MOLAP Data Warehouse of a Software Products Servicing Call Center

Transcription

1 MOLAP Data Warehouse of a Software Products Servicing Call Center Z. Kazi, B. Radulovic, D. Radovanovic and Lj. Kazi Technical faculty "Mihajlo Pupin" University of Novi Sad Complete Address: Technical faculty "Mihajlo Pupin", Djure Djakovica bb, Zrenjanin, SERBIA Phone: ( ) Fax ( ) zkazi@ptt.rs Abstract - This paper presents data warehouse system design with example of a software importer and distributor Call center. Data analyses of telephone requests for servicing software products at Call center have been implemented. Call center business processes have been described. Conceptual model of database is presented, as well as aggregated data created with data warehouse. Multi-Dimensional On-Line Analytical Processing (MOLAP) cubes based on star and snowflake schema are implemented. Additional features are developed such as executing MDX queries and exporting analytical data to spreadsheet application, for the purposes of reports in the form of pivot tables and charts. I. INTRODUCTION Data warehouse is a concept of data integration into a specific storage that is used for conducting analysis and generating various reports. Data from relational databases is extracted, filtered, organized and stored in the storage form which enables complex and multidimensional access to data in aim to compute answers to questions of company management decisions support. Thereby different software tools, methods, models and hardware support are used for this purpose. The term "data warehouse" was first introduced by W.H. Inmon in who defines data warehouse is subjectoriented, integrated, non-volatile, time-variant collection of data in support of management s decisions [1]. The main goal of data warehousing is data collecting and distribution of information throughout the firm and use of any information, anywhere, at any time due to the realization of the principle of "always be at the service for information users". One of the main data warehouse building goals is not only to store data, but also to enable managers to conduct data analysis. Decision makers in organizations are often under pressure, because they must make their decisions on the basis of analysis of current facts obtained from various business situations, processes and data sources. These facts are stored mostly in on-line transaction (OLTP) systems as well as some the data sources. Integrated obtaining data for analysis is not very easy to perform. Purpose of data warehouse system is to transform the data obtained from existing OLTP system into a form suitable for processing which enables performing analysis with tools for business decision making support. End user of data warehouse system needs are met by enabling functionality such as using large amount of data from the company business processes for analysis, setting questions /getting answers about different business issues and diverse reporting. II. OLAP ARCHITECTURES Data warehouse systems are based on online analytical processing (OLAP), which is intended for interactive analysis and reporting, as opposed to the production system designed for data update and transaction processing OLTP systems. Analytical online processing as a term was introduced by mathematician Dr. Edgar F. Codd. While working for IBM in 1970, he published a paper "A relational model of data for large share data banks". This paper presents the theory of relational databases, where data analysis in business can be done by common queries over relational databases. The first technology introduced at beginning of data processing is based on relatively simple queries, performed by SQL statements in OLTP systems. Later on, queries became so complex that OLTP tools were not able to give answers in an acceptable time period. Therefore more efficient are OLAP systems that enable easy synthesis, analysis and consolidation of data. They are used for intuitive and flexible manipulation of transaction data. OLAP systems include variety of tools for complex processing that enable analysis of data from different perspectives. OLAP systems like data warehouses are using multidimensionality principle of de-normalized data. The basic elements of OLAP systems are: Database - provides data for analysis. OLAP server process and manipulate data. Interface system for interaction with a user and other applications. Administrative tools. There are two approaches regarding relationship of OLAP system with OLTP data resources: Some OLAP tools physically transmit all data from a relational database and other sources to multidimensional database by using meta-. Refreshing data is performed with full data warehouse batch access and complete data recovery in certain intervals. Another approach, called online, carries each change in relational to multidimensional database and refreshes data warehouse only with those data that were changed in a specified time interval. These two basic approaches caused the emergence of two basic OLAP architectures: multidimensional (MOLAP) and relational (ROLAP) architecture. There are also some other OLAP architectures such as HOLAP - Hybrid OLAP and DOLAP - Desktop OLAP.

