Tiểu luận môn hệ hỗ trợ quyết định OLAP CUBE OVERVIEW AND CASE STUDY

NATIONAL UNIVERSITY OF HO CHI MINH CITY UNIVERSITY OF INFORMATION TECHNOLOGY (UIT) DECISION MAKING SYSTEM SEMINAR TOPIC OLAP & USING SSAS TO ANALYZE OLAP CUBE IN RETAIL DATABASE GROUP 1. 2. 3. 4. MAI HOÀNG THẮNG - CH1401032 TRẦN THANH TRÂM – CH1401038 BÙI BÁ NGUYÊN – CH1401029 NGUYỄN THỊ THÙY LINH - CH1401027 Advisor: Associate Professor Do Phuc HCM, 07/2015 OLAP CUBE OVERVIEW AND CASE STUDY CONTENTS INTRODUCTION In the past decade, Microsoft SQL Server Analysis Services established itself as one of the leaders in the Business Intelligences systems market. Analysis Services helps managers, employees, customers, and partners to make more informed business decisions by enabling them to analyze information accumulated during a company’s day-to-day operations. Success of Analysis Services and the entire Business Intelligence market was predefined by incredible growth of amounts of data accumulated as a result of everyday functioning of a large number of companies. Today it’s hard to imagine a business or an organization that doesn’t use an online transaction processing (OLTP) system. OLTP systems provide means to highly efficient execution of a large number of small transactions and reliable access to data stored in the result of the transactions. It’s easy to see why analysis of data has become so important to the management of modern enterprises. However, OLTP systems are not well suited to analyzing data. In the past decades, an entire new market has emerged for systems that can provide reliable and fast access for analyzing very large amounts of data: online analytical processing (OLAP). OLAP enables managers, executives, and analysts to gain insight into data using fast, interactive, and consistent interfaces to a wide variety of possible views of information. Because OLAP systems are designed specifically for analysis, they typically don’t need to both read and write data. All that is necessary for analysis is reading data. With this emphasis on reading only, OLAP systems enjoy a speed advantage over their OLTP cousins. However, a read-only approach to the database architecture is not the only distinction of the OLAP solution. In scope of studying Decision Support System at Computer Science Department, University of Information Technology, Associate Professor Do Phuc brings much helpful knowledge on decision support system, specially on using OLAP technique to make decision. Number Four group would like to thank a lot to this support from Mr Phuc, then our group has some research and presentation on OLAP. Here are some main ideas will be in this document: • Chapter – Olap Overview • Chapter - Olap With Sql Server Analysis Service (SSAS) • Chapter - Olap Case Study - Decision Making With Shopping Malls Number Four group believes that these information will give readers more details of OLAP and how to use OLAP with SSAS through a sample case study. NUMBER FOUR GROUP OLAP CUBE OVERVIEW AND CASE STUDY CHAPER - OLAP OVERVIEW 1.1. OLAP Definition OLAP (online analytical processing) is computer processing that enables a 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 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 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 sub-attributes. OLAP is an acronym, standing for "On-Line Analytical Processing". This, in itself, does not provide a very accurate description of OLAP, but it does distinguish it from OLTP or "On-Line Transactional Processing". Difference between OLAP and OLTP Online Transaction Processing (OLTP) Online Analytical Processing (OLAP) Designed to support Daily DML Operations Designed to hold historical data for analyses of your application and forecast business needs Holds daily Latest Transactional Data related to your application Data is consistent up to the last update that occurred in your Cube Data stored in normalized format Data stored in denormalized format Databases size is usually around 100 MB to Databases size is usually around 100 GB to a 100 GB few TB Used by normal users Used by users who are associated with the decision making process, e.g., Managers, CEO. CPU, RAM, HDD space requirement is less. CPU, RAM, HDD space requirement is higher. Query response may be slower if the amount of data is very large, it can Query Response is quicker, management can Trend analysis on their data easily and NUMBER FOUR GROUP OLAP CUBE OVERVIEW AND CASE STUDY Online Transaction Processing (OLTP) Online Analytical Processing (OLAP) impact the reporting performance. generate quicker reports. T-SQL language used for query MDX is used for querying on OLAP Cube 1.2. Why we need OLAP When first investigating OLAP, it is easy to question the need for it. If an end user requires high-level information about their company, then that information can always be derived from the underlying transactional data, hence we can achieve every requirement with an OLTP application. Were this true, OLAP would not have become the important topic that it is today. OLAP exists & continues to expand in usage because there are limitations with the OLTP approach. The limits of OLTP applications are seen in three areas. 