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Multidimensional Prediction Models When the Resolution Context Changes

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Multidimensional Prediction Models When the Resolution Context Changes

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Martínez Usó, A.; Hernández Orallo, J. (2015). Multidimensional Prediction Models When the Resolution Context Changes. En Machine Learning and Knowledge Discovery in Databases. Springer. 509-524. doi:10.1007/978-3-319-23525-7_31

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/65588

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Title: Multidimensional Prediction Models When the Resolution Context Changes
Author:
UPV Unit: Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Issued date:
Abstract:
Multidimensional data is systematically analysed at multiple granularities by applying aggregate and disaggregate operators (e.g., by the use of OLAP tools). For instance, in a supermarket we may want to predict sales of ...[+]
Subjects: Multidimensional data , Operating context aggregation , Disaggregation , OLAP cubes , Quantification
Copyrigths: Reserva de todos los derechos
ISBN: 978-3-319-23524-0
Source:
Machine Learning and Knowledge Discovery in Databases. (issn: 0302-9743 )
DOI: 10.1007/978-3-319-23525-7_31
Publisher:
Springer
Publisher version: http://link.springer.com/chapter/10.1007/978-3-319-23525-7_31
Conference name: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD 2015)
Conference place: Porto, Portugal
Conference date: September 7-11, 2015
Series: Lecture Notes in Computer Science;9285
Description: The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-23525-7_31
Type: Capítulo de libro Comunicación en congreso

References

Agrawal, R., Gupta, A., Sarawagi, S.: Modeling multidimensional databases. In: Proceedings of the Thirteenth International Conference on Data Engineering, ICDE 1997, pp. 232–243. IEEE Computer Society (1997)

Bella, A., Ferri, C., Hernández-Orallo, J., Ramírez-Quintana, M.: Quantification via probability estimators. In: IEEE ICDM, pp. 737–742 (2010)

Bella, A., Ferri, C., Hernández-Orallo, J., Ramírez-Quintana, M.J.: Aggregative quantification for regression. DMKD 28(2), 475–518 (2014) [+]
Agrawal, R., Gupta, A., Sarawagi, S.: Modeling multidimensional databases. In: Proceedings of the Thirteenth International Conference on Data Engineering, ICDE 1997, pp. 232–243. IEEE Computer Society (1997)

Bella, A., Ferri, C., Hernández-Orallo, J., Ramírez-Quintana, M.: Quantification via probability estimators. In: IEEE ICDM, pp. 737–742 (2010)

Bella, A., Ferri, C., Hernández-Orallo, J., Ramírez-Quintana, M.J.: Aggregative quantification for regression. DMKD 28(2), 475–518 (2014)

Bickel, R.: Multilevel analysis for applied research: It’s just regression! Guilford Press (2012)

Cabibbo, L., Torlone, R.: A logical approach to multidimensional databases. In: Schek, H.-J., Saltor, F., Ramos, I., Alonso, G. (eds.) EDBT 1998. LNCS, vol. 1377, p. 183. Springer, Heidelberg (1998)

Chaudhuri, S., Dayal, U.: An overview of data warehousing and OLAP technology. ACM Sigmod Record 26(1), 65–74 (1997)

Chen, B.C.: Cube-Space Data Mining. ProQuest (2008)

Chen, B.C., Chen, L., Lin, Y., Ramakrishnan, R.: Prediction cubes. In: Proc. of the 31st Intl. Conf. on Very Large Data Bases, pp. 982–993 (2005)

Datahub: Car fuel consumptions and emissions 2000–2013 (2013). http://datahub.io/dataset/car-fuel-consumptions-and-emissions

Dhurandhar, A.: Using coarse information for real valued prediction. Data Mining and Knowledge Discovery 27(2), 167–192 (2013)

Forman, G.: Quantifying counts and costs via classification. Data Min. Knowl. Discov. 17(2), 164–206 (2008)

Goldstein, H.: Multilevel Statistical Models, vol. 922. John Wiley & Sons (2011)

Golfarelli, M., Maio, D., Rizzi, S.: The dimensional fact model: a conceptual model for data warehouses. Intl. J. of Coop. Information Systems 7, 215–247 (1998)

Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: An update. SIGKDD Explor. 11(1), 10–18 (2009)

Hernández-Orallo, J.: Probabilistic reframing for cost-sensitive regression. ACM Transactions on Knowledge Discovery from Data 8(3) (2014)

IBM Corporation: Introduction to Aroma and SQL (2006). http://www.ibm.com/developerworks/data/tutorials/dm0607cao/dm0607cao.html

Kamber, M., Jenny, J.H., Chiang, Y., Han, J., Chiang, J.Y.: Metarule-guided mining of multi-dimensional association rules using data cubes. In: KDD, pp. 207–210 (1997)

Lin, T., Yao, Y., Zadeh, L.: Data Mining, Rough Sets and Granular Computing. Studies in Fuzziness and Soft Computing. Physica-Verlag HD (2002)

Páircéir, R., McClean, S., Scotney, B.: Discovery of multi-level rules and exceptions from a distributed database. In: Proc. of the 6th ACM SIGKDD Intl. Conf. on Knowledge discovery and data mining, pp. 523–532. ACM (2000)

Pastor, O., Casamayor, J.C., Celma, M., Mota, L., Pastor, M.A., Levin, A.M.: Conceptual Modeling of Human Genome: Integration Challenges. In: Düsterhöft, A., Klettke, M., Schewe, K.-D. (eds.) Conceptual Modelling and Its Theoretical Foundations. LNCS, vol. 7260, pp. 231–250. Springer, Heidelberg (2012)

Perlich, C., Provost, F.: Distribution-based aggregation for relational learning with identifier attributes. Machine Learning 62(1–2), 65–105 (2006)

Team, R., et al.: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2012)

Ramakrishnan, R., Chen, B.C.: Exploratory mining in cube space. Data Mining and Knowledge Discovery 15(1), 29–54 (2007)

Raudenbush, S.W., Bryk, A.S.: Hierarchical linear models: applications and data analysis methods, vol. 1. Sage (2002)

UCI Repository: UJIIndoorLoc data set (2014). http://archive.ics.uci.edu/ml/datasets/UJIIndoorLoc

Vassiliadis, P.: Modeling multidimensional databases, cubes and cube operations. In: Proc. of the 10th SSDBM Conference, pp. 53–62 (1998)

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