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Year 2019, Volume: 12 Issue: 1, 379 - 410, 24.03.2019

Anıl Utku Muhammet Ali Akcayol

https://doi.org/10.18185/erzifbed.473008

Abstract

References

  • Abrol, S., Rajasekar, G., Khan, L., Khadilkar, V., Nagarajan, S., Mcdaniel, N., Thuraisingham, B. 2017. “Real-time, stream data information integration and analytics system”, U.S. Patent Application No. 14/746,576.
  • Aggarwal, C.C. 2014. “Data classification: Algorithms and applications”, CRC Press.
  • Aggarwal, C.C. 2007. “Data streams: models and algorithms”, Springer Science & Business Media.
  • Aggarwal, C.C., Han, J., Wang, J., Yu, P. S. 2003. “A framework for clustering evolving data streams”, In Proceedings of the 29th international conference on Very large data bases, 81-92.
  • Augenstein, C., Spangenberg, N., Franczyk, B. 2017. “Applying machine learning to big data streams: An overview of challenges”, In Soft Computing & Machine Intelligence (ISCMI), 2017 IEEE 4th International Conference on, 25-29.
  • Babcock, B., Babu, S., Datar, M., Motwani, R., Widom, J. 2002. “Models and issues in data stream systems”, In Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, 1-16.
  • Bargiela, A., Pedrycz, W. 2016. “Granular computing”, In Handbook On Computational Intelligence: Volume 1: Fuzzy Logic, Systems, Artificial Neural Networks, and Learning Systems, 43-66.
  • Bar-Yossef, Z., Jayram, T. S., Kumar, R., Sivakumar, D., Trevisan, L. 2002. “Counting distinct elements in a data stream”, In International Workshop on Randomization and Approximation Techniques in Computer Science, 1-10.
  • Bengio, Y., Courville, A., Vincent, P. 2013. “Representation learning: A review and new perspectives”, IEEE transactions on pattern analysis and machine intelligence, 35(8), 1798-1828.
  • Bifet, A. 2016. “Mining Internet of Things (IoT) Big Data Streams”, In SIMBig, 15-16.
  • Bramer, M. 2007. “Principles of data mining”, Springer.
  • Brief, A. I. 2012. “Qualitative and Quantitative Research Techniques for Humanitarian Needs Assessment”, Acaps.
  • Chandler, N., Hostmann, B., Rayner, N., Herschel, G. 2011. “Gartner’s business analytics framework. Processes and platforms that need to be integrated and aligned to take a more strategic approach to business intelligence”, Gartner.
  • Chen, X. W., Lin, X. 2014. “Big data deep learning: challenges and perspectives”, IEEE access, 2, 514-525.
  • Crawford, M. M., Tuia, D., Yang, H. L. 2013. “Active learning: Any value for classification of remotely sensed data?”, Proceedings of the IEEE, 101(3), 593-608.
  • Debnath, P., Chobe, S. 2014. “A Quick Survey on Data Stream Mining”, Int. J. Comput. Sci. Inf. Technol., 5(3).
  • D'Andrea, E., Ducange, P., Lazzerini, B., Marcelloni, F. 2015. “Real-time detection of traffic from twitter stream analysis”, IEEE transactions on intelligent transportation systems, 16(4), 2269-2283.
  • Ellis, B. 2014. “Real-time analytics: Techniques to analyze and visualize streaming data”, John Wiley & Sons.
  • Elnahrawy, E. 2003. “Research directions in sensor data streams: solutions and challenges”, Rutgers University, Tech. Rep.
  • Gaber, M. M., Zaslavsky, A., Krishnaswamy, S. 2005. “Mining data streams: a review”, ACM Sigmod Record, 34(2), 18-26.
  • Gagarin, O. O., Toporivskyi, B.P. 2016. “Research issues of mining big data streams”, Infocom 2016.
  • Gama, J. 2010. Knowledge discovery from data streams, CRC Press.
  • Han, J., Pei, J., Kamber, M. 2011. “Data mining: Concepts and techniques”, Elsevier.
  • Huang, F., Yates, A. 2012. “Biased representation learning for domain adaptation”, In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 1313-1323.
  • Huang, Y., Cui, B., Zhang, W., Jiang, J., Xu, Y. 2015. “Tencentrec: Real-time stream recommendation in practice”, In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, 227-238.
  • Huisman, D. O. 2015. “To What Extent do Predictive, Descriptive and Prescriptive Supply Chain Analytics Affect Organizational Performance”, Yüksek lisans tezi, Twente Üniversitesi, Twente.
  • Hurwitz, J., Nugent, A., Halper, F., Kaufman, M. 2013. “Big data for dummies”, John Wiley & Sons.
  • IBM. 2013. “Descriptive, predictive, prescriptive: Transforming asset and facilities management with analytics”, IBM Software Thought Leadership White Paper.
  • Ikonomovska, E., Loskovska, S., Gjorgjevik, D. 2007. “A survey of stream data mining”, In Proceedings of 8th National Conference with International participation, 19-21.
  • Johnson, A. 2015. “Quantitative vs. Qualitative Data.Natural Resources”, Online Courses.
