Transaction Processing Systems Research Articles

Enterprise Resource Planning Systems Implementation: Effects on Accounting Information Processing

The use of computerized accounting system has reached remarkable heights in recent years. However this pattern in developing economies does not seem to have attained similar strength. The era of technological sophistication has enabled the use of specialized transactions processing systems. Consequently this study chose to investigate the effects of enterprise resource planning systems on accounting information processing with particular reference to Nigerian. This paper builds upon the technologies acceptance model on factors influencing individual's choice of technology when faced with particular tasks. Using a survey approach and applying both descriptive and inferential statistics the study found significant use of enterprise resource planning systems on accounting information processing in Nigeria. This research sets a baseline for academia in bridging the theory and practise gap to continue to support scholars in learning computerized accounting information processing for practise relevance. In addition the use of computerized systems must remain within the ambience of generally accepted accounting principles and procedures to ensure the art continues to present true and fair view of underlying transactions for users of financial reports. This paper is unique in that there are no studies evidencing the use of enterprise resource planning systems in Nigeria. Hence, it is a fundamental point of reference for future research in this area.

Bellwether Garden Supply: Revising Purchase and Disbursement Processes and Controls in the Implementation of a New Accounting Application

ABSTRACT An appreciation for the complementary nature of manual and automated controls is fundamental for the proper evaluation and maintenance of accounting information systems (AIS). Additionally, the implementation of application access controls is often critical for ensuring an appropriate separation of duties and, ultimately, the reliability of a given transaction processing system. This paper describes a project where students act as consultants for Bellwether Garden Supply (BGS), a small retailer in Norcross, GA.1 In the case setting, BGS is in the process of completing the implementation of Sage 50. BGS management has asked student consulting teams to evaluate its current purchases and disbursements processes and organizational structure, to make any revisions necessary to accommodate the new technology, and to document the post-implementation process using Business Process Model and Notation (BPMN). The case provides a rich, semi-structured setting where students can: improve their understanding of application access controls, separation of duties, and other key controls in the purchases and disbursements process; gain an appreciation of the importance of the context in which accounting applications are implemented; and enhance their communication skills using BPMN.

Hash-based Byzantine fault tolerant agreement with enhanced view consistency

This paper presents an authentication-based Byzantine fault tolerant agreement protocol that is capable of detecting the tentative failures in transaction processing systems. The protocol employs a cryptographic hash function to detect crash as well as a few more types of commonly faced arbitrary behaviours manifested by the participating server replicas in the system. We show that the use of hash functions can successfully handle various types of arbitrary faults. In addition, it suggests a preemptive mechanism to proactively detect the malicious replicas. The protocol avoids frequent view changes that contributes to the liveness of the system and ensures continuity in transaction processing.

Design of a Unified Data with Business Rules Storage Model for OLTP and OLAP Systems

This paper reviews the literature concerning the practice of using Online Analytical Processing (OLAP) systems to recall information stored by Online Transactional Processing (OLTP) systems. Such a review provides a basis for discussion on the need for the information that are recalled through OLAP systems to maintain the contexts of transactions with the data captured by the respective OLTP system. The paper observes an industry trend involving the use of OLTP systems to process information into data, which are then stored in databases without the business rules that were used to process information and data stored in OLTP databases without associated business rules. This includes the necessitation of a practice, whereby sets of business rules are used to extract, cleanse, transform and load data from disparate OLTP systems into OLAP databases to support the requirements for complex reporting and analytics. These sets of business rules are usually not the same as business rules used to capture data in particular OLTP systems. The paper argues that differences between the business rules used to interpret these same data sets risk gaps in semantics between information captured by OLTP systems and information recalled through OLAP systems. The literature concerning the modeling of business transaction information as facts with context as part of the modelling of information systems was reviewed to identify design trends that are contributing to the design quality of OLTP and OLAP systems. The paper then argues that the quality of OLTP and OLAP systems design has a critical dependency on the capture of facts with associated context, encoding facts with contexts into data with business rules, storage and sourcing of data with business rules, decoding data with business rules into the facts with the context and recall of facts with associated contexts. The paper proposes UBIRQ, a design model to aid the co-design of data with business rules storage for OLTP and OLAP purposes. The proposed design model provides an opportunity for the implementation and use of multi-purpose databases and business rules stores for OLTP and OLAP systems. Such implementations would enable the use of OLTP systems to record and store data with executions of business rules, which will allow for the use of OLTP and OLAP systems to query data with business rules used to capture the data, thereby ensuring that information recalled via OLAP systems preserves the contexts of transactions as per the data captured by the respective OLTP system.

