Browsing by Author "Zhang, D."
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Conference paperPublication Metadata only Realtime healthcare services via nested complex event processing technology(The ACM Digital Library, 2012) Liu, M.; Ray, M.; Zhang, D.; Rundensteiner, E.; Dougherty, D. J.; Gupta, C.; Wang, S.; Arı, İsmail; Computer Science; ARI, IsmailComplex Event Processing (CEP) over event streams has become increasingly important for real-time applications ranging from healthcare to supply chain management. In such applications, arbitrarily complex sequence patterns as well as non existence of such complex situations must be detected in real time. To assure real-time responsiveness for detection of such complex pattern over high volume high-speed streams, efficient processing techniques must be designed. Unfortunately the efficient processing of complex sequence queries with negations remains a largely open problem to date. To tackle this shortcoming, we designed optimized strategies for handling nested CEP query. In this demonstration, we propose to showcase these techniques for processing and optimizing nested pattern queries on streams. In particular our demonstration showcases a platform for specifying complex nested queries, and selecting one of the alternative optimized techniques including sub-expression sharing and intermediate result caching to process them. We demonstrate the efficiency of our optimized strategies by graphically comparing the execution time of the optimized solution against that of the default processing strategy of nested CEP queries. We also demonstrate the usage of the proposed technology in several healthcare services.Conference paperPublication Metadata only VisDrone-MOT2021: The vision meets drone multiple object tracking challenge results(IEEE, 2021) Chen, G.; Wang, W.; He, Z.; Wang, L.; Yuan, Y.; Zhang, D.; Zhang, J.; Zhu, P.; Gool, L. V.; Han, J.; Hoi, S.; Hu, Q.; Liu, M.; Sciarrone, A.; Sun, C.; Garibotto, C.; Tran, D. N. N.; Lavagetto, F.; Haleem, H.; Motorcu, Hakkı; Ateş, H. F.; Jeon, H. J.; Bisio, I.; Jeon, J. W.; Li, J.; Pham, J. H.; Jeon, M.; Feng, Q.; Li, S.; Tran, T. H. P.; Pan, X.; Song, Y. M.; Yao, Y.; Du, Y.; Xu, Z.; Luo, Z.; Motorcu, HakkıVision Meets Drone: Multiple Object Tracking (VisDrone-MOT2021) challenge - the forth annual activity organized by the VisDrone team - focuses on benchmarking UAV MOT algorithms in realistic challenging environments. It is held in conjunction with ICCV 2021. VisDrone-MOT2021 contains 96 video sequences in total, including 56 sequences (~24K frames) for training, 7 sequences (~3K frames) for validation and 33 sequences (~13K frames) for testing. Bounding-box annotations for novel object categories are provided every frame and temporally consistent instance IDs are also given. Additionally, occlusion ratio and truncation ratio are provided as extra useful annotations. The results of eight state-of-the-art MOT algorithms are reported and discussed. We hope that our VisDrone-MOT2021 challenge will facilitate future research and applications in the field of UAV vision.