IDEAS home Printed from https://ideas.repec.org/a/eee/jomega/v93y2020ics0305048317310666.html
   My bibliography  Save this article

An exact method for shrinking pivot tables

Author

Listed:
  • Boschetti, Marco A.
  • Golfarelli, Matteo
  • Graziani, Simone

Abstract

Pivot tables are one of the most popular tools for data visualization in both business and research applications. Although they are in general easy to use, their comprehensibility becomes progressively lower when the quantity of cells to be visualized increases (i.e., information flooding problem). Pivot tables are largely adopted in OLAP, the main approach to multidimensional data analysis. To cope with the information flooding problem in OLAP, the shrink operation enables users to balance the size of query results with their approximation, exploiting the presence of multidimensional hierarchies. The only implementation of the shrink operator proposed in the literature is based on a greedy heuristic that, in many cases, is far from reaching a desired level of effectiveness.

Suggested Citation

  • Boschetti, Marco A. & Golfarelli, Matteo & Graziani, Simone, 2020. "An exact method for shrinking pivot tables," Omega, Elsevier, vol. 93(C).
    • Handle: RePEc:eee:jomega:v:93:y:2020:i:c:s0305048317310666
    DOI: 10.1016/j.omega.2019.03.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0305048317310666
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.omega.2019.03.002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Huang, Chun-Che & (Bill) Tseng, Tzu-Liang & Li, Ming-Zhong & Gung, Roger R., 2006. "Models of multi-dimensional analysis for qualitative data and its application," European Journal of Operational Research, Elsevier, vol. 174(2), pages 983-1008, October.
    2. Łatuszko, Marek & Pytlak, Radosław, 2015. "Methods for solving the mean query execution time minimization problem," European Journal of Operational Research, Elsevier, vol. 246(2), pages 582-596.
    3. Balaji Padmanabhan & Alexander Tuzhilin, 2003. "On the Use of Optimization for Data Mining: Theoretical Interactions and eCRM Opportunities," Management Science, INFORMS, vol. 49(10), pages 1327-1343, October.
    4. P. S. Bradley & Usama M. Fayyad & O. L. Mangasarian, 1999. "Mathematical Programming for Data Mining: Formulations and Challenges," INFORMS Journal on Computing, INFORMS, vol. 11(3), pages 217-238, August.
    5. M.A. Boschetti & A. Mingozzi & S. Ricciardelli, 2004. "An Exact Algorithm for the Simplified Multiple Depot Crew Scheduling Problem," Annals of Operations Research, Springer, vol. 127(1), pages 177-201, March.
    6. A. Mingozzi & M. A. Boschetti & S. Ricciardelli & L. Bianco, 1999. "A Set Partitioning Approach to the Crew Scheduling Problem," Operations Research, INFORMS, vol. 47(6), pages 873-888, December.
    7. Egon Balas & Maria C. Carrera, 1996. "A Dynamic Subgradient-Based Branch-and-Bound Procedure for Set Covering," Operations Research, INFORMS, vol. 44(6), pages 875-890, December.
    8. Olafsson, Sigurdur & Li, Xiaonan & Wu, Shuning, 2008. "Operations research and data mining," European Journal of Operational Research, Elsevier, vol. 187(3), pages 1429-1448, June.
    9. Alberto Caprara & Paolo Toth & Matteo Fischetti, 2000. "Algorithms for the Set Covering Problem," Annals of Operations Research, Springer, vol. 98(1), pages 353-371, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Marco Antonio Boschetti & Vittorio Maniezzo, 2022. "Matheuristics: using mathematics for heuristic design," 4OR, Springer, vol. 20(2), pages 173-208, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Heydari Majeed & Yousefli Amir, 2017. "A new optimization model for market basket analysis with allocation considerations: A genetic algorithm solution approach," Management & Marketing, Sciendo, vol. 12(1), pages 1-11, March.
    2. Meisel, Stephan & Mattfeld, Dirk, 2010. "Synergies of Operations Research and Data Mining," European Journal of Operational Research, Elsevier, vol. 206(1), pages 1-10, October.
    3. Lan, Guanghui & DePuy, Gail W. & Whitehouse, Gary E., 2007. "An effective and simple heuristic for the set covering problem," European Journal of Operational Research, Elsevier, vol. 176(3), pages 1387-1403, February.
