research on Laboratory Construction and Management Based on Internet of Things and Deep Learning

A computer laboratory intelligent management and control system based on Internet of Things technology and deep learning is proposed to address the shortcomings of traditional university laboratory management.The system adopts the main embedded technology, mobile communication technology, wireless sensor technology and database storage technology in the Internet of Things technology, laboratory areas and equipment, identify and collect relevant data, monitor abnormal conditions in real time, and alarm. At the same time, the user is provided with a preview of the experimental content and a demonstration of the results through the handheld client APP. The experimental results show that: when the system is powered by 0.35A current, the total system power is about 3W. The NTC thermistor used has a B value of 3435. Under normal temperature, T2 = 298.15K,R = 10KΩ,R0 = 10KΩ. The current temperature value can be calculated according to the value obtained after the analog-to-digital conversion of the microcontroller. The output frequency of the humidity detection circuit is measured by the single-chip microcomputer, and the current ambient humidity can be obtained by substituting the result into the above formula, that is, when the output frequency dimension of the circuit is 6853Hz, the ambient humidity is 40%. Through the application of this system, the inefficient and extensive manual management method of the computer experimental teaching center is completely solved, so that it can bring more intelligent and efficient services to the teachers and students of the whole school, so as to comprehensively promote the experimental teaching of the school.

Radio Frequency Identification of Attendance System

Abstract: An RFID (Radio Frequency Identification) based attendance system is a technology-driven approachthat automates the process of recording attendance. This abstract outlines the key components and functionality of such a system. RFID technology employs electromagnetic fields to automatically identify and track tags attached to objects or individuals. In the context of attendance tracking, RFIDtags are assigned to students, employees, or participants. These tags are embedded with unique identification information. The attendance system consists of several components including RFID tags, RFID readers, a database, and a software interface. Each participant is provided with an RFID tag which is either worn (like an ID card) or embedded into an object (like a wristband).When participants enter a designated area monitored by RFID readers, the readers detect the presence of the RFID tags within range. The reader captures the unique ID associated with the tag and transmits this data to a centralized database. The database stores the attendance records, associating each tag ID with a timestamp and participant identity. The advantages of an RFID-based attendance system include accuracy, efficiency, and automation. It reduces the manual effort required for traditional attendance taking, minimizes errors associated with human intervention, and provides real-time data for monitoring attendance trends. This abstract summarizes the fundamental concepts and benefits of an RFID attendance system, highlighting its role in streamlining attendance management.

Smart Slot Assignment for Urban EV Charging Using Folium API Integration

Abstract: Efficient management of electric vehicle (EV) charging distribution in urban areas is crucial amid increasing demand and varying traffic conditions. This study proposes a novel web-based solution that integrates Folium Map API for realtime location tracking and Python Flask backend with MySQL database storage to allocate charging demands. By leveraging real-time traffic information from the Folium Map API, the proposed model optimizes charging resource allocation across multiple stations and road networks. The web interface, built using HTML, CSS, and JavaScript, allows users to interact with the system seamlessly. Charging allocation is dynamically adjusted based on real-time data, prioritizing maxi- mum returns. Additionally, an emergency charge counter facility is introduced to cater to unforeseen charging needs, enhancing user flexibility. The system offers two interfaces and a dashboard for both station administrators and users, en- abling pre-booking services for travelers during their journey, facilitating access to nearby charging stations, and providing a facility for prebooking. These features contribute to improved station revenue and pile utilization, highlighting the efficacy of the proposed web-based approach. By leveraging advanced web technologies and APIs, this comprehensive strategy enhances charging infrastructure efficiency, fosters the widespread adoption of electric vehicles, and contributes to sustainable urban mobility management.

Sistem Informasi Kepegawaian Pada PT Graha Sriwijaya Sejahtera Sentosa

A personnel information system is a system that provides information about personnel to help companies manage employee data more easily and quickly. PT Graha Sriwijaya Sejahtera Sentosa is a company engaged in the distribution of car batteries under the Massiv brand. Problems that exist in companies include the accumulation of prospective employee application files resulting in the destruction of rejected employee application files, searching for employee data which takes a lot of time because employee data has to be searched in a storage cupboard so it is less efficient and employee leave applications are still recorded manually. in written media such as books and paper which have a fairly high risk of being lost or damaged, which causes data processing related to the company's human resource information system to still have shortcomings and inefficiencies. The system development method used is the RUP method (Rational Unified Process) by observing the running system. This research aims to collect data and convey information about employees. The results of interviews with the company's HRD revealed problems in collecting employee data adapted to the PIECES framework as welldiagram usecase as well as in planning to useclass diagram anddiagram activity andMySQL for database storage. The purpose of creating this system is to simplify the process of collecting personnel data in companies. Therefore, having a personnel information system in the company can help the company more easily collect employee data, provide information to employees regarding the company's internal affairs, and help employees more easily apply for leave without having to contact their superiors directly.

