Cite

Ahmadov, A., Thiele, M., Eberius, J., Lehner, W. and Wrembel, R. (2015). Towards a hybrid imputation approach using web tables, IEEE/ACM International Symposium on Big Data Computing (BDC), Limassol, Cyprus, pp. 21-30.Search in Google Scholar

Bekkerman, R., Bilenko, M. and Langford, J. (2011). Scaling Up Machine Learning: Parallel and Distributed Approaches, Cambridge University Press, New York, NY.10.1145/2107736.2107740Search in Google Scholar

Benjelloun, O., Garcia-Molina, H., Menestrina, D., Su, Q., Whang, S.E. and Widom, J. (2009). Swoosh: A generic approach to entity resolution, The VLDB Journal 18(1): 255-276. 10.1007/s00778-008-0098-xOpen DOISearch in Google Scholar

Bayer, M.A. and Edjlali, R. (2014). Magic Quadrant for Data Warehouse Database Management Systems, Gartner Publications, Stamford, CT, https://www.gartner.com/doc/2678018/magic-quadrant-data-warehouse-database.Search in Google Scholar

Beyer, M. and Laney, D. (2012). The Importance of “Big Data”: A Definition, Gartner Publications, Stamford, CT. Search in Google Scholar

Boyd, D. and Crawford, K. (2012). Critical questions for big data, Information, Communication and Society 15(5): 662-679.10.1080/1369118X.2012.678878Open DOISearch in Google Scholar

Brzezinski, D. and Stefanowski, J. (2014). Combining block-based and online methods in learning ensembles from concept drifting data streams, Information Sciences 265: 50-67.10.1016/j.ins.2013.12.011Search in Google Scholar

Che, D., Safran, M. and Peng, Z. (2013). From big data to big data mining: Challenges, issues, and opportunities, in10.1007/978-3-642-40270-8_1Search in Google Scholar

B. Hong et al. (Eds.), International Conference on Database Systems for Advanced Applications, Lecture Notes in Computer Science, Vol. 7827, Springer, Berlin/Heidelberg, pp. 1-15.Search in Google Scholar

Chen, C.L.P. and Zhang, C. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on big data, Information Sciences 275(10): 314-347.10.1016/j.ins.2014.01.015Search in Google Scholar

Custers, B., Calders, T., Schermer, B. and Zarsky, T.Z. (Eds.) (2013). Discrimination and Privacy in the Information Society-Data Mining and Profiling in Large Databases, Studies in Applied Philosophy, Epistemology and Rational Ethics, Vol. 3, Springer, Berlin/Heidelberg.Search in Google Scholar

Ditzler, G., Roveri, M., Alippi, C. and Polikar, R. (2015). Learning in nonstationary environments: A survey, IEEE Computational Intelligence Magazine 10(4): 12-25.10.1109/MCI.2015.2471196Search in Google Scholar

Domingos, P. and Hulten, G. (2000). Mining high-speed data streams, ACM SIGKDD International Conference on Knowledge Discovery Data Mining, Boston, MA, USA, pp. 71-80.Search in Google Scholar

Duggan, J., Elmore, A.J., Stonebraker, M., Balazinska, M., Howe, B., Kepner, J., Madden, S., Maier, D., Mattson, T. and Zdonik, S. (2015). The BigDAWG polystore system, SIGMOD Record 44(2): 11-16.10.1145/2814710.2814713Search in Google Scholar

Elmagarmid, A., Rusinkiewicz, M. and Sheth, A. (Eds.) (1999). Management of Heterogeneous and Autonomous Database Systems, Morgan Kaufmann, San Francisco, CA.Search in Google Scholar

Fernández, A., del Río, S., Chawla, N.V. and Herrera, F. (2017). An insight into imbalanced big data classification: Outcomes and challenges, Complex & Intelligent Systems 3(2): 105-120.10.1007/s40747-017-0037-9Search in Google Scholar

Francisco, P. (2012). Oracle Exadata and IBM Netezza data warehouse appliance compared, IBM White Paper, www.ibmbigdatahub.com/pdf/Oracle_Exadata_IBMNetezza_Compared_WP_EN.pdf.Search in Google Scholar

Gama, J. (2010). Knowledge Discovery from Data Streams, Chapman and Hall, Boca Raton, FL.10.1201/EBK1439826119Search in Google Scholar