1.2.1. Increasing data storage The trend towards companies storing more & more data about their business shows no sign of stopping. Retrieving many thousands of records for immediate analysis is a time and resource consuming process, particularly when many users are using an application at the same time. Database engines that can quickly retrieve a few thousand records for half-a-dozen users struggle when forced to return the results of large queries to a thousand concurrent users. Caching frequently requested data in temporary tables & data stores can relieve some of the symptoms, but only goes part of the way to solving the problem, particularly if each user requires a slightly different set of data. In a modern data warehouse where the required data might be spread across multiple tables, the complexity of the query may also cause time delays & require more system resources which means more money must be spent on database servers in order to keep up with user demands. 1.2.2. Data versus Information Business users need both data and information. Users who make business decisions based on events that are happening need the information contained within their company's data. A stock controller in a superstore might want the full list of all goods sold in order to check up on stock levels, but the manager might only want to know the amount of fruit & frozen goods being sold. Even more useful would be the trend of frozen good sales over the last three months. NUMBER FOUR GROUP OLAP CUBE OVERVIEW AND CASE STUDY In order to answer the question "How many frozen goods did we sell today?", an OLTP application must retrieve all of the frozen good sales for the day and then count them, presenting only the summarized information to the end-user. To make a comparison over three months, this procedure must be repeated for multiple days. Multiply the problem by several hundred stores, so that the managing director can see how the whole company is performing and it is easy to see that the problem requires considerable amounts of processing power to provide answers within the few seconds that a business user would be prepared to wait. Database engines were not primarily designed to retrieve groups of records and then sum them together mathematically and they tend not to perform well when asked to so. An OLTP application would always be able to provide the answers, but not in the typical few-seconds response times demanded by users. Caching results doesn't help here either, because in order to be effective, every possible aggregation must be cached, or the benefit won't always be realized. Caching on this scale would require enormous sets of temporary tables and enormous amounts of disk space to store them. 1.2.3. Data layout The relational database model was designed for transactional processing and is not always the best way to store data when attempting to answer business questions such as "Sales of computers by region" or "Volume of credit-card transactions by month". These types of queries require vast amounts of data to be retrieved & aggregated on-demand, something that will require time & system resources to achieve. More significantly, related queries such as "Product sales broken down by region" and "Regions broken down by product sales" require separate queries to be performed on the same data set. The answer to the limitations of OLTP is not to spend more & more money on bigger & faster databases, but to use a different approach altogether to the problem and that approach is OLAP. OLAP applications store data in a different way from the traditional relational model, allowing them to work with data sets designed to serve greater numbers of users in parallel. Unlike databases, OLAP data stores are designed to work with aggregated data, allowing them to quickly answer high-level questions about a company's data whilst still allowing users to access the original transactional data when required. 1.3. OLAP Cube The cube is the conceptual design for the data store at the center of all OLAP applications. Although the underlying data might be stored using a number of different NUMBER FOUR GROUP OLAP CUBE OVERVIEW AND CASE STUDY methods, the cube is the logical design by which the data is referenced. The easiest way to explain a cube is to compare storing data in a cube with storing it in a database table. A relational table containing sale records An OLAP cube is a technology that stores data in an optimized way to provide a quick response to various types of complex queries by using dimensions and measures. Most cubes store pre-aggregates of the measures with its special storage structure to provide quick response to queries. The data are shown as a two-dimentional cube. The two-dimensional cube reoriented. NUMBER FOUR GROUP OLAP CUBE OVERVIEW AND CASE STUDY SSRS Reports and Excel Power Pivot is used as front end for Reporting and data analysis