  • Kamburugamuve, S., Fox, G., Leake, D., Qiu, J. 2013. “Survey of Streaming Data Algorithms”, Tech. Rep., Indiana University.
  • Klein, A., Do, H. H., Hackenbroich, G., Karnstedt, M., Lehner, W. 2007. “Representing data quality for streaming and static data”, In Data Engineering Workshop, 2007 IEEE 23rd International Conference on, 3-10.
  • Kleppmann, M. 2016. “Making Sense of Stream Processing”, O’Reilly Media Inc.
  • Kohler, H. 2002. “Statistics for Business and Economics: Minitab Enhanced”, South-Western, Thomason Learning.
  • Krawczyk, B., Minku, L. L., Gama, J., Stefanowski, J., Woźniak, M. 2017. “Ensemble learning for data stream analysis: A survey”, Information Fusion, 37, 132-156.
  • Krishnaswamy, S., Gama, J., Gaber, M.M. 2012. “Mobile data stream mining: from algorithms to applications”, In Mobile Data Management (MDM), 2012 IEEE 13th International Conference on, 360-363.
  • LeCun, Y., Bengio, Y., Hinton, G. 2015. “Deep learning”, Nature, 521(7553), 436-444.
  • Lennert, D., Maynard, W., Mehta, V., Huss, T. 2018. “Systems and methods for cross-platform batch data processing”, 2018. U.S. Patent Application No. 15/397,583.
  • Leskovec, J., Rajaraman, A., Ullman, J.D. 2014. “Mining of massive datasets”, Cambridge University Press.
  • Najafabadi, M.M., Villanustre, F., Khoshgoftaar, T.M., Seliya, N., Wald, R., Muharemagic, E. 2015. “Deep learning applications and challenges in big data analytics”, Journal of Big Data, 2(1).
  • Nguyen, H. L., Woon, Y. K., Ng, W.K. 2015. “A survey on data stream clustering and classification”, Knowledge and information systems, 45(3), 535-569.
  • Oussous, A., Benjelloun, F.Z., Lahcen, A.A., Belfkih, S. 2017. “Big Data Technologies: A Survey”, Journal of King Saud University-Computer and Information Sciences.
  • Peary, B. D., Shaw, R., Takeuchi, Y. 2012. “Utilization of social media in the east Japan earthquake and tsunami and its effectiveness”, Journal of Natural Disaster Science, 34(1), 3-18.
  • Peteiro-Barral, D., Guijarro-Berdiñas, B. 2013. “A survey of methods for distributed machine learning”, Progress in Artificial Intelligence, 2(1), 1-11.
  • PhridviRaj, M.S.B., GuruRao, C.V. 2014. “Data mining–past, present and future–a typical survey on data streams”, Procedia Technology, 12, 255-263.
  • Psaltis, A.G. 2017. “Streaming Data: Understanding the Real-Time Pipeline”, Manning.
  • Pyle, D. 1999. “Data preparation for data mining”, Morgan Kaufmann.
  • Qiu, J., Wu, Q., Ding, G., Xu, Y., Feng, S. 2016. “A survey of machine learning for big data processing”, EURASIP Journal on Advances in Signal Processing, 1(67).
  • Read, J., Perez-Cruz, F., Bifet, A. 2015. “Deep learning in partially-labeled data streams”, In Proceedings of the 30th Annual ACM Symposium on Applied Computing, 954-959.
  • Settles, B. 2010. “Active learning literature survey”, University of Wisconsin, Madison, 52, 55-66.
  • Silva, J.A., Faria, E.R., Barros, R.C., Hruschka, E.R., De Carvalho, A.C., Gama, J. 2013. “Data stream clustering: A survey”, ACM Computing Surveys (CSUR), 46(1), 13.
  • Skowron, A., Jankowski, A., Dutta, S. 2016. “Interactive granular computing”, GranularComputing, 1(2), 95-113.
  • Son, N.H. 2006. “Data cleaning and Data preprocessing”, Mimuw University.
  • Steinmetz, R., Nahrstedt, K. 2002. “Multimedia fundamentals, Volume 1: Media coding and content processing”, Pearson Education.
  • Stephens, S., Tamayo, P. 2003. “Supervised and unsupervised data mining techniques for the life sciences”, Curr. Drug Discov, 34(36).
  • Tan, P.N. 2006. “Introduction to data mining”, Pearson Education India.
  • Weiss, K., Khoshgoftaar, T.M. ve Wang, D. 2016. “A survey of transfer learning”, Journal of Big Data, 3(1).
  • Wrench, C., Stahl, F., Di Fatta, G., Karthikeyan, V., Nauck, D.D. 2016. “Data stream mining of event and complex event streams: A survey of existing and future technologies and applications in big data”, In Enterprise Big Data Engineering, Analytics, and Management, 24-47.
  • Xiang, E. W., Cao, B., Hu, D. H., Yang, Q. 2010. “Bridging domains using world wide knowledge for transfer learning”, IEEE Transactions on Knowledge and Data Engineering, 22(6), 770-783.
  • Xu, S., Balazinska, M. 2011. “Sensor Data Stream Exploration for Monitoring Applications”, DMSN.
  • Zang, W., Zhang, P., Zhou, C., Guo, L. 2014. “Comparative study between incremental and ensemble learning on data streams: Case study”, Journal of Big Data, 1(1).
  • Zheng, H., Kulkarni, S.R. ve Poor, H.V. 2011. “Attribute-distributed learning: models, limits, and algorithms”, IEEE Transactions on Signal processing, 59(1), 386-398.

