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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. [ABSTRACT FROM AUTHOR]

The article is devoted to the description of the method of multidimensional database, which is an effective method of data storage, which allows analyzing data qualitatively, and most importantly in a short time. The article discusses the capabilities of multidimensional databases, in particular, multidimensional OLAP (On-Line Analytical Processing) cubes when analyzing large amounts of data. Provides an overview and features of a multidimensional database and discusses the steps you need to take with a multidimensional database to understand the structure and capabilities of an OLAP cube. To create a knowledge base, it describes the steps you can take to create and execute a multidimensional database that you can collect from various sources, save to a database, and then prepare a report using OLAP analysis. Various information system data processing technologies such as OLTP and OLAP were considered. The algorithm of the data storage process for analysis purposes was studied. A model of a multidimensional database in the form of a three-dimensional cube was presented. Examples of analysis and ways of obtaining information from the data cube were also given. The use of a multidimensional database in higher education institutions as a simple and effective method of data storage is considered. There are also illustrations of the structure of a higher educational institution to see the bulkiness of information, and what kind of information database operates in the educational institution.

In our initial DaMoN paper, we set out the goal to revisit the results of "Starring into the Abyss [...] of Concurrency Control with [1000] Cores" (Yu in Proc. VLDB Endow 8: 209-220, 2014). Against their assumption, today we do not see single-socket CPUs with 1000 cores. Instead, multi-socket hardware is prevalent today and in fact offers over 1000 cores. Hence, we evaluated concurrency control (CC) schemes on a real (Intel-based) multi-socket platform. To our surprise, we made interesting findings opposing results of the original analysis that we discussed in our initial DaMoN paper. In this paper, we further broaden our analysis, detailing the effect of hardware and workload characteristics via additional real hardware platforms (IBM Power8 and 9) and the full TPC-C transaction mix. Among others, we identified clear connections between the performance of the CC schemes and hardware characteristics, especially concerning NUMA and CPU cache. Overall, we conclude that no CC scheme can efficiently make use of large multi-socket hardware in a robust manner and suggest several directions on how CC schemes and overall OLTP DBMS should evolve in future. [ABSTRACT FROM AUTHOR]

Nowadays data analytics OLAP (online Analytical Processing) is mostly accepted domain of current researchers and the concept of data mining serves better for the same. There are so many data mining methodologies defined for data analytics. Mining Association rule is widely used in data mining methods for data categorization. Apriori Algorithm is popular method for defining n-element. Frequent item set form k number of huge transactional data set online transaction processing (OLTP) using Association Mining rule (AMR). In this paper, researchers executed original Apriori on transactional data set containing 35039 number of transactions, divided into three data sets DS-1 to DS-3 with 20039, 12000, 5000 number of transactions with variable length with minimum support of 30%, 60% and 80% respectively. Researchers carried out experimental work and compared results of Apriori Algorithm with our proposed algorithm (enhanced version of Apriori algorithm) on the same perimeter and state improvement with 11%, 30% and 27% of Rate of Improvements in DS-1 to Ds-3 respectively for 30% minimum support. Our proposed algorithm is working far much better then Apriori algorithm at each parameter which was included to conclude the results. [ABSTRACT FROM AUTHOR]

The article is devoted to the description of the method of multidimensional database, which is an effective method of data storage, which allows analyzing data qualitatively, and most importantly in a short time. The article discusses the capabilities of multidimensional databases, in particular, multidimensional OLAP (On-Line Analytical Processing) cubes when analyzing large amounts of data. Provides an overview and features of a multidimensional database and discusses the steps you need to take with a multidimensional database to understand the structure and capabilities of an OLAP cube. To create a knowledge base, it describes the steps you can take to create and execute a multidimensional database that you can collect from various sources, save to a database, and then prepare a report using OLAP analysis. Various information system data processing technologies such as OLTP and OLAP were considered. The algorithm of the data storage process for analysis purposes was studied. A model of a multidimensional database in the form of a three-dimensional cube was presented. Examples of analysis and ways of obtaining information from the data cube were also given. The use of a multidimensional database in higher education institutions as a simple and effective method of data storage is considered. There are also illustrations of the structure of a higher educational institution to see the bulkiness of information, and what kind of information database operates in the educational institution. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Micro-architectural behavior of traditional disk-based online transaction processing (OLTP) systems has been investigated extensively over the past couple of decades. Results show that traditional OLTP systems mostly under-utilize the available micro-architectural resources. In-memory OLTP systems, on the other hand, process all the data in main-memory and, therefore, can omit the buffer pool. Furthermore, they usually adopt more lightweight concurrency control mechanisms, cache-conscious data structures, and cleaner codebases since they are usually designed from scratch. Hence, we expect significant differences in micro-architectural behavior when running OLTP on platforms optimized for in-memory processing as opposed to disk-based database systems. In particular, we expect that in-memory systems exploit micro-architectural features such as instruction and data caches significantly better than disk-based systems. This paper sheds light on the micro-architectural behavior of in-memory database systems by analyzing and contrasting it to the behavior of disk-based systems when running OLTP workloads. The results show that, despite all the design changes, in-memory OLTP exhibits very similar micro-architectural behavior to disk-based OLTP: more than half of the execution time goes to memory stalls where instruction cache misses or the long-latency data misses from the last-level cache (LLC) are the dominant factors in the overall execution time. Even though ground-up designed in-memory systems can eliminate the instruction cache misses, the reduction in instruction stalls amplifies the impact of LLC data misses. As a result, only 30% of the CPU cycles are used to retire instructions, and 70% of the CPU cycles are wasted to stalls for both traditional disk-based and new generation in-memory OLTP. [ABSTRACT FROM AUTHOR]