with SSAS (SQL Server Analysis Services) OLAP Cube. SSAS (SQL Server Analysis Services) is Microsoft BI Tool for creating Online Analytical Processing and data mining functionality. BIDS (Business Intelligence Development Studio) provides environment for developing your OLAP Cube and Deploy on SQL Server. BIDS (Business Intelligence Development Studio) comes with Microsoft SQL Server 2005, 2008 (e.g. Developer, Enterprise Edition) . We have to choose OLAP Cube when performance is a key factor, the key decision makers of the company can ask for statistics from the data anytime from your huge database. We can perform various types of analysis on data stored in Cube, it is also possible to create data mining structure on this data which can be helpful in forecasting, prediction. In our case, data warehouse is used as a source of data to Cube in BIDS. Once Cube gets ready with data, users can run queries on Cube created in SSAS. SSRS Reports and Excel Pivoting/Power Pivot can use OLAP Cube as source of data instead of OLTP database to get performance for resolving Complex Queries. SSRS Reports, Excel Power Pivot can be used for visualization/analysis of data from cube. 1.4. History Of OLAP The term OLAP was created as a slight modification of the traditional database term OLTP (Online Transaction Processing). Databases configured for OLAP employ a multidimensional data model, allowing for complex analytical and ad-hoc queries with a rapid execution time. They borrow aspects of navigational databases and hierarchical databases that are speedier than their relational kind. Nigel Pendse has suggested that an alternative and perhaps more descriptive term to describe the concept of OLAP is Fast Analysis of Shared Multidimensional Information (FASMI). The first product that performed OLAP queries was Express, which was released in 1970 (and acquired by Oracle in 1995 from Information Resources). However, the term did not appear until 1993 when it was coined by Ted Codd, who has been described as "the father of the relational database". NUMBER FOUR GROUP OLAP CUBE OVERVIEW AND CASE STUDY 1.5. OLAP Operations The user-initiated process of navigating by calling for page displays interactively, through the specification of slices via rotations and drill down/up is sometimes called "slice and dice". a) Slice: A slice is a subset of a multi-dimensional array corresponding to a single value for one or more members of the dimensions not in the subset. OLAP Slicing b) Dice: The dice operation is a slice on more than two dimensions of a data cube (or more than two consecutive slices). OLAP Dicing c) Drill Down/Up: Drilling down or up is a specific analytical technique whereby the user navigates among levels of data ranging from the most summarized (up) to the most detailed (down). NUMBER FOUR GROUP OLAP CUBE OVERVIEW AND CASE STUDY OLAP Drill-up and drill-down d) e) Roll-up: A roll-up involves computing all of the data relationships for one or more dimensions. To this, a computational relationship or formula might be defined. Pivot: To change the dimensional orientation of a report or page display. The output of an OLAP query is typically displayed in a matrix (or pivot) format. The dimensions form the row and column of the matrix; the measures, the values. OLAP pivoting 1.6. Data warehouse and OLAP 1.6.1. OLAP Solution components In a typical OLAP implementation, the solution architecture has the following components: Data sources, ETL, Data Warehouse and OLAP. In this section, we briefly describe each of these components. NUMBER FOUR GROUP OLAP CUBE OVERVIEW AND CASE STUDY a) Data Sources When it comes to data sources used in data warehouse and OLAP solutions, data in any format and structure is possible: RDBMS, legacy DBMS, Flat files, XML, Web Service, etc. b) Extraction, Transformation and Load (ETL) ETL is a process that reads data, transforms it to multidimensional format and loads it to data warehouse. While ETL can be implemented by basic programming, there are various ETL-specific tools developed by different vendors. Using a specific ETL tool provides faster development, easier maintenance and improved Meta data management. c) Data Warehouse A data Warehouse is the repository of data in multidimensional format. (Inmon 1995) Data warehouses are intended to help data reporting and analysis. A data warehouse is usually specific to a subject, like Marketing. If it covers different subjects, it is easy to find all data items related to one subject together. Hence, a data warehouse is Subject Oriented. A data warehouse is Non-Volatile; after data entered into the warehouse, data is not supposed to change. Data in a data warehouse is integrated from all data sources that contain data items related to the subject(s) that is (are) covered in data warehouse. In order to be able to analyze trends over time, historical data should be collected in a data warehouse. This is in contrast with Online Transaction Processing (OLTP) databases and is the Time Invariant characteristic of the data warehouse. d) OLAP Data in a data warehouse is still in relational format, not able to meet performance and ease of use requirements of complex analytical queries that are multidimensional in their nature; OLAP provides data in so called OLAP cubes, designed specifically to improve query performance and ease of use when analytical queries are posed. As this is the focus of this report, the rest of this document covers OLAP concepts in SQL Server Analysis Services (SSAS). SSAS is one of Microsoft SQL Server 2008 components that provide OLAP support together with data mining functionalities. 