Online Transaction Processing Research Articles

A system design for elastically scaling transaction processing engines in virtualized servers

Online Transaction Processing (OLTP) deployments are migrating from on-premise to cloud settings in order to exploit the elasticity of cloud infrastructure which allows them to adapt to workload variations. However, cloud adaptation comes at the cost of redesigning the engine, which has led to the introduction of several, new, cloud-based transaction processing systems mainly focusing on: (i) the transaction coordination protocol, (ii) the data partitioning strategy, and, (iii) the resource isolation across multiple tenants. As a result, standalone OLTP engines cannot be easily deployed with an elastic setting in the cloud and they need to migrate to another, specialized deployment. In this paper, we focus on workload variations that can be addressed by modern multi-socket, multi-core servers and we present a system design for providing fine-grained elasticity to multi-tenant, scale-up OLTP deployments. We introduce novel components to the virtualization software stack that enable on-demand addition and removal of computing and memory resources. We provide a bi-directional, low-overhead communication stack between the virtual machine and the hypervisor, which allows the former to adapt to variations coming both from the workload and the resource availability. We show that our system achieves NUMA-aware, millisecond-level, stateful and fine-grained elasticity, while it is not intrusive to the design of state-of-the-art, in-memory OLTP engines. We evaluate our system through novel use cases demonstrating that scale-up elasticity increases resource utilization, while allowing tenants to pay for actual use of resources and not just their reservation.

Implementation of Data Backup and Synchronization Based on Identity Column Real Time Data Warehouse

Failure in the process of loading data from the Online Transactional Processing(OLTP) system to the Normalized Data Store (NDS) database can occur. This caused by a disruption in the network so that the OLTP system is unable to save data to the OLTP and NDS databases. Backup and synchronization data scenarios are needed to maintain data consistency and data availability. In this research, the process of data backup and synchronization is done by providing an identity column for the table in the OLTP database. An identity column is used to give status data, value '0' if the inserting process fails, and value '1' if successful. Data backup is done by storing temporary data into a CSV file format, then the CSV file is read, and an insert process is carried out into the OLTP database. After the insertion process into the OLTP database is successful, it continues with the synchronization process between the OLTP database and the NDS. Data synchronization between OLTP and NDS databases is done by checking the value of the Identity Column in each table in the OLTP database.

Genetic algorithm based key sequence generation for cipher system

ABSTRACT Stream cipher system seems to be one of the best alternatives in order to provide confidentiality and security in an on line and high-speed transmission. Cryptography is required to secure the secret information transmitting over the communication channel. Day by day the importance of security increases due to increase of online transaction processing and e-commerce. As well as encryption / decryption algorithm, randomness characteristics of key sequences prove the strength of the stream cipher. In this work the Linear Feedback Shift Register (LFSR) is used to produce non binary pseudo random key sequence. The length of the sequence has been enhanced by designing hybrid model using LFSR and Genetic Algorithm (GA). Achieving the length more than the maximum length of LFSR is the primary intention of this work. The statistical tests is conducted to assess the randomness of key sequence generated from hybrid model. Generated key sequences are used as key sequences in cryptographic applications and results are analyzed.

Micro-architectural analysis of OLAP

Understanding micro-architectural behavior is important for efficiently using hardware resources. Recent work has shown that in-memory online transaction processing (OLTP) systems severely underutilize their core micro-architecture resources [29]. Whereas, online analytical processing (OLAP) workloads exhibit a completely different computing pattern. OLAP workloads are read-only, bandwidth-intensive, and include various data access patterns. With the rise of column-stores, they run on high-performance engines that are tightly optimized for modern hardware. Consequently, micro-architectural behavior of modern OLAP systems remains unclear. This work presents a micro-architectural analysis of a set of OLAP systems. The results show that traditional commercial OLAP systems suffer from their long instruction footprint, which results in high response times. High-performance columnstores execute tight instruction streams; however, they spend 25 to 82% of their CPU cycles on stalls both for sequential- and random-access-heavy workloads. Concurrent query execution can improve the utilization, but it creates interference in the shared resources, which results in sub-optimal performance.

