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X-ORIGINAL-URL:https://isg.ics.uci.edu
X-WR-CALDESC:Events for Information Systems Group
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DTSTART;TZID=America/Los_Angeles:20230505T130000
DTEND;TZID=America/Los_Angeles:20230505T140000
DTSTAMP:20260711T214506
CREATED:20230502T052932Z
LAST-MODIFIED:20230512T193154Z
UID:1553-1683291600-1683295200@isg.ics.uci.edu
SUMMARY:Farzad Habibi: Metastable Failures in Consensus Algorithms
DESCRIPTION:Abstract\nMetastable failure is a recent abstraction of a pattern of failures in distributed systems. A metastable failure is characterized as “permanent overload with an ultra-low goodput.”\nPrior research has proposed a framework for understanding metastable failure and has observed various cases of such failures in real-world settings.\nIn this talk\, we discuss the challenge of metastable fault tolerance in replication systems\, focusing specifically on a basic problem in distributed systems—consensus. Consensus is a basic building block for many distributed systems and protocols which means that such a focus would have an impact on a large class of systems.\nThe main points covered in this talk include (1) an introduction to metastable failures\, (2) a comprehensive analysis of various metastable failure cases in replication systems\, (3) a reproduction of a metastable failure case study\, (4) modeling of these failures using queuing theory\, and (5) a machine learning approach for predicting metastable failures. \nBio:\nFarzad Habibi is a second-year Ph.D. student in Computer Science at the University of California\, Irvine. He earned his Bachelor’s degree in Computer Engineering from the University of Tehran. Prior to his current research focus\, Farzad explored the resiliency of blockchain systems in the face of network partitioning. His current research endeavors are centered around investigating and addressing metastable failures in distributed systems.
URL:https://isg.ics.uci.edu/event/farzad-habibi-metastable-failures-in-consensus-algorithms/
LOCATION:DBH 4011
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230512T110000
DTEND;TZID=America/Los_Angeles:20230512T120000
DTSTAMP:20260711T214506
CREATED:20230512T193453Z
LAST-MODIFIED:20230522T212929Z
UID:1564-1683889200-1683892800@isg.ics.uci.edu
SUMMARY:CS Seminar: Prof. Arun Kumar: The New DBfication of ML/AI
DESCRIPTION:The Department of Computer Science\, UC Irvine  \nWELCOMES \nProf. Arun Kumar \nUCSD \n5/12/2023\, Friday\, 11:00 am – noon \nPlace DBH 6011 \nAbstract: \nThe recent boom in ML/AI applications has brought into sharp focus the pressing need for tackling the concerns of scalability\, usability\, and manageability across the entire lifecycle of ML/AI applications. The ML/AI world has long studied the concerns of accuracy\, automation\, etc. from theoretical and algorithmic vantage points. But to truly democratize ML/AI\, the vantage point of building and deploying practical systems is equally critical. \nIn this talk\, I will make the case that it is high time to bridge the gap between the ML/AI world and a world that exemplifies successful democratization of data technology: databases. I will show how new bridges rooted in the principles\, techniques\, and tools of the database world are helping tackle the above pressing concerns and in turn\, posing new research questions to the world of ML/AI. As case studies of such bridges\, I will briefly describe two lines of work from my group: query optimization for scalable deep learning systems and benchmarking data preparation in AutoML platforms. \nBio: \nArun Kumar is an Associate Professor in the Department of Computer Science and Engineering and the Halicioglu Data Science Institute and an HDSI Faculty Fellow at the University of California\, San Diego. He is a member of the Database Lab and Center for Networked Systems and an affiliate member of the AI Group. His primary research interests are in data management and systems for machine learning/artificial intelligence-based data analytics. Systems and ideas based on his research have been released as part of the Apache MADlib open-source library\, shipped as part of products from Cloudera\, IBM\, Oracle\, and Pivotal\, and used internally by Facebook\, Google\, LogicBlox\, Microsoft\, and other companies. He is a recipient of three SIGMOD research paper awards\, five distinguished reviewer/meta reviewer awards from SIGMOD/VLDB\, the IEEE TCDE Rising Star Award\, an NSF CAREER Award\, a UCSD oSTEM Faculty of the Year Award\, and research award gifts from Amazon\, Google\, Oracle\, and VMware.