1.6.2. Architecture and design options There are several possible architectures to choose when implementing data warehouse and OLAP solutions. The architecture choice is completely dependent on the requirements. For example, it is possible in some cases to implement OLAP directly on top of operational OLTP databases. In practice, most of the OLAP solutions rely on a data warehouse in star schema. NUMBER FOUR GROUP 10 OLAP CUBE OVERVIEW AND CASE STUDY Data warehouse Architecture Data warehouse Architecture includes tools for extracting data from multiple operational databases and external sources; for cleaning, transforming and integrating this data; for loading data into the data warehouse; and for periodically refreshing the warehouse to reflect updates at the sources and to purge data from the warehouse, perhaps onto slower archival storage. In addition to the main warehouse, there may be several departmental data marts. Data in the warehouse and data marts is stored and managed by one or more warehouse servers, which present multidimensional views of data to a variety of frontend tools: query tools, report writers, analysis tools, and data mining tools. Finally, there is a repository for storing and managing metadata, and tools for monitoring and administering the warehousing system. The warehouse may be distributed for load balancing, scalability , and higher availability. In such a distributed architecture, the metadata repository is usually replicated with each fragment of the warehouse, and the entire warehouse is administered centrally . An alternative architecture, implemented for expediency when it may be too expensive to construct a single logically integrated enterprise warehouse, is a federation of warehouses or data marts, each with its own repository and decentralized administration. Designing and rolling out a data warehouse is a complex process, consisting of the following activities: • • • • • • Define the architecture, capacity planning, and select the storage servers, database and OLAP servers, and tools. Integrate the servers, storage, and client tools. Design the warehouse schema and views. Define the physical warehouse organization, data placement, partitioning, and access methods. Connect the sources using gateways, ODBC drivers, or other wrappers. NUMBER FOUR GROUP 11 OLAP CUBE OVERVIEW AND CASE STUDY • • • • Design and implement scripts for data extraction, cleaning, transformation, load, and refresh. Populate the repository with the schema and view definitions, scripts, and other metadata. Design and implement end-user applications. Roll out the warehouse and applications. Type Of OLAP 1.7.1. Relational OLAP(ROLAP) • Extended RDBMS with multidimensional data mapping to standard relational operation. ROLAP provides functionality by using relational databases and relational query tools to store and analyze multidimensional data. • Build on existing relational technologies and represent extension to all those companies who already used RDBMS. • Data access language and query performance are optimized for multidimensional data. • ROLAP supports for very large databases. 1.7. Multidimensional OLAP(MOLAP) MOLAP implemented operation in multidimensional data. MOLAP extends OLAP functionality to MDBMS. Best suited to manage, store and analyze multidimensional data. Proprietary techniques used in MDBMS. MDBMS and users visualize the stored data as a 3-Dimensional Cube i.e Data Cube. MOLAP Databases are known to be much faster than the ROLAP counter parts. Data cubes are held in memory called “Cube Cache” 1.7.2. • • • • • 1.7.3. Hybrid Online Analytical Processing (HOLAP) HOLAP is a hybrid approach to the solution where the aggregated totals are stored in a multidimensional database while the detail data is stored in the relational database. This is the balance between the data efficiency of the ROLAP model and the performance of the MOLAP model. 