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Below is a list of selected publications, which may not be up to date. Please see my Google Scholar profile or DBLP page for the complete list.

⊛ Aydin, B. & Angryk, R. Spatiotemporal Frequent Pattern Mining from Evolving Region Trajectories (SpringerBriefs in Computer Science) (Springer, 2018). DOI: 10.1007/978-3-319-99873-2.
⊛ Filali Boubrahimi, S., Aydin, B., Schuh, M., Kempton, D., Angryk, R. & Ma, Ruizhe. Spatiotemporal interpolation methods for solar event trajectories. The Astrophysical Journal Supplement Series 236, 23 (2018). DOI: 10.3847/1538-4365/aab763.
⊛ Aydin, B., Kucuk, A., Filali Boubrahimi, S. & Angryk, R. Top-(R%,K) spatiotemporal event sequence mining. In IEEE International Conference on Data Mining Workshops, ICDM Workshops 2017 (2017). DOI: 10.1109/ICDMW.2017.39.
⊛ Aydin, B., Kucuk, A., Angryk, R. A. & Martens, P. C. Measuring the significance of spatiotemporal co-occurrences. ACM Transactions on Spatial Algorithms and Systems (TSAS) 3, 9 (2017). DOI: 10.1145/3139351.
⊛ Kucuk, A., Aydin, B., Filali Boubrahimi, S., Kempton, D. & Angryk, R. A. An integrated solar database (ISD) with extended spatiotemporal querying capabilities. In International Symposium on Spatial and Temporal Databases, 405–410 (Springer, Cham, 2017). DOI: https://doi.org/10.1007/978-3-319-64367-0_25.
⊛ Aydin, B. & Angryk, R. A graph-based approach to spatiotemporal event sequence mining. In 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), 1090–1097 (2016). DOI: 10.1109/ICDMW.2016.0157.
⊛ Aydin, B. & Angryk, R. Spatiotemporal event sequence mining from evolving regions. In 23rd International Conference on Pattern Recognition (ICPR), Cancún, México, December 4-8, 2016, 4167–4172 (2016). DOI: 10.1109/ICPR.2016.7900288.
⊛ Pillai, K. G. et al. Mining at most top-k% spatiotemporal co-occurrence patterns in datasets with extended spatial representations. ACM Transactions on Spatial Algorithms and Systems (TSAS) 2, 10 (2016). DOI: 10.1145/2936775.
⊛ Aydin, B., Akkineni, V. & Angryk, R. Mining spatiotemporal co-occurrence patterns in non-relational databases. GeoInformatica 20, 801–828 (2016). DOI: https://doi.org/10.1007/s10707-016-0255-0.
⊛ Aydin, B., Akkineni, V. & Angryk, R. Modeling and indexing spatiotemporal trajectory data in non-relational databases. In Managing Big Data in Cloud Computing Environments, 133–162 (IGI Global, 2016). DOI: 10.4018/978-1-4666-9834-5.ch006.
⊛ Aydin, B. & Angryk, R. Spatiotemporal frequent pattern mining on solar data: Current algorithms and future directions. In IEEE International Conference on Data Mining Workshop, ICDMW 2015, Atlantic City, NJ, USA, November 14-17, 2015, 575–581 (2015). DOI: 10.1109/ICDMW.2015.10.
⊛ Aydin, B., Akkineni, V. & Angryk, R. Time-efficient significance measure for discovering spatiotemporal co-occurrences from data with unbalanced characteristics. In Proceedings of the 23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Seattle, WA, USA, November 3-6, 2015, 80–1 (2015). DOI: 10.1145/2820783.2820871.
⊛ Aydin, B., Kempton, D., Akkineni, V., Angryk, R. & Pillai, K. Mining spatiotemporal co-occurrence patterns in solar datasets. Astronomy and Computing 13, 136–144 (2015). DOI: 10.1016/j.ascom.2015.10.003.
⊛ Aydin, B., Kempton, D., Akkineni, V., Govaparam, S., Pillai, K.G., Angryk, R. Spatiotemporal indexing techniques for efficiently mining spatiotemporal co-occurrence patterns. In 2014 IEEE International Conference on Big Data, Big Data 2014, Washington, DC, USA, October 27-30, 2014, 1–10 (IEEE, 2014). DOI: 10.1109/BigData.2014.7004398.
⊛ Pillai, K. G., Angryk, R. A. & Aydin, B. A filter-and-refine approach to mine spatiotemporal co-occurrences. In Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 104–113 (ACM, 2013). DOI: 10.1145/2525314.2525367.

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