© Springer-Verlag Berlin Heidelberg 2015 James J. (Jong Hyuk) Park et al. (eds.), Computer Science and Its Applications,

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Lecture Notes in Electrical Engineering 330, DOI: 10.1007/978-3-662-45402-2_89

Application Research of University Decision Support System Based on Data Warehouse

Liang Gao* and Yun Chen

Informatization Office，Shanghai University of Finance and Economics Shanghai 200433, China

{gao.liang,chenyun}@mail.shufe.edu.cn

Abstract. There had accumulated a large amount data in business system of university, providing favorable condition for building decision support system based on data warehouse. This paper has introduced the architecture, data ware-house subject and construction process of decision support system in detail. Then the paper introduces a application example to show the value of the deci-sion support system. This system can not only support daily management, but also provide data evidence for leader to draw up policies and regulations of uni-versity, promoting the development of the university.

Keywords: Data Warehouse, Decision Support System, University.

1 Introduction

All kinds of management information system in university have been mature through years of construction. There has accumulated a large number of business data. It is a research focus of how to use these valuable data resources to provide services for the management and decision in university. There are some problems, for example data inconsistency, redundancy, heterogeneity, because the data distributes in different business systems. This causes there has a bottleneck in analysis and query cross dif-ferent business system. Constructing enterprise data warehouse will extract, clean and transform data from different business system. This process completes data integra-tion and can eliminate data inconsistency, redundancy, heterogeneity. The decision support system based on data warehouse not only can meet the requirement of daily query and statistics, but also can plan and manage all kinds of resources globally. It is more important to make full use of the results of data analysis to provide decision-making basis for the management layer, and develop historical data, explore the potential and valuable information by data mining.

* Corresponding author.

632 L. Gao and Y. Chen

2 System Architecture

The framework of university decision support system shown figure 1 includes data source layer, data processing layer, basis data layer, data mart layer, application ser-vice layer and access layer mainly.

Fig. 1. Architecture of Decision Support System

(1) Data Source Layer The data source layer is the data source of data warehouse system. The data source

of university business distributes mainly in human resources system, student system, teaching system, scientific research system, financial system, asset system and other core business systems. (2) Data Processing Layer

The data processing layer extracts, cleans, transforms original data, and organizes the original data according to the structure of the data warehouse model, then loads it into the data warehouse. After all procedures, the original data becomes standard data of data warehouse with multi granularity, providing support for decision-making. (3) Basic Data Layer

Basic data layer is the core of data warehouse system, which uses 3NF building re-lational data model. It stores and manages all kinds of business data by subject area

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and keeps historical data. Basic data layer normalizes business data, establishes uni-fied code rule, making various business system with the same or similar data format. (4) Data Mart Layer

The data mart layer establishes summary information following dimension for dif-ferent business applications. The data of data mart layer comes from basic data layer, ensuring the data quality and data consistency. (5) Application Service Layer

The application service layer is the core of accessing data warehouse information. It receives user’s requests to access the data in OLAP or data warehouse, producing various of complicate reports, and shows them in a variety of ways, such as print out-put, file output, Email output, Web release. (6) User Access Layer

User access layer is the interface between data service and user. This layer pro-vides users browsing, requesting, accessing the data in data warehouse, also includes user authentication, authority management.

3 Construction Process and Methods

3.1 Data Warehouse Subject

Combined with university business and the characteristics of data in university, we design eight subjects for university data warehouse, including public, organization, person, teaching, scientific research, asset, finance, event. Relations between subjects are shown in figure 2.

Fig. 2. Subjects of University Data Warehouse

634 L. Gao and Y. Chen

The public subject stores dictionary code of business activities, such as internation-al standard, university standard, Industry standard and so on.

Organization subject includes all kinds of organization structure of school, such as campus, departments, research institutes, party organizations, etc..

The person subject mainly includes all types of personnel data, such as faculty in human resources system, all kinds of students in teaching system and all types of person in other systems. This subject is the most basic and most central subject of data warehouse, because all business activities are focused on all kinds of person and this subject has close connection with other subjects.

