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DTSTART;TZID=America/Los_Angeles:20241011T130000
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SUMMARY:Arnab Nandi (OSU): Data Exploration in a Camera-first World: Query and Result Challenges
DESCRIPTION:Prof. Arnab Nandi \n \nAssociate Professor\, Computer Science and Engineering \nThe Ohio State University \nFriday\, October 11\, 2024\nat 11 a.m.\nDonald Bren Hall 6011 \nTitle: “Data Exploration in a Camera-first World: Query and Result Challenges” \nAbstract: The pervasive availability of cameras in smartphones\, vehicles\, drones and more has triggered a new “camera-first” data revolution across industries. When combined with rapid advances in computer vision and machine learning\, this video data deluge presents several data exploration challenges. Here\, we will talk about two complementary challenges for end-users: query specification and result consumption. \nWhen searching large video collections\, the first challenge is that the user is often unaware of the contents of the video\, its structure\, and the exact terminology to use in the user query\, putting them at a loss for where to begin specifying the query. Here\, we present methods to guide the user through the query construction process by building on vision language models and search query interfaces. \nOnce users have executed a search\, they are faced with a new challenge of result consumption. Presenting query results as a list of links poses an impedance mismatch: they are cumbersome to skim through and are in a different modality compared to the source data. However\, processing large video collections within interactive response times has performance implications. We present V2V\, a system to efficiently synthesize video results for video queries. V2V returns a fully-edited video\, allowing the user to consume results in the same modality as the source videos\, resulting in a fluid\, user-centric video exploration experience. \nBio:  Arnab’s work focuses on bridging data infrastructure with human interaction\, spanning areas of database systems\, human factors\, and next-generation interfaces. Arnab is a recipient of the US National Science Foundation’s CAREER Award\, IEEE’s TCDE Early Career Award for his contributions towards user-focused data interaction\, The Ohio State University’s Alumni Award for Distinguished Teaching\, and the University’s Early Career Innovator of the Year Award. \nOver the years\, Arnab has served as Program Committee member and Associate Editor for several database systems journals and conferences including SIGMOD\, VLDB\, ICDE\, and HILDA. Most recently\, Arnab served as Vice President of Data Science at Azuga Inc. (a Bridgestone company) after the acquisition of his connected vehicles analytics startup\, Mobikit. https://arnab.org/ \n 
URL:https://isg.ics.uci.edu/event/arnab-nandi-osu-data-exploration-in-a-camera-first-world-query-and-result-challenges/
LOCATION:DBH 3011
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20241017T150000
DTEND;TZID=America/Los_Angeles:20241017T160000
DTSTAMP:20260604T143350
CREATED:20241011T010505Z
LAST-MODIFIED:20250211T004309Z
UID:2128-1729177200-1729180800@isg.ics.uci.edu
SUMMARY:Nika Mansouri Ghiasi (ETH): Storage-Centric Computing for Genomics and Metagenomics
DESCRIPTION:Title: Storage-Centric Computing for Genomics and Metagenomics \nAbstract \nGenomics and metagenomics applications have enabled significant advancements in many critical areas. The exponential growth of genomic data poses unprecedented challenges in genomics and metagenomic applications. These applications suffer from significant data movement overheads from the storage system. To fundamentally address these overheads\, we make a case for storage-centric computing. \nFirst\, we propose GenStore\, the first in-storage processing system designed for genome sequence analysis that greatly reduces both data movement and computational overheads of genome sequence analysis by exploiting low-cost and accurate in-storage filters. We address the challenges of in-storage processing\, supporting reads with 1) different read lengths and error rates\, and 2) different degrees of genetic variation. Through rigorous analysis of read mapping processes\, we design low-cost hardware accelerators and data/computation flows inside a NAND flash-based SSD. Our evaluation using a wide range of real genomic datasets shows that GenStore significantly improves the read mapping performance of state-of-the-art software (hardware) baselines by 2.07-6.05× (1.52-3.32×) for read sets with high similarity to the reference genome and 1.45-33.63× (2.70-19.2×) for read sets with low similarity to the reference genome. \nSecond\, we propose MegIS\, the first in-storage processing system designed to significantly