Online Analytical Processing Database Research Articles

PERANCANGAN DASHBOARD UNTUK VISUALISASI HARGA DAN PASOKAN BERAS DI PASAR INDUK BERAS CIPINANG

Food Station is a city-owned enterprise owned by the Jakarta Provincial Government whose tasks include monitoring and publication of supply data and rice price through Cipinang Rice Market so that it is useful as a guide for traders and policy makers in relevant government institutions. This study aims to design a dashboard to visualize the price and supply of rice through Cipinang Rice Market. The research stages consist of identification of information requirement, data warehouse design and On-line Analytical Processing (OLAP) database, transfer of operational data from excel file and web scrapping into OLAP database through Extract, Transform, Load (ETL) process and create dashboard using PowerBI application. The result of this research is a data warehouse that can facilitate the analysis with some fact table that is price, quantity of rice enter, quantity of rice out and stock and some dimension table that is place dimension (city of origin / destination), dimension time, dimension of rice type and dimension market and dashboard of various price and supply charts that can be selected based on available dimensions. Keywords: dashboard, data warehouse, rice price, rice supply, visualization

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.

An Approach Toward a Conceptual Collaborative Framework Based on a Case Study in a Wood Supply Chain

To face the current economic context and better respond to market demand, many companies have started creating collaborations with different organizations inside and outside their supply chain. The objective is to have a greater visibility over supply chain operations, decision making and planning with positive impacts on the productivity and performance. In this paper, a collaborative framework is proposed to improve the business competitiveness of supply chains. Different dimensions including business and marketing, interfirm collaborative aspects, and quality and conformity in this framework are taken into account. Competence as an encompassing factor for human aspect to implement and operate the proposed framework is also scrutinized. All stated dimensions are then put together under one umbrella and implemented within a decision support system using online analytical processing database. The wood industry in Quebec North Shore region is chosen as an example of a supply chain and the case study throughout this paper.

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.

Compacting transactional data in hybrid OLTP&OLAP databases

Growing main memory sizes have facilitated database management systems that keep the entire database in main memory. The drastic performance improvements that came along with these in-memory systems have made it possible to reunite the two areas of online transaction processing (OLTP) and online analytical processing (OLAP): An emerging class of hybrid OLTP and OLAP database systems allows to process analytical queries directly on the transactional data. By offering arbitrarily current snapshots of the transactional data for OLAP, these systems enable real-time business intelligence. Despite memory sizes of several Terabytes in a single commodity server, RAM is still a precious resource: Since free memory can be used for intermediate results in query processing, the amount of memory determines query performance to a large extent. Consequently, we propose the compaction of memory-resident databases. Compaction consists of two tasks: First, separating the mutable working set from the immutable "frozen" data. Second, compressing the immutable data and optimizing it for efficient, memory-consumption-friendly snapshotting. Our approach reorganizes and compresses transactional data online and yet hardly affects the mission-critical OLTP throughput. This is achieved by unburdening the OLTP threads from all additional processing and performing these tasks asynchronously.

중소기업성장지원 OLAP 데이터베이스 시스템 구축 - 중소기업 정책금융지원 사업을 중심으로-

본 연구에서는 중소기업청의 정책자금지원 사업을 도와 줄 OLAP 데이터베이스 시스템을 구축하는 것을 목적으로 하고 있다. 정책자금을 단계적으로 지원하기 위해서는 필수적으로 이질 차원 스키마를 채택해야 한다. 따라서 본 연구에서는 단계적 정책금융을 지원하도록 하는 FREQUENCY 차원테이블을 작성한 것이다. 또한 본 연구에서는 중소기업의 크기를 측정할 모델을 작성하였는데, 이 모델은 매출액, 고용인력, 기술보유, 매출액 영업이익률, 부채 비율, 유동비율의 6가지 기업크기 측정지표로 구성되어 있다. FREQUENCY 차원을 포함한 3개 차원과 기업크기 측정모델을 사용하여 OLAP 데이터베이스 시스템을 구축한 것이다. 아울러 이렇게 구축한 OLAP 데이터베이스 시스템을 이용하여 2004년~2007년 간 기 시행된 중소기업 정책자금 지원사업의 적정성을 평가해 보고 있다. The purpose of this paper is to develop an OLAP (online analytical processing) database system that supports the SMBA (Small and medium Business Administration) policy funding. A heterogeneous dimension schema will be central in staged support of policy funds. In this paper, therefore, we designed the FREQUENCY dimension table which has a heterogeneous dimension schema structure. In this paper, we made a model of measuring SME (small and medium-sized enterprise) size first. The model is composed of six determinants of firm growth such as sales, employment, own technology, the operating profit to sales ratio, the debt ratio, and the current ratio. We developed the OLAP database system by using three dimensions including the FREQUENCY dimension, and using the model of measuring SME size. Also we assessed past decisions on policy funding in the Small Business Policy Funds Support Project (2004-2007) by using the OLAP database system.