Akan Veri Karakterizasyonu, Üretimi Ve Analitiği Üzerine Kapsamlı Bir İnceleme

Year 2019, Volume: 12 Issue: 1, 379 - 410, 24.03.2019

Anıl Utku Muhammet Ali Akcayol

https://doi.org/10.18185/erzifbed.473008

Abstract

Gelişen donanım ve yazılım
teknolojileri sayesinde anlık olarak üretilen veri miktarı ve hızı giderek
artmaktadır. Akan veriler, statik verilerden farklı olarak, potansiyel olarak
sonsuz, hızlı, zamanla değişen ve gelişen bir yapıya sahiptir. Bu sebeple akan
verilerin tamamını depolamak zaman ve depolama alanı kısıtları sebebiyle
imkânsız olabilmektedir. Akan veri madenciliğinde kullanılan yöntemlerin,
statik veri madenciliğinden farklı olarak akan verilerin doğasına uygun bir
şekilde optimize edilmesi gerekmektedir. Bu çalışma kapsamında akan veriler,
statik veriler ile karşılaştırmalı olarak analiz edilmiştir.  Akan veri madenciliği, akan veri
karakterizasyonu ve akan veri üretimi üzerine detaylı araştırmalar yapılmıştır.
Akan verilerin türleri, kaynakları ve akan veriden öğrenme yöntemleri ile akan
veri uygulamalarında kullanılan yöntemler ve algoritmalar kapsamlı bir şekilde
incelenmiştir.Yapılan
literatür araştırmaları ve incelemeler sonucunda, veri gizliliği, geleceğe
dönük öngörülerin oluşturulması, veri ön işleme, kaynak kullanımı ve çevrimiçi
güncelleme konularının geliştirilecek akan veri uygulamalarında
değerlendirilmesi gereken konular olduğu ortaya konulmuştur. 

