Transaction Processing Systems Research Articles

Implementation and evaluation of a decentralized medical data exchange system based on the IOTA Tangle and approximate algorithms for optimal data placement


 
 
 
 
 
 
 
 © Voronin V., Rybalchenko A., Shmatko O., Kolomiitsev O., Tretiak V., Kliuchka Ya., 2023
 
 
 
 
 
 
 
 
 Facilitating the transfer of patient medical information across healthcare providers is crucial for ensuring high-quality care. However, this process encounters obstacles pertaining to privacy, security, and centralised control. This article outlines the creation of a decentralised system for exchanging medical data. The system utilises Distributed Ledger Technology, specifically the IOTA Tangle (open protocol (and network) for data and value transfer). This document outlines the structure and fundamental elements of a system designed for the secure and unalterable storage and transfer of medical records. The system employs masking and encryption methodologies to protect patient confidentiality while enabling healthcare practitioners to access complete information with patient authorization. The IOTA Tangle facilitates transactions without any fees and ensures the verification of data integrity through its Directed Acyclic Graph (DAG) topology. Simulation trials validate the system’s capability to securely communicate medical data on a large scale, while incurring lower resource costs than typical blockchain systems. The system demonstrates the feasibility of a decentralised and self-governing method for efficient and confidential sharing of medical data, utilising Distributed Ledger Technology (DLT). The secure data sharing platform facilitates the implementation of novel care and research frameworks, all the while upholding patient confidentiality and adhering to healthcare ethical standards. Also, in the development of billing Online Transaction Processing (OLTP) systems, which are designed for input, structured storage and processing of information in real time, the use of cloud technology is proposed. The problems that arise and their relevance to the solution of an integer linear programming problem with Boolean variables are shown. Approximate algorithms for optimal data placement and mathematical models for optimizing the structure of a distributed database of a cloud system are proposed, taking into account the limitations on the amount of node memory, the available costs of renting cloud resources, and the number of replicas of fragments of the distributed database.

Leveraging Technology Integration in Kenya's Food & Beverage Industry for Competitiveness.

Leveraging Technology Integration in Kenya's Food & Beverage Industry for Competitiveness.

Proposed framework for enhancing integrity technique using distributed query operation

Database growth and Storage problems are main concern for large and small enterprises nowadays, which significantly influences the efficiency of database applications. Database archiving is one of the solutions available for management. Many issues have been identified as a result of the use of archive databases, including the elimination of inactive data from online transaction processing systems (OLTP), performance and integrity management. The aim of this paper is to propose a framework for storing OLTP and archiving data in a distributed database environment as part of an integrated system. Maintaining the integrity between an OLTP database and an archiving database is crucial, but even more critical is the performance of the required query. The main aspect of the proposed framework is that it will not only ensure data integrity for primary and unique keys between OLTP and archive database, but it will also improve query performance by introducing parallel processing and query execution plans to maintain that integrity.

A Blockchain-Based Framework to Make the Rice Crop Supply Chain Transparent and Reliable in Agriculture

Rice is one of the major food crops across the globe, and its quality and safety highly influence human health. It is the basis of many different products, including rice flour, rice bread, noodles, rice vinegar, and others. Therefore, the rice supply chain has garnered increasing attention due to the high demand for food safety. Furthermore, malpractices in the rice supply chain can impact farmers by generating low revenues despite their great efforts in rice cultivation. In addition, they would cause governments to suffer significant economic losses due to the high cost of importing rice crops from other countries during the off-season. These issues derive from the lack of reliability, trust, transparency, traceability, and security in the rice supply chain. In this research, we propose a secure, trusted, reliable, and transparent framework based on a Blockchain for rice crop supply chain’s traceability from farm to fork. A new crypto token, the Rice Coin (RC), is introduced to keep a record of all transactions between the stakeholders of the rice supply chain. Moreover, the proposed framework includes an economic model and a crypto wallet and introduces an Initial Coin Offering (ICO) for the RC. Based on smart contracts, a transaction processing system was developed for the transparency and traceability of rice crops, including the conversion of the RC to fiat currency. Furthermore, the InterPlanetary File System (IPFS) is proposed in this research to store encrypted data of companies, retailers, and farmers, so to increase data security, transparency, and availability. In the end, the experimental results showed a better performance of the proposed framework compared to already available supply chain solutions in terms of transaction verification time, transaction average gas cost, and new block latency.

