Strategies for Detection of Correlated Data Streams

Alseghayer, Rakan; Petrov, Daniel; Chrysanthis, Panos K.

doi:10.1145/3214708.3214714

Cited by 3 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These policies can increase the efficiency when detecting correlated live data streams and/or address different exploration requirements. By appropriately tuning the parameters of the utility function, the different policies exhibit different detection-recall, overlapping-recall, and diversity results [3,5]. -Develop and evaluate analytically and experimentally e-Racoon, a novel in-memory access structure that weaves spatial and temporal indexing to efficiently processes temporal aggregation join queries at a users' mobile devices.…”

Section: Summary Of Contributionsmentioning

confidence: 99%

See 1 more Smart Citation

Optimizing Operators for Temporal and Spatiotemporal Data

Alseghayer

2024

2024 25th IEEE International Conference on Mobile Data Management (MDM)

View full text Add to dashboard Cite

Without their support I would not have been able to finish this eventful and rewarding journey. Your belief in me, support, humor, and positiveness kept me going through the path until the end. This dissertation is dedicated for you. Above all, I would like to thank my academic advisor Panos K. Chrysanthis for his admirable guidance and support during my PhD years. His incremental step-wise research style, holistic view of research problems, and story telling talent have shaped me to be the scientific researcher I am. I also would like to thank Constantinos Costa for his technical support, dedication to shape this work, and high availability for feedback. I would like to thank the rest of the committee members for their valuable feedback and support. I would like also to thank my colleagues and co-authors of several papers Daniel Mossé and Daniel Petrov. Working with you has been a pleasure and a very rewarding experience. Moreover, I would like to extend my gratitude to the rest of the Advanced Data Management Technologies Lab members and my colleagues and friends at PITT that I have known during this journey, for you have been available for feedback and personal support when I needed

show abstract

Section: Summary Of Contributionsmentioning

confidence: 99%

“…Inspired by [41,3], we reduce the cost of processing the temporal aggregation join query by stopping the processing once reaching a duration that meets the…”

Section: Ct Early Terminationmentioning

confidence: 99%