URL:https://isg.ics.uci.edu/event/the-new-dbfication-of-ml-ai/
LOCATION:DBH 6011
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230519T130000
DTEND;TZID=America/Los_Angeles:20230519T140000
DTSTAMP:20260711T214506
CREATED:20230516T180316Z
LAST-MODIFIED:20230516T180316Z
UID:1567-1684501200-1684504800@isg.ics.uci.edu
SUMMARY:Yiming Lin: Auto-BI: Automatically Build BI-Models Leveraging Local Join Prediction and Global Schema Graph
DESCRIPTION:Abstract: \nBusiness Intelligence (BI) is crucial in modern enterprises and billion-dollar business. Traditionally\, technical experts like database administrators would manually prepare BI-models (e.g.\, in star or snowflake schemas) that join tables in data warehouses\, before less-technical business users can run analytics using end-user dashboarding tools. However\, the popularity of self-service BI (e.g.\, Tableau and Power-BI) in recent years creates a strong demand for less technical end-users to build BI-models themselves. We develop an Auto-BI system that can accurately predict BI models given a set of input tables\, using a principled graph-based optimization problem we propose called k-Min-Cost-Arborescence (k-MCA)\, which holistically considers both local join prediction and global schema-graph structures\, leveraging a graph-theoretical structure called arborescence. While we prove k-MCA is intractable and inapproximate in general\, we develop novel algorithms that can solve k-MCA optimally\, which is shown to be efficient in practice with sub-second latency and can scale to the largest BI-models we encounter (with close to 100 tables). Auto-BI is rigorously evaluated on a unique dataset with over 100K real BI models we harvested\, as well as on 12 popular TPC benchmarks. It is shown to be both efficient and accurate\, achieving over 0.9 F1-score on both real and synthetic benchmarks.\n\n\nBio:\nYiming is a final year PhD student working with Prof. Sharad Mehrotra. His research area focuses on data management\, and especially on efficient query processing\, query optimization\, data quality and data integration.
URL:https://isg.ics.uci.edu/event/yiming-lin-auto-bi-automatically-build-bi-models-leveraging-local-join-prediction-and-global-schema-graph/
LOCATION:DBH 4011
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230526T130000
DTEND;TZID=America/Los_Angeles:20230526T140000
DTSTAMP:20260711T214506
CREATED:20230522T213103Z
LAST-MODIFIED:20230522T213103Z
UID:1577-1685106000-1685109600@isg.ics.uci.edu
SUMMARY:Qiushi Bai: Improving SQL Performance Using Middleware-Based Query Rewriting
DESCRIPTION:Abstract: \nQuery performance is critical in database-supported applications where users need answers quickly to make timely decisions. Traditional databases rely on rewriting queries to improve SQL performance. With the emergence of business intelligence and interactive visualization applications\, databases often miss opportunities to rewrite their queries\, due to reasons such as failure to adopt high-accuracy time estimators to choose efficient plans\, and missing domain-specific rewriting rules valid only for specific datasets. We focus on middleware-based query-rewriting solutions to address the problem since\, in many cases\, both the application and database layer are black boxes. First\, we develop Maliva\, a machine-learning-based technique that leverages high-accuracy query-time estimators to rewrite queries under time constraints. Second\, we present QueryBooster\, a human-centered query rewriting framework that provides an easy-to-use language for users to formulate rewriting rules based on their domain knowledge. Finally\, to make it easy for users to optimize their application queries\, we propose a middleware-based system called Squidster that provides query rewriting as a service. Our experiments in real and synthetic datasets show the effectiveness of the proposed solutions in improving end-to-end SQL query performance. \n  \nIn this talk\, we will focus on the QueryBooster and Squidster work. We will show a demo to motivate the problem and demonstrate the effectiveness and convenience of using the QueryBooster system to improve SQL query performance. \n  \nBio: \nQiushi Bai is a final year Ph.D. candidate in the Computer Science Department at UC Irvine. He received his Master’s and Bachelor’s degrees in CS from Northeastern University in China. His research interests have focused on improving query performance for big data analytics.
URL:https://isg.ics.uci.edu/event/qiushi-bai-improving-sql-performance-using-middleware-based-query-rewriting/
LOCATION:DBH 4011
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