Skip to main content
SpringerLink
Log in

Theoretical and empirical validation of comprehensive complexity metric for multidimensional models for data warehouse

  • Original Article
  • Published:
International Journal of System Assurance Engineering and Management Aims and scope Submit manuscript

Abstract

Structural complexity metrics have been widely used to assess quality of an artefact. Researchers in past have defined complexity metrics to assess the quality of multidimensional models for data warehouse. These metrics have been defined considering various elements like facts, dimensions, dimension hierarchies etc., but have not taken into account the relationships among these elements of the models. In our previous work, a comprehensive complexity metric for multidimensional models for data warehouse has been proposed which not only considered complexity due to the elements but also structural complexity due to relationships among these elements. However, the proposal lacks theoretical and empirical validation of the metric. Hence, practical utility of the metric could not be established. This paper validates the proposed metric theoretically as well as empirically. The theoretical validation using Briand’s framework shows that the proposed metric satisfies most of the properties required for a complexity measure. Empirical validation is carried out to observe the relationship between the complexity metric and understandability-a sub-characteristic of maintainability of multidimensional models. The results show that the metric has significant positive correlation with understandability of multidimensional models. Predictive model based on Ordinal Regression proposed in this work indicates that the proposed complexity metric may act as objective indicator for understandability as accuracy of the model is 86.3 % which is quite high.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Basili VR, Rombach HD (1988) The TAME Project: towards improvement-oriented software environment. IEEE Trans Softw Eng 14(6):758–773

    Article  Google Scholar 

  • Batra D (2007) Cognitive complexity in data modelling: causes and recommendations. Requir Eng 12:231–244

    Article  Google Scholar 

  • Berenguer G, Romero R, Trujillo J, Serrano M, Piattini M (2005) A set of quality indicators and their corresponding metrics for conceptual models of data warehouses. Proceeding of 7th International conference on Data Warehousing and Knowledge Discovery (Lecture Notes in Computer), Denmark, 22–26 August 2005, pp.95–104

  • Boehm B (1981) Software engineering economics. Prentice Hall, New Jersey

    MATH  Google Scholar 

  • Briand LC, Morasca S, Basili VR (1996) Property based software engineering measurement. IEEE Trans Softw Eng 22:68–86

    Article  Google Scholar 

  • Briand LC, Morasca S, Basili VR (1997) Response to: comments “property- based software engineering measurement: refining the additivity properties”. IEEE Trans Softw Eng 22(3):196–197

    Article  Google Scholar 

  • Briand LC, Wuest J, Ikonomovski S and Lounis H (1999) Investigation of quality factors in object oriented designs: an industrial case study. Proceedings of 21st International Conference on Software Engineering, Los Angeles, pp 345–354

  • Briand LC, Morasca S, Basili VR (1999b) Defining and validating measures for object-based high-level design. IEEE Trans Softw Eng 25(5):722–743

    Article  Google Scholar 

  • Canfora G, Garcia F, Piattin M, Ruiz F, Visaggio CA (2005) A family of experiments to validate metrics for software process models. J Syst Softw 77(2):113–129

    Article  Google Scholar 

  • Card DN, Agresti WW (1988) Measuring software design complexity. J Syst Softw 8(3):185–197

    Article  Google Scholar 

  • Carver J, Jaccheri L, Morasca S, Shull F (2003) Using empirical studies during software courses. Experimental Software Engineering Research Network (LNCS 2765), pp. 81–103

  • Si-Saıd Cherfi S, Prat N (2003) Multidimensional schemas quality: assessing and balancing analyzability and simplicity, ER Workshop 2003, pp. 140–151

  • Chidamber SR, Kemerer CF (1994) A metrics suite for object-oriented design. IEEE Trans Softw Eng 20(6):476–493

    Article  Google Scholar 

  • Ciolkowski M, Shull F, Biffle S (2002) A family of experiments to investigate the influence of context on the effect of inspection techniques. 6th International Conference on empirical assessment in software engineering, Keele, UK, pp 48–60