2 MOLAP is a concept that requires data from a relational database to be transferred to multidimensional structure called a cube. Data extracting and transferring into the cube, creating aggregations, dimensions, calculated fields and other elements of the cube is a time consuming process whose duration depends on server performance. User can start an analysis only after completion of the whole process. It is difficult to add new dimension to the cube after creating a cube. It is necessary to start a completely new process of creating a cube that is defined upon all required elements. These systems have limited size of data sets that can handle with, because they require large disks and high system performance. Therefore, MOLAP system is suitable for use in cases when it is possible to divide large data sets into several smaller data sets, also called data mart. Advantage of MOLAP architecture is excellent performance of aggregated data presentation for analysis, since aggregated data are already available they are created in the process of creating a cube. ROLAP is a concept of direct access to data in relational databases from data warehouse. These systems can work with large amounts of data, because it does not transfer data to the warehouse. Once you determine the source of data, the user can immediately begin the analysis. Because of the direct work with relational database, users always have current data available, and it is easy to add new dimensions. After defining the model of data warehouse, aggregated data from the transactional system can be load to the warehouse. Multidimensional analysis is transformed into a series of SQL statements that are transmitted to the relational database. Each ROLAP report represent one or more queries which execution can last a long time, because a large number of operations that must be executed over data during the process. HOLAP architecture is a combination of MOLAP and ROLAP architecture. It is based on the multidimensional and relational databases. It aims to support good features of multidimensional analysis, i.e. short response time with analytical capabilities, as well as support to dynamic approach of relational systems. HOLAP data warehouse can perform very complex SQL statements in acceptable time period. Development of DOLAP architecture systems started with the idea that analysts do their work on regular desktop computers. DOLAP systems enable better performance of analysis with segmented and de-centralized data. Cube data sets, data marts and other data sources are stored at desktop computers. Data extraction and analysis are performed at a desktop computer, that is temporary disconnected from complex OLTP queries, which enables better analytical processing performance. This approach allows inconsistency of results because under these conditions the analysis can be done on multiple computers and at different time periods. Data that are stored at desktop computers could not be up-to-date with their origins, i.e. transactional data that are processed in relational OLTP systems. Results from different cubes may differ. III. CALL CENTER DATA WAREHOUSE DESIGN Data warehouse should have the following characteristics: subject/problem orientation, integration, names convention, time dependence of data and permanent data in the warehouse. For data warehouse development it is necessary to make analysis of data sources, prepare the data and build a data warehouse. Subject/problem orientation of data warehouse system in this paper is related to call center of a company that is engaged in servicing software products. The main purpose of data warehouse in this case is to provide storage, search, processing and analysis of data which is collected by call center from customers telephone requests about software products servicing. Data needed for servicing these requests are collected from companies that are vendors and software suppliers. The OLTP database contains data about applications that are serviced (type of application, application name, manufacturer, software package, class, version, year of production), service users (user, user name, region, place, and his primary business), customer phone calls, complaints and servicing (CallId, users, type of application, type of problem, call arrival time, date and hour, time of beginning and end of service, time that user spend waiting and the length of phone calls). Software user (i.e. user of software products that are serviced) may refer one or more calls to service several different software products, i.e. applications of different manufacturers. For each application must be known the manufacturer, product type (word processing, spreadsheet, presentation development, database, photo editing, etc), software package version, production year etc. The database contains only data related to applications that are serviced, but not for those which yet has not been invited for servicing. It is necessary to categorize, set name and describe the problem that customer refers to, as well as to classify problem difficulty. Conceptual model of the Call center relational database is shown in the following scheme: Fig. 1. Conceptual model of the Call Center database The main aim of data warehouse system development in this case is collecting problems and requirements for software products features (functional and non functional) that are required by software products users. Therefore, data warehouse is designed and built upon the analysis of these data, to provide decision support. MOLAP cube is generated in a short period of time, so any exceptions that occur could be tightly controlled.

