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X-WR-CALDESC:Events for Information Systems Group
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DTSTART;TZID=America/Los_Angeles:20250411T110000
DTEND;TZID=America/Los_Angeles:20250411T120000
DTSTAMP:20260502T202829
CREATED:20250401T171717Z
LAST-MODIFIED:20260401T210231Z
UID:2188-1744369200-1744372800@isg.ics.uci.edu
SUMMARY:Jiawei Han (distinguished lecture): A Retrieval-and-Structuring Approach for LLM-Enhanced\, Theme-Focused Scientific Exploration
DESCRIPTION:“A Retrieval-and-Structuring Approach for LLM-Enhanced\, Theme-Focused Scientific Exploration” \nAbstract:  Large Language Models (LLMs) may bring unprecedented power for scientific exploration.  However\, current LLMs may still encounter major challenges for effective scientific exploration due to their lack of in-depth\, theme-focused data and knowledge.  Retrieval augmented generation (RAG) has recently become an interesting approach for augmenting LLMs with grounded\, theme-specific datasets.  We discuss the challenges of RAG and propose a retrieval and structuring (RAS) approach\, which enhances RAG by improving retrieval quality and mining structures (e.g.\, extracting entities and relations and building knowledge graphs) to ensure its effective integration of theme-specific data with LLM.  We show the promise of this approach at augmenting LLMs and discuss its potential power for LLM-enabled science exploration.  \n\n\n\n\nBio: Jiawei Han is Michael Aiken Chair Professor in the Siebel School of Computing and Data Science\, University of Illinois Urbana-Champaign.  He received ACM SIGKDD Innovation Award (2004)\, IEEE Computer Society Technical Achievement Award (2005)\, IEEE Computer Society W. Wallace McDowell Award (2009)\, Japan’s Funai Achievement Award (2018)\, and being elevated to Fellow of Royal Society of Canada (2022).  He is Fellow of ACM and Fellow of IEEE and served as the Director of Information Network Academic Research Center (INARC) (2009-2016) supported by the Network Science-Collaborative Technology Alliance (NS-CTA) program of U.S. Army Research Lab and co-Director of KnowEnG\, a Center of Excellence in Big Data Computing (2014-2019)\, funded by NIH Big Data to Knowledge (BD2K) Initiative.  Currently\, he is serving on the executive committees of two NSF funded research centers:  MMLI (Molecular Make Research Institute)—one of NSF funded national AI centers since 2020 and I-Guide—The National Science Foundation (NSF) Institute for Geospatial Understanding through an Integrative Discovery Environment (I-GUIDE) since 2021.
URL:https://isg.ics.uci.edu/event/jiawei-han-distinguished-lecture/
LOCATION:DBH 6011
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250418T130000
DTEND;TZID=America/Los_Angeles:20250418T140000
DTSTAMP:20260502T202829
CREATED:20250401T171754Z
LAST-MODIFIED:20250423T180252Z
UID:2190-1744981200-1744984800@isg.ics.uci.edu
SUMMARY:Abhishek Singh: Transaction Processing in Hybrid Edge Data Management Systems
DESCRIPTION:Title: Transaction Processing in Hybrid Edge Data Management Systems \nAbstract:\nAdvances in computing and storage systems have enabled end users to run complex workloads on relatively cheap machines. These advancements have given rise to a novel infrastructure in data management: Edge-Cloud. Edge-Cloud data management systems allow data to be stored at the edge and managed by the cloud. The availability of Edge-Cloud systems has given rise to interesting research directions in data management. \nIn this talk\, we deal with the problem of building data management systems that use resources from Edge and Cloud. We use the idea of the `Cloud’ and `Edge’ as abstractions of Trusted and Untrusted systems respectively. The `Cloud’ in this thesis is treated as a trusted resource. This abstraction is motivated by the fact that applications deployed on the cloud (such as Gmail\, Facebook\, etc) are trusted by end users. Applications on the cloud are deployed and managed by large corporations that have a financial incentive to ensure that the data and applications they manage are secure. In contrast to the Cloud\, Edge data management systems use cheaper components and no assumption can be made about them. \nIn order to execute transactions on an integrated edge-cloud system we propose LogPoseDB\, an edge-cloud database that spans both edge and cloud nodes. LogPoseDB aims to overcome the two challenges above. LogPoseDB does not require any dedicated edge infrastructure. Rather\, clients may utilize their edge nodes – if desired – to perform the processing and storage of their data while they need it. (Other clients can still process their data on cloud nodes.) To enable this type of processing\, LogPoseDB proposes state disentanglement\, where the state (storage and processing) is treated as a shared resource between the cloud and the edge. \nLogPoseDB’s transaction processing protocol ensures fast response by avoiding wide-area coordination with the cloud or other faraway edge nodes. This is done by leveraging data locality of detached state and by methods that build on the areas of transaction chopping and commutativity. To address the trust challenges\, we propose a byzantine fault-tolerant (BFT) protocol that targets edge nodes. LogPoseDB’s BFT replication protocol proposes the principle of remote lazy trust that enables efficient BFT edge coordination by utilizing a remote trusted node asynchronously. \nBio:\nAbhishek Alfred Singh is a PhD Candidate working with Professor Faisal Nawab. His research interests are in transaction processing in emerging edge-cloud data management systems. His work deals with transaction processing in loosely coupled distributed systems.
URL:https://isg.ics.uci.edu/event/abhishek-singh/
LOCATION:DBH 3011
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250425T130000
DTEND;TZID=America/Los_Angeles:20250425T140000
DTSTAMP:20260502T202829
CREATED:20250401T171824Z
LAST-MODIFIED:20250521T184951Z
UID:2192-1745586000-1745589600@isg.ics.uci.edu
SUMMARY:Yiming Lin (Berkeley): Toward Building Efficient Document Analytics Systems from the Lens of Document Structure
DESCRIPTION:Abstract:\nThe vast majority—over 80%—of data today exists in unstructured formats\, and querying and extracting value from unstructured document collections remains a considerable challenge. While Large Language Models (LLMs) have made remarkable progress in document understanding\, they fail to provide high-accuracy results for analytical queries on documents and incur high costs. \nIn this talk\, we demonstrate that document collections often have hidden structure\, and discovering them can facilitate multiple downstream data analytics tasks on documents effectively. At one extreme\, we explore documents sharing a similar high-level template that impart a common semantic structure\, such as scientific papers from the same venue. We introduce ZenDB\, a document analytics system that leverages this semantic structure\, coupled with LLMs\, to answer ad-hoc SQL queries on document collections. At another extreme\, we explore documents that are form-like\, such as invoices\, order bills\, containing structured data like tables or key-value pairs\, which are programmatically generated by populating fields in a visual blueprint. We present TWIX\, a document analytics tool that first infers the common blueprint and then extracts structured data from documents efficiently. For both extremes explored\, we provide theoretical guarantees on the correctness of structure extraction\, present empirical results demonstrating their potential for document analytics\, and show their early impact on our collaborators\, including Big Local News at Stanford and California Police Data Applications. \nBio: \nYiming Lin is a postdoctoral researcher at UC Berkeley\, and he received his PhD in Computer Science from UC Irvine. His research interests span document analytics\, query processing and optimization\, and data cleaning\, with a current focus on developing data management systems for document analytics. Yiming has closely collaborated with and interned at industrial pioneers in data analytics\, including Microsoft Research and Amazon. His work has been published in several flagship conferences\, including VLDB\, SIGMOD\, and ICDE.
URL:https://isg.ics.uci.edu/event/yiming-lin-berkeley/
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