Optimized Authentication Model for Online Transaction Payments

The rapid growth of the internet across the globe has gained attention in the world of business. The internet has become the major driver for business growth in the world; due to several security lapses online; it is necessary to implement measures and standards on the internet to protect transactions online. These security lapses have led to the development of various online payment protocols to ensure the safety of online transactions such as Secure Electronic Transaction (SET), internet Keyed Payment (iKP) and The Secure Socket Layer (SSL). There are several methods of paying for online transactions; these include direct payment with bank accounts and the use of electronic card. The payments are subjected to the verification system, which ensures no one uses someone’s card to transact business online. Each card has a security feature known as the Card Verification Value (CVV) number, which is used as authentication for online business. The key feature of the card which validates the card owner as the user is the CVV number, which is found at the back of the card. The problem is that when the card gets lost or falls into the hands of another person, it is likely the person might use the card for a fraudulent activity online. This is because all the information required for e-payment is on the card. In this paper, we propose an optimized conceptual model which ensures the removal of the CVV number from the all-electronic card, the paper also recommended a framework that deployed Unstructured Supplementary Services Data (USSD) technology in the online transaction and payment process. In a real-world implementation, the proposed optimized model shall enhance e-commerce payments, card user participation, reduce threats, improves the security of conducting online business and then offer the card user the opportunity to deny or accepts payment.

Compatible byte-addressable direct I/O for peripheral memory devices in Linux

Memory devices can be used as storage systems to provide a lower latency that can be achieved by disk and flash storage. However, traditional buffered input/output (I/O) and direct I/O are not optimized for memory-based storages. Traditional buffered I/O includes a redundant memory copy with a disk cache. Traditional direct I/O does not support byte addressing. Memory-mapped direct I/O, which optimizes file operations for byte-addressable persistent memory and appears to the CPU as a main memory. However, it has an interface that is not always compatible with existing applications. In addition, it cannot be used for peripheral memory devices (e.g., networked memory devices and hardware RAM drives) that are not interfaced with the memory bus. This paper presents a new Linux I/O layer, byte direct I/O (BDIO), that can process byte-addressable direct I/O using the standard application programming interface. It requires no modification of existing application programs and can be used not only for the memory but also for the peripheral memory devices that are not addressable by a memory management unit. The proposed BDIO layer allows file systems and device drivers to easily support BDIO. The new I/O achieved 18% to 102% performance improvements in the evaluation experiments conducted with online transaction processing, file server, and desktop virtualization storage.

Development of Online Egg Grading Information Management System with Data Warehouse Technique

Highlights Digital data collection and management system is developed for the USDA-AMS’s shell-egg grading program. Database system consisting of OLTP, data warehouse and OLAP databases enables online data entry and trend reporting. Data and information management is done through web application servers. Users access the databases via web browsers. Abstract . This paper is concerned with development of web-based online data entry and reporting system, capable of centralized data storage and analytics of egg grading records produced by USDA egg graders. The USDA egg grading records are currently managed in paper form. While there is useful information for data-driven knowledge discovery and decision making, the paper-based egg grading record system has fundamental limitations in effective and timely management of such information. Thus, there has been a demand to electronically and digitally store and manage the egg grading records in a database for data analytics and mining, such that the quality trends of eggs observed at various levels (e.g., nation or state) are readily available to decision makers. In this study, we report the design and implementation of a web-based online data entry and reporting information system (called USDA Egg Grading Information Management System, EGIMS), based on a data warehouse framework. The developed information system consisted of web applications for data entry and reporting, and internal databases for data storage, aggregation, and query processing. The internal databases consisted of online transaction processing (OLTP) database for data entry and retrieval, data warehouse (DW) for centralized data storage and online analytical processing (OLAP) database for multidimensional analytical queries. Thus, the key design goal of the system was to build a system platform that could provide the web-based data entry and reporting capabilities while rapidly updating the OLTP, DW and OLAP databases. The developed system was evaluated by a simulation study with statistically-modeled egg grading records of one hypothetical year. The study found that the EGIMS could handle approximately up to 600 concurrent users, 32 data entries per second and 164 report requests per second, on average. The study demonstrated the feasibility of an enterprise-level data warehouse system for the USDA and a potential to provide data analytics and data mining capabilities such that the queries about historical and current trends can be reported. Once fully implemented and tested in the field, the EGIMS is expected to provide a solution to modernize the egg grading practice of the USDA and produce the useful information for timely decisions and new knowledge discovery. Keywords: Data warehouse, Database, OLTP, OLAP, Egg grading, Information management, Web application, Information system, Data.

