BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Information Systems Group - ECPv6.4.0.1//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://isg.ics.uci.edu
X-WR-CALDESC:Events for Information Systems Group
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20230312T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20231105T090000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230413T120000
DTEND;TZID=America/Los_Angeles:20230413T130000
DTSTAMP:20260426T192442
CREATED:20230420T175420Z
LAST-MODIFIED:20260401T210127Z
UID:1541-1681387200-1681390800@isg.ics.uci.edu
SUMMARY:C. Mohan: A Survey of Cloud Database Systems
DESCRIPTION:C. Mohan\nDistinguished Visiting Professor\, Tsinghua University\, China & Member\, Board of Governors (Digital University Kerala\, India) & Retired IBM Fellow (IBM Research\, USA)\n“A Survey of Cloud Database Systems” \nABSTRACT:  In this talk\, I will first introduce traditional (non-cloud) parallel and distributed database systems. Concepts like SQL and NoSQL systems\, data replication\, distributed and parallel query processing\, and data recovery after different types of failures will be covered. Then\, I will discuss how the emergence of the (public) cloud has introduced new requirements on parallel and distributed database systems\, and how such requirements have necessitated fundamental changes to the architectures of such systems. I will illustrate the related developments by discussing some of the details of systems like Alibaba POLARDB\, Microsoft Azure SQL DB\, Microsoft Socrates\, Azure Synapse POLARIS\, Google Spanner\, Google AlloyDB\, CockroachDB\, Amazon Aurora and Snowflake. \nBio: Dr. C. Mohan is currently a Distinguished Visiting Professor at Tsinghua University in China\, a Member of the inaugural Board of Governors of Digital University Kerala\, and an Advisor of the Kerala Blockchain Academy (KBA) and the Tamil Nadu e-Governance Agency (TNeGA) in India. He retired in June 2020 from being an IBM Fellow at the IBM Almaden Research Center in Silicon Valley. He was an IBM researcher for 38.5 years in the database\, blockchain\, AI and related areas\, impacting numerous IBM and non-IBM products\, the research and academic communities\, and standards\, especially with his invention of the well-known ARIES family of database locking and recovery algorithms\, and the Presumed Abort distributed commit protocol. This IBM (1997-2020)\, ACM (2002-) and IEEE (2002-) Fellow has also served as the IBM India Chief Scientist (2006-2009). In addition to receiving the ACM SIGMOD Edgar F. Codd Innovations Award (1996)\, the VLDB 10 Year Best Paper Award (1999) and numerous IBM awards\, Mohan was elected to the United States and Indian National Academies of Engineering (2009)\, and named an IBM Master Inventor (1997). This Distinguished Alumnus of IIT Madras (1977) received his PhD at the University of Texas at Austin (1981). He is an inventor of 50 patents. More information can be found in the Wikipedia page and his homepage.
URL:https://isg.ics.uci.edu/event/c-mohan-a-survey-of-cloud-database-systems/
LOCATION:DBH 3011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230414T130000
DTEND;TZID=America/Los_Angeles:20230414T140000
DTSTAMP:20260426T192442
CREATED:20230417T220007Z
LAST-MODIFIED:20230417T220007Z
UID:1537-1681477200-1681480800@isg.ics.uci.edu
SUMMARY:Zuozhi Wang: Texera: A System for Collaborative and Interactive Data Analytics Using Workflows (PhD Final Defense)
DESCRIPTION:Abstract\nIn the world of data analytics\, domain experts\, such as public health scientists and medical researchers\, play a crucial role as their domain knowledge can unlock valuable insights from data. However\, they face several challenges in the current landscape of data analytics tools. They often lack the technical skills necessary to analyze large datasets\, requiring collaboration with technical experts who may not have relevant domain knowledge. Moreover\, when processing large volumes of data\, processing times can be lengthy\, and non-technical users are left in the dark without feedback.Over the past six years\, our team has been developing Texera\, a workflow-based data analytics system specifically designed to enable non-technical users to perform data analytics tasks with ease by promoting seamless collaboration and responsive interactions. Texera enables multiple users to collaboratively construct workflows\, offering an experience similar to that of Google Docs. Furthermore\, Texera allows users to interact with the workflow execution\, enabling them to pause/resume workflows\, inspect execution states\, and modify logic as needed. In this talk\, we will explore the design choices and the associated tradeoffs of several key components within Texera that enable these powerful features. \n  \nBio\nZuozhi Wang is a sixth year PhD student at UC Irvine\, under the supervision of Professor Chen Li. His main research focuses are on the areas of distributed big data processing and query optimization.
URL:https://isg.ics.uci.edu/event/zuozhi-wang-texera-a-system-for-collaborative-and-interactive-data-analytics-using-workflows-phd-final-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230428T130000
DTEND;TZID=America/Los_Angeles:20230428T140000
DTSTAMP:20260426T192442
CREATED:20230418T205830Z
LAST-MODIFIED:20230420T003058Z
UID:1539-1682686800-1682690400@isg.ics.uci.edu
SUMMARY:Quishi Bai: Maliva: Using Machine Learning to Rewrite Visualization Queries Under Time Constraints
DESCRIPTION:Abstract:\nAs a powerful way for people to gain insights from data quickly and intuitively\,  visualization is becoming increasingly important in the Big Data era. Considering data-visualization systems where a middleware layer translates a frontend request to a SQL query to a backend database to compute visual results.  In this talk\, we study the problem of answering a visualization request within a limited time due to the responsiveness requirement.  We propose a novel middleware solution called Maliva based on machine learning (ML) techniques.  Maliva applies the Markov Decision Process (MDP) model to decide how to rewrite queries and uses instances to train an agent to make a sequence of decisions judiciously for an online request.  Our experiments on both real and synthetic datasets show that Maliva performs significantly better than a baseline solution that does not do any rewriting\, in terms of both the probability of serving requests interactively and query execution time.\n\nBio:\nQiushi Bai is a 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 and visualizations.
URL:https://isg.ics.uci.edu/event/quishi-bai-maliva-using-machine-learning-to-rewrite-visualization-queries-under-time-constraints/
LOCATION:DBH 4011
END:VEVENT
END:VCALENDAR