  • Costagliola G, Ferrucci F, Tortora G, Vitiello G (2005) Class points: an approach for the size estimation of object-oriented systems. IEEE Trans Softw Eng 31(1):52–74

    Article  Google Scholar 

  • Fenton NE, Pfeelger SL (1997) Software metrics – a rigorous and practical approach. International Thomson Computer Press, London

    Google Scholar 

  • Finkelstein L (2003) Widely, strongly and weakly defined measurement. Measurement 34(1):39–48

    Article  Google Scholar 

  • Flood RL, Carson ER (1988) Dealing with complexity: an introduction to the theory and application of system sciences. Plenum, New York

    Google Scholar 

  • Gemino A, Wand Y (2004) A framework for empirical evaluation of conceptual modelling techniques. Requir Eng 9:248–260

    Article  Google Scholar 

  • Genero M, Esperanza M, Visaggio A, Canfora G, Piattini M (2007) Building measure-based prediction models for UML class diagram maintainability. Empir Softw Eng 12(5):517–549

    Article  Google Scholar 

  • Genero M, Poels G, Piattini M (2008) Defining and validating metrics for assessing the understandability of entity–relationship diagrams. Data Knowl Eng 64(3):534–557

    Article  Google Scholar 

  • Gosain A, Nagpal S, Sabharwal S (2011a) Quality metrics for conceptual models for data warehouse focusing on dimension hierarchies. ACM SIGSOFT Software Engineering Notes 36(4):1–5

    Article  Google Scholar 

  • Gosain A, Sabharwal S, Nagpal S (2011b) Assessment of quality of data warehouse multidimensional model. Int J Inf Qual 2(4):344–358

    Google Scholar 

  • Gosain A, Nagpal S, Sabharwal S (2013) Validating dimension hierarchy metrics for the understandability of multidimensional models for data warehouse. To appear in IET software

  • Henry S, Kafura D (1981) Software structure metrics based on information flow. IEEE Trans Softw Eng 7(5):510–518

    Article  Google Scholar 

  • Inmon WH (1997) Building data warehouse. Wiley, New York

    Google Scholar 

  • IS0/IEC 9126 (2001)-Software engineering –product quality – part 1: quality model

  • Kaner C (2004) Software engineering metrics: what do they measure and how? Proceedings of the 10th IEEE Intertnational Software Metrics Symposium (Metrics 2004), Chicago, pp: 1–10

  • Kesh S (1995) Evaluating the quality of entity relationship model. Inf Softw Technol 37(12):681–689

    Article  Google Scholar 

  • Kitchenhem B, Pfleeger S, Pickard L, Jones P, Hoaglin D, EI Emam K, Rosenberg J (2002) Preliminary guidelines for empirical research in software engineering. IEEE Trans Softw Eng 28(8):721–734

    Article  Google Scholar 

  • Lindland OI, Sindre G, Solvberg A (1994) Understanding quality in conceptual modelling. IEEE Softw 11(2):42–49

    Article  Google Scholar 

  • Malinowski E, Zimanyi E (2006) Hierarchies in a multidimensional model: from conceptual modeling to logical representation. Data Knowl Eng 59(2):348–377

    Article  Google Scholar 

  • Mishra S, Akman I, Koyunku M (2011) An inheritance complexity metric for object-oriented code: a cognitive approach. Sadhana (Indian Academy of Sciences) 36(3):317–337

    Article  Google Scholar 

  • Moody DL (2005) Theoretical and practical issues in evaluating the quality of conceptual models: current state and future directions. Data Knowl Eng 55(3):243–276

    Article  MathSciNet  Google Scholar 

  • Moody DL, Shank G (2003) Improving the quality of data models: empirical validation of quality management framework. Int J Inf Syst 28(6):619–650