Database Analytics: A Reconfigurable-Computing Approach

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.

Data Warehouse Design in Academic Environment

The popularity of Data Warehouses (DW)s for data analysis has grown tremendously, asconventional transaction processing systems have matured, becoming faster and more stable. Conventionalsystems in universitiesworking in the same area, storedaily activities and display or report these events on a regular basis. Therefore, the establishment of the DWs will help the top management in the development of strategies and take appropriate decisions inthese universities .This paper addresses issues related to designing a DW for private university in Jakarta . The purpose of the project isto provide a process model that will enhance decision making capabilities in a private university in order to facilitate and improve academic activities. Keywords: requirements analysis, conceptual design , data warehouse , academic

Autonomic Prioritization of Enterprise Transactions Based on Bid-Price Controls

We study the business-driven prioritization of workflows in enterprise transaction processing systems. Today, IT services are increasingly shared amongst workflows, and static prioritization is applied during phases of high system workload to mitigate the business impact of service queuing and resulting workflow response-time increase. Static prioritization rules can only be optimal for one particular workflow definition and demand mix scenario, while in practice workflow definitions are continuously re-engineered and demand varies over time. We propose a technique to automatically adapt workflow priorities aimed at minimizing overload-related costs resulting from workflow delays. We continuously align maximum service buffer lengths and bid-prices reflecting current marginal opportunity costs of invoking a shared service. New prioritization is derived by comparing expected costs when queuing a workflow with its current opportunity costs, computed as the sum of bid-prices of invoked services. Simulation outcomes based on industry data illustrate that the approach dominates standard queuing disciplines as well as static prioritization.

Leveraging Big Data Analytics to Reduce Healthcare Costs

The healthcare sector deals with large volumes of electronic data related to patient services. This article describes two novel applications that leverage big data to detect fraud, abuse, waste, and errors in health insurance claims, thus reducing recurrent losses and facilitating enhanced patient care. The results indicate that claim anomalies detected using these applications help private health insurance funds recover hidden cost overruns that aren't detectable using transaction processing systems. This article is part of a special issue on leveraging big data and business analytics.

Analysis of Data Quality and Performance Issues in Data Warehousing and Business Intelligence

A Data Warehouse is an integral part of those enterprises which want to have a clear business insights from customer and operational data. It includes collection of technologies aimed at enabling the knowledge worker (executive, manager, analyst) to make better and faster decisions. It is expected to present the right information in the right place at the right time with the right cost in order to support the right decision. Over the years ,the practice of Data warehousing proved that the traditional online Transaction Processing (OLTP) systems are not fully appropriate for decision support. From the survey and evaluation of the literature related to Data Warehouse and with consultation and feedback of the data warehouse practitioners working in renowned IT giants ,it has been observed that the fundamental problems arise in populating a warehouse with quality data. . This paper mainly focuses on the study of the issues that hinder the data quality and performance of the Data warehouse and some of the means that may be opted to realize a better performance with respect to accuracy and quality to meet the challenging and dynamic needs of the corporate world.

Information architecture analysis using business intelligence tools based on the information needs of executives

Devising an information architecture system that enables an organization to centralize information regarding its operational, managerial and strategic performance is one of the challenges currently facing information technology. The present study aimed to analyze an information architecture system developed using Business Intelligence (BI) technology. The analysis was performed based on a questionnaire enquiring as to whether the information needs of executives were met during the process. A theoretical framework was applied consisting of information architecture and BI technology, using a case study methodology. Results indicated that the transaction processing systems studied did not meet the information needs of company executives. Information architecture using data warehousing, online analytical processing (OLAP) tools and data mining may provide a more agile means of meeting these needs. However, some items must be included and others modified, in addition to improving the culture of information use by company executives.