Health-care databases and its role in transformation of medicine

Health-care database is a collection of health-care data organized for storage, accessibility, and retrieval. Health-care database serves to replace the paper documents, filing cabinets of old, and file folders, which will be more convenient and immediate. At present, health-care system is in a transition from a patient care perspective toward information-based medicine requiring data from various sources such as laboratory tests, genetic tests, medical images, family histories, and patient medical records, which need to be easily stored, integrated, and analyzed. The most commonly used database in health care is the online transaction processing (OLTP) database among different types of databases available. OLTP database is the one that is a single computer application runs on, and the main strength of it is quick and real-time transaction processing. Electronic health record is a prime example of the OLTP, and practitioners enter routine clinical and laboratory data into them during usual practice as a record of the patient's care. Data can be stored externally in a secure place and can be backed up to prevent data loss with these health-care databases. The back-end data can therefore become more accurate and standardized because of the front-end software providing tip text and enforce data. Furthermore, the health-care database allows the rapid processing of classical transactions such as laboratory results and payment claims as the data are computerized.

A Novel Optimized Case-Based Reasoning Approach With K-Means Clustering and Genetic Algorithm for Predicting Multi-Class Workload Characterization in Autonomic Database and Data Warehouse System

Data management systems are essential elements for any organization which is dealing with large volume of data now a days. Due to increase in data volume, and its complexities, it has become more challenging job for workload management system to maintain its performance. So, there is a need of such a system that can autonomically deal with such complexities with less or without human involvement. Performance of these systems can be improved by making the systems well-aware about the workload entering into the system. The workload of a prevalent typical database and data warehouse system can be characterized into three types that is Online Transaction Processing (OLTP), Decision Support Systems (DSS) and Mixed type of workload. Currently, autonomic characterization of workload into a binary class such as OLTP and DSS is being carried out as reported in the literature, however, characterizing the workload into three types that refers to a multi-class classification problem is relatively a more challenging task. In this study, we propose a novel optimized Case-based Reasoning (CBR) approach based on clustering for autonomically characterizing the workload into multi-class types before entering into the system. We implement four phases of CBR along with case-base generation and map it to the elements of autonomic MAPE-K model. In Retrieve phase, k-means clustering is used for enhancing retrieval efficiency and workload types predictions are made in Reuse phase. Genetic Algorithm is used in Revise and Adapt phase of CBR. Few autonomic self_* characteristics are incorporated to make it autonomic. We performed various experiments and results show that the proposed model outperforms in prediction as compared to existing approaches. We performed post-hoc test for the validation of results in comparison with other machine learning classifiers using the Friedman test that show that the proposed model stands out as the best classifier.

Equilibrium of Redundancy in Relational Model for Optimized Data Retrieval

Conceptual and relational data models of online transaction processing (OLTP) applications are usually created and maintained following the principle of normalization, which implies avoidance of redundancy. Data retrieval from a disk-based normalized relational database often requires complex and inefficient queries that may cause noticeable performance issues when executed on larger volumes of data. Computer professionals sometimes intentionally trade off the strict normal form to optimize data retrieval queries through error-prone manual tuning and denormalization. We propose a fully automatic optimization approach, based on data redundancy, that relies on a formal cost-benefit model. We prove that finding the optimal level of data redundancy, for given workload statistics, is an NP-Complete optimization problem. A detailed reduction of the problem to binary linear programming is presented in the paper. The proposed optimization approach was evaluated using the TPCE benchmark for OLTP systems. The evaluation has shown that the proposed optimization approach is highly scalable, and that it can be efficiently applied to real-life relational data models.

Decision support system using data warehouse for retail system

CMK company is a retail company that sell and distributes miniature vehicle items to small stores. CMK has several branches in various countries that use for operational and storage warehouse. This company faces the problem for analysing sales to decide the useful strategy since this company only uses Online Transaction Processing (OLTP) database. This study implements Online Analysis Processing (OLAP) database and data warehouse to provide useful information for this company by using the nine-step method designed by Kimball & Ross. The data will be performed into dashboard to make easier for CMK Company to analyze data. Furthermore, many useful information can be provided in short and efficient time.

Query Optimization for Big Data Analytics

Organizations adopt different databases for big data which is huge in volume and have different data models. Querying big data is challenging yet crucial for any business. The data warehouses traditionally built with On-line Transaction Processing (OLTP) centric technologies must be modernized to scale to the ever-growing demand of data. With rapid change in requirements it is important to have near real time response from the big data gathered so that business decisions needed to address new challenges can be made in a timely manner. The main focus of our research is to improve the performance of query execution for big data.