Highly-available datastores are widely deployed for Internet-based applications. However, many Internet-based applications are not contented with the simple data access interface provided by highly-available datastores. Distributed transaction support is demanded by applications such as massive online payment used by Alipay, Paypal or Baidu Wallet. Current solutions to distributed transaction can spend more than half of the whole transaction processing time in distributed commit. The culprits are the multiple write-ahead logging steps and communication roundtrips in the commit process. This paper presents the HACommit protocol, a logless one-phase commit protocol for highly-available datastores. HACommit has transaction participants vote for a commit before the client decides to commit or abort the transaction; in comparison, the state-of-the-art practice for distributed commit is to have the client decide before participants vote. The change enables the removal of both the participant's write-ahead logging and the coordinator's write-ahead logging steps in the distributed commit process; it also makes possible that, after the client initiates the transaction commit, the transaction data is visible to other transactions within one communication roundtrip time (i.e., one phase). In the evaluation with extensive experiments, HACommit outperforms recent atomic commit solutions for highly-available datastores under different workloads. In the best case, HACommit can commit in one fifth of the time the widely-used two-phase commit (2PC) does. [ABSTRACT FROM AUTHOR]

Проведено аналіз можливості впровадження хмарних технологій для забезпечення діяльності банківських установ і підтримки функціонування бізнес-процесів. Розглянуто проблеми та переваги хмарних технологій на різних рівнях архітектурного ландшафту банку з урахуванням специфіки нормативно-правового регулювання діяльності фінансової установи. Результати дослідження можуть бути апробовані шляхом впровадження відповідних проектів, зумовлених викликами та тенденціями банківської сфери, ринковими та регуляторними змінами.

Обмен данными между двумя и более информационными системами всегда являлся актуальной и сложной задачей. За последние три десятилетия данная задача прошла свой эволюционный путь от ручного способа обмена до возможности автоматического обмена данными между системами. В данной работе рассматриваются различные современные технологии для организации обмена данными между информационными системами, в том числе между OLTP- и OLAP-системами. Приведено описание технологий ETL, MDM и технологий, основанных на пространствах имен, в том числе "Синхронизатор ТОФИ" , разрабатываемый в Компании системных исследований "Фактор". Подробно излагается формальное понятие "данных" , используемое в технологии ТОФИ, пространство имен ТОФИ и функциональные возможности синхронизатора ТОФИ по обмену данными и метаданными. Функционально синхронизатор осуществляет сопоставление метаданных различных информационных систем на основе систем кодирования, автоматически (по расписанию) или вручную производит формирование метаданных, обмен метаданными с другими системами, формирование данных для передачи другой системе, прием и передачу набора данных из/в другой(-ую) информационной(-ую) системы(-у) и т.д. Обмен данными, основанный на пространстве имен ТОФИ, может осуществляться в автоматическом режиме на основе синхронизации метаданных.

The purpose of this study is to provide an overview of the Head of Distribution of PT. X to support product distribution monitoring activities in terms of the number of products to be distributed to each branch office as needed. The problem that occurs is the large number and diversity of products required where each branch office is located far apart, cause the company difficulty in collecting product data that functions for the monitoring process. The solution to this problem is there needs a model that can be used to collect all the necessary data for monitoring product distribution using the Data warehouse method. This method is used to collect diverse data into a storage area so that users can quickly analyze the data needed. OLTP is part of the Data Warehouse, which is the initial stage of modeling data sources, then with ETL processes, which are the basis for modeling data warehouse schemes. So, this model can be used as a reference to produce useful information for managerial parties PT. X in the next study.