Management Information Systems vs. Other Information Systems

A Management Information System (MIS) is a systematic organization and presentation of information that is generally required by the management of an organization. In one sense MIS is a part or subset of the Information Management System of an Organization. There are different types of information systems such as, Transaction Processing System (TPS), Decision Support System, Executive Support System (ESS) etc. having some differences and some overlapping. MIS is different from DSS as the later uses unstructured data to take non-standard decisions. MIS is different from TPS and the later is used for day-to-day transactions of the organization. Thus the purpose of MIS is to organize and present such information that would help the middle and higher level management for taking better and more effective decisions to increase the productivity of the organization and gain competitive advantage.

Toward scalable transaction processing

Designing scalable transaction processing systems on modern multicore hardware has been a challenge for almost a decade. The typical characteristics of transaction processing workloads lead to a high degree of unbounded communication on multicores for conventional system designs. In this tutorial, we initially present a systematic way of eliminating scalability bottlenecks of a transaction processing system, which is based on minimizing the unbounded communication. Then, we show several techniques that apply the presented methodology to minimize logging, locking, latching etc. related bottlenecks of transaction processing systems. In parallel, we demonstrate the internals of the Shore-MT storage manager and how they have evolved over the years in terms of scalability on multicore hardware through such techniques. We also teach how to use Shore-MT with the various design options it offers through its application layer Shore-Kits and Metadata Frontend.

The Technology of Using a Data Warehouse to Support Decision-Making in Health Care

This paper describes the technology of data warehouse in healthcare decision-making and tools for support of these technologies, which is used to cancer diseases. The healthcare executive managers and doctors needs information about and insight into the existing health data, so as to make decision more efficiently without interrupting the daily work of an On-Line Transaction Processing (OLTP) system. This is a complex problem during the healthcare decision-making process. To solve this problem, the building a healthcare data warehouse seems to be efficient. First in this paper we explain the concepts of the data warehouse, On-Line Analysis Processing (OLAP). Changing the data in the data warehouse into a multidimensional data cube is then shown. Finally, an application example is given to illustrate the use of the healthcare data warehouse specific to cancer diseases developed in this study. The executive managers and doctors can view data from more than one perspective with reduced query time, thus making decisions faster and more comprehensive.

Next Generation Data Warehouse Design with OLTP and OLAP Systems Sharing same Database

Online Transaction Processing (OLTP) systems have been using the traditional relational Database management system for many years. Online Analytical Processing (OLAP) system for analytical reporting is optimized to aggregate many records which involve more read operations. Hence to enhance the performance in OLAP systems various modeling options like star schema, extended star schema was implemented. But with the latest innovations in hardware technologies like availability of multi core dual processors, availability of 64 bit processors, decreasing cost of main memory and software technology innovations like In-Memory computing, Columnar storage and other with various other new features now there is less concern towards the performance degradation and performance optimization in a data warehouse system. If there is an option to use same database for the OLTP and OLAP systems then the business intelligence system can get the real time data for the analytical query reporting. This document presents the overview of the next generation data warehouse architecture which shares the database along with OLTP and discuss how it is going to use the in memory capability to apply real time analytics.