Gama, J., Zliobaite, I., Bifet, A., Pechenizkiy, M. and Bouchachia, A. (2014). A survey on concept drift adaptation, ACM Computing Surveys 46(4): 44:1-44:37.10.1145/2523813Open DOISearch in Google Scholar

Gens, F. (2011). IDC predictions 2012: Competing for 2020. IDC analyze the future, https://www.virtustream.com/sites/default/files/IDCTOP10Predictions2012.pdf.Search in Google Scholar

Gessert, F., Schaarschmidt, M., Wingerath, W., Witt, E., Yoneki, E. and Ritter, N. (2017). Quaestor: Query web caching for database-as-a-service providers, PVLDB 10(12): 1670-1681.10.14778/3137765.3137773Search in Google Scholar

Glavic, B. (2014). Big data provenance: Challenges and implications for benchmarking, in T. Rabl et al. (Eds.), Specifying Big Data Benchmarks, Springer, New York, NY, pp. 72-80.10.1007/978-3-642-53974-9_7Search in Google Scholar

Gupta, A. (2009). Data provenance, in L. Liu and M.T. O¨ zsu (Eds.), Encyclopedia of Database Systems, Springer, Berlin, pp. 608-608.10.1007/978-0-387-39940-9_1305Search in Google Scholar

Han, J. and Kamber, M. (Eds.) (2011). Data Mining. Concepts and Techniques, Morgan Kaufmann, San Francisco, CA.Search in Google Scholar

Hashem, H. and Ranc, D. (2016). Pre-processing and modeling tools for bigdata, Foundations of Computing and Decision Sciences 41(3): 151-162.10.1515/fcds-2016-0009Search in Google Scholar

Japkowicz, N. and Stefanowski, J. (2016a). A machine learning perspective on big data analysis, in N. Japkowicz and J.10.1007/978-3-319-26989-4_1Open DOISearch in Google Scholar

Stefanowski (Eds.), Big Data Analysis: New Algorithms for a New Society, Springer, Cham, pp. 1-31.Search in Google Scholar

Japkowicz, N. and Stefanowski, J. (Eds.) (2016b). Big Data Analysis: New Algorithms for a New Society, Studies in Big Data, Vol. 16, Springer, Cham.10.1007/978-3-319-26989-4Search in Google Scholar

Kingma, D.P. and Welling, M. (2013). Auto-encoding variational Bayes, ArXiv e-prints, 1312.6114a.Search in Google Scholar

Krawczyk, B., Minku, L.L., Gama, J., Stefanowski, J. and Wozniak, M. (2017). Ensemble learning for data stream analysis: A survey, Information Fusion 37: 132-156.10.1016/j.inffus.2017.02.004Search in Google Scholar

Krempl, G., Zliobaite, I., Brzezinski, D., H¨ullermeier, E., Last, M., Lemaire, V., Noack, T., Shaker, A., Sievi, S., Spiliopoulou, M. and Stefanowski, J. (2014). Open challenges for data stream mining research, SIGKDD Explorations 16(1): 1-10.10.1145/2674026.2674028Search in Google Scholar

Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks, in F. Pereira et al. (Eds.), Advances in Neural Information Processing Systems 25, Curran Associates, Inc., Red Hook, NY, pp. 1097-1105.Search in Google Scholar

Krawiec, K. (2016). Evolutionary feature selection and construction, in S. Claude and G. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining, Springer, Boston, MA.10.1007/978-1-4899-7502-7_90-1Search in Google Scholar

Langegger, A., Wöß, W. and Blöchl, M. (2008). A semantic web middleware for virtual data integration on the web, European Semantic Web Conference on the Semantic Web: Research and Applications (ESWC), Tenerife, Canary Islands, Spain, pp. 493-507.Search in Google Scholar

LeCun, Y., Bengio, Y. and Hinton, G. (2015). Deep learning, Nature 521(7553): 436-444.10.1038/nature1453926017442Search in Google Scholar

Liu, M. and Wang, Q. (2016). Rogas: A declarative framework for network analytics, International Conference on Very Large Data Bases (VLDB), New Delhi, India, pp. 1561-1564.Search in Google Scholar