Keywords

Akan Veri Akan Veri Karakterizasyonu Akan Veri Üretimi Akan Veri Analitiği

References

  • Abrol, S., Rajasekar, G., Khan, L., Khadilkar, V., Nagarajan, S., Mcdaniel, N., Thuraisingham, B. 2017. “Real-time, stream data information integration and analytics system”, U.S. Patent Application No. 14/746,576.
  • Aggarwal, C.C. 2014. “Data classification: Algorithms and applications”, CRC Press.
  • Aggarwal, C.C. 2007. “Data streams: models and algorithms”, Springer Science & Business Media.
  • Aggarwal, C.C., Han, J., Wang, J., Yu, P. S. 2003. “A framework for clustering evolving data streams”, In Proceedings of the 29th international conference on Very large data bases, 81-92.
  • Augenstein, C., Spangenberg, N., Franczyk, B. 2017. “Applying machine learning to big data streams: An overview of challenges”, In Soft Computing & Machine Intelligence (ISCMI), 2017 IEEE 4th International Conference on, 25-29.
  • Babcock, B., Babu, S., Datar, M., Motwani, R., Widom, J. 2002. “Models and issues in data stream systems”, In Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, 1-16.
  • Bargiela, A., Pedrycz, W. 2016. “Granular computing”, In Handbook On Computational Intelligence: Volume 1: Fuzzy Logic, Systems, Artificial Neural Networks, and Learning Systems, 43-66.
  • Bar-Yossef, Z., Jayram, T. S., Kumar, R., Sivakumar, D., Trevisan, L. 2002. “Counting distinct elements in a data stream”, In International Workshop on Randomization and Approximation Techniques in Computer Science, 1-10.
  • Bengio, Y., Courville, A., Vincent, P. 2013. “Representation learning: A review and new perspectives”, IEEE transactions on pattern analysis and machine intelligence, 35(8), 1798-1828.
  • Bifet, A. 2016. “Mining Internet of Things (IoT) Big Data Streams”, In SIMBig, 15-16.
  • Bramer, M. 2007. “Principles of data mining”, Springer.
  • Brief, A. I. 2012. “Qualitative and Quantitative Research Techniques for Humanitarian Needs Assessment”, Acaps.
  • Chandler, N., Hostmann, B., Rayner, N., Herschel, G. 2011. “Gartner’s business analytics framework. Processes and platforms that need to be integrated and aligned to take a more strategic approach to business intelligence”, Gartner.
  • Chen, X. W., Lin, X. 2014. “Big data deep learning: challenges and perspectives”, IEEE access, 2, 514-525.
  • Crawford, M. M., Tuia, D., Yang, H. L. 2013. “Active learning: Any value for classification of remotely sensed data?”, Proceedings of the IEEE, 101(3), 593-608.
  • Debnath, P., Chobe, S. 2014. “A Quick Survey on Data Stream Mining”, Int. J. Comput. Sci. Inf. Technol., 5(3).
  • D'Andrea, E., Ducange, P., Lazzerini, B., Marcelloni, F. 2015. “Real-time detection of traffic from twitter stream analysis”, IEEE transactions on intelligent transportation systems, 16(4), 2269-2283.
  • Ellis, B. 2014. “Real-time analytics: Techniques to analyze and visualize streaming data”, John Wiley & Sons.
  • Elnahrawy, E. 2003. “Research directions in sensor data streams: solutions and challenges”, Rutgers University, Tech. Rep.
  • Gaber, M. M., Zaslavsky, A., Krishnaswamy, S. 2005. “Mining data streams: a review”, ACM Sigmod Record, 34(2), 18-26.
