On-Line Analytical Processing Tools for LandRevenue Management System

ShamsuJ Sahibuddin & Nor Hawaniah ZakariaCollaborative Working & Distributed System GroupFaculty ofComputer Science & Information System

University Tectmology Malaysia(shamsu/,hawaniah}@fsksm.utm.my

Abstract- A functional data warehouse organizesand stores all of the available data needed forinformational, analytical processing over ahistorical time perspective. The whole purposeof building the data warehouse is to give anidentified set of users a particular set of data.This study is focusing on data analysis tools fortbe land revenue system used by a stategovernment. Land Office is one of tbe majorrevenue providers for tbe state government.According to the report provided by tbe landoffice in one of the states in 1999, the totalincome collected through land office was morethan RM15 million [61. A system was developedin 1995 to facilitate the collection process. Thissystem is called Sistem Pentadbiran Basil Tanah(SPHT) or Land Revenue Management System.This paper will describe bow a data analyticaltool is going to be implemented in the system inorder to provide a better mecbanism in thedecision-making process. New systemarchitecture is proposed in order to maintainthe existing system while at the same timeproviding new analytical processing toolscapabilities. Issues regarding the databasearchitecture are also discussed.

I. INTRODUCTION

[n today's world. the competitive edge iscontributed less by optimization but more from theproactive use of the information that computersystems have been collecting over the years.Organization is beginning to realize the vastpotential of the infonnation that they hold in theirestablishment. Tapping into this information cansignificantly improve the quality of their decisionmaking through focus actions.

To make this possible, a response in thefonn of data warehouse system was initiated. Thedata warehouse is more than just data; it is also theprocesses involved in getting the data from table toanalysts. The data warehouse provides facility forintegrating the data generated in a world ofunintegrated infonnation systems [I]. Datawarehouses collect information from many sourcesinto a single database [5]. A functional datawarehouse organizes and stores all of the available

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data needed for informational, analytical processingover a historical time perspective.

The whole purpose of building the data warehouse isto give an identified set of users to a particular set ofdata. There are as many different tools as there arepeople to use them. These tools can be classified intothree broad categories, namely;

Data dipping tools.Data mining tools andData analysis tools [I].

This study is focusing on data analysis tools for theland revenue system use by a state government. This I

paper will describe how a data analytical tool isimplemented in the system in order to provide a bettermechanism in decision-making process.

II LAND REVENUE SYSTEM

Land Office is one of the major revenue providers forthe state government. According to the report providedby the land office in one of the state in Malaysia, in1999 the total of income collected through land officewas more than RM15 million [6]. A system wasdeveloped in 1995 to facilitate the collection process.This system is called Sistem Pentadbiran Hasil Tanah(SPHT) or Land Revenue Management System.

SPHT is used to minimize processing backlogand the maintenance of payment record and to reducepaper trail. The main objective of SPHT is to enhancethe tax collection process without sacrificing thesecurity aspect while providing easy access to theinformation. The system was fully operational in 1999.The system allows payment to be made at any districtoffice. The payment transaction occurs daily.

At the end of the month and at the end of the year anumber of reports are generated. Among the statementsgenerated are the following:

PTG-L(lO),KEW-249,LPR-650,LPR·D40 and other statements.

These statements are important to the management inmaking decision and planning for the future of theorganizations [6]. These statements provide the overallinformation on the income collected. With the currentsystem, the management has to rely on the clerical staffin order to get the report. This creates delay in getting

the report to the management and in the process,some on the information becomes outdated.

There exist a number of other weaknesses inSPHT. The system does not provide the correctestimate value, which is used to calculate thecollected percentage value. The estimate valueused by the current system is based on the nearestvalue and the highest value that was used in theprevious year's calculation. Also this value isstatic and as a result the organization will notachieve the exact percentage collected value.Another problem exists in the system is when thereis a request by the management for the totalcollected amount. The data has to be extracted andsorted out from the PTG-L(10) statement manuallyand hence wasting the time and effort of themanagement [6].

ROLAP tools are SQL-oriented and have tightintegration with the relational model. Metadata is useto isolate the user from the underlying complexities. Italso differs from the data differs by their range anddepth of analytical functionality. It can also be use asdata browsers with drill-down capability fromaggregation of detailed data [1].

Multidimensional analysis

In order to analyze the data in many dimensions atonce, a multidimensional analysis technique isemployed. In this technique, matrix arithmetic andsparse matrix optimizations allow the data to be storedefficiently and analyzed rapidly by the loadeddimensions. Allowing the use of multiple separate but

Table 1 : Exilmples ofPTG-L(10) Statements

Kod Hasil PerihaJ Tahull Anggaran Sehillgga Pungutan Jumlah Peratusan(Revenue (Description) (Year) (Estimate) BulanLaiu Bulall Pungulan Pungulan

Code) (Until Last Semasa (Total (PercentageMonth) (Current Collection) of

Month Collection)Collection)

5611100 Cukai Tanah 1999 5,000,000 2,500,000 1,000,000 3,500,000 70%Sema!a 2000 5000,000 3.000.000 1.000000 4.000.000 80%

5611101 Cukai Tanah I.... .... .... .... ..... ....Tun22akan

.... .... .... .... .... ,... ., .. ....