3 Some of analytical requirements for creating data warehouse of software products call center are defined according to answering following questions: How many calls for service are made from each geographical location? How many calls are made, grouped by diverse geographical regions? How many calls are made according to products categories and business domains? What is the frequency of servicing different types of applications - which are to be serviced more and which less? Number of applications services / requests grouped by manufacturers? Number of applications services grouped by manufacturers and types of software products? Number of applications services grouped by manufacturers, types of software products and program packages? Numbers of services by types of problems? Show services by months; during which month should we give discounts? What is the total amount and average price of servicing by applications and regions? How many calls for services users have sent? Which type of users sent most calls? OLAP cubes are organized by dimensions and measures. Dimensions are taken from the dimensional tables and measures from the fact table. Table dimensions contain hierarchically arranged data which are the subject of various calculations. Dimension is a category for analyzing business data. It can be, for example: temporal, geographical and others. Fact table is used for application of mathematical functions such as summarizing, counting, average, maximum and minimum values of some columns, which are then appointed in accordance with the convention and are specified as derivative, i.e. aggregated data. There are standard cube dimensions, which are to be specified according to user requirements. In this subject orientation, i.e. specific semantics of software servicing call center, some of important dimensions are: Problem -> Name problems Time -> Global hour containers Class name ->Class name -> Application name City > City name Application -> Application Name Region -> City name -> Region name Manufacturer -> Name of the manufacturer -> Application name Program package -> Package name -> Application name User -> User Name Activity -> Activity description -> User Name Incoming container id -> Global container hour Table of facts and dimension tables are related, i.e. connected in data model. Organization of these tables is presented by schemas. The most frequently used methods of designing a scheme is snow-flake schema and star schema. Fig. 2. Star schema of the Call Center cube Star schema of data warehouse design is more often used, especially because of the structure that support fact tables to be associated with dimensions tables at only one. Star schema example (Fig. 2) presents a data model for the design of the cube. This cube will enable analyzing number of calls to call center in a certain period of time, with registered particular type of problem applications. It consists of the fact table CallLog that is connected with four dimensional tables: user, application, time and problem. Fig. 3. Snowflake schema of the Call Center cube Snowflake schema has more complicated structure where fact table is connected to dimensional tables that are at two or higher. Snowflake schema requires more joins between the tables and therefore is more resource intensive. Snowflake scheme example of the call center cube (Fig. 3) presents the fact table CallLog (the same as in the previous example of star schema) that has been associated with the eight-dimensional tables. As we can see from Fig. 3, dimension City is at two from the fact table CallLog, and dimension Region is at three. OLAP cubes in our example were designed and created in OLAP cube editor (Fig 4.) of Microsoft SQL Server Analysis Services tool, which is an upgrade to SQL Server

4 database management system. Following steps of using this tool are needed for creating an OLAP cube: a) Creating a database at server, b) Determination of data sources, the path to a relational database and DBMS drivers (DB providers), c) Creating new OLAP cube, d) Selection of fact table and creating measures, e) Specifying dimensions, f) Setting data storage options, by choosing types of storage: MOLAP, ROLAP or HOLAP type of storage. g) Determination of the options for grouping and aggregating data, relating to the limitations of disk space that cube will occupy, h) Processing the cube with the choice of processing methods (new cube, changing existing cube and refresh data in existing cube). After the cube is created, initial analysis of aggregated data can be accessed (Fig. 4), by using cube reader within the cube editor. Cube reader enables aggregated data and dimensions to be flexibly shown and hidden, depending on required analysis to be performed. business decision support need to set of actions that are to be performed over the analysing tool. Analysing tool performs necessary calculations and manipulations to get the desired results, which are formatted to fit to the display form of reports. These reports may consist of a combination of text, graphics, video, audio and they facilitate user understanding. The report is delivered to end users (management) by using diverse media - paper, computer monitor etc. Reports that are presented to users may