Application Programming Interface (API) And Management of Web-Based Accounting Information System (AIS): Security of Transaction Processing System, General Ledger and Financial Reporting System

The paper present exploratory research on application programming interface (API), management of accounting information system, security of transaction processing system, general ledger and financial reporting system. An application programming interface, or API, enables businesses to make the data and functionality of their applications available to external third-party developers, commercial partners, and internal departments inside their own organizations. Using a defined interface enables services and products to interact with one another and benefit from each other's information and features. The interface is used by developers to interact with other software and services; they are not required to understand how an API is developed, and neither are the software’s end-users. In short, an API is a contract between pieces of applications serving the main software once integrated into the source code of the main application. These pieces of applications communicate with each other in the language they both understand and over a network if needed. As the name implies, APIs serve as interfaces between programs. The interface is usually between a software developer and software developer’s application. Basically, the API allows one software to access some functionalities of another software. In its development, information systems develop and run well using a web-base so that it can be reached via an online computer network. Web applications have become complex and important for many companies, especially when combined with areas such as AIS. All AIS transaction data will be stored in the database management system and will be used if there is a query data request. Based on the IFRS (International Financial Reporting Standard) document, a proper accounting system must at least include the following aspects (IFRS Standard Requirements): Financial position statement, cash flows statement. Web-based AIS application for recording, grouping, processing

Fake Face Detection in Identity Cards Using Stegoface

Abstract: Identification and Machine-Readable Travel Documents (MRTDs) play a crucial role in verifying and validating identities in various situations, such as international travel, civil applications, online commerce, and access to transaction processing systems. These documents incorporate multiple security features aimed at preventing document forgery. However, criminals have shifted their focus to obtaining genuine documents fraudulently and manipulating facial portraits, as the existing security systems are challenging to bypass. To address this issue and mitigate the risks associated with such fraud, it is imperative for governments and ID/MRTD manufacturers to continually enhance and develop security measures. In this context, we present StegoFace, an innovative and efficient steganography method specifically designed for concealing secret messages within facial images found on common IDs and MRTDs. StegoFace employs an end-to-end approach, consisting of an ensemble of n Deep Convolutional Auto Encoders, which encode the secret message into a stegofacial image, and a Deep Convolutional Auto Decoder, capable of extracting the hidden message from the stegofacial image. Notably, our StegoFace approach outperforms the StegaStamp method in terms of perceptual quality, as evidenced by the results of metrics such as Peak Signal-to-Noise Ratio, hiding capacity, and imperceptibility on the test dataset.

Learning Graph Databases: Neo4j an overview

As the internet is growing day by day, the amount of data being generated is huge. This data includes structured data and unstructured data. The data along with its relationship with other data makes the most powerful and meaningful information. Maximum data exists in the form of the relationship between different or same objects and the noticeable thing is the relationship between the data is more important than the data itself. These relationships are handled efficiently by Relational databases that store data having structures and which have several records. The important point to be noted here is that these Relational database management Systems use tables with normalization concept. If the amount of data in such tables is huge, then handling such a large amount of data with its relationships is a tedious task. Here, Graph Databases come into picture. Entities and their relationships in relational databases will be reflected with nodes and relationships in graph databases. Graph databases provide very simple data model than databases with Online Transaction Processing systems. Graph databases provide features such as transactional integrity and operational availability. This paper introduces the idea of graph database systems in conjunction with Neo4j encompassing its query features, consistency, transactions, availability, and scaling.