Rancang Bangun Engine ETL Data Warehouse dengan Menggunakan Bahasa Python

Big companies that have many branches in different locations often have difficulty with analyzing transaction processes from each branch. The problem experienced by the company management is the rapid delivery of massive data provided by the branch to the head office so that the analysis process of the company's performance becomes slow and inaccurate. The results of this process used as a consideration in decision making which produce the right information if the data is complete and relevant. The right method of massive data collection is using the data warehouse approach. Data warehouse is a relational database designed to optimize queries in Online Analytical Processing (OLAP) from the transaction process of various data sources that can record any changes in data that occur so that the data becomes more structured. In applying the data collection, data warehouse has extracted, transform, and load (ETL) steps to read data from the Online Transaction Processing (OLTP) system, change the form of data through uniform data structures, and save to the final location in the data warehouse. This study provides an overview of the solution for implementing ETL that can work automatically or manually according to needs using the Python programming language so that it can facilitate the ETL process and can adjust to the conditions of the database in the company system.

STRATEGIC HUMAN RESOURCES MANAGEMENT AND BUSINESS INTELLIGENCE SYSTEMS IN THE PURPOSE OF SOCIAL AND ECONOMIC DEVELOPMENT

Interesting knowledge is gained by researching and re-examining how human resources management works in our region - the city. Human resource management in every business system becomes completely different from what it has been. It is no longer understood only as a mere functional activity, but as a new business philosophy to be implemented at all managerial levels. In this century, human resources are not only the key competitive advantage of any organization, but of every country. A holistic, coherent human resource management of the organization in order to ensure the maximum value added and the best positioning for the achievement of organizational strategic goals, in broad terms, leads directly to general socio-economic development and progress. One of the key functions of HRM is to spread the various components of the strategic plan to all employees in the organization. An important function of human resources management can and should be to provide information on the strategic readiness of human capital - whether and to what extent available human resources have the required knowledge, skills and motivation to support and implement an organization’s strategy. There is no efficient strategic management of human resources without the support of information technologies and information systems. The development of information technologies and information systems dramatically changes the management of human resources. The strategic readiness of information capital (IT and systems) is the most meaningful measure of the value of the information capital of the organization. The greatest availability of information capital in human resources management is measured by the level of development of online transaction processing systems, whether it is a module of an ERP system, or that it is HRIS (Human Resources Information Systems). Another technology that has even more strategic significance is BI (Business Intelligence) technology and BI decision support systems. A particular challenge is to examine the state of and opportunities for the development of human resources in profit and non-profit institutions in our narrower region. This scientific work will attempt to obtain the missing information on the content and method of human resources management in the profit and non-profit sector, observed in the context of information systems.

Fine-grained, secure and efficient data provenance on blockchain systems

The success of Bitcoin and other cryptocurrencies bring enormous interest to blockchains. A blockchain system implements a tamper-evident ledger for recording transactions that modify some global states. The system captures entire evolution history of the states. The management of that history, also known as data provenance or lineage, has been studied extensively in database systems. However, querying data history in existing blockchains can only be done by replaying all transactions. This approach is applicable to large-scale, offline analysis, but is not suitable for online transaction processing. We present LineageChain , a fine-grained, secure and efficient provenance system for blockchains. LineageChain exposes provenance information to smart contracts via simple and elegant interfaces, thereby enabling a new class of blockchain applications whose execution logics depend on provenance information at runtime. LineageChain captures provenance during contract execution, and efficiently stores it in a Merkle tree. LineageChain provides a novel skip list index designed for supporting efficient provenance query processing. We have implemented LineageChain on top of Hyperledger and a blockchain-optimized storage system called ForkBase. Our extensive evaluation of LineageChain demonstrates its benefits to the new class of blockchain applications, its efficient query, and its small storage overhead.