    4. Masoud Yaghini & Mohammad Karimi & Mohadeseh Rahbar, 2015. "A set covering approach for multi-depot train driver scheduling," Journal of Combinatorial Optimization, Springer, vol. 29(3), pages 636-654, April.
    5. Patrizia Beraldi & Andrzej Ruszczyński, 2002. "The Probabilistic Set-Covering Problem," Operations Research, INFORMS, vol. 50(6), pages 956-967, December.
    6. Marco Antonio Boschetti & Vittorio Maniezzo, 2022. "Matheuristics: using mathematics for heuristic design," 4OR, Springer, vol. 20(2), pages 173-208, June.
    7. Saglam, Burcu & Salman, F. Sibel & Sayin, Serpil & Turkay, Metin, 2006. "A mixed-integer programming approach to the clustering problem with an application in customer segmentation," European Journal of Operational Research, Elsevier, vol. 173(3), pages 866-879, September.
    8. Kaiquan Xu & Stephen Shaoyi Liao & Raymond Y. K. Lau & J. Leon Zhao, 2014. "Effective Active Learning Strategies for the Use of Large-Margin Classifiers in Semantic Annotation: An Optimal Parameter Discovery Perspective," INFORMS Journal on Computing, INFORMS, vol. 26(3), pages 461-483, August.
    9. Heil, Julia & Hoffmann, Kirsten & Buscher, Udo, 2020. "Railway crew scheduling: Models, methods and applications," European Journal of Operational Research, Elsevier, vol. 283(2), pages 405-425.
    10. Ibarra-Rojas, O.J. & Delgado, F. & Giesen, R. & Muñoz, J.C., 2015. "Planning, operation, and control of bus transport systems: A literature review," Transportation Research Part B: Methodological, Elsevier, vol. 77(C), pages 38-75.
    11. Cassioli, A. & Chiavaioli, A. & Manes, C. & Sciandrone, M., 2013. "An incremental least squares algorithm for large scale linear classification," European Journal of Operational Research, Elsevier, vol. 224(3), pages 560-565.
    12. Olafsson, Sigurdur & Li, Xiaonan & Wu, Shuning, 2008. "Operations research and data mining," European Journal of Operational Research, Elsevier, vol. 187(3), pages 1429-1448, June.
    13. Gao, Chao & Yao, Xin & Weise, Thomas & Li, Jinlong, 2015. "An efficient local search heuristic with row weighting for the unicost set covering problem," European Journal of Operational Research, Elsevier, vol. 246(3), pages 750-761.
    14. Youngho Lee & Hanif D. Sherali & Ikhyun Kwon & Seongin Kim, 2006. "A new reformulation approach for the generalized partial covering problem," Naval Research Logistics (NRL), John Wiley & Sons, vol. 53(2), pages 170-179, March.
    15. Coslovich, Luca & Pesenti, Raffaele & Ukovich, Walter, 2006. "Minimizing fleet operating costs for a container transportation company," European Journal of Operational Research, Elsevier, vol. 171(3), pages 776-786, June.
    16. Aardal, Karen & van den Berg, Pieter L. & Gijswijt, Dion & Li, Shanfei, 2015. "Approximation algorithms for hard capacitated k-facility location problems," European Journal of Operational Research, Elsevier, vol. 242(2), pages 358-368.
    17. Mark Gilchrist & Deana Lehmann Mooers & Glenn Skrubbeltrang & Francine Vachon, 2012. "Knowledge Discovery in Databases for Competitive Advantage," Journal of Management and Strategy, Journal of Management and Strategy, Sciedu Press, vol. 3(2), pages 2-15, April.
    18. Karl Martin & Parag Chitalia & Murugan Pugalenthi & K. Raghava Rau & Sudeep Maity & Rahul Kumar & Rohit Saksena & Randhir Hebbar & Mahesh Krishnan & Ganesh Hegde & Chandrasekhar Kesanapally & Tejinder, 2014. "Dell’s Channel Transformation: Leveraging Operations Research to Unleash Potential Across the Value Chain," Interfaces, INFORMS, vol. 44(1), pages 55-69, February.
    19. Zhang, Zhiwang & Gao, Guangxia & Shi, Yong, 2014. "Credit risk evaluation using multi-criteria optimization classifier with kernel, fuzzification and penalty factors," European Journal of Operational Research, Elsevier, vol. 237(1), pages 335-348.
    20. Maysam Eftekhary & Peyman Gholami & Saeed Safari & Mohammad Shojaee, 2012. "Ranking Normalization Methods for Improving the Accuracy of SVM Algorithm by DEA Method," Modern Applied Science, Canadian Center of Science and Education, vol. 6(10), pages 1-26, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jomega:v:93:y:2020:i:c:s0305048317310666. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/375/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.