Objectives: Osteochondral lesions of the distal tibial plafond (OLTP) are rare and far less common than osteochondral lesions of the talus. Literature data do not report clinical records with significant number of cases and follow-up. The aim of our study was to evaluate clinical and MRI outcomes following arthroscopic treatment of distal tibia osteochondral lesions and to report our results with treating these rare lesions.Methods: Between October 2010 and November 2011, a consecutive series of 27 patients, 15 males and 12 females, were treated arthroscopically with the one-step BMDCT for OLTPs. Exclusion criteria were: age < 18 or > 50 years, patients with severe osteoarthritis (stage III according to Van Dijk classification), presence of kissing lesions of the ankle and patients with rheumatoid or hemophilic arthritis. All patients were evaluated through X-rays; MRI was performed preoperatively and at the final follow-up with MOCART score; clinical evaluation was assessed by AOFAS score at various follow-ups of 12, 24, 36, 60 and 72 months.Results: No complications were observed post-surgery or during the rehabilitation period. The AOFAS score improved from 52.4 preoperatively to 80.6 at the mean final follow-up. All the patients were satisfied with the procedure. In 14 cases the MRI showed a complete filling of the osteochondral defect, in three patients a hypertrophic tissue was observed, and in the other two patients an incomplete repair of the lesion associated with a persistent slight subchondral edema was reported. A topographic study was also performed.Conclusions: Osteochondral lesions of the distal tibia represent a challenge for the orthopedic surgeon because of their difficulty diagnostic and rarities. The high incidence of good outcome in our series indicates that the one-step BMDCT could be a valid option for the treatment of this rare type of lesions. Further studies with a longer follow-up and more accurate imaging studies are necessary to confirm these results. [ABSTRACT FROM AUTHOR]

This paper presents a model for improving query performance in Virtual Data Warehouse (VDW) by simulating VDW environment on a cellular phone billing and customer care system which involves processing millions of Call Detail Records (CDRs) generated by thousands of counters across the country. Processing aggregations on millions of CDRs requires expensive systems, especially when analysing customers’ traffic trends and encompasses several performance optimization techniques used for improvement of query performance in VDW. In this regard, VDW offers several advantages such as real-time analytic reports, reduced maintenance, low cost solution and flexible data integration, but performance is still one of its critical shortcomings. This paper enhances performance of VDW by using techniques like partitioned materialized views, index performance optimization, query rewrite in materialized views, analytic functions, sub-queries and enabling parallel execution etc. The study uses Oracle 10 g as a backend database; Oracle management console and SQL query analyser are used for monitoring performance concerns during validation of VDW model; standard PL/SQL developer is used for extracting and loading test data; and finally, Hyperion Development suite is used for testing time comparisons of datasets both in normal OLTP and simulated VDW environments. [ABSTRACT FROM AUTHOR]

Importance of information management procedures and practice based on of defined and accepted frame within an organization, is discussed. It is a common presumption that transformation of data into information brings the level of knowledge to support right decision making. However, these efforts and investments often do not result in increased efficiency, due to absence of well defined and designed transactional and informational systems. As a consequence, many organizations are 'rich' in data, but still 'poor' in information. Difference between transactional (or operational) and informational systems are highlighted, too.

Modern hardware is abundantly parallel and increasingly heterogeneous. The numerous processing cores have non-uniform access latencies to the main memory and processor caches, which causes variability in the communication costs. Unfortunately, database systems mostly assume that all processing cores are the same and that microarchitecture differences are not significant enough to appear in critical database execution paths. As we demonstrate in this paper, however, non-uniform core topology does appear in the critical path and conventional database architectures achieve suboptimal and even worse, unpredictable performance. We perform a detailed performance analysis of OLTP deployments in servers with multiple cores per CPU ( multicore) and multiple CPUs per server ( multisocket). We compare different database deployment strategies where we vary the number and size of independent database instances running on a single server, from a single shared-everything instance to fine-grained shared-nothing configurations. We quantify the impact of non-uniform hardware on various deployments by (a) examining how efficiently each deployment uses the available hardware resources and (b) measuring the impact of distributed transactions and skewed requests on different workloads. We show that no strategy is optimal for all cases and that the best choice depends on the combination of hardware topology and workload characteristics. Finally, we argue that transaction processing systems must be aware of the hardware topology in order to achieve predictably high performance. [ABSTRACT FROM AUTHOR]