III OATA ANALYSIS TOOL

Data analysis tools are used to perform complexanalysis of data. They normally have a set ofanalytic functions that allow sophisticated analysisof the data. On-line analytical processing (OLAP)is one example of the tools available. OLAP toolsare designed to allow reasonably large quantity ofdata to be analyzed online.

related hypercubes reduces the scarcity of the matricesfurther.

MOLAP does have an SQL interface in order toallow them to extract data from a relational database.MOLAP tools are used for analyzing aggregated data inconjunction with its dimension data. Theseaggregations are normally data mart designedspecifically for the MOLAP tools. These tools are notrecommended for drill down to detailed data at the factlevel [1].

On-line Analytical Processing (OLAP) IV OLAP FOR LAND REVENUE SYSTEM

OLAP describes a class of technologies that aredesigned for live ad-hoc data access technologies.It is about the methods, structures and toolsrequired rapidly accessing and analyzing data.OLAP tools are designed to allow reasonably largequantity of data to be analyzed online. It allowsusers to quickly perform standard analytical,functions on the data and to represent both data

, and results graphically. OLAP 'tools can bedivided into two categories, which is ROLAP(relational OLAP) and MOLAP (multidimensionalOLAP). Both tools differ in the approach used toanalyze data. which is the relational approach orthe multidimensional approach [1 ][7][8].

An OLAP tools for Land Revenue ManagementSystem was proposed based on the weaknesses ofcurrent system.

System Architecture

The system architecture for an OLAP is based onWeb-based client-server architecture. In a classicalclient-server architecture, there exist two-tier, namely aclient and a server (Figure 1). But for a system that isrelying on a legacy database and at the same time istrying to take advantage of the world-wide-webinfrastructure, then hyper-tier architecture is a bettersolution

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Issues in System Architecture

There are a number of issues that is affecting thedesign of the OLAP system architecture. Amongthe issues are as follows:

• The need to maintain the mainframe­based legacy database. The investmenton the legacy system is enormous.Thus, there is a requirement to maintainthe legacy system. The legacy databasecan also act as the data warehouse forthe whole system. The majority ofother transactional systems also use thelegacy database.

• The need to take advantage of theworld-wide-web infrastructure. £norder to make the system available ondifferent types of platform, there is aneed to utilize the world-wide-web.The web will act as a platform ofintegration with existing environment.

• The integration of the legacy databasewith the multi-dimensional databaseand OLAP server. The requirement to

section. In this approach, the back-end server or thefourth-tier is the mainframe, storing the originaldatabase. The third-tier is the multi-dimensionaldatabase and the OLAP server. The second-tier is theWeb server and the Java server connecting the webclient with the OLAP system. And at the other end onthe fU'St-tier is the web client for the system.

£n this proposed architecture, the legacy database onthe fourth-tier allows existing transactional system toco-exist. The multi-dimensional database on the third­tier allows the on-line analytical processing to beconducted by the OLAP server. The Web and Javaserver on the second-tier act as an integrating platformbetween the OLAP server and the Web client.

Enabling Technologies

The OLAP systems must be implemented on someform of network. There are a number of problems ifthe system is to be implemented in a local area networkor wide area network from scratch. One is the enablinginfrastructure in terms of development tools have to beset up [3]. The problem of integration with existingenvironments and migration is also an issue.

1. Web browserinterpret user's

~q~st gL~~

Web Browser

2. Request is send to the WebServer via the internet

4. The information requested issend to the Web browser

Web Server

Figure 1: Web-based Client-Server Architecture

3. Web serverinterpret request fromWeb browser and getthe necessaryinformation

maintain the existing legacy databasenecessitates the setting up of a differentmUlti-dimensional database to be usedby the OLAP system. The OLAPserver not only is required to handle theanalytical query, but it also has tocommunicate with the data warehouse.

• The integration of the OLAP serverwith the world-wide-web. In order tomake the OLAP system accessible onthe web, the system must be integratedwith the web server and web clients.One way to achieve this is by usingJava technology.

Hyper- Tier Solutions for OLAP System

The client-server architecture proposed is ahyper-tier approach (Figure 2). This architectureis considered the best solution availableconsidering the issues raised in the previous

Two types of technologies that are of interest to thesystem architecture are World Wide Web (WWW) andJava Technology (Java). The World Wide Web(WWW) provides a great deal of potential. It can beviewed as a platform of integration with existingenvironment and as an enabling infrastructure fornetworked system. The WWW offers simple andunified access for users to retrieve information or tomake it available for others (Figure 1).