On the Effects of Transaction Data Access Patterns on Performance in Lock-Based Concurrency Control

Transaction Processing (TP) plays a primary role in the design and implementation of IT applications and services. Many TP systems exploit lock-based concurrency control to guarantee atomicity and isolation of transactions that access shared data. In this article, we show that transaction data access patterns, in particular the order of data accesses along the transaction execution, have a noticeable impact on how lock-based concurrency control affects performance. We show that the performance can remarkably change depending on whether transactions, or a percentage of them, access data items following some common ordering rule or not. We investigate on this aspect and its root causes through an analytical modeling approach, and with the evidence of data gathered through both simulation and the execution of real transactional workloads. Finally, we show how the findings of our study can be easily exploited for improving the performance of common transactional workloads.

DBPA: A Benchmark for Transactional Database Performance Anomalies

Anomaly diagnosis is vital to the performance of online transaction processing (OLTP) systems. In the meanwhile, machine learning techniques can reason complex relationships beyond human abilities and perform well on such problems. However, they rely on a large number of training samples for anomalies, which are in serious shortage in both industry and academia due to the difficulty of collection. The problem raises the demand of a benchmark for anomaly reproduction and data collection. In this paper, we propose DBPA, a benchmark for transactional database performance anomalies. Specifically, we identify nine common anomalies rooted in the diverse influence factors. For each anomaly, we carefully design a reproduction procedure, which consists with its root cause in real-world databases. With the reproduction procedures, users can easily generate a dataset in a new environment and extend new anomaly types. For compound anomalies, we provide a generation algorithm that allows users to generate compound anomalies data of any possible combinations with existing collected data. We also provide a large dataset of both normal and anomalous monitoring data collected from various environments, facilitating the training of machine learning models and the evaluation of new algorithms for anomaly diagnosis.

Management Information System and Performance of Cement Firms in Southeast Nigeria

Purpose: The study examined how management information system influences the performance of cement-producing firms in Southeast Nigeria. Specifically, the influence of the transaction processing system, decision support system and executive support system on firm performance was assessed. Research methodology: The research design deployed in the study was a descriptive survey while the population of the study compromised 143 staff in the accounting and management information system departments of the selected four (4) cement companies in Southeast Nigeria. Yamane formula for sample size was applied in determining the sample size of 141 out of the population size of 143. The researchers collected primary data using a structured questionnaire administered to the respondents. Pearson Product Moment Correlational analysis was applied to establish the relationship between the variables of the study at a 5% level of significance. Results: Transaction processing system, decision support system and executive support system have a significant and positive effect on the firm performance of cement-producing companies in Southeast Nigeria. Limitations: The results are more fittingly applicable to only cement manufacturing companies. Therefore, they cannot be generalized to other sub-sectors of the Nigerian manufacturing industry. Also, the findings are valid to the extent that the responses to the questionnaire are free from bias. Contribution: The study contributes to knowledge by filling the gap created by the lack of empirical research that uncovers the influence of management information systems on the performance of cement manufacturing firms in the context of Southeast Nigeria.

Mitigation of Database Security Threats in Transaction Processing System

The main function of the transaction processing system (TPS) in the business world is to handle data transactions. Hence, it is crucial to secure the database, we discuss the problem of database threats in a transaction processing system in this article. A transaction process system (TPS) refers to an information processing system for business transactions that collects, modifies, and retrieves all transaction data. Database security denotes the safeguarding of database management systems using a variety of strategies against malicious cyberattacks, unlicensed access, and illegitimate use. The elements that need to be protected by database security systems include the data that has to be stored, the database where it is stored, and the database management system used to administer the data. It involves safeguarding the system from harm, infiltration, and exploitation. Additionally, it safeguards any database-related applications. The aim of this study is to explore possible database security threats in a TPS, along with some security challenges, and mitigation techniques to these threats.