Matwin, S. (2013). Privacy-preserving data mining techniques: Survey and challenges, in B. Custers et al. (Eds.), Discrimination and Privacy in the Information Society, Vol 3. Springer, Berlin/Heidelberg, pp. 209-221.10.1007/978-3-642-30487-3_11Search in Google Scholar

Mauro, A.D., Greco, M. and Grimaldi, M. (2015). What is big data? A consensual definition and a review of key research topics, International Conference on Integrated Information, Madrid, Spain, pp. 97-104.Search in Google Scholar

Miao, X., Gao, Y., Guo, S. and Liu, W. (2017). Incomplete data management: A survey, Frontiers of Computer Science, DOI: 10.1007/s11704-016-6195-x.10.1007/s11704-016-6195-xOpen DOISearch in Google Scholar

Moreau, L., Clifford, B., Freire, J., Futrelle, J., Gil, Y., Groth, P., Kwasnikowska, N., Miles, S., Missier, P., Myers, J., Plale, B., Simmhan, Y., Stephan, E. and den Bussche, J.V. (2011). The open provenance model core specification (v1.1), Future Generation Computer Systems 27(6): 743-756.10.1016/j.future.2010.07.005Open DOISearch in Google Scholar

Napierala, K. and Stefanowski, J. (2016). Types of minority class examples and their influence on learning classifiers from imbalanced data, Journal of Intelligent Information Systems 46(3): 563-597.10.1007/s10844-015-0368-1Open DOISearch in Google Scholar

Naumann, F. (2014). Data profiling revisited, SIGMOD Record 42(4): 40-49.10.1145/2590989.2590995Open DOISearch in Google Scholar

Rudin, C. (2014). Algorithms for interpretable machine learning, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, pp. 1519-1519.Search in Google Scholar

Russom, P. (2017). Data lakes: Purposes, practices, patterns, and platforms. TDWI White Paper, https://info.talend.com/rs/talend/images/WP_EN_BD_TDWI_DataLakes.pdf.Search in Google Scholar

Schmidhuber, J. (2015). Deep learning in neural networks: An overview, Neural Networks 61(C): 85-117.10.1016/j.neunet.2014.09.00325462637Search in Google Scholar

Shaker, A. and Hüllermeier, E. (2014). Survival analysis on data streams: Analyzing temporal events in dynamically changing environments, International Journal of Applied Mathematics and Computer Science 24(1): 199-212, DOI: 10.2478/amcs-2014-0015.10.2478/amcs-2014-0015Open DOISearch in Google Scholar

Soltanpoor, R. and Sellis, T. (2016). Prescriptive analytics for big data, Australasian Database Conference on Databases Theory and Applications (ADC), Sydney, Australia, pp. 245-256.Search in Google Scholar

Sun, Y., Tang, K., Minku, L.L., Wang, S. and Yao, X. (2016). Online ensemble learning of data streams with gradually evolved classes, IEEE Transactions on Knowledge and Data Engineering 28(6): 1532-1545.10.1109/TKDE.2016.2526675Search in Google Scholar

Terrizzano, I., Schwarz, P., Roth, M. and Colino, J.E. (2015). Data wrangling: The challenging journey from the wild to the lake, Conference on Innovative Data Systems Research (CIDR), Asiloma, CA, USA.Search in Google Scholar

Wang, J., Crawl, D., Purawat, S., Nguyen, M.H. and Altintas, I. (2015). Big data provenance: Challenges, state of the art and opportunities, IEEE International Conference on Big Data, Santa Clara, CA, USA, pp. 2509-2516.Search in Google Scholar

Wiederhold, G. (1992). Mediators in the architecture of future information systems, IEEE Computer 25(3): 38-49.10.1109/2.121508Open DOISearch in Google Scholar

Wylot, M., Cudré-Mauroux, P., Hauswirth, M. and Groth, P.T. (2017). Storing, tracking, and querying provenance in linked data, IEEE Transactions on Knowledge and Data Engineering 29(8): 1751-1764.10.1109/TKDE.2017.2690299Search in Google Scholar

Zakhary, V., Agrawa, D. and El Abbadi, A. (2017). Caching at the web scale, International Conference on World Wide Web Companion, Perth, Australia, pp. 909-912.Search in Google Scholar

eISSN:
2083-8492
Idioma:
Inglés
Calendario de la edición:
4 veces al año
Temas de la revista:
Mathematics, Applied Mathematics