With Java, the issue of migration can be tackled, andprovides the tools for extending the WWW andintegrating it with existing end-user environment. TheJava programming language was designed to meet thechallenges of application development in the context ofheterogeneous network-wide distributed environment[2]. Java standard classes also provide all the basicsblocks necessary for client-server implementation [4].Java servers allows the request from the Web client toredirected to the OLAP server which in turn will accessthe multidimensional database.

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Figure 2: Four-Tier Cllent-Server Architecture for OLAP Toolsfor Land Revenue System

Web Browser

~L~~Web Browser

Web Server MUltidimensional Database& OLAP Server

Data Warehouse

Database Architecture

There are a number of different databases that isinvolved in OLAP systems. Normally, a separatedatabase is maintained for the operational usage.A different database is also maintained for the datawarehouse and the data marts. In this discussion,we are only concern with the database architecturefor the multi-dimensional database, which will beused by the OLAP server.

Issues in Database Structure

Organization of data is essential for systemperformance, especially for OLAP system. One ofmajor technical challenge is to structure a solutionthat will be effective for a reasonable period oftime. The solution offered should not berestructured when business changes or queryprofiles change.

In designing the database for the OLAP system,several issues shaH be focused, among which are:

• The database schema. The schemashould answer the question of how adatabase should be designed to answerunknown queries to be performed.

• The portioning strategy. The issue here ishow the database should be partitioned toallow best performance andmanageability of data; and

• The aggregation (summary) strategy.This will allow common queries to beperformed fast.

Multi-Dimensional Data Model

Arrangement of data is important to allow formultidimensional analysis to happen fast especially inlarge databases [1][7]. In multidimensional data model,database is viewed as a set offacts in multi-dimensionalspace. A database schema called star schema describesthe physical architecture of the dimensional model.

In order to come-up with a star schema for thisOLAP system, there is a need to define the userrequirement. It is critical to understand how theinformation in the data mart will be used. The userrequirement will identify the possible analyticalfunctions provided by the systems [8].

In general, the star schema consists of a single facttable and a single table for each dimension. The facttable consists of data that is being used to measure oranalyze, and numerical data being tracked, for examplethe amount of tax paid, or the size of land owned.Whereas, dimension tables are made up of businessparameters, for example the type of land info, or thetype of tax info. Dimension tables are being set in adenormalized form. The factual data provides theinformation for analysis.

Several advantages of applying star schema in amulti-dimensional database are as follows:• Reduce the number of physical joins that normally

occur in a normal joined relational tables;• Simplify the view of the data model;• Allow relatively easy maintenance on the data

V CONCLUSION

As a conclusion. this paper has presented a proposalfor an On-line Analytical Processing Tools for LandRevenue System. The system architecture proposed isbased on four-tier client-server architecture. The need

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to maintain the mainframe-based legacy database.taking advantage of the world-wide-webinfrastructure, the integration of the legacydatabase with the muiti-dimensional database andOLAP server, and the integration of the OLAPserver with the world-wide-web are the issues thatwas taken into consideration during the design ofthe system architecture. The database schema forthe multi-dimensional database was also discussed.Star schema, which describes the physicalarchitecture of the dimensional model, is used asthe database schema.

[5J Labio, W.J., Quass.D., & Adelberg, B., PhysicalDatabase Design for Data Warehouses,http://www.cs.stanford.edu, 20 September, 2000.

[6] Nazri Mohd Nawi, Pengurusan Hasi/ Tanahmenggunakan Kaedah OLAP, Laporan Projek IM.Sc., Skudai: FSKSM, Mei, 2000

[7] Pilot Software, An Introducion to OLAP:Multidimensional Terminology and Technology,http://www.pilotsw.com/news/olap_white.htm. 21July, 2000.

[8] Efrem, G.M, Decision Support and DataWarehouse Systems, Boston: Irwin McGraw-Hili,2000

REFERENCES

[1] Anahory, Sam and Murray, Dennis, DataWarehousing in the Real World, Addison­Wesley: Harlow, England, 1997.

[2] Sahibuddin, Shamsul, "FlexSIG: FlexibleSoftware Inspection Groupware", PhDThesis, Aston University, Birmingham,United Kingdom, 1999

[3] Doherty, B.S. & Sahibuddin, S., "ModellingDistributed Code Inspection System",Proceedings of the REDECS'96, Serdang,Malaysia: Universiti Putra MalaysiaPublications, June, pp. 69-73, 1996

[4] Sun. A Java Language Environment: A WhitePaper, Mountain View, California: SunMicrosystems Computer Company, October,1995

Information Sources Data WarehouseServer

OLAP Servers Clients

DataWarehouse

Data Marts

MOl.AP

/. IIII_serv_,:> o~"'

ROLAP lW

d8~·. ~.

SemistructuredSources

Operational

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Figure 3 : A Generic Data Warehouse Architecture

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