Arriving at the Results by Comparing with Traditional Approach to My Approach in Deriving Function Points for ETL Operations

It can be hard to guess how much data will need to be put into the data warehouse when the whole history of the transaction system is moved there. This is especially true when the transfer process could take weeks or even months. The ETL system's parts must be broken down into its three independent stages, nevertheless, when estimating a big starting load. Data extraction from source systems, Creating the dimensional model from data, Loading the data warehouse and timing estimates for the extraction process. Surprisingly, data extraction from the source system may take up the majority of the ETL procedure. Online transaction processing (OLTP) systems are simply not built to return those massive data sets from the data warehouse's historic load, which extracts a tremendous quantity of data in a single query. However, the daily incremental loads and the breath-of-life historic database loads are very different. In any case, fact-table filling requires data to be pulled in a different way than what transaction systems are able to do. ETL extraction procedures frequently call for time-consuming techniques like views, cursors, stored procedures, and correlated subqueries. It is essential to anticipate how long an extract will take to begin before it does. Calculating the extract time estimate is challenging. Due to the hardware mismatch between the test and production servers, estimates based on the execution of the ETL operations in the test environment may be greatly distorted. Sometimes working on certain projects where an extract task would run continuously and until it eventually failed, at which point it would be restarted and run once more until it failed. Without producing anything, days or even weeks passed. One must divide the extract process into two simpler steps in order to overcome the challenges of working with large amounts of data. Response time for queries. the interval between when the query is conducted and when the data starts to be returned. It is pertinent that effort arrival for ETL Operations for Data Marts and DWH projects in terms of Function Points which is a scientific way is essential. In the last paper, I have talked about general System Characteristics to arrive at Value Adjustment Factor. In this paper, I came up with results. I compared my findings with the conventional FPA on industrial projects in order to evaluate the Function Point Analysis’s suitability for Data Mart projects. I outline the strategy, implementation, and outcomes analysis of this validation in this section.

Optimized CPU–GPU collaborative acceleration of zero-knowledge proof for confidential transactions

The potential application scope of blockchain is much broader than its origin of digital currencies, which may include managing smart contract, copyrighted works, and digitization of commercial or organizational registries. Not surprisingly, the confidentiality of transaction information is desired in these various blockchain applications, and is unfortunately not well supported yet. Zero-knowledge proofs such as bulletproofs are good candidate solutions to provide transaction confidentiality in blockchain, due to the ability to verify the truth of information without revealing the information directly. However, zero-knowledge proofs still suffer from high computation overhead and low throughput, and Bulletproofs are still computationally inefficient to be applied in blockchain applications. Edge computing meets confidential transactions’ strong need for high performing by providing powerful GPUs and can be a helpful option. In this paper, we first present a CPU–GPU collaborative framework to accelerate the inner-product arguments of Bulletproofs. The experiments show that our implementation achieves an average speedup ratio of 3.7x. On this basis, this paper also proposes to build a high efficient bulletproofs based transaction processing system that supports both confidential and transparent transactions at GPU-enabled edge. Compared with the original bulletproofs version, a GPU accelerated implementation can obtain a speedup ratio up to 4.7x. When multiple bulletproofs are required to execute in bundle, the work focuses on memory optimization and data segmentation, which further increases the speed by 30%. Finally, the overall transaction processing system achieves a 1.5x speedup when both confidential and transparent transactions are parallelized at the edge.

Optimization of Transaction Processing System (TPS) Using RAD With FAST Method

The use of computer-based information systems today has been understood by most companies in order to survive and succeed well. The transaction processing system (TPS) is a computerized system that runs and records daily routine business transactions that serve the operational level in the company. Information systems of purchase, inventory, sales are part of the TPS widely used by companies. The problem here is how to ensure that the system is connected to the organization's business plan and information requirements, where often a system that is built is not in accordance with the wishes of the user. Meanwhile, manual information systems can cause problems such as data redundancy, inconsistent data, irregular recording, miscalculations, difficulty in finding data and delays in presenting reports. To build the information system, the Framework for the Application of System Thinking (FAST) method will be used with a Rapid Application Development (RAD) approach that uses selected prototype features. The solution is expected to build an information system that can bring together user requirements and help companies process purchase, inventory and sales data in an integrated manner, so as to increase the efficiency and effectiveness of retail data processing.

