...Application of OLAP to the Analysis of the Curriculum Chosen by Students Hua-long Zhao Department of Geography Dezhou University Dezhou Shandong 253023, China Zhl1970@dzu.edu.cn Abstract—With development of the scale of higher education, more and more data about curriculum chosen by students has been produced. This paper analyzes the application of data warehouse, on-line analytical processing and data mining on the analysis of curriculum chosen by students, accomplishes the design of data warehouse about universities curriculum chosen by students, extracts and transforms curriculum chosen by students data and then loads them into the data warehouse. This paper builds curriculum chosen by students multidimensional cube analysis data model by taking use of OLAP technology, and realizes the query, analysis and show of curriculum chosen by students multidimensional data, so it can analyze the curriculum's establishment situation from many angles, and serve the university teaching decision support system. (Abstract) Keywords-Data Warehouse, OLAP, Dimensional Table, Fact Table, Star Model. Ⅰ. INTRODUCTION Data warehouse is a new database technology that has rapidly developed since 1990s. It can serve the policy-maker well, and it is one of data congregations that have the features of facing theme, integration, variation with time and supporting decision. In the past more than 20 years, with the development and widespread application of database, enterprise has accumulated massive...
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...Tarea #6 Estructura informática de la Empresa (AC 9202-T005) Profesor Dr. Dexter Mena Jose Mauricio Ureña Jimenez 1 er Trimestre 2013 2 INDICE Página Que significa Gestión del Conocimiento 3 Qué herramientas informáticas asocian este concepto 5 Qué significa (BI Business Intelligence) 7 Bibliografía 11 3 ¿Que significa Gestión del Conocimiento? Gestión del conocimiento es el proceso por el cual una organización, facilita la trasmisión de informaciones y habilidades a sus empleados, de una manera sistemática y eficiente. Es importante aclarar que las informaciones y habilidades no tienen por qué estar exclusivamente dentro de la empresa, sino que pueden estar o generarse generalmente fuera de ella. Este matiz final es muy importante. Generalmente la mayoría de las empresas identifican gestión del conocimiento solamente con la información y habilidades internas de la empresa, lo que se conoce como Business Intelligence o inteligencia empresarial. De esta forma casi todos los esfuerzos se orientan a canalizar la información y habilidades que ya posee una organización centrándose en la eficiencia de los procesos de comunicación interna a través de la implantación de sistemas como CRM, ERP y un CMI Esto ha sido tradicionalmente así por que siempre ha sido mucho más fácil controlar los volúmenes de información interna que la información externa que se encuentra fuera de la organización que es más difícil de encontrar, buscar, seleccionar y organizar...
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...последнее время много написано про OLAP. Можно сказать, что наблюдается некоторый бум вокруг этих технологий. Правда, для нас этот бум несколько запоздал, но связано это, конечно, с общей ситуацией в стране. Информационные системы масштаба предприятия, как правило, содержат приложения, предназначенные для комплексного многомерного анализа данных, их динамики, тенденций и т.п. Такой анализ в конечном итоге призван содействовать принятию решений. Нередко эти системы так и называются – системы поддержки принятия решений. Системы поддержки принятия решений обычно обладают средствами предоставления пользователю агрегатных данных для различных выборок из исходного набора в удобном для восприятия и анализа виде. Как правило, такие агрегатные функции образуют многомерный (и, следовательно, нереляционный) набор данных (нередко называемый гиперкубом или метакубом), оси которого содержат параметры, а ячейки – зависящие от них агрегатные данные – причем храниться такие данные могут и в реляционных таблицах, но в данном случае мы говорим о логической организации данных, а не о физической реализации их хранения). Вдоль каждой оси данные могут быть организованы в виде иерархии, представляющей различные уровни их детализации. Благодаря такой модели данных пользователи могут формулировать сложные запросы, генерировать отчеты, получать подмножества данных. Технология комплексного многомерного анализа данных получила название OLAP (On-Line Analytical Processing). OLAP – это ключевой компонент организации...