Accelerating in-memory transaction processing using general purpose graphics processing units

High throughput is critical for on-line transaction processing (OLTP) applications with a large amount of users. With massive parallel processing units and high memory bandwidth, GPUs are suitable for accelerating OLTP transactions. However, it is challenge to implement transaction execution on GPUs, due to (1) the branch divergences caused by the single instruction multiple threads (SIMT) execution paradigm, and (2) the lack of fine-grained synchronization mechanism and pointer-based dynamic data structures in the GPU ecosystem.In this paper, we present a high-performance in-memory transaction processing system on GPUs to accelerate OLTP applications, named GPU-TPS. Firstly, we propose a transaction execution model to improve GPU hardware utilization and perform synchronization among transactions. Secondly, we optimize the indexing data structures that used extensively in OLTP systems (i.e., hash table for unordered store, and b＋ tree for ordered store) for fast storing on GPUs.To evaluate GPU-TPS, we apply it to two popular OLTP workloads (SmallBank and TPCC), and compare it with the state-of-the-art hardware transactional memory based CPU OLTP system (DrTM) and a GPU OLTP system (GPUTx). The experimental results show that GPU-TPS outperforms the CPU implementation by 3.8X for SmallBank and by 1.9X for TPCC, and outperforms the GPU implementation by 1.6X for SmallBank and by 1.8X for TPCC.

IMPLEMENTATION OF NAÏVE BAYESIAN DATA MINING ALGORITHM ON DECEASED REGISTRATION DATA

The use of data mining algorithm in different domains like marketing, finance, retail, cyber security, fraud detection, medical science, etc… is well known. In recent times, the data mining is also implemented for e-governance data. Normally, e-governance data are only used for Online Transaction Processing System (OLTP). However, one can use data mining algorithm to uncover hidden and new trends from e-governance data. In this paper, Naive Bayes data mining algorithm is applied on Deceased Registration data to find hidden trends.

RETRACTED ARTICLE: User information protection of e-commerce platform business based on credit evaluation system

Self-governance of e-commerce platform business has become a governance model recognized by the e-commerce industry and state agencies. However, the leakage of user information during the online transaction process, the abuse problem has occurred frequently, seriously jeopardizing the legitimate rights and interests of network users. By analyzing the autonomy rules of the online e-commerce platform business, the author found that the platform obtains the right to use the user’s private information by setting various mandatory clauses, which is not protected and leads to the abuse and disclosure of information. The article proposes that third-party institutions as the evaluation party of the network autonomy rules, through the scientific and rigorous evaluation index system to make a score on the autonomy rules of the online e-commerce platform business, urge the platform to improve its autonomy rules, thereby protecting the legitimate rights and interests of users.

Legal Protection of E-Commerce Consumers in Online Transactions in Indonesia

The use of e-commerce in economic transactions, continues to increase every year. However, various complaints arise from online consumers. They complain of experiencing various cases of fraud through online transactions. Based on this phenomenon, the increase in public consumption of online transactions was not matched by consumer protection that had to be accepted as a customer. The most pressing problem is the weak protection of consumer law. The purpose of this study is to describe the consumer legal protection model in the online transaction process and to ascertain whether the current regulatory framework in Indonesia adequately addresses relevant risks and issues. Based on normative legal studies conducted by researchers that the agreement on e-commerce is inseparable from the basic concept of the agreement established in the UNCITRAL Model Law on eCommerce and the UNCITRAL Model Law on eSignature. Where online trading is basically the same as general trade agreements, only in exchange for transactions conducted with electronic media. We found that to resolve the case of legal protection against consumers is to improve the quality of human resources who must have professional competence, academic competence, resilience, techniques and good attitude and values. Ecommerce contracts must be based on the principles of good trade transactions and enforce the law fairly. We also include that the current laws and regulations on information and electronic transactions for consumer protection have not been able to effectively protect consumers because in practice there are still many cases that cause consumer losses due to violations.

Prototype Data Warehouse Aplikasi eM-Tilang

The development of information technology today is very advanced. One example of the development of information technology is the implementation of a data warehouse. The data warehouse is a reacting database that can be used for query and analysis, object-oriented, integrated, time-variant, unchanging that is used to help decision makers. The eM-Tilang application system that is managed by the police requires the implementation of a data warehouse in order to improve community control on the highway and reduce traffic violations. the implementation of the data warehouse in the eM-Tilang system certainly requires supporting technologies such as data mining, OLAP (Online Analytical Processing), OLTP (Online Transaction Processing), ETL (Extract Transform Loading) / ELT (Extraxt Loading Transform), especially Cloud Computing IAAS (As A Services infrastructure). The implementation of the data warehouse is expected to assist the police in making informed decisions in order to improve public order on the highway and reduce traffic violations. Keywords : Data Warehouse; eM-Tilang; Prototype; Traffic Ticketing