DrTM+B: Replication-Driven Live Reconfiguration for Fast and General Distributed Transaction Processing

Recent in-memory database systems leverage advanced hardware features like RDMA to provide transaction processing at millions of transactions per second. Distributed transaction processing systems can scale to even higher rates, especially for partitionable workloads. Unfortunately, it is challenging to sustain such high rates during live reconfiguration of partitions. In this article, we observe that state-of-the-art approaches would cause notable performance disruption under fast transaction processing. To this end, this article presents DrTM+B, a live reconfiguration approach that seamlessly repartitions data with little performance disruption to running transactions. DrTM+B uses a pre-copy-based mechanism to avoid excessive data transfer by leveraging common properties in recent transactional systems. DrTM+B's reconfiguration plans reduce data movement by preferring existing data replicas, while copying data from multiple replicas asynchronously and in parallel. It further reuses the log forwarding mechanism in primary-backup replication to seamlessly track and forward dirty database tuples and avoids iterative copying costs. To commit a reconfiguration plan in a transactional-safe way, DrTM+B designs a cooperative commit protocol for synchronization of data and state among replicas. To boost the performance during data migration, DrTM+B combines the pre-copy and post-copy schemes to propose a hybrid copy scheme. The live reconfiguration approach can also coexist with fault-tolerance mechanisms of primary-backup replication to provide high availability. Evaluation on a working system based on DrTM+R with 3-way replication using typical OLTP workloads like TPC-C and SmallBank shows that DrTM+B incurs only very small performance degradation during live reconfiguration and provides high availability. Both the reconfiguration time and the downtime are also minimal.

SwitchTx

Online-transaction-processing (OLTP) applications require the underlying storage system to guarantee consistency and serializability for distributed transactions involving large numbers of servers, which tends to introduce high coordination cost and cause low system performance. In-network coordination is a promising approach to alleviate this problem, which leverages programmable switches to move a piece of coordination functionality into the network. This paper presents a fast and scalable transaction processing system called SwitchTx. At the core of SwitchTx is a decentralized multi-switch in-network coordination mechanism, which leverages modern switches' programmability to reduce coordination cost while avoiding the central-switch-caused problems in the state-of-the-art Eris transaction processing system. SwitchTx abstracts various coordination tasks (e.g., locking, validating, and replicating) as in-switch gather-and-scatter (GaS) operations, and offloads coordination to a tree of switches for each transaction (instead of to a central switch for all transactions) where the client and the participants connect to the leaves. Moreover, to control the transaction traffic intelligently, SwitchTx reorders the coordination messages according to their semantics and redesigns the congestion control combined with admission control. Evaluation shows that SwitchTx outperforms current transaction processing systems in various workloads by up to 2.16X in throughput, 40.4% in latency, and 41.5% in lock time.

Near-data processing in database systems on native computational storage under HTAP workloads

Today's Hybrid Transactional and Analytical Processing (HTAP) systems, tackle the ever-growing data in combination with a mixture of transactional and analytical workloads. While optimizing for aspects such as data freshness and performance isolation, they build on the traditional data-to-code principle and may trigger massive cold data transfers that impair the overall performance and scalability. Firstly, in this paper we show that Near-Data Processing (NDP) naturally fits in the HTAP design space. Secondly, we propose an NDP database architecture, allowing transactionally consistent in-situ executions of analytical operations in HTAP settings. We evaluate the proposed architecture in state-of-the-art key/value-stores and multi-versioned DBMS. In contrast to traditional setups, our approach yields robust, resource- and cost-efficient performance.

