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X-WR-CALDESC:Events for Information Systems Group
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DTSTART;TZID=America/Los_Angeles:20230303T130000
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DTSTAMP:20260427T160154
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SUMMARY:Alex Behm (Databricks): Photon: How to think vectorized
DESCRIPTION:The Department of Computer Science\, Information Systems Group\, UC Irvine \nWELCOMES \nDr. Alex Behm \nDatabricks \nPhoton: How to think vectorized \n3/3/2023\, Friday\, 1:00 – 2 pm \nPlace DBH 4011 \nI’m presenting Photon\, a new vectorized execution engine powering Databricks written from scratch in C++. I will introduce you to its basic building blocks by walking you through the evaluation of an example query with code snippets. You will learn about expression evaluation\, compute kernels\, runtime adaptivity\, filter evaluation\, and vectorized operations against hash tables. After the talk\, you will understand why vectorization is not just about SIMD for database people! \nBio: Alex has been building databases for over a decade in academia and industry and maintains a passion for speed and quality. He is the tech lead for Photon\, a new vectorized engine written from scratch in C++ that powers Databricks. Before joining Databricks\, Alex helped build Apache Impala as the second engineer on the project. Alex holds a PhD in databases from UC Irvine.
URL:https://isg.ics.uci.edu/event/photon-how-to-think-vectorized/
LOCATION:DBH 4011
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DTSTART;TZID=America/Los_Angeles:20230310T130000
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DTSTAMP:20260427T160154
CREATED:20230307T042058Z
LAST-MODIFIED:20230307T042058Z
UID:1533-1678453200-1678456800@isg.ics.uci.edu
SUMMARY:Fangqi Liu: DOME: Drone-assisted Monitoring of Emergent Events For Wildland Fire Resilience
DESCRIPTION:Abstract:\n\nBy serving as “eyes in the sky\,” data obtained from a carefully coordinated set of drones equipped with sensors have the potential to enable continuous monitoring of mission-critical events. We develop a Drone-assisted Monitoring system\, DOME\, that gathers real-time data for situational awareness in emergent and evolving events. The driving use case for this work is a prescribed burn event (Rx fire)\, often used to reduce hazardous fuels in forests. DOME coordinates the use of multiple heterogeneous drone platforms to support the observation of emergent physical phenomena (e.g.\, fire spread) by leveraging domain expert input and physics-based modeling/simulation methods. We propose an executable rule-based system for drone task generation; here\, a high-level mission specification utilizes physics-based models for fire spread prediction and automatically generates detailed monitoring instructions with locations\, periods\, and frequency for individual drones. DOME integrates algorithms for task allocation (mapping tasks to drones) and flight path planning while considering trade-offs between sensing coverage and accuracy. In addition\, DOME will guide in-flight drones to store and upload data under challenged communication settings (out of transmission range\, external signal blocking by trees). We evaluate the performance of DOME in real events (with expert-developed burn plans for a forest in North America). We test the applicability of the DOME system using simulated Rx burns at the Blodgett Forest Research Station and evaluate our proposed algorithms by comparing their performance with multiple baseline algorithms. Our experiments illustrate the effectiveness of the composite mechanisms in DOME that outperforms other approaches with higher rewards (capturing data of higher quality) and coverage (reduction of missed tasks).\n\nBio:\n\nFangqi Liu is a final year Ph.D. student in the Distributed Systems Middleware (DSM) group led by Professor Nalini Venkatasubramanian. Her research interests include wireless mobile networks\, the Internet of things\, motion planning and scheduling of mobile vehicles\, and drone-based monitoring applications.
URL:https://isg.ics.uci.edu/event/fangqi-liu-dome-drone-assisted-monitoring-of-emergent-events-for-wildland-fire-resilience/
LOCATION:DBH 4011
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