1.8. OLAP Application The Online Analytical Processing framework allows users to things that would not be possible or would take a lot of time with the database offering only a two-dimensional correlational analysis. OLAPApplication takes data that is originally in different formats and brings this data together in a way that is more user-friendly, efficient, and helpful. The basic operating NUMBER FOUR GROUP 12 OLAP CUBE OVERVIEW AND CASE STUDY principles of OLAP-based web development are data consolidation or roll up, data drilling down, and what is rather gastronomically called slicing and dicing of data. The different approaches allow the users to aggregate some data while going into the most minute details on a different data set and observing a third data set from various viewpoints at the same time. The complex mathematical and logical algorithms make an OLAP web application indispensable in any situation where large volumes of data are involved. Advantages and Disadvantages 1.9.1. Advantages a) Offer quicker analysis 1.9. An important advantage of OLAP cubes is that they enhance the speed that an organization can investigate large data amounts. This is critical business tool in the current competitive industry. The OLAP cubes can easily uncover relationships, identify trends and offer numerous perspectives regarding an organization’s performance. All these can be attained with only a few seconds. b) Great reporting tool OLAP cubes are normally used by an organization for the reporting advantage that they offer. In fact, to really extend the advantages of these OLAP cubes in an organization, you require a good reporting tool. This reporting tool enables the organization to leverage data directly from the cubes and offer scalability for important business intelligence that it delivers to managers and employees in all departments. c) Improves decision making Through enabling the employees within an organization to view the analysis got from the OLAP cubes, better decisions can be made and this allows the company to become more competitive. Financial planners and analysts can attain better visibility concerning financial performance, thereby improve reporting. Furthermore, OLAP cubes enable operation managers to make good decisions regarding asset utilization so as to locate cost savings opportunities. d) Flexible OLAP cubes are flexible and users can easily view their business data using several reporting tools such as Microsoft Excel. It offers enhanced reporting flexibility via highly customized and rich reports. Provided OLAP cubes are used in the correct place, they have no shortcomings. They essentially allow users to dissect information in different dimensions. 1.9.2. Disadvantage NUMBER FOUR GROUP 13 OLAP CUBE OVERVIEW AND CASE STUDY OLAP, and its reliance on the data warehousing environment, are two of the most significant new technology areas. Moreover, the use of relational design and relational database technology are not feasible implementations to support OLAP design because of the complexity of the queries. The business problem is that OLAP queries are not real-time queries because of the refresh cycle of data into the OLAP data repository. Conventional designs call for integration of data into an operational data store where it can be cleansed, transformed, extracted, & then loaded into the OLAP data repository. This is accomplished through the use of (ETL) tools. The ETL process is generally complicated because data must be integrated and transformed for loading into the nonnormalized relational schema usually associated with OLAP environments. As such, the process can be complicated and time consuming, and with large amounts of data may only occur at monthly or quarterly time intervals. This creates the problem of not having real-time data in the OLAP repository. Real-time data exists in the OLTP environment where the time horizon of data within the OLTP environment is much shorter because performance decreases can occur with growing amounts of data. This is opposite of the nature and goals of the OLAP environment where data is aggregated and the time horizon of data grows to some large amount as determined by the information life cycle policy of the organization. The main problems you have to face using OLAP as a source is that OLAP engines, in general, are designed to return small result sets from highly aggregated data, whereas data mining, in general, is designed to perform operations on large sets of raw (or preprocessed) data. The implementation of OLAP in Analysis Services, requires that all of the result set be materialized in memory before returning to the client. This generally isn't a big deal for typical OLAP queries, but if you are, for instance, trying to mine all of your transaction data for the past 10 years, you will run into difficulties, in short the data gathered may not be (relatively) recent enough to qualify as real-time data for business intelligence purposes. NUMBER FOUR GROUP 14 OLAP CUBE OVERVIEW AND CASE STUDY CHAPER - OLAP WITH SQL SERVER ANALYSIS SERVICE (SSAS) 2.1. SQL Server Analysis Services (SSAS) Microsoft SQL Server Analysis Services, SSAS, is an online analytical processing (OLAP), data mining and reporting tool in Microsoft SQL Server. SSAS is used as a tool by organizations to analyze and make sense of information possibly spread out across multiple databases, or in disparate tables. Microsoft has included a number of services in SQL Server related to business intelligence and data warehousing. These services include Integration Services and Analysis Services. Analysis Services includes a group of OLAP and data mining capabilities. 