These days, huge amounts of data are created by not only humans but also the Internet of Things (loT). As databases increase in size, their buffer pool is becoming more crucial to the overall performance. Buffer flushing, i.e., writing dirty pages, is a key factor in buffer pool management. Tuning the buffer-flushing activity in a real system is often difficult because it depends on the workload and system configuration. It is usually done through trial and error. In this paper, we propose a self-tuning algorithm for automatically controlling the buffer-flushing activity. Our algorithm not only avoids synchronous writes, which block other operations, but also preserves the effectiveness of the buffer pool. Experimental results using TPC-C and TPC-H show that our algorithm gives up to a 2.1-fold higher throughput compared with the original buffer-flushing algorithm of MySQL and 1.5-fold compared with that of Percona Server, which is a variant of MySQL. [ABSTRACT FROM AUTHOR]

Coroutine is able to increase program concurrency and processor core utilization. However, for adapting the coroutine-to-transaction model, the existing coroutine package has the following disadvantages: (1) Additional scheduler threads incur synchronization overhead when the load between scheduler threads and worker threads is unbalanced. (2) Coroutines are swapped out periodically to prevent deadlocks, which will increase the conflict rate by adding suspended transactions. (3) Supporting only the swap-out function (yield, await, etc.) cannot flexibly control the transaction swap-in time. In this paper, we present SGPM, a coroutine framework for transaction processing. To adapt to the coroutine-to-transaction model, SGPM has the following properties: First, it eliminates scheduler threads and the periodic coroutine switch. Second, it provides a variety of coroutine scheduling strategies to make all types of concurrency control protocols run on SGPM reasonably. We implement eight well-known concurrency control on SGPM and, particularly, we use SGPM to optimize the performance of four wound-wait concurrency control among them, including 2PL, SS2PL, Calvin, and EWV. The experiment result demonstrates that after SGPM optimization 2PL and SS2PL outperform OCC and MVCC, and the throughput of Calvin and EWV is also improved by 1.2x and 1.3x respectively. [ABSTRACT FROM AUTHOR]

What are the next challenges and trends for online transaction-processing (OLTP) database systems? Here, Andrew Pavlo opines on how database management system developers will need to respond as new hardware technologies become widely available. [ABSTRACT FROM AUTHOR]

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In this paper, we survey the use of advanced hardware features for optimizing main-memory database systems in the context of our HyPer project. We exploit the virtual memory management for snapshotting the transactional data in order to separate OLAP queries from parallel OLTP transactions. The access behavior of database objects from simultaneous OLTP transactions is monitored using the virtual memory management component in order to compact the database into hot and cold partitions. Utilizing many-core NUMA-organized database servers is facilitated by the morsel-driven adaptive parallelization and partitioning that guarantees data locality w.r.t. the processing core. The most recent Hardware Transactional Memory support of, e.g., Intel's Haswell processor, can be used as the basis for a lock-free concurrency control scheme for OLTP transactions. Finally, we show how heterogeneous processors of 'wimpy' devices such as tablets can be utilized for high-performance and energy-efficient query processing. [ABSTRACT FROM AUTHOR]

In the era of big data, organizations today rely of huge quantity of data from diverse sources and need to integrate this data in a speedy manner to gain any strategic advantage out of the data. Data warehouse is becoming increasingly popular in organizations due to the need for enterprises to gather all of their data in a single place for in-depth analysis and also to segregate such analytical work form on-line transaction processing systems. In carrying out this qualitative case study, researchers examined the limitation and issues of current data warehousing system architecture of the financial institution. The researchers discuss the benefits of migration from tradition data warehouse to enterprise data management and present the new architecture. Contributions are noted and conclusions drawn. [ABSTRACT FROM AUTHOR]

Businesses are experiencing a growing need for performing real-time analytics on ever-increasing enterprise data. While providing useful business insights and improved market responsiveness, analytics also adds a computational burden on traditional online transaction processing (OLTP) systems and could adversely affect the performance of OLTP workloads. The authors present a highly pipelined, high-throughput query-processing engine on a field-programmable gate array (FPGA) to offload expensive queries for database analytics. The proposed solution provides a mechanism for a database management system (DBMS) to seamlessly harness the FPGA accelerator without requiring any changes in the application or the existing data layout. The authors' system, which uses an off-the-shelf server and a PCIe-attached FPGA card and is integrated into a commercial DBMS platform, achieves an order-of-magnitude speedup on various real-life queries. [ABSTRACT FROM PUBLISHER]