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...database structures, specialized servers, and Web-enable software products (O'Brien & Marakas, 2011). The ability to analyze and synthesize the available data can be a source of a competitive advantage for any firm. Online Analytical Processing (OLAP) is one of tools that can assist managers in making sound business decisions. OLAP is a powerful technology behind many Business Intelligence (BI) applications. It offers many capabilities for data discovery, report viewing, complex analytical calculations, and planning (Olap.com, n.d.). In other words, OLAP is a “computer-enhanced multidimensional analysis” (Achor, 2002). The term OLAP was created by E.F. Codd in 1993. According to Codd and associates, OLAP is made up of many speculative “what-if” and/or “why” data model scenarios conducted within the context of the specific historical basis (Codd, Codd and Salley, 1993). Under these scenarios, the values of major parameters are changed to show potential variances in “supply, production, the economy, sales, marketplace, costs, and/or other environmental and internal factors” (Codd, Codd and Salley, 1993, p.6). These variable groups or dimensions make up a base for the company’s planning, analysis and reporting activities (Bogue, 2005). OLAP tools do not keep individual transaction records in a row-by-column format, like relational databases. Instead, they store consolidated information in multidimensional cubes (Olap.com, n.d.). When necessary, analysts use operations called consolidation...
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...ABSTRACT Online Analytical Processing is a powerful framework for the analysis of organizational data. OLAP is often supported by a logical structure known as a data cube, a multidimen- sional data model that offers an intuitive array-based per- spective of the underlying data. Supporting efficient index- ing facilities for multi-dimensional cube queries is an issue of some complexity. In practice, the difficulty of the in- dexing problem is exacerbated by the existence of attribute hierarchies that sub-divide attributes into aggregation layers of varying granularity. In this paper, we present a hierar- chy and caching framework that supports the efficient and transparent manipulation of attribute hierarchies within a parallel ROLAP environment. Experimental results verify that, when compared to the non-hierarchical case, very little overhead is required to handle streams of arbitrary hierar- chical queries. Categories and Subject Descriptors H.2.7.b [Database Management]: Data Warehouse and Repository; H.2.2.a [DatabaseManagement]: AccessMeth- ods General Terms Algorithms Design Performance Keywords Hierarchies, Caching, Data Cubes, Aggregation, Indexing, OLAP, Granularity, Materialization, Parallelization 1. INTRODUCTION Online Analytical Processing (OLAP) has become an im- portant component of contemporary Decision Support Sys- tems (DSS). Central to OLAP is the data cube, a multidi- mensional data model that presents an intuitive cube-like Permission...
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...Introduction to Data Warehousing and Business Intelligence Slides kindly borrowed from the course “Data Warehousing and Machine Learning” Aalborg University, Denmark Christian S. Jensen Torben Bach Pedersen Christian Thomsen {csj,tbp,chr}@cs.aau.dk Course Structure • Business intelligence Extract knowledge from large amounts of data collected in a modern enterprise Data warehousing, machine learning Acquire theoretical background in lectures and literature studies Obtain practical experience on (industrial) tools in practical exercises Data warehousing: construction of a database with only data analysis purpose • Purpose Business Intelligence (BI) Machine learning: find patterns automatically in databases 2 •1 Literature • Multidimensional Databases and Data Warehousing, Christian S. Jensen, Torben Bach Pedersen, Christian Thomsen, Morgan & Claypool Publishers, 2010 • Data Warehouse Design: Modern Principles and Methodologies, Golfarelli and Rizzi, McGraw-Hill, 2009 • Advanced Data Warehouse Design: From Conventional to Spatial and Temporal Applications, Elzbieta Malinowski, Esteban Zimányi, Springer, 2008 • The Data Warehouse Lifecycle Toolkit, Kimball et al., Wiley 1998 • The Data Warehouse Toolkit, 2nd Ed., Kimball and Ross, Wiley, 2002 3 Overview • • • • Why Business Intelligence? Data analysis problems Data Warehouse (DW) introduction DW topics Multidimensional modeling ETL Performance optimization 4 •2 What is Business Intelligence (BI)? • From...
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...between AQL and OLAP Auteur Peter den Heijer Adres Veldhofstraat 16 Postcode 7213 AM Plaatsnaam Gorssel Emailadres pdheijer@gmail.com Telefoonnummer 0575-490719 Datum 26 mei 2012 Opleidingsinstituut CAI Opleiding Business Intelligence Opleidingscode BUSI1201UTRx Docent Emiel Caron Versie 3.0 Pagina 1 van 19 Inhoud 1. Introduction ..................................................................................................................................... 3 2. Business Case................................................................................................................................... 3 3. Central Research Question .............................................................................................................. 3 4. Purpose............................................................................................................................................ 3 5. Sub Questions.................................................................................................................................. 4 6. Research Methodology and Scope .................................................................................................. 4 7. Introduction Qlikview ...................................................................................................................... 4 8. Definition of OLAP ................