2.2. Data Mining Data mining algorithms supported in SSAS could be classified into five categories. Predicting a sequence of events, for example, to analyze sequence of weather situation that ends to a specific phenomenon. Finding groups of common items in transactions, most common example here is market basket analysis. Finding groups of similar items, a typical application in this area is segmenting customer data to find distinct group of customers. Another category is Predicting a discrete attribute, for example, predicting whether one specific customer is likely to buy the new product or not. Predicting a continuous attribute, for example, forecasting next month revenue. Data Mining within SQL Server 2.3. OLAP - Multi Dimensional Data in SQL Server OLAP facilities are provided within SQL Server as a whole and also within SSAS. NUMBER FOUR GROUP 15 OLAP CUBE OVERVIEW AND CASE STUDY Multidimensional Data within SQL Server Multidimensional Data provides means for developers to design, publish and modify data cubes. A data cube represents data in multidimensional format which is suitable for advanced ad-hoc and analytical queries. Data in a cube is aggregated based on cube dimensions. Cube data can come from relational databases, data warehouses and data marts. 2.4. SSAS Architecture From the architecture point of view, SSAS is a separated service running on windows that follows standard client/server architecture. Each server can contain several Analysis Services instances. Each analysis services instance contains several Analysis Services databases. Analysis services databases contain multidimensional data structures, data mining structures, data sources, and data source views. Logical SSAS Architecture NUMBER FOUR GROUP 16 OLAP CUBE OVERVIEW AND CASE STUDY AMO (Analysis Management Object) Applications are software programs that are uses to define manage and publish SSAS cubes and data mining structures. Physical SSAS Architecture All communications with SSAS is done using XML for Analysis (XMLA). SSAS provides a number of ways to client applications to access data. They share a common protocol to communicate with an instance of Analysis Services which is XMLA. Asoftheclientside,SSASsupportsthinclients,meaningallqueriesareprocessedin server side. There exist several data access providers to support different programming languages; ADO MD, OLEDB for OLAP and ADO MD.net. All data access providers communicate to the server using XMLA. NUMBER FOUR GROUP 17 OLAP CUBE OVERVIEW AND CASE STUDY SSAS Clients; Physical Architecture 2.5. SSAS Tools SQL Server Management studio 2.5.1. SQL Server Management Studio is a general management tool to manage relational databases, Analysis Services databases, Reporting Services objects, and integration services packages. By connecting SQL Server Management studio to an Analysis Services instance on a machine, following database management tasks can be performed: e) Processing analysis services objects Processing an analysis services object means populating it with data. For example, SQL Server Management studio provides the facilities to process data cubes, which is populating them with data from data sources. f) Browsing analysis services objects The brows facility is a graphical query builder. By browsing, the content of Analysis Services objects is queried. For example, browsing a cube includes dragging attributes / hierarchies and cube measures to respective pane and brows the data in the cube. g) Constructing queries NUMBER FOUR GROUP 18 OLAP CUBE OVERVIEW AND CASE STUDY Multidimensional queries(MDX),Data Mining queries(DMX) and XML. A queries can be posed to Analysis Services database via SQL Server Management studio. h) Scripting Analysis Services objects Scripting an object makes it an XMLA script so that it could be executed in another analysis services instance to make the same object. Scripting only includes structures and definitions. The data is provided after processing. i) Managing Analysis Services Databases Other general database management concepts are also included in SQL Server Management Studio; Defining roles and security aspects of accessing the database and making backups from analysis services databases. 2.5.2. Microsoft Business Intelligence Development Studio (BIDS) BIDS is the development environment for OLAP cubes and data mining models. BIDS is Microsoft Visual Studio with Analysis Services projects extension. After development is done, BIDS publishes the analysis services project to an Analysis Services database. Processing the database can be performed both from BIDS and SQL Server Management Studio. BIDS components: j) Analysis Services Solution Explorer The solution explorer show different objects within the analysis database that is being developed. These include data sources, data source views, cubes, dimensions, mining structures, roles, assemblies, and miscellaneous. By double clicking on any item in the solution explorer, you can open that object