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Please use this identifier to cite or link to this item: http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/15223
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dc.contributor.authorKumar, Dhruv-
dc.date.accessioned2024-08-13T06:42:35Z-
dc.date.available2024-08-13T06:42:35Z-
dc.date.issued2023-07-
dc.identifier.urihttps://ieeexplore.ieee.org/document/10181222-
dc.identifier.urihttp://dspace.bits-pilani.ac.in:8080/jspui/xmlui/handle/123456789/15223-
dc.description.abstractToday, data is generated in a geographically distributed manner in a wide variety of domains such as social networks, e-commerce, search engines, online advertisements, audio and video streaming, energy, smart cities, IoT sensors etc. Consequently, this data is stored across geographically distributed edges and data centers (DCs) near to the end-users and end-devices, the very sources of this data. Analyzing this geographically distributed data is challenging primarily due to two reasons: 1) constrained and costly WAN bandwidth links which connect the geo-distributed edges and DCs (henceforth collectively called as sites) [1], and 2) limited compute availability at each siteen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectComputer Scienceen_US
dc.subjectGeo distributed joinsen_US
dc.subjectClouden_US
dc.subjectEdgeen_US
dc.titleAggFirstJoin: Optimizing Geo-Distributed Joins using Aggregation-Based Transformationsen_US
dc.typeArticleen_US
Appears in Collections:Department of Computer Science and Information Systems

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