cause users to define additional quiries to clarify set of answers or to gather more information. The process continues until users reach desired results. MDX (Multidimensional Expressions) Language is used to create queries over OLAP cubes. MDX began to appear in commercial applications during year MDX was designed by Microsoft as a standard for formulation of queries with multidimensional data sources. Setting MDX queries as an additonal functional feature is developed within MSSQL Analysis Services tool. The query syntax uses keywords that are common with SQL, such as SELECT and WHERE. Even though there are apparent similarities of the two languages, there are also significant differences. The general form of MDX query: WITH calculated values SELECT axis 1 ON COLUMNS, axis 2 ON ROWS FROM cubename WHERE data selecting criteria Figure 5. shows MDX query editor, supported in Microsoft Analysis Services tool. Fig. 4. OLAP cube editor IV. MAKING QUERIES OVER OLAP CUBES Analysis of data by using queries and reports is a process of getting answers to questions. It is performed by extracting relevant data from data warehouse system, transformation and setting in the context and formulation to adequate and readable format, according to end user needs. This process is conducted by an analyst, who must request answers to his questions. Defining the query is the process of taking the business questions or hypotheses and their translation in the format of queries that can be formalized for using in specific decision making support tools. During the execution of a query, a tool generates commands to obtain derived data from cube to be processed as a result of a query, i.e. set of data that present answers to a query question. Resulting data could be original subset of derived data from cube, or another of derivation of data upon original derived data from the cube. Analyst formulate the Fig. 5. MDX query editor MDX query over cube [Calls by application classes and regions] to display the number of calls for Microsoft products, by region, along the months of November (represented as [11]) and December (represented as [12]) in the year 2009 (represented as [2009]) shows an example of application of specified MDX syntax.

5 SELECT {[Time dimension].[2009].[11], [Time dimension].[2009].[12]} on Columns, Region.Members on Rows FROM [Calls by application classes and regions] WHERE [Manufacturer].[All Manufacturer].[Microsoft] made to the Call Center, grouped by types of applications and complexity s of using software. Another example of MDX query displays the number of calls, total for all calls and average cost per call, for value Belgrade from dimension table City: SELECT FROM WHERE {[Call number], [Total], [Average cost per call]} on columns [CallLog Cube] [City].[All City].[Belgrade] V. REPORTING ANALYTICAL DATA Additional analysis and graphical display of data, which are organized into OLAP cubes, can be done by using Pivot tables and graphs. These could be created by connecting OLAP cube as a data source to a spreadsheet application. It is also possible to create special applications by using ADO reporting mechanisms, for the same purpose. TABLE I Fig. 6. Chart made from pivot table VI. CONCLUSION Using data warehouse system of a software products servicing call center by implementation of OLAP cubes and exporting data for graphic display shows that the data warehouse system may affect the quality of operation of the business. It can significantly improve business operations by providing data to enhance quality of services to customers and clients, by analysis of data from the database in a specific time period. This system can provide answers to questions of management and can serve as a support for making strategic business decisions. Managers together with their business analysts can make their own analysis, since using software tools, methods, models and hardware support do not require specific and expert knowledge from areas such as programming, database design, transaction processing data, working with complex report generators and similar tools. User All User REFERENCES Call number Problem Application Installation name Advanced Basic Medium Grand Total Access Acrobat Reader Excel Flash Photoshop Power Point Premier Word Grand Total In this example, the analytical data are exported to Microsoft Excel from OLAP cube to create pivot tables. Microsoft Excel has efficient mechanisms for connecting to the OLAP source and excellent tools for various forms of diagrams and charts. Figure 6. shows columnar diagram that was created with data from the previously created (Table I) pivot table, which contains the number of calls [1] R. Elmasri, S. B. Navathe, «Fundamentals of Database Systems», Addison Wesley, [2] M. Gunderloy, J. L. Jorden, «SQL Server 2000», Mikro knjiga, Beograd, [3] P. Mogin, I. Luković, M. Govedarica, «Principi projektovanja baza podataka», Univerzitet u Novom Sadu, Stylos, Novi Sad, [4] A. Veljović, «Menadžment informacioni sistemi u praksi», Kompjuter biblioteka Č ačak, [5] W. Pearson, «MDX at First Glance: Introduction to SQL Server MDX Essentials», [6] B. Radulović, Lj. Kazi, Z. Kazi, «Informacioni sistemi - odabrana poglavlja, Tehnički fakultet Mihajlo Pupin, Zrenjanin, 2006, ISBN