with its specific designer. k) Analysis Services Designers There are four designers in BIDS, Data Source View Designer, Cube Designer, Dimension Designer, and Data Mining Designer. Some of these designers are reviewed in this report while performing tutorials. l) Analysis Services Menus There are four menus related to analysis services projects, database menu, cube menu, dimension menu and mining model menu. Each of these are activated when the respective designer is open. m) Analysis Services Tools/Options NUMBER FOUR GROUP 19 OLAP CUBE OVERVIEW AND CASE STUDY The option menu provides some analysis services specific options on top of the general ones: Connection and query timeouts, Default Deployment Server Edition, Default Target Server, and Data Mining Viewers. NUMBER FOUR GROUP 20 OLAP CUBE OVERVIEW AND CASE STUDY CHAPER - OLAP CASE STUDY - DECISION MAKING WITH SHOPPING MALLS 3.1. Overview on shopping malls and activity Number Four Global is having different malls in our city, where daily sales take place for various products. Higher management is facing an issue while decision making due to non availability of integrated data they can’t study on their data as per their requirement. So they asked us to design a system which can help them quickly in decision making and provide Return on Investment (ROI) based on reports using Data warehouse and OLAP model. 3.2. Develop database & data warehouse - Database and data warehouse for Number - Four Global were designed based on Microsoft SQL Server Database was named as "NumberFourGroup" Data warehouse was named as "NumberFourceGroupDW" 3.3. Develop OLAP Cube - Finance cube Finance cube for Number Four Data Warehouse - Internet Sales cube NUMBER FOUR GROUP 21 OLAP CUBE OVERVIEW AND CASE STUDY for Number Four Data Warehouse NUMBER FOUR GROUP 22 OLAP CUBE OVERVIEW AND CASE STUDY - Reseller Sales cube Reseller Sales cube 3.4. - Process the cube All the cubes for the NumberFourGroupDW will be deployed and processed using SSAS, especially SQL Server Business Intelligence Development Studio or SQL Server Management Studio in Microsoft SQL Server NUMBER FOUR GROUP 23 OLAP CUBE OVERVIEW AND CASE STUDY Process cube using Microsoft SQL Server Managment Studio Reports System will use reports which are created using SSRS. All the reports will be deployed to SSRS Server and integrate to the NumberFourGroup web application system sothat the managers can view easily. 3.5. NUMBER FOUR GROUP 24 OLAP CUBE OVERVIEW AND CASE STUDY CONCLUSIONS Microsoft SQL Server has robust capabilities for development, maintenance and querying of OLAP cubes. SSAS provides the combination of flexible architecture and robust user friendly tools. The OLAP storage settings provide adequate options for system designers. SSAS flexible architecture makes it possible to integrate SSAS with any type of data source, and any means of end user interaction. Since OLAP is additional to basic database technologies and information technology solutions, there are always other database related technologies and user interfaces that OLAP has to integrate to. SSAS architecture flexibility makes it easier to integrate OLAP with existing database and interface technologies. SQL Server provides user friendly development tools. Business Intelligence Development Studio(BIDS) is based on Microsoft Visual Studio 2008, one of the most popular and user friendly development environments. Since BIDS has been used in other Microsoft products before, it has become mature enough to facilitate OLAP development and design. SSAS gives many options to architecture designers to propose problem specific OLAP storage solutions. There are seven predefined OLAP storage options In addition to predefined storage options, architecture designers are given the chance to define even more specific storage options based on particular application. The scalability test reveals that query response time grows linearly with the database size. As the main purpose behind OLAP is to support ad-hoc queries, the linear complexity is a major problem in applications with huge data. On the other hand, real time queries capabilities provided by ROLAP storage mode could be very helpful if the database size is moderate. NUMBER FOUR GROUP 25 OLAP CUBE OVERVIEW AND CASE STUDY REFERENCES [1] Himanshu Tiwari. Data Mining, Warehousing and OLAP Technology. Galgotia’s college of Engineering and technology Gr.Noida. 2014 [2] Siddhant Mehta, PoonamRawat, Prerna Malik. Overview of Multidimensional Data Model – OLAP. International Journal Of Research. 2014 [3] Dr Walid Qassim Qwaider. Apply Online Analytical Processing (OLAP) With Data Mining For Clinical Decision Support. International Journal Of Managing Information Technology. 2012 [4] Robert Wrembel & Christian Konci. Data Warehouses And OLAP – Concepts, Architectures and Solutions. Pozan University Of Technology, Poland. 