Prof. Arnab Nandi Associate Professor, Computer Science and Engineering The Ohio State University Friday, October 11, 2024 at 11 a.m. Donald Bren Hall 6011 Title: "Data Exploration in a Camera-first […]

Title: Storage-Centric Computing for Genomics and Metagenomics Abstract Genomics and metagenomics applications have enabled significant advancements in many critical areas. The exponential growth of genomic data poses unprecedented challenges in […]

Yannis Papakonstantinou Distinguished Engineer, Query Processing and GenAI at Google Cloud Databases Abstract: Semantic search ability, via embedding (vectors) and vector indexing, has been added to Google Cloud Platform (GCP) […]

A Compiler-Centric Query Engine Design for Mixed Workloads and Modern Hardware 11/1/2024, 1:00 PM 2 PM, DBH 3011 Michael Jungmair, Technical University of Munich, Germany Abstract: Relational query engines are increasingly expected to handle more than just relational queries and also run on modern hardware that is increasingly parallel and distributed. However, it is not clear how existing system designs can deal with these two challenges effectively. We propose a holistic, compiler-centric design for data processing systems that is designed for tightly integrated optimization and execution of relational queries, non-relational workloads and user-defined functions on modern hardware. Bio: Michael Jungmair is a third year PhD student at the Technical University of Munich. Supervised by Jana Giceva, he is performing research in the intersection of database engines and compiler technology. So far, this research culminated in the design and implementation of LingoDB (lingo-db.com), a novel query engine based on the MLIR compiler framework

 CloudMapper: A Pay-as-you-go Solution for Accelerating Genomics Sequence Alignment Using Public Clouds Abstract: Single-cell RNA sequencing (scRNA-seq) alignment remains a computational bottleneck in bioinformatics data analysis. As datasets grow in size […]

Abstract: In database applications involving sensitive data, the dual imperatives of data confidentiality and provable (verifiable) query processing are important. This paper introduces PoneglyphDB, a database system that leverages non-interactive zero-knowledge proofs […]

Abstract: As data analytics grow in popularity, the increasing volume of data and complexity of jobs require users to wait longer to see results, hindering productivity and causing frustration. To address […]

Abstract: Emerging IoT and edge applications demand fast response times that cannot be achieved by faraway cloud datacenters. This motivates building edge-cloud systems where nodes on the edge can participate in […]

Abstract:  We have entered a data-centric computing era, characterized by the coexistence of diverse parallel and specialized hardware accelerators along with general-purpose processors. In this ecosystem, minimizing data movement has become […]

Abstract In an era where data-driven decision-making shapes industries, governments, and everyday life, the ability to leverage data science has become an essential skill. Modern data science tools—encompassing data collection, […]

Practical Approaches for Private and Scalable Information Data Management Systems   Amr El Abbadi Professor of Computer Science University of California at Santa Barbara  Abstract.  Increasingly countries and regions have […]

Abstract In this talk, we will first present our paper at the DSE-K12 conference with the title “DS4ALL: Teaching High-School Students Data Science and AI/ML Using the Texera Workflow Platform […]

Abstract: The Dept of Energy (DOE) complex comprises of many science facilities that could be classified as data producing (eg. the Advanced Photon Source at Argonne National Laboratory) and consuming (eg. […]

ABSTRACT Data-based systems are increasingly used in applications that have far-reaching consequences and long-lasting societal impact. However, the development process remains highly specialized, tedious, and unscalable. This produces a manually […]

Knowledge Graphs and AI: Bridging Enterprise Data and Knowledge Graphs to Leverage AI Applications Abstract: In today’s data-driven world, enterprises face significant challenges in integrating, structuring, and utilizing vast amounts […]

“A Retrieval-and-Structuring Approach for LLM-Enhanced, Theme-Focused Scientific Exploration” Abstract:  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.  Bio: 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.

Title: Transaction Processing in Hybrid Edge Data Management Systems Abstract: Advances in computing and storage systems have enabled end users to run complex workloads on relatively cheap machines. These advancements have […]