Eliminating unscalable communication in transaction processing

Multicore hardware demands software parallelism. Transaction processing workloads typically exhibit high concurrency, and, thus, provide ample opportunities for parallel execution. Unfortunately, because of the characteristics of the application, transaction processing systems must moderate and coordinate communication between independent agents; since it is notoriously difficult to implement high performing transaction processing systems that incur no communication whatsoever. As a result, transaction processing systems cannot always convert abundant, even embarrassing, request-level parallelism into execution parallelism due to communication bottlenecks. Transaction processing system designers must therefore find ways to achieve scalability while still allowing communication to occur. To this end, we identify three forms of communication in the system--unbounded, fixed, and cooperative--and argue that only the first type poses a fundamental threat to scalability. The other two types tend not impose obstacles to scalability, though they may reduce single-thread performance. We argue that proper analysis of communication patterns in any software system is a powerful tool for improving the system's scalability. Then, we present and evaluate under a common framework techniques that attack significant sources of unbounded communication during transaction processing and sketch a solution for those that remain. The solutions we present affect fundamental services of any transaction processing engine, such as locking, logging, physical page accesses, and buffer pool frame accesses. They either reduce such communication through caching, downgrade it to a less-threatening type, or eliminate it completely through system design. We find that the later technique, revisiting the transaction processing architecture, is the most effective. The final design cuts unbounded communication by roughly an order of magnitude compared with the baseline, while exhibiting better scalability on multicore machines.

ElasTraS

A database management system (DBMS) serving a cloud platform must handle large numbers of application databases (or tenants ) that are characterized by diverse schemas, varying footprints, and unpredictable load patterns. Scaling out using clusters of commodity servers and sharing resources among tenants (i.e., multitenancy ) are important features of such systems. Moreover, when deployed on a pay-per-use infrastructure, minimizing the system's operating cost while ensuring good performance is also an important goal. Traditional DBMSs were not designed for such scenarios and hence do not possess the mentioned features critical for DBMSs in the cloud. We present ElasTraS, which combines three design principles to build an elastically-scalable multitenant DBMS for transaction processing workloads. These design principles are gleaned from a careful analysis of the years of research in building scalable key-value stores and decades of research in high performance transaction processing systems. ElasTraS scales to thousands of tenants, effectively consolidates tenants with small footprints while scaling-out large tenants across multiple servers in a cluster. ElasTraS also supports low-latency multistep ACID transactions , is fault-tolerant, self-managing, and highly available to support mission critical applications. ElasTraS leverages Albatross, a low overhead on-demand live database migration technique, for elastic load balancing by adding more servers during high load and consolidating to fewer servers during usage troughs. This elastic scaling minimizes the operating cost and ensures good performance even in the presence of unpredictable changes to the workload. We elucidate the design principles, explain the architecture, describe a prototype implementation, present the detailed design and implementation of Albatross, and experimentally evaluate the implementation using a variety of transaction processing workloads. On a cluster of 20 commodity servers, our prototype serves thousands of tenants and serves more than 1 billion transactions per day while migrating tenant databases with minimal overhead to allow lightweight elastic scaling. Using a cluster of 30 commodity servers, ElasTraS can scale-out a terabyte TPC-C database serving an aggregate throughput of approximately one quarter of a million TPC-C transactions per minute.

Avoiding disruptive failovers in transaction processing systems with multiple active nodes

We present a highly available system for environments such as stock trading, where high request rates and low latency requirements dictate that service disruption on the order of seconds in length can be unacceptable. After a node failure, our system avoids delays in processing due to detecting the failure or transferring control to a back-up node. We achieve this by using multiple primary nodes which process transactions concurrently as peers. If a primary node fails, the remaining primaries continue executing without being delayed at all by the failed primary. Nodes agree on a total ordering for processing requests with a novel low overhead wait-free algorithm that utilizes a small amount of shared memory accessible to the nodes and a simple compare-and-swap like protocol which allows the system to progress at the speed of the fastest node. We have implemented our system on an IBM z990 zSeries eServer mainframe and show experimentally that our system performs well and can transparently handle node failures without causing delays to transaction processing. The efficient implementation of our algorithm for ordering transactions is a critically important factor in achieving good performance.