The teaching subject stores entities related to teaching activities, including training plan, class information, curriculum, teaching activity, course arrangement, course selection, examination arrangement, exam results , students' graduation thesis activi-ties, evaluation of teaching activities and other related contents.

The scientific research subject stores entities related to scientific research activi-ties, including scientific research project, project funds, all kinds of scientific research product and so on.

Asset subject stores a variety of of asset data, including building, room, furniture, equipment, instrument and other kinds of asset data in university.

Finance subject includes all kinds of budget, revenue, expenditure data. Event subject mainly stores transaction data related to business activities, such as

payroll records, performance appraisal records, recruitment records, declare scholar-ship activities and so on.

3.2 Data Modeling

The essence of data modeling is to integrate the data coming from different business system, specificate data storage, and establish clear relationship of business data. The data warehouse storage architecture generally divides into ODS layer, base layer and data mart layer, corresponding to different modeling method.

(1) ODS Layer Modeling ODS (Operational Data Store) is a permanent storage area for business system da-

ta, as a buffer zone for extracting business data. The model of ODS layer is consistent with business system’s model. The data of ODS layer maintains consistent with origi-nal data as much as possible. All tables in ODS layer are added the loading date field, provideing the convenience for the future operation and management of ETL.

(2) Base Layer Modeling The base layer is the core of data warehouse, and the data is stored according to the

subject classification. The function mainly includes:

• Store the clean data through validation rules • Code business data, realizing unification and standardization • Store and manage the business entity according to subject classification, as

the business view for business user and data view for technical user

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• Use relational model organizing entities and reducing the data redundancy • Retain all the historical changes in business, especially business state

We build the data model of base layer according to 3NF, reorganizing the source data and making the data storage structure more reasonable. The base layer includes two categories of table, current table and history table. The data in current table re-flects the latest business state and the data in history table reflects historical business state, can query every day history data.

(3) Data Mart Layer Modeling The data mart calculates the statistical metric for specific user in advance, meeting

the performance requirement. The target of data mart layer is to solve practical re-quirements, so there not have strict restraint for the model. Considering execution time and story space comprehensively, we use dimension modeling method structur-ing the data mart layer. When there is enough storage space, we take more effective redundancy, meeting the needs of performance. We mainly use wide table designing the model, combining more dimensions and metrics in one table to meet a variety of different application requirements. Storage form can be tables, materialized views and views.

3.3 Data ETL

Different data storage layers need ETL to complete data extracting, cleansing, trans-forming, loading, finally finishing the entire data process and coming into the target data.

(1) ODS Layer ETL ODS layer ETL is the process of importing data from source system into the data

buffer. Because the table structure of ODS is consistent with business system, the ETL process does not involve data extraction and cleaning, and the essence of ETL is to copy the table from source system to ODS layer. We can use ETL tool or script to complete batch development.

(2) Base Layer ETL Base layer ETL achieves data extraction, transformation, cleaning and loading

from ODS layer to base layer according to the mapping document. In the way of load-ing, we choose incremental mode or total mode according whether there is business date. In the loading strategy, we design loading sequence according to the dependency relationship between the tables, generally loading code table firstly, loading business entity tables secondly. In the development tool, we can use commercial tools such as Informatica, open source tools such as Kettle, stored procedure.

(3) Data Mart Layer ETL Data mart layer ETL develop the data oriented application, generally useing view,

materialized view and storage process. We select the specific development method according to the execution performance and the development complexity.

636 L. Gao and Y. Chen

4 Application Example

Fig.3 is an application example of university decision support system. We can watch the trend of paper quantity and forecast future quantity according to the quantity of published paper. In Fig.3, the blue line signifies actual quantity, the red line signifies estimate quantity, the solid line signifies the real situation, the dotted line signifies the trend.

Fig. 3. Trend and Forecast of Paper Quantity

5 Summary

With the accumulation of business data continuously, building a decision support system based on data warehouse is a inevitable trend. The system of our university has shown the value in management and decision. The content of this paper has strong guiding significance for university to build decision support system.

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