The expansion of higher education (HE) institutions and their increased emphasis on strategic planning have raised the demand for integrated information systems that can support strategic analysis. A data warehouse (DW) provides decision data for managers in a form that facilitates their access, using business intelligence (BI) tools to enhance their decision making process. However, available DW models and design methods do not address the HE industry’s specific needs. In this paper, we aim to fill this gap by providing a methodological framework that encompasses methods tailored to meet the needs of HE. Also, a prototype has been developed using actual student data to validate the framework. It shows the efficacy of the framework in supporting strategic analysis at universities, leading them to develop effective strategies and policies. The paper further provides guidelines for researchers on the important issues to be considered for designing efficient DWs.
AllanRGMayDR (2000) Data models for a registrar’s data mart. In: Higher Education Administrative Technology Conference (CUMREC), Arlington, Virginia, USA. EDUCAUSE.
2.
AniaCSamuelS (2014) Multidimensional design paradigms for data warehouses: a systematic mapping study. Journal of Software Engineering and Applications7(1): 53–61.
3.
AsraniDJainR (2014) Review of techniques used in data warehouse implementation: an initiative towards designing a frame work for effective data warehousing.in: International Conference on Advances in Engineering and Technology Research (ICAETR), Unnao, India, 1-2 August 2014, pp. 1-5.IEEE.
4.
BaranovicMMadunicMMekterovicI (2003) Data warehouse as a part of the higher education information system in Croatia. In: Proceedings of the 25th International Conference on Information Technology Interfaces(ITI), Cavtat, Croatia. pp. 121–126. IEEE.
5.
BonifatiACattaneoFCeriS. (2001) Designing data marts for data warehouses. ACM Transactions on Software Engineering and Methodology10(4): 452–483.
6.
BrasilMABPereiraGFontouraLM (2012) Software process tailoring using Situational Method Engineering based on criteria of quality improvement. In: XXXVIII Conferencia Latinoamericana En Informatica (CLEI), Medellin, Colombia, 1-5 October 2012, pp. 1–8. IEEE.
7.
FreitasGMLaenderAHFCamposMLM (2002) MD2 - Getting users involved in the development of data warehouse applications. In: 4th International Workshop on Design and Management of Data Warehouses (DMDW’02), Toronto, Canada, 27 May 2002. pp. 3–12.CEUR-WS.
8.
GiorginiPRizziSGarzettiM (2005) Goal-oriented requirement analysis for data warehouse design. In: Proceedings of the 8th ACM International Workshop on Data Warehousing and OLAP. Bremen, Germany, 4-5 November 2005. pp. 47–56. ACM.
9.
GosainASinghJ (2015) Conceptual multidimensional modeling for data warehouses: a survey. In: SatapathySCBiswalBNUdgataSK. (eds.) Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014. Springer International Publishing, pp. 305–316.
10.
GrahamJW (2002) Constructing a student data warehouse. In: Proceedings of the 30th Annual ACM SIGUCCS Conference on User Services, Providence, Rhode Island, USA, 20-23 November 2002. pp. 174–175. ACM.
11.
GuanJNunezWWelshJF (2002) Institutional strategy and information support: the role of data warehousing in higher education. Campus-Wide Information Systems19(5): 168–174.
12.
Hua-longZ (2008) Application of OLAP to the analysis of the curriculum chosen by students. In: 2nd International Conference on Anti-counterfeiting, Security and Identification (ASID), Guiyang, China, 20-23 August 2008. pp. 97–100. IEEE.
13.
InmonWH (2005) Building the Data Warehouse. New York: John Wiley.
14.
JohnMA (2009) Data driven vs. metric driven data warehouse design. In: JohnW (ed.) Encyclopedia of Data Warehousing and Mining, Second Edition. Hershey, PA, USA: IGI Global. pp. 382–387.
15.
JukicNNicholasJ (2010) A framework for requirement collection and definition process for data warehousing projects. In: 32nd International Conference on Information Technology Interfaces (ITI), Cavtat/Dubrovnik, Croatia21-24 June 2010. pp. 187–192. IEEE.
16.
KhouriSBellatrecheLJeanS. (2014) Requirements driven data warehouse design: we can go further. In: MargariaTSteffenB (eds.) Leveraging Applications of Formal Methods, Verification and Validation. Specialized Techniques and Applications. Springer Berlin Heidelberg. pp. 588–603.
17.
KimballRMundyJThornthwaiteW (2011) The Microsoft Data Warehouse Toolkit: With SQL Server 2008 R2 and the Microsoft Business Intelligence Toolset. New York: John Wiley.
18.
KimballRRossM (2002) The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling. New York: John Wiley.
19.
KimballRRossM (2013) The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling. New York: John Wiley.
20.
KimballRRossMThornthwaiteW. (2008) The Data Warehouse Lifecycle Toolkit. New York: John Wiley.