reduce the data movement overhead of the end-to-end metagenomic analysis pipeline. MegIS is enabled by our lightweight design that effectively leverages and orchestrates processing inside and outside the storage system. Through our detailed analysis of the end-to-end metagenomic analysis pipeline and careful hardware/software co-design\, we address \nin-storage processing challenges for metagenomics via specialized and efficient 1) task partitioning\, 2) data/computation flow coordination\, 3) storage technology-aware algorithmic optimizations\, 4) data mapping\, and 5) lightweight in-storage accelerators. MegIS’s design is flexible\, capable of supporting different types of metagenomic input datasets\, and can be integrated into various metagenomic analysis pipelines. Our evaluation shows that MegIS outperforms the state-of-the-art performance- and accuracy-optimized software metagenomic tools by 2.7×–37.2× and 6.9×–100.2×\, respectively\, while matching the accuracy of the accuracy-optimized tool. MegIS achieves 1.5×–5.1× speedup compared to the state-of-the-art metagenomic hardware-accelerated (using processing-in-memory) tool\, while achieving significantly higher accuracy. \n Bio \nNika Mansouri Ghiasi is a Ph.D. candidate in the SAFARI Research Group at ETH Zürich\, working with Professor Onur Mutlu. Her current research interests are in computer architecture and bioinformatics\, focusing on 1) large-scale bioinformatics applications\, storage systems\, and their interactions\, and 2) emerging technologies such as ultra-dense 3D integrated systems. Nika has co-authored several works on these topics in major computer architecture venues such as ISCA\, ASPLOS\, and MICRO\, as well as major bioinformatics venues such as ISMB\, Bioinformatics\, and Nature Reviews. \n 
URL:https://isg.ics.uci.edu/event/nika-mansouri-ghiasi-eth-storage-centric-computing-for-genomics-and-metagenomics/
LOCATION:DBH 3011
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20241018T110000
DTEND;TZID=America/Los_Angeles:20241018T120000
DTSTAMP:20260604T143350
CREATED:20241008T004750Z
LAST-MODIFIED:20241009T163638Z
UID:2109-1729249200-1729252800@isg.ics.uci.edu
SUMMARY:Yannis Papakonstantinou (Google): Vector Search and Databases
DESCRIPTION:Yannis Papakonstantinou\nDistinguished Engineer\, Query Processing and GenAI at Google Cloud Databases\nAbstract:\nSemantic search ability\, via embedding (vectors) and vector indexing\, has been added to Google Cloud Platform (GCP) databases in order to enable GenAI applications. The inclusion of vectors in databases confers many of the traditional benefits of databases: Developers can now develop GenAI applications on their familiar and trusted databases. Furthermore\, developers can be sure that the vectors are also up-to-date and transactionally consistent. The rapid adoption of the postgres pgvector extension is evidence of the appreciation of these benefits by the database developer community.\nThe inclusion of vectors in databases raises three R&D questions\, which we will discuss in this talk.\nFirst\, can databases with vector abilities perform as well as purpose-built vector databases in pure vector search? What does it take to achieve this?\nSecond\, what are the opportunities and respective R&D challenges that emerge at the intersection of SQL data and vectors?\nFinally\, what does it take to facilitate and align the experience of SQL developers with the world of vector management and vector indexing? \nBio:\nYannis Papakonstantinou is a Distinguished Engineer\, working on Query Processing and GenAI\, at Google Cloud. He is also an Adjunct Professor of Computer Science and Engineering at the University of California\, San Diego\, following many years of having been a UCSD regular faculty member. Previously he was an architect in query processing & ETL at Databricks. Earlier\, he was a Senior Principal Scientist at Amazon Web Services from 2018-2021 and was a consultant for AWS since 2016. He was the CEO and Chief Scientist of Enosys Software\, which built and commercialized an early Enterprise Information Integration platform for structured and semistructured data. The Enosys Software was OEM’d and sold under the BEA Liquid Data and BEA Aqualogic brand names\, eventually acquired in 2003 by BEA Systems.\nHis R&D work has been mostly on query processing with focus on querying semistructured data. He has published over one hundred twenty research articles that have received over 21\,000 citations. Yannis holds a Diploma of Electrical Engineering from the National Technical University of Athens\, MS and Ph.D. in Computer Science from Stanford University (1997). \n 
URL:https://isg.ics.uci.edu/event/yannis-papakonstantinou-google-vector-search-and-database/
LOCATION:DBH 6011
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