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...examples of how OLAP works and its Predictive Analytics INTRODUCTION On-line analytical Processing (OLAP) refers to a computer-based processing with a capability of manipulating and analyzing large volumes of data from multiple perspectives (different points of view). It is one technique you can use to transform data into information. The original system of the OLAP is also known as the multi-dimensional cube or hyper cube. For example, a user can request that data be analyzed to display a spread-sheet showing all of a company's products sold in Ghana in the month of January, compare revenue figures with those for the same products in March, and then see a comparison of other product sales in Ghana in the same time period. Historical Background The first fully functional on-line analytical system was introduced in 1970 by Express, and later in 1995, the Oracle acquired the release for the resource of information. The formal launching for acquisition of OLAP was held in 2007. Oracle also released its own system called Essbase using the OLAP theoretical background and functionality. In 1998, Microsoft stepped in for upgrading and advancement in the OLAP technology. Microsoft worked on the mainstream idea and developed highly advanced online analytical system that is deployed in many large organizations today. Types of OLAP There are 3 types of the on-line analytical systems each with different properties according to the level of use. Multi-dimensional OLAP Multi-dimensional...
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...Taken literally, business intelligence is just that—intelligence or understanding of your business. You get that understanding by analyzing your business operations. That analysis is accomplished by collecting the information that represents your marketing, sales, and service activities, the behavior of your customers in responding to these activities, and the behavior of your internal systems and your suppliers’ systems in responding to your customers’ behavior. Once you have collected this information, and its collection is a continuous process, not a one-time event, you organize and store it in a manner to facilitate its access, processing, and presentation through a broad range of techniques including, reporting, query and analysis, OLAP, and data mining. Finally, you use the results of applying these techniques to improve your business operations and start the analysis cycle all over again. This business intelligence process can deliver significant, bottom-line results. Implementing its technologies and applying its process can help make your business more effective and more efficient, increasing revenue, decreasing costs, and improving your relationships with customers and suppliers. Business Intelligence Platforms In order to deliver business intelligence to the widest audience and to maximize the benefits that it can deliver its technologies must be organized. They must be deployed within an infrastructure with the capabilities to implement the business intelligence...
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...What is Data Mining? What is OLAP? How is data mining different from OLAP? Data mining: Data mining is essential utilized today by organizations with an in number buyer center retail, budgetary, correspondence, and promoting associations. It empowers these organizations to figure out relationships around "interior" components, for example, value, item positioning, or staff abilities, and "outer" variables, for example, financial pointers, rivalry, and client demographics. Also, it empowers them to figure out the effect on deals, client fulfillment, and corporate benefits. At long last, it empowers them to "penetrate down" into synopsis data to view part transactional information. OLAP: OLAP stays for Online Analytical Processing and is designing used to accumulate, regulate and process multidimensional data and outfit brisk access to this data for demonstrative purposes. OLAP is for the most part used inside business reporting for promoting, deals, human possession organization and diverse business fields. OLAP mulls over brisk execution of complex database requests continuously. OLAP energizes complex data sees through data turning, complex data transforming, and data showing. OLAP manages dimensional information, which takes into account much quicker execution of complex database inquiries contrasted with social database administration frameworks. The information structure that OLAP make from the social information is called OLAP block. OLAP solid shapes might be considered...
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... I would to like to convey my sense of gratitude to Professor.Rajni Palikhey who helped and supported us right throughout the semester. This paper would not have been possible without her cooperation and technical assistance. We would also thank our Institution and our faculty members without whom this project would have been a distant reality. We also extend our heartfelt thanks to our family and well wishers. I would like to take this occasion to specially thank University of Northern Virginia to provide us with excellent faculty and also in supporting us getting quality education remotely. Contents SL No Title Page no 1 Abstract 5 2 Introduction to Databases 6 3 OLTP and OLAP Systems 7 4 Difference between OLTP and OLAP 9 5 Data Modeling 13 6 Workflows in Enterprise level Data warehousing 18 7 Business Intelligence tools used in Data flow and Data Warehousing 21 8 Analysis in Data warehousing 24 9 Conclusion 28 10 Foot Note 30 11 References 31 ABSTRACT These days majority of the applications, may it be web applications or windows applications or mobile applications, are completely database dependent. Most of the application developments are becoming database driven environments, hence rendering databases as one of the most key elements in a software environment. This dependency on databases can attributed to the increasing number of data requirements from the...