21.
KornyshovaEDeneckereRClaudepierreB (2010) Contextualization of method components. In: Fourth International Conference on Research Challenges in Information Science (RCIS), Nice, France, 19-21 May 2010, pp. 235–246. IEEE.
22.
LailaNMaris SolodovnikovaTLigaG (2009) Development of data warehouse conceptual models: method engineering approach. In: DavidT (ed.) Progressive Methods in Data Warehousing and Business Intelligence: Concepts and Competitive Analytics. Hershey, PA, USA: IGI Global. pp. 1–23.
23.
LinMC (2001) University data warehouse design issues: a case study. In: Proceedings of the 2001 American Society for Engineering Education Annual Conference & Exposition, Albuquerque, New Mexico, USA. pp. 6.1085.1081–1086.1085.1089. American Society for Engineering Education (ASEE).
24.
MalinowskiEZimànyiE, (2009) Advanced Data Warehouse Design: From Conventional to Spatial and Temporal Applications. Springer-Verlag.
25.
MatteoG (2010) From user requirements to conceptual design in warehouse design: a survey. In: LadjelB (ed.) Data Warehousing Design and Advanced Engineering Applications: Methods for Complex Construction. Hershey, PA, USA: IGI Global. pp. 1–16.
26.
MeylianaWidjaja HAESantosoSW (2014) University dashboard: an implementation of executive dashboard to university. In: 2nd International Conference on Information and Communication Technology (ICoICT), Bandung, Indonesia, 28-30 May 2014. pp. 282–287. IEEE.
27.
MoreiraJLCordeiroKdFCamposMLM. (2015) Hybrid multidimensional design for heterogeneous data supported by ontological analysis: an application case in the Brazilian electric system operation. In: Proceedings of the EDBT/ICDT 2015 Joint Conference(EDBT/ICDT), Brussels, Belgium, 27 March 2015. pp. 72–77.CEUR-WS.
28.
NobreTTrigoASanchesP (2014) SBIAES; business intelligence system for analysis of access to higher education: the case of the Polytechnic Institute of Coimbra. In: 9th Iberian Conference on Information Systems and Technologies (CISTI), Barcelona, Spain, 18-21 June 2014. pp. 1–6. IEEE.
29.
OscarRAlbertoA (2009) A survey of multidimensional modeling methodologies. International Journal of Data Warehousing and Mining (IJDWM)5(2): 1–23.
30.
PaimFRSCarvalhoAECastroJBD (2002) Towards a methodology for requirements analysis of data warehouse systems. In: 16th Brazilian Symposium on Software Engineering, SBES’02, Gramado, Rio Grande do Sul, Brasil, 14-18 October 2002. pp. 1–16.
31.
PiedadeMBSantosMY (2010) Business intelligence in higher education: enhancing the teaching-learning process with a SRM system. In: 5th Iberian Conference on Information Systems and Technologies (CISTI), Santiago de Compostela, Spain, 16-19 June 2010. pp. 1–5. IEEE.
32.
PorterJDRomeJJ (1995) Lessons from a successful data warehouse implementation. CAUSE/EFFECT18(4): 43–50.
33.
RamamurthyKSenASinhaAP. (2008) An empirical investigation of the key determinants of data warehouse adoption. Decision Support Systems44(4): 817–841.
34.
RizziSAbellóALechtenbörgerJ. (2006) Research in data warehouse modeling and design: dead or alive? In: Proceedings of the 9th ACM International Workshop on Data Warehousing and OLAP, Arlington, Virginia, USA, pp. 3–10.1183515. ACM.
35.
SelmouneNAlimazighiZ (2008) A decisional tool for quality improvement in higher education. In: 3rd International Conference on Information and Communication Technologies: From Theory to Applications (ICTTA), Damascus, Syria, 7-11 April 2008. pp. 1–6. IEEE.
36.
SimitsisA (2005) Mapping conceptual to logical models for ETL processes. In: Proceedings of the 8th ACM International Workshop on Data Warehousing and OLAP, Bremen, Germany, 4-5 November 2005. pp. 67–76. ACM.
37.
SommervilleI (2011) Software Engineering. New York: Pearson Education Ltd.
38.
ŞuşneaE (2013) Improving decision making process in universities: a conceptual model of intelligent decision support system. Procedia - Social and Behavioral Sciences76(0): 795–800.
39.
TriaFDLefonsETangorraF (2012) Hybrid methodology for data warehouse conceptual design by UML schemas. Information and Software Technology54(4): 360–379.
40.
ZhengHY (2015) Business intelligence as a data-based decision support system and its roles in support of institutional research and planning. In: WebberKLCalderonAJ (eds.) Institutional Research and Planning in Higher Education: Global Context and Themes. New York: Routledge Press/Taylor & Francis.