A Novel Approach for Discovering the Patterns by using PDBD Model in Big Data

Big data is a term that refers to information collected from a wide range of sources, including transaction processing systems, sensors, digital photographs, internet click stream logs, movies, and social media. The goal of text mining is to reveal new patterns and relationships that could lead to the discovery of previously unknown sources. It provides ample scope for interpretation of genuine user intent or contextual meanings as the case be. This research focuses on the creation of a novel approach for discovering patterns in big data using the Pattern Discovery in Big Data (PDBD) model in text mining. From textual sources, text-mining techniques extract a range of patterns and important facts. Sequential pattern mining helps to extract informative elements from a set of sequences based on the frequency of occurrences. The Pattern Discovery in Big Data (PDBD) model study has four primary contributions to uncovering patterns uniquely and effectively: Semantic information extraction, pattern improvements, LDA Topic Modeling, and cluster assignments. The proposed work compared the Pattern Taxonomy Model (Inner Pattern Evolving) pattern inner evolving model with other existing similar benchmark models to evaluate the model performance. The empirical results prove that the proposed model outperformed the compared models with significant improvement in accuracy Improvement in this Pattern Discovery in Big Data (PDBD) model yields better results in discovering the patterns.

Reducing Transaction Processing Latency in Hardware Transactional Memory-based Database with Non-volatile Memory

PDF HTML XML Export Cite reminder Reducing Transaction Processing Latency in Hardware Transactional Memory-based Database with Non-volatile Memory DOI: 10.21655/ijsi.1673-7288.00274 Author: Affiliation: Clc Number: Fund Project: National Key Research and Development Program of China (2020YFB2104100); National Science Fund for Distinguished Young Scholars of China (61925206) Article | Figures | Metrics | Reference | Related | Cited by | Materials | Comments Abstract:The emergency of Hardware Transactional Memory (HTM) has greatly boosted the transaction processing performance in in-memory databases. However, the group commit protocol, aiming at reducing the impact from slow storage devices, leads to high transaction commit latency. Non-Volatile Memory (NVM) opens opportunities for reducing transaction commit latency. However, HTM cannot cooperate with NVM together: flushing data to NVM will always cause HTM to abort. In this paper, we propose a technique called parity version to decouple the process of HTM execution and NVM write. Thus, the transactions can correctly and efficiently use NVM to reduce their commit latency with HTM. We have integrated this technique into DBX, a state-of-the-art HTM-based database, and propose DBXN: a low-latency and high-throughput in-memory transaction processing system. Evaluations using typical OLTP workloads including TPC-C show that it has 99% lower latency and 2.1 times higher throughput than DBX. Reference Related Cited by

PERANCANGAN SISTEM PEMPROSES TRANSAKSI PENJUALAN SUKU CADANG MESIN OUTBOAT PADA CV. DINAMIKA MARINE

The aim of this research is; 1) To find out the Design of Outboat Engine Parts Sales Transaction Processing System on CV. Dinamika Marine, 2) To design a Transaction Processing System for Sales of Outboat Engine Parts on CV. Web-based Marine Dynamics, AND 3) The design that will be built later is a sales transaction prototype that runs on the Windows operating system and does not require an internet connection. In making this model, the researcher compiled a data flow diagram that used a logical data flow diagram to describe the system to be proposed, while the prototype development process used the waterfall method. the results of the research include; 1) This Outboat Engine Spare Parts Sales application makes it easier to process transaction data and stock data based on sales, firstly the old system analysis, a user needs analysis, system feasibility analysis, system design which includes process modeling with DAD as a design tool, data modeling by making table designs, and designing interface designs and built using a web-based programming language with a database created using MySQL, and 2) The level of accuracy of this application in terms of recording transactions and stocks of Outboat Engine Parts can be seen by day, month and certain years so as to reduce the level of human error. This application can handle changes that occur, such as price changes and additional types of goods, these changes can be processed on the sales transaction form. The application is simple, the language used is easy for the user to understand, so it does not require a long time for training in operating this application