Perancangan Sistem Labeling ODP (Optical Distribution Point) Berbasis Web di PT. Telkom Akses Balikpapan

PT. Telkom Akses has an assurance division whose job is to carry out preventive maintenance on the Indihome network and handle problems directly with the TTR (Time To Repair) concept of &lt; 3 hours. However, in handling disruptions, technicians often encounter obstacles regarding invalid ODP data and customer data, which makes the work of assurance technicians less efficient. To overcome this problem, efforts were made to create an ODP label system which contains information regarding ODP status and customer data. This research uses the waterfall design method which focuses on development using UML (Unified Model language) and using the PHP programming language, as well as Xampp with Apache as the web server. and MySQL which is used as database storage. So this research produces an application that can make it easier for technicians to search for customer data. Based on the test results, the web-based Optical Distribution Point (ODP) labeling system has met the expected usability aspects. Testing has been carried out using the functional blackbox testing method, namely by using a series of case experiments on functions that have been built both from the admin and user side. The output results provided were as expected and there were no errors or output that was outside of what was expected.

A Performance Analysis of CassandraDB and ScyllaDB

Abstract: Big Data encompasses vast amounts of data, reaching exabytes or even zettabytes. In the current landscape, large databases play a crucial role, particularly in generating substantial data for daily analyses of social media and multimedia content. The enormity of Big Data poses challenges, given its extensive volume of structured, semi-structured, and unstructured data, making traditional database systems and software techniques insufficient. Big Data is frequently defined by its 9 V’s: velocity, variety, volume, veracity, validity, variability, volatility, visualization, and value. This complexity highlights the need for a simple information management strategy that integrates various new data types alongside traditional data. The significance of Big Data databases is emphasized by the daily generation of millions of terabytes of data from sources like social media posts and multimedia. This study aims to evaluate the performance of two Wide-Column Store Big Data database systems, Apache CassandraDB and ScyllaDB, using the CassandraStress Benchmarking Tool. Key metrics such as total operation time, operation rate, partition rate, row rate, and maximum latency will be assessed as the number of records and operations increase. The development of these databases, motivated by diverse industry requirements, emphasizes their adaptation to specific needs. The research methodology outlines the tool used to compare these WideColumn Store databases on various parameters, contributing valuable insights into their performance in real-world scenarios

أثر الذكاء الاصطناعي في الأداء المؤسسي: متغير وسيط رأس المال الفكري في البنوك التجارية العاملة بالمملكة العربية السعودية

The aim of this study was to investigate the impact of artificial intelligence (AI) with its dimensions (expert systems, automatic machine learning, usability) on organizational performance, mediated by intellectual capital, in commercial banks operating in the Kingdom of Saudi Arabia. The study population comprised all employees in commercial banks in Saudi Arabia, and the study relied on a proportionate stratified random sample of administrators in these banks (managers, department heads, supervisors, administrators), totaling 338 administrators. The study utilized a descriptive-analytical methodology, developing a questionnaire consisting of 38 items. Statistical Package for the Social Sciences (SPSS) was used for data analysis and hypothesis testing. The study yielded several key findings: There is a significant impact of artificial intelligence, with its dimensions (expert systems, automatic machine learning, usability), on organizational performance. Additionally, there is a significant impact of artificial intelligence dimensions on intellectual capital, and intellectual capital significantly impacts organizational performance. Moreover, there is a significant indirect impact of artificial intelligence dimensions on organizational performance through intellectual capital in commercial banks. The study recommended that bank management focus on expert systems provided by artificial intelligence to acquire knowledge from stored databases for problem-solving and application through organizational performance and intellectual capital. It also suggested developing systems to logically and programmatically address accounting errors to enhance the ability to automatically monitor and reevaluate internal processes continuously in line with changes in the external work environment. This can be achieved through the use of modern technology, knowledge transfer, research, and development.

IoT Based Smart Trolley for Auto Billing

This paper proposes the development of a Smart Trolley system aimed at enhancing the shopping experience by automating the billing process. The system integrates Raspberry Pi, a low-cost, credit-card-sized computer, and RFID (Radio-Frequency Identification) technology to create an efficient and seamless checkout process for customers in retail environments. The Smart Trolley employs RFID tags attached to products and an RFID reader mounted on the trolley to identify items as they are placed inside. The Raspberry Pi processes the RFID data and maintains a running total of the items in the trolley. Additionally, it communicates with the store's database to retrieve product information and pricing. Through this integration, the Smart Trolley eliminates the need for manual scanning of each item at checkout, reducing waiting times and enhancing overall efficiency. Moreover, the system provides real-time updates on the total bill, allowing customers to track their expenses as they shop. This paper outlines the design, implementation, and testing of the Smart Trolley system, demonstrating its effectiveness in automating the billing process while improving the shopping experience for customers. The proposed system holds promise for revolutionizing retail operations, offering a glimpse into the future of smart, technology-driven shopping solutions. Key Words: Autobilling, Rasberry Pi, RFID(EM-18), LCD, Smart Shopping, IoT.