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pro vyhledávání: '"Sabek, Ibrahim"'
We show how to utilize machine learning approaches to improve sliding window algorithms for approximate frequency estimation problems, under the ``algorithms with predictions'' framework. In this dynamic environment, previous learning-augmented algor
Externí odkaz:
http://arxiv.org/abs/2409.11516
Autor:
Pandey, Varun, van Renen, Alexander, Zacharatou, Eleni Tzirita, Kipf, Andreas, Sabek, Ibrahim, Ding, Jialin, Markl, Volker, Kemper, Alfons
Spatial data is ubiquitous. Massive amounts of data are generated every day from a plethora of sources such as billions of GPS-enabled devices (e.g., cell phones, cars, and sensors), consumer-based applications (e.g., Uber and Strava), and social med
Externí odkaz:
http://arxiv.org/abs/2309.06354
Autor:
Sabek, Ibrahim, Kraska, Tim
In-memory join is an essential operator in any database engine. It has been extensively investigated in the database literature. In this paper, we study whether exploiting the CDF-based learned models to boost the join performance is practical or not
Externí odkaz:
http://arxiv.org/abs/2111.08824
In this work, we aim to study when learned models are better hash functions, particular for hash-maps. We use lightweight piece-wise linear models to replace the hash functions as they have small inference times and are sufficiently general to captur
Externí odkaz:
http://arxiv.org/abs/2107.01464
Autor:
Zacharatou, Eleni Tzirita, Kipf, Andreas, Sabek, Ibrahim, Pandey, Varun, Doraiswamy, Harish, Markl, Volker
Spatial approximations have been traditionally used in spatial databases to accelerate the processing of complex geometric operations. However, approximations are typically only used in a first filtering step to determine a set of candidate spatial o
Externí odkaz:
http://arxiv.org/abs/2010.12548
Autor:
Pandey, Varun, van Renen, Alexander, Kipf, Andreas, Sabek, Ibrahim, Ding, Jialin, Kemper, Alfons
Spatial data is ubiquitous. Massive amounts of data are generated every day from billions of GPS-enabled devices such as cell phones, cars, sensors, and various consumer-based applications such as Uber, Tinder, location-tagged posts in Facebook, Twit
Externí odkaz:
http://arxiv.org/abs/2008.10349
Akademický článek
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Autor:
Sabek, Ibrahim, Youssef, Moustafa
Device-free (DF) localization is an emerging technology that allows the detection and tracking of entities that do not carry any devices nor participate actively in the localization process. Typically, DF systems require a large number of transmitter
Externí odkaz:
http://arxiv.org/abs/1308.0768
Inferring evaluation scores based on human judgments is invaluable compared to using current evaluation metrics which are not suitable for real-time applications e.g. post-editing. However, these judgments are much more expensive to collect especiall
Externí odkaz:
http://arxiv.org/abs/1307.1872
Autor:
Sabek, Ibrahim, Youssef, Moustafa
Device-free (DF) localization in WLANs has been introduced as a value-added service that allows tracking indoor entities that do not carry any devices. Previous work in DF WLAN localization focused on the tracking of a single entity due to the intrac
Externí odkaz:
http://arxiv.org/abs/1207.4265