Feasibility and Scalability of G2C Social-Welfare Transactions on Cloud-Based 'Aadhaar' UID of India

‘Aadhaar’ – the Unique Identification (UID) Project of India has been much in the focus as well as in controversy over the last three years, more so since February 2009 when the Unique Identification Authority of India (UIDAI) was established. In April 2010, the project got rechristened as 'Aadhaar,' meaning 'foundation,' whereas it also is interpreted by the UIDAI as 'Aap Ka Adhikar' (meaning 'Your Right') to 'Aam Admi Ka Adhikaar' ('The Right of a Common Man') in its various advertisements to reach out the vast 1.21 billion populace, subsequently curtailed to 600 million and existing ones. The ambitious project had remained much talked about – due to a variety of reasons, starting from security purposes, privacy issues, reliability of biometric identification in a nation like India to even to take care of a leaky and non-targeted subsidy schemes of the Government towards social welfare. In India, from late 1980s, when former Prime Minister Mr. Rajiv Gandhi highlighted the degree of leaks in subsidies meant for the poor and under privileged, it is often stated that hardly 10% of the actual subsidy spent by the Government reaches the intended recipient. Subsequently, future uses of Aadhaar UID have been often extended to possibilities of direct cash transfers – be it for fertilizers to domestic LPGs to PDS (Public Distribution System covering essential food and fuel) to even taking care of absenteeism of students and teachers alike in primary classes in Government-run schools. Media reports suggested that actual spent on social welfare oriented subsidies for financial year 2012-13 is likely to be close to $55 billion. There has been also active speculation in media that in financial year 2013-14, Government wants to direct around $40 billion of this subsidy through Direct cash Transfer, a large part of which will be through Aadhaar-based verification process. This paper takes views from traditional Project Management perspective of Aadhaar UID from various secondary sources (primary data collection failed as questionnaire to UIDAI remained unanswered), and scrutinizes how this project is supposed to deliver to the various social welfare needs identified, both from the feasibility and scalability perspectives. Essentially many of the uses of the Aadhaar UID fall under the traditional Transaction Processing Systems (TPS) category. These demographic data, which covers socio-economic to others needed for the TPS to be completed are dynamic, and are not of the type of 'timeless' data. This paper examines the project scope, the creeping scope, the methodology adopted and its evolution – and thereby examines how the project is likely to deliver on much of these often reported Aadhaar-linked TPS-services in areas of G2C social welfare subsidies, on a scalable and sustainable manner, for a country of India’s size and diversity. The findings highlight a reinventing the wheel approach where probably other less complex, more feasible and scalable solutions exists.

Fuzzy Metagraph and Vague Metagraph based Techniques and their Applications

Metagraphs are graphical hierarchical structure in which every node is a set having one or more elements. Fuzzy Metagraph and Vague Metagraph are an emerging technique used in the design of many information processing systems like transaction processing systems, Decision Support Systems (DSS), and workflow Systems. In this paper, distinct matrixes have been proposed for Fuzzy Metagraph and Vague Metagraph respectively. This method has reduced time complexity and space complexity. In complex situations, our Fuzzy Expert System integrated with the metagraphs will yield goods decision as quickly as possible. The main purpose of this DSS is to help a user make effective and quick decisions that the user can concentrate only on solving the problem. General Terms

Scalable and dynamically balanced shared-everything OLTP with physiological partitioning

Scaling the performance of shared-everything transaction processing systems to highly parallel multicore hardware remains a challenge for database system designers. Recent proposals alleviate locking and logging bottlenecks in the system, leaving page latching as the next potential problem. To tackle the page latching problem, we propose physiological partitioning (PLP). PLP applies logical-only partitioning, maintaining the desired properties of sharedeverything designs, and introduces a multi-rooted B+Tree index structure (MRBTree) that enables the partitioning of the accesses at the physical page level. Logical partitioning and MRBTrees together ensure that all accesses to a given index page come from a single thread and, hence, can be entirely latch free; an extended design makes heap page accesses thread private as well. Moreover, MRBTrees offer an infrastructure for easy repartitioning and allow us to have a lightweight dynamic load balancing mechanism (DLB) on top of PLP. Profiling a PLP prototype running on different multicore machines shows that it acquires 85 and 68%fewer contentious critical sections, respectively, than an optimized conventional design and one based on logical-only partitioning. PLP also improves performance up to almost 50 % over the existing systems, while DLB enhances the system with rapid and robust behavior in both detecting and handling load imbalances.