  • Gagarin, O. O., Toporivskyi, B.P. 2016. “Research issues of mining big data streams”, Infocom 2016.
  • Gama, J. 2010. Knowledge discovery from data streams, CRC Press.
  • Han, J., Pei, J., Kamber, M. 2011. “Data mining: Concepts and techniques”, Elsevier.
  • Huang, F., Yates, A. 2012. “Biased representation learning for domain adaptation”, In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 1313-1323.
  • Huang, Y., Cui, B., Zhang, W., Jiang, J., Xu, Y. 2015. “Tencentrec: Real-time stream recommendation in practice”, In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, 227-238.
  • Huisman, D. O. 2015. “To What Extent do Predictive, Descriptive and Prescriptive Supply Chain Analytics Affect Organizational Performance”, Yüksek lisans tezi, Twente Üniversitesi, Twente.
  • Hurwitz, J., Nugent, A., Halper, F., Kaufman, M. 2013. “Big data for dummies”, John Wiley & Sons.
  • IBM. 2013. “Descriptive, predictive, prescriptive: Transforming asset and facilities management with analytics”, IBM Software Thought Leadership White Paper.
  • Ikonomovska, E., Loskovska, S., Gjorgjevik, D. 2007. “A survey of stream data mining”, In Proceedings of 8th National Conference with International participation, 19-21.
  • Johnson, A. 2015. “Quantitative vs. Qualitative Data.Natural Resources”, Online Courses.
  • Kamburugamuve, S., Fox, G., Leake, D., Qiu, J. 2013. “Survey of Streaming Data Algorithms”, Tech. Rep., Indiana University.
  • Klein, A., Do, H. H., Hackenbroich, G., Karnstedt, M., Lehner, W. 2007. “Representing data quality for streaming and static data”, In Data Engineering Workshop, 2007 IEEE 23rd International Conference on, 3-10.
  • Kleppmann, M. 2016. “Making Sense of Stream Processing”, O’Reilly Media Inc.
  • Kohler, H. 2002. “Statistics for Business and Economics: Minitab Enhanced”, South-Western, Thomason Learning.
  • Krawczyk, B., Minku, L. L., Gama, J., Stefanowski, J., Woźniak, M. 2017. “Ensemble learning for data stream analysis: A survey”, Information Fusion, 37, 132-156.
  • Krishnaswamy, S., Gama, J., Gaber, M.M. 2012. “Mobile data stream mining: from algorithms to applications”, In Mobile Data Management (MDM), 2012 IEEE 13th International Conference on, 360-363.
  • LeCun, Y., Bengio, Y., Hinton, G. 2015. “Deep learning”, Nature, 521(7553), 436-444.
  • Lennert, D., Maynard, W., Mehta, V., Huss, T. 2018. “Systems and methods for cross-platform batch data processing”, 2018. U.S. Patent Application No. 15/397,583.
  • Leskovec, J., Rajaraman, A., Ullman, J.D. 2014. “Mining of massive datasets”, Cambridge University Press.
  • Najafabadi, M.M., Villanustre, F., Khoshgoftaar, T.M., Seliya, N., Wald, R., Muharemagic, E. 2015. “Deep learning applications and challenges in big data analytics”, Journal of Big Data, 2(1).
  • Nguyen, H. L., Woon, Y. K., Ng, W.K. 2015. “A survey on data stream clustering and classification”, Knowledge and information systems, 45(3), 535-569.
  • Oussous, A., Benjelloun, F.Z., Lahcen, A.A., Belfkih, S. 2017. “Big Data Technologies: A Survey”, Journal of King Saud University-Computer and Information Sciences.
  • Peary, B. D., Shaw, R., Takeuchi, Y. 2012. “Utilization of social media in the east Japan earthquake and tsunami and its effectiveness”, Journal of Natural Disaster Science, 34(1), 3-18.