    MATH  Google Scholar 

  • Nagpal S, Gosain A, Sabharwal S (2012) Complexity metric for multidimensional model for data warehouse. International Information Technology Conference and Exhibition, Pune, 3–5 Sep 2012

  • Olague HM, Etzkorn LH, Messimer SL, Delugach HS (2008) An empirical validation of object-oriented class complexity metrics and their ability to predict error-prone classes in highly iterative, or agile, software: a case study. J Softw Maintenance Evol Res Pract 20(3):171–197

    Article  Google Scholar 

  • Olive A (2002) Specific relationship types in conceptual modeling: the cases of generic and with common participants. keynote lecture In: 4th International Conference on Enterprise Information Systems (ICEIS’ 02), Ciudad Real, 3–6 April 2002

  • Poels G, Dedene G (2000) Distance: A framework for software measure construction. Research Report DTEW9937, Dept Applies Economics Katholieke Universiteit Lueven, Belgium

  • Reijers HA, Mendling J (2011) A study into the factors that influence the understandability business process model. IEEE Trans Syst Man Cybern Part A 41:449–462

    Article  Google Scholar 

  • Rizzi S, Abello A, Lechtenbörger J, Trujillo J (2006) Research in data warehouse modelling: dead or alive? Proceedings 9th International Workshop on Data Warehousing and OLAP, Arlington, pp 3–10

  • Schneidewind N (1992) Methodology for validating software metrics. IEEE Tans Softw Eng 18(5):410–422

    Article  Google Scholar 

  • Schuff D, Karen Corral, Turetken O (2011) Comparing understandability of alternative data warehouse schemas: an empirical study. Decis Support Syst 52(1):9–20

    Article  Google Scholar 

  • Calero C, Piattini M, Pascual C, Serrano, MA (2001) Towards data warehouse quality metrics. 3rd International workshop on design and management of data warehouses, Interlaken, Switzerland

  • Serrano M, Calero C, Piattini M (2002) Validating metrics for data warehouse. IEE Proc Softw 149(5):161–166

    Article  Google Scholar 

  • Serrano M, Calero C, Piattini M (2005) An experimental replication with data warehouse metrics. Int J Data Warehouse Min 1(4):1–21

    Article  Google Scholar 

  • Serrano M, Trujillo J, Calero C, Piattini M (2007) Metrics for data warehouse conceptual models understandability. J Inf Softw Technol 49(8):851–870

    Article  Google Scholar 

  • Serrano M, Calero C, Sahraouli H, Piattini M (2008) Empirical studies to assess the undesrstandability of data warehouse schemas using structural metrics. Softw Qual J 16(1):79–106

    Article  Google Scholar 

  • Weyuker EJ (1988) Evaluating software complexity measure. IEEE Trans Softw Eng 14:1357–1365

    Article  MathSciNet  Google Scholar 

  • Wohlin C, Runeson P, Host M, Ohlsson MC, Regnell B, Wesslen A (2000) Experimentation in software engineering. Kluwer Academic, Norwell

    Book  MATH  Google Scholar 

  • Zuse H (1998) Framework of software measurement. Walter de Guyter, Berlin, 1998. http://www.norusis.com/pdf/ASPC_v13.pdf. Accessed July 2012

Download references

Author information

Authors and Affiliations

  1. Netaji Subhas Institute of Technology, New Delhi, India

    Sushama Nagpal & Sangeeta Sabharwal

  2. GGSIPU, New Delhi, India

    Anjana Gosain

Authors
  1. Sushama Nagpal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anjana Gosain
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sangeeta Sabharwal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sushama Nagpal.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Nagpal, S., Gosain, A. & Sabharwal, S. Theoretical and empirical validation of comprehensive complexity metric for multidimensional models for data warehouse. Int J Syst Assur Eng Manag 4, 193–204 (2013). https://doi.org/10.1007/s13198-013-0158-5

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13198-013-0158-5

Keywords

Search

Navigation