Advertisement

Selection of a Green Logical Data Warehouse Schema by Anti-monotonicity Constraint

  • Issam GhabriEmail author
  • Ladjel Bellatreche
  • Sadok Ben Yahia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12011)

Abstract

In the era of social media and big data, many organizations and countries are devoting considerable effort and money to reduce energy consumption. Despite that, current research mainly focuses on improving performance without taking into account energy consumption. Recently, great importance has been attached to finding a good compromise between energy efficiency and performance in data warehouse (DW) applications. For a given DW, multiple logical schemes may exist due to the presence of dependencies and hierarchies among the attributes. In this respect, it has been shown that varying the logical schema has an impact on energy saving. In this paper, we introduce a new approach for efficient exploration of the different logical schemes of a DW. To do so, we prune the search space by relying on anti-monotonicity based constraint to swiftly find the most energy-efficient logical schema. The carried out experiments show the sharp impact of the logical design on energy saving.

Keywords

Data warehouse Logical schema Variability Anti-monotonicity Energy consumption Green computing 

References

  1. 1.
    Abadi, D., et al.: The beckman report on database research. Commun. ACM 59(2), 92–99 (2016)CrossRefGoogle Scholar
  2. 2.
    Acar, H., Alptekin, G.I., Gelas, J., Ghodous, P.: The impact of source code in software on power consumption. Int. J. Electron. Bus. Manag. 14 (2016). http://ijebm-ojs.ie.nthu.edu.tw/IJEBM_OJS/index.php/IJEBM/article/view/693
  3. 3.
    Bellatreche, L., Missaoui, R., Necir, H., Drias, H.: A data mining approach for selecting bitmap join indices. J. Comput. Sci. Eng. 1, 177–194 (2007)CrossRefGoogle Scholar
  4. 4.
    Bellatreche, L., Roukh, A., Bouarar, S.: Step by step towards energy-aware data warehouse design. In: Marcel, P., Zimányi, E. (eds.) eBISS 2016. LNBIP, vol. 280, pp. 105–138. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-61164-8_5CrossRefGoogle Scholar
  5. 5.
    Bouarar, S., Bellatreche, L., Jean, S., Baron, M.: Do rule-based approaches still make sense in logical data warehouse design? In: Manolopoulos, Y., Trajcevski, G., Kon-Popovska, M. (eds.) ADBIS 2014. LNCS, vol. 8716, pp. 83–96. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-10933-6_7CrossRefGoogle Scholar
  6. 6.
    Bouarar, S., Bellatreche, L., Roukh, A.: Eco-data warehouse design through logical variability. In: Steffen, B., Baier, C., van den Brand, M., Eder, J., Hinchey, M., Margaria, T. (eds.) SOFSEM 2017. LNCS, vol. 10139, pp. 436–449. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-51963-0_34CrossRefGoogle Scholar
  7. 7.
    Guo, B., Yu, J., Liao, B., Yang, D., Lu, L.: A green framework for DBMS based on energy-aware query optimization and energy-efficient query processing. J. Netw. Comput. Appl. 84, 118–130 (2017)CrossRefGoogle Scholar
  8. 8.
    Inmon, W.H.: Building the Data Warehouse. Wiley, New York (1992)Google Scholar
  9. 9.
    Liebert, E.: Five strategies for cutting data center energy costs through enhanced cooling efficiency. White paper (2007)Google Scholar
  10. 10.
    Pitoura, E.: Query optimization. In: Liu, L., Özsu, M.T. (eds.) Encyclopedia of Database Systems. Springer, New York (2018).  https://doi.org/10.1007/978-1-4614-8265-9_861CrossRefGoogle Scholar
  11. 11.
    Roukh, A., Bellatreche, L.: Eco-processing of OLAP complex queries. In: Madria, S., Hara, T. (eds.) DaWaK 2015. LNCS, vol. 9263, pp. 229–242. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-22729-0_18CrossRefGoogle Scholar
  12. 12.
    Roukh, A., Bellatreche, L., Boukorca, A., Bouarar, S.: Eco-physic: eco-physical design initiative for very large databases. Inf. Syst. 68, 44–62 (2017)CrossRefGoogle Scholar
  13. 13.
    Roukh, A., Bellatreche, L., Ordonez, C.: Enerquery: energy-aware query processing. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 2465–2468. ACM (2016)Google Scholar
  14. 14.
    Steinbrunn, M., Moerkotte, G., Kemper, A.: Heuristic and randomized optimization for the join ordering problem. VLDB J. 6(3), 191–208 (1997)CrossRefGoogle Scholar
  15. 15.
    Svahnberg, M., van Gurp, J., Bosch, J.: A taxonomy of variability realization techniques: research articles. Softw. Pract. Exper. 35(8), 705–754 (2005)CrossRefGoogle Scholar
  16. 16.
    Tsirogiannis, D., Harizopoulos, S., Shah, M.A.: Analyzing the energy efficiency of a database server. In: SIGMOD, pp. 231–242 (2010)Google Scholar
  17. 17.
    Tu, Y.C., Wang, X., Zeng, B., Xu, Z.: A system for energy-efficient data management. ACM SIGMOD Record 43(1), 21–26 (2014)CrossRefGoogle Scholar
  18. 18.
    Xu, Z., Tu, Y., Wang, X.: Online energy estimation of relational operations in database systems. IEEE Trans. Comput. 64(11), 3223–3236 (2015)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Xu, Z., Tu, Y.C., Wang, X.: PET: reducing database energy cost via query optimization. Proc. VLDB Endow. 5(12), 1954–1957 (2012)CrossRefGoogle Scholar
  20. 20.
    Yu, P.S., Han, J., Faloutsos, C.: Link Mining: Models, Algorithms, and Applications, 1st edn. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Issam Ghabri
    • 1
    • 2
    Email author
  • Ladjel Bellatreche
    • 2
  • Sadok Ben Yahia
    • 1
    • 3
  1. 1.Faculty of Sciences of Tunis, LIPAH-LR11ES14University of Tunis El ManarTunisTunisia
  2. 2.LIAS/ISAE-ENSMA - Poitiers UniversityPoitiersFrance
  3. 3.Department of Software ScienceTallinn University of TechnologyTallinnEstonia

Personalised recommendations