중소기업성장지원 OLAP 데이터베이스 시스템 구축 -중소기업기술개발지원사업을 중심으로-

본 연구에서는 중소기업성장을 지원할 OLAP 데이터베이스 시스템을 구축하는 것을 목적으로 하고 있다. 본 연구의 특징은 중소기업의 크기를 측정할 모델을 작성하는 것이다. 이 모델은 고용인력, 매출액, 수출액, 기술보유, 매출액 대비 기술개발투자비 비율 등의 5가지 기업크기 측정지표로 구성된다. 또한 단계적 기술개발자금 지원을 가능케 할 FREQUENCY 차원 테이블을 설계하는 것이다. FREQUENCY 차원을 포함한 3개 차원과 기업크기 측정모델을 사용하여 OLAP 데이터베이스 시스템을 구축한 것이다. 그리고 이 OLAP 데이터베이스 시스템을 이용하여 기 시행된 중소기업기술개발지원사업의 적정성을 평가해 보고 있다. The purpose of this paper is to develop an online analytical processing (OLAP) database system for small and medium-sized enterprises (SMEs) growth support. In this paper, we made a model of measuring SME size first. The model is composed of five determinants of firm growth such as employment, sales, the amount of export, own technology, and the ratio of R&D expenditure to sales. Second, we designed the FREQUENCY dimension table which will make staged support of R&D expenditure. We developed the OLAP database system by using three dimensions including the FREQUENCY dimension, and using the model of measuring SME size. Also we assessed past decisions on R&D expenditure support in the Small Business R&D Support Project by using the OLAP database system.

High-Fidelity Aerothermal Engineering Analysis for Planetary Probes Using DOTNET Framework and OLAP Cubes Database

This publication presents the architecture integration and implementation of various modules inSpartaframework.Spartais a trajectory engine that is hooked to an Online Analytical Processing (OLAP) database for Multi-dimensional analysis capability. OLAP is an Online Analytical Processing database that has a comprehensive list of atmospheric entry probes and their vehicle dimensions, trajectory data, aero-thermal data and material properties like Carbon, Silicon and Carbon-Phenolic based Ablators. An approach is presented for dynamic TPS design. OLAP has the capability to run in one simulation several different trajectory conditions and the output is stored back into the database and can be queried for appropriate trajectory type. An OLAP simulation can be setup by spawning individual threads to run for three types of trajectory:Nominal,Undershoot and Overshoot trajectory. Sparta graphical user interface provides capabilities to choose from a list of flight vehicles or enter trajectory and geometry information of a vehicle in design. DOTNET framework acts as a middleware layer between the trajectory engine and the user interface and also between the web user interface and the OLAP database. Trajectory output can be obtained in TecPlot format, Excel output or in a KML (Keyhole Markup Language) format. Framework employs an API (application programming interface) to convert trajectory data into a formatted KML file that is used by Google Earth for simulating Earth-entry fly-by visualizations.

Explanation of exceptional values in multi-dimensional business databases

In this paper, we describe an extension of the OnLine Analytical Processing (OLAP) framework with causal explanation, offering the possibility to automatically generate explanations for exceptional cell values. This functionality can be built into conventional OLAP databases using a generic explanation formalism, which supports the work of managers in diagnostic processes. The central goal is the identification of specific knowledge structures and reasoning methods required to construct computerized explanations from multi-dimensional data and business models. The methodology was tested on a case study involving the comparison of financial figures of a firm’s business units. The findings suggest improved decision-making by managers because the current tedious and error-prone manual analysis process is enhanced by automated problem identification and explanation generation. It is also noted that this novel methodology has general utility for decision-support systems, for example, for automated diagnosis in the financial and accountancy domain.