  • Peteiro-Barral, D., Guijarro-Berdiñas, B. 2013. “A survey of methods for distributed machine learning”, Progress in Artificial Intelligence, 2(1), 1-11.
  • PhridviRaj, M.S.B., GuruRao, C.V. 2014. “Data mining–past, present and future–a typical survey on data streams”, Procedia Technology, 12, 255-263.
  • Psaltis, A.G. 2017. “Streaming Data: Understanding the Real-Time Pipeline”, Manning.
  • Pyle, D. 1999. “Data preparation for data mining”, Morgan Kaufmann.
  • Qiu, J., Wu, Q., Ding, G., Xu, Y., Feng, S. 2016. “A survey of machine learning for big data processing”, EURASIP Journal on Advances in Signal Processing, 1(67).
  • Read, J., Perez-Cruz, F., Bifet, A. 2015. “Deep learning in partially-labeled data streams”, In Proceedings of the 30th Annual ACM Symposium on Applied Computing, 954-959.
  • Settles, B. 2010. “Active learning literature survey”, University of Wisconsin, Madison, 52, 55-66.
  • Silva, J.A., Faria, E.R., Barros, R.C., Hruschka, E.R., De Carvalho, A.C., Gama, J. 2013. “Data stream clustering: A survey”, ACM Computing Surveys (CSUR), 46(1), 13.
  • Skowron, A., Jankowski, A., Dutta, S. 2016. “Interactive granular computing”, GranularComputing, 1(2), 95-113.
  • Son, N.H. 2006. “Data cleaning and Data preprocessing”, Mimuw University.
  • Steinmetz, R., Nahrstedt, K. 2002. “Multimedia fundamentals, Volume 1: Media coding and content processing”, Pearson Education.
  • Stephens, S., Tamayo, P. 2003. “Supervised and unsupervised data mining techniques for the life sciences”, Curr. Drug Discov, 34(36).
  • Tan, P.N. 2006. “Introduction to data mining”, Pearson Education India.
  • Weiss, K., Khoshgoftaar, T.M. ve Wang, D. 2016. “A survey of transfer learning”, Journal of Big Data, 3(1).
  • Wrench, C., Stahl, F., Di Fatta, G., Karthikeyan, V., Nauck, D.D. 2016. “Data stream mining of event and complex event streams: A survey of existing and future technologies and applications in big data”, In Enterprise Big Data Engineering, Analytics, and Management, 24-47.
  • Xiang, E. W., Cao, B., Hu, D. H., Yang, Q. 2010. “Bridging domains using world wide knowledge for transfer learning”, IEEE Transactions on Knowledge and Data Engineering, 22(6), 770-783.
  • Xu, S., Balazinska, M. 2011. “Sensor Data Stream Exploration for Monitoring Applications”, DMSN.
  • Zang, W., Zhang, P., Zhou, C., Guo, L. 2014. “Comparative study between incremental and ensemble learning on data streams: Case study”, Journal of Big Data, 1(1).
  • Zheng, H., Kulkarni, S.R. ve Poor, H.V. 2011. “Attribute-distributed learning: models, limits, and algorithms”, IEEE Transactions on Signal processing, 59(1), 386-398.
There are 62 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Makaleler
Authors

Anıl Utku 0000-0002-7240-8713

Muhammet Ali Akcayol 0000-0002-6615-1237

Publication Date March 24, 2019
Published in Issue Year 2019 Volume: 12 Issue: 1

Cite

APA Utku, A., & Akcayol, M. A. (2019). Akan Veri Karakterizasyonu, Üretimi Ve Analitiği Üzerine Kapsamlı Bir İnceleme. Erzincan University Journal of Science and Technology, 12(1), 379-410. https://doi.org/10.18185/erzifbed.473008