2006 [4] How to Create OLAP Cube in Analysis Services, http://www.wikihow.com/CreateOLAP-Cube-in-Analysis-Services, View on 30 July 2015 [5] OLAP and Business Intelligence Tutorials, http://olap.com/learn-bi-olap/tutorials/, View on 26 July 2015 [6] Trần Vũ Hải. Áp Dụng Kỹ Thuật Phân Tích Dữ Liệu Trực Tuyến (OLAP) Phục Vụ Công Tác Quản Lý Điều Hành. Luận Văn Thạc Sĩ. Học Viện Công Nghệ Bưu Chính Viễn Thông, Hà Nội, 2011 [7] DSS course material provided by Associate Professor Phuc Do, 2015 NUMBER FOUR GROUP 26 [...]... "NumberFourceGroupDW" 3.3 Develop OLAP Cube - Finance cube Finance cube for Number Four Data Warehouse - Internet Sales cube NUMBER FOUR GROUP 21 OLAP CUBE OVERVIEW AND CASE STUDY for Number Four Data Warehouse NUMBER FOUR GROUP 22 OLAP CUBE OVERVIEW AND CASE STUDY - Reseller Sales cube Reseller Sales cube 3.4 - Process the cube All the cubes for the NumberFourGroupDW will be deployed and processed using SSAS,... GROUP 19 OLAP CUBE OVERVIEW AND CASE STUDY The option menu provides some analysis services specific options on top of the general ones: Connection and query timeouts, Default Deployment Server Edition, Default Target Server, and Data Mining Viewers NUMBER FOUR GROUP 20 OLAP CUBE OVERVIEW AND CASE STUDY CHAPER 3 - OLAP CASE STUDY - DECISION MAKING WITH SHOPPING MALLS 3.1 Overview on shopping malls and activity... Server NUMBER FOUR GROUP 23 OLAP CUBE OVERVIEW AND CASE STUDY Process cube using Microsoft SQL Server Managment Studio Reports System will use reports which are created using SSRS All the reports will be deployed to SSRS Server and integrate to the NumberFourGroup web application system sothat the managers can view easily 3.5 NUMBER FOUR GROUP 24 OLAP CUBE OVERVIEW AND CASE STUDY CONCLUSIONS Microsoft... and access methods Connect the sources using gateways, ODBC drivers, or other wrappers NUMBER FOUR GROUP 11 OLAP CUBE OVERVIEW AND CASE STUDY • • • • Design and implement scripts for data extraction, cleaning, transformation, load, and refresh Populate the repository with the schema and view definitions, scripts, and other metadata Design and implement end-user applications Roll out the warehouse and. .. revenue Data Mining within SQL Server 2.3 OLAP - Multi Dimensional Data in SQL Server OLAP facilities are provided within SQL Server as a whole and also within SSAS NUMBER FOUR GROUP 15 OLAP CUBE OVERVIEW AND CASE STUDY Multidimensional Data within SQL Server Multidimensional Data provides means for developers to design, publish and modify data cubes A data cube represents data in multidimensional format... for multidimensional data • ROLAP supports for very large databases 1.7 Multidimensional OLAP( MOLAP) MOLAP implemented operation in multidimensional data MOLAP extends OLAP functionality to MDBMS Best suited to manage, store and analyze multidimensional data Proprietary techniques used in MDBMS MDBMS and users visualize the stored data as a 3-Dimensional Cube i.e Data Cube MOLAP Databases are known to... Disadvantage NUMBER FOUR GROUP 13 OLAP CUBE OVERVIEW AND CASE STUDY OLAP, and its reliance on the data warehousing environment, are two of the most significant new technology areas Moreover, the use of relational design and relational database technology are not feasible implementations to support OLAP design because of the complexity of the queries The business problem is that OLAP queries are not real-time... the main purpose behind OLAP is to support ad-hoc queries, the linear complexity is a major problem in applications with huge data On the other hand, real time queries capabilities provided by ROLAP storage mode could be very helpful if the database size is moderate NUMBER FOUR GROUP 25 OLAP CUBE OVERVIEW AND CASE STUDY REFERENCES [1] Himanshu Tiwari Data Mining, Warehousing and OLAP Technology Galgotia’s... Concepts, Architectures and Solutions Pozan University Of Technology, Poland 2006 [4] How to Create OLAP Cube in Analysis Services, http://www.wikihow.com/CreateOLAP -Cube- in-Analysis-Services, View on 30 July 2015 [5] OLAP and Business Intelligence Tutorials, http:/ /olap. com/learn-bi -olap/ tutorials/, View on 26 July 2015 [6] Trần Vũ Hải Áp Dụng Kỹ Thuật Phân Tích Dữ Liệu Trực Tuyến (OLAP) Phục Vụ Công Tác... intelligence purposes NUMBER FOUR GROUP 14 OLAP CUBE OVERVIEW AND CASE STUDY CHAPER 2 - OLAP WITH SQL SERVER ANALYSIS SERVICE (SSAS) 2.1 SQL Server Analysis Services (SSAS) Microsoft SQL Server Analysis Services, SSAS, is an online analytical processing (OLAP) , data mining and reporting tool in Microsoft SQL Server SSAS is used as a tool by organizations to analyze and make sense of information possibly . more details of OLAP and how to use OLAP with SSAS through a sample case study. NUMBER FOUR GROUP 2 OLAP CUBE OVERVIEW AND CASE STUDY CHAPER 1 - OLAP OVERVIEW 1.1. OLAP Definition OLAP (online analytical. two-dimentional cube. The two-dimensional cube reoriented. NUMBER FOUR GROUP 6 OLAP CUBE OVERVIEW AND CASE STUDY SSRS Reports and Excel Power Pivot is used as front end for Reporting and data analysis. a cube includes dragging attributes / hierarchies and cube measures to respective pane and brows the data in the cube. g) Constructing queries NUMBER FOUR GROUP 18 OLAP CUBE OVERVIEW AND CASE