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...Open Object Business Intelligence Release 1.0 Tiny SPRL 2009-04-09 CONTENTS i ii Open Object Business Intelligence, Release 1.0 I 1 2 Part 1 : Introduction Goal of the project What is for User? 2.1 2.2 2.3 For the end-user: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . For the administrator user: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . For the developer: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 7 9 9 9 9 11 12 15 3 OLAP 3.1 Who uses OLAP and Why? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Terminologies II 5 6 Part 2 : Architecture Schema Components 6.1 6.2 6.3 6.4 6.5 6.6 The Cube . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The CLI interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Cube Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Web Client . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The OpenOffice plugin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Open ERP interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 19 21 21 21 21 22 22 22 23 25 26 7 Extra libraries 8 Introduction to the OpenObject Module 8.1...
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... OLAP (online analytical processing) Star schema What is OLAP (online analytical processing) Fact table OLAP (online analytical processing) is computer processing that enables a Big data analytics Data modeling Ad hoc analysis user to easily and selectively extract and view data from different points of view. For example, a user can request that data be analyzed to display a spreadsheet showing all of a company's beach ball products sold in Florida in the month of July, compare revenue figures with those for the same products in September, and then see a comparison of other product sales in Data visualization Extract, transform, load (ETL) Florida in the same time period. To facilitate this kind of analysis, OLAP data is stored in a multidimensional database. Whereas a relational database can be thought of as two-dimensional, a multidimensional database considers each data attribute (such as product, geographic sales region, and time Association rules (in data mining) Relational database period) as a separate "dimension." OLAP software can locate the intersection of dimensions (all products sold in the Eastern region above a certain price during a certain time period) and display them. Attributes such as time periods can be broken down into subattributes. Denormalization OLAP can be used for data mining or the discovery of previously Master data management (MDM) undiscerned relationships between data items. An OLAP database...
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...Continuous Assurance Auditing (CAA)dan XBRL Continuous assurance auditing (CAA) adalah proses monitoring terkait pengendalian dalam sistem teknologi informasi yang mana monitoring ini akan mengirimkan pemberitahuan kepada auditor (biasanya internal auditor) jika terdapat penyimpangan sistem dari yang batasi auditor. Konsep ini telah ada sejak awal audit berbasis teknologi informasi muncul. Beberapa program yang muncul pada saat itu adalah Integraed Test Facility (ITFs) ataupun System Continuous Audit Review File (SCARF). Konsep sistem ITFs dan SCARF saat ini berevolusi menjadi teknik pemantauan CAA. Saat ini, CAA dibuat sedemikian hingga menjadi sangat mudah diterapkan sebagai sistem audit otomatis. 29.1 Implementasi CAA Dengan banyaknya aplikasi IT sekarang perbedaan dalam pengendalian untuk dipertimbangkan dalam meningkatkan efisiensi audit, auditor mulai menekankan hanya pada pengendalian internal yang lebih beresiko tinggi melalui analisa resiko secara formal. A. Apakah yang dimaksud dengan proses pemantauan CAA? CAA adalah proses audit yang menghasilkan hasil audit secara simultan dengan atau diantara waktu yang singkat setelah peristiwa sebenarnya terjadi. CAA umumnya merupakan bentuk independen dari aplikasi bisnis dengan proses yang menguji data transaksi dibandingkan dengan parameter pengendalian atau peraturan. Meskipun memiliki konsep yang mirip, terkadang kita bingung antara CAA dengan continuous monitoring.Berikut beberapa perbedaannya: 1) CAA * Perangkat...
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...F4: DW Architecture and Lifecycle Erik Perjons, DSV, SU/KTH perjons@dsv.su.se The data warehouse architecture The back room The front room Analysis/OLAP Productt Product2 Product3 Product4 Time1 Time2 Time3 Time4 Value1 Value2 Value3 Value4 Value11 Value21 Value31 Value41 Data warehouse External sources Extract Transform Load Serve Query/Reporting Operational source systems Data marts Data mining Falö aöldf flaöd aklöd falö alksdf Operational source Data staging systems (RK) area (RK) Legacy systems Back end tools OLTP/TP systems Data presentation area (RK) ”The data warehouse” Presentation (OLAP) servers Data access tools (RK) End user applications Business Intelligence tools Operational Source Systems Operational source systems characteristics: Operational source systems • the source data often in OLTP (Online Transaction Processing) systems, also called TPS (Transaction Processing Systems) • high level of performance and availability • often one-record-at-a time queries • already occupied by the normal operations of the organisation OLTP vs. DSS (Decision Support Systems) OLTP vs. OLAP (Online analytical processing) Operational Source Systems More operational source systems characteristics: Operational source systems • a OLTP system may be reliable and consistent, but there are often inconsistencies between different OLTP systems • different types of data format and data structures in different OLTP systems AND DIFFERENT...
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