Database Lab Research Seminar

Announcements

If you are interested in database research, you are welcome to join us in our weekly database seminar. Every week a local or visiting researcher gives a talk on their research topic.

Location/Time: In Fall 2022, the seminar is held on Friday 1:00-2:20pm PT as a mixed modality meeting at 4217 CSE and sometimes on Zoom, organized by Arun Kumar. The Zoom link will be emailed to the DBTalks mailing list.

Course Number: If you are a student and plan to attend, you can enroll in CSE 239A for 1 credit.

Mailing List: Announcements about the seminar are sent to the DBTalks mailing list. To get subscribed, please submit this Google Form.


DB Seminars

  • Milvus: A Cloud-Native Vector Database

    Frank Liu (Zilliz) • Oct 28, 2022
    The total amount of digital data generated worldwide is increasing at a rapid rate. Simultaneously, approximately 80% (and growing) of this newly generated data is... Read more

  • Dream the Stream: High Velocity Event Processing with a Converged Database

    Shasank Chavan (Oracle) • Oct 21, 2022
    Event stream processing is a rapidly growing category of workloads including IoT, Timeseries, Clickstream, Quality Control, Security, Auditing, Metrics, and Monitoring, etc. Analysts estimate the... Read more

  • Autonomics in Amazon Redshift

    Dr. Chunbin Lin (Amazon Web Services) • Oct 7, 2022
    Amazon Redshift is Amazon’s petabyte-scale data warehouse service. It uses machine learning techniques in multiple areas of the service, e.g., automatic workload management. In this... Read more

  • SpeakQL2: A Dialect System for Improving Speech-driven Querying of Structured Data

    Kyle Luoma (UC San Diego) • Sep 30, 2022
    SpeakQL2 builds upon prior work done within the ADALab on a speech + touch SQL query interface designed to enable effective SQL querying against databases... Read more

  • DataPrep: Accelerate Data Preparation for AI

    Dr. Jiannan Wang (Simon Fraser University) • Dec 1, 2021
    Data scientists have been complaining about data preparation (data collection –> data understanding –> data cleaning –> data enrichment –> data integration –> feature engineering)... Read more

  • Efficient and Reliable Query Processing using Machine Learning

    Daniel Kang (Stanford University) • Nov 17, 2021
    Given the rise of increasingly powerful models, machine learning (ML) can now be used to answer a range of queries over unstructured data (e.g., videos,... Read more

  • A 3-year History of Instance Optimized DB Research at Microsoft

    Dr. Umar Farooq Minhas (Microsoft Research) • Nov 3, 2021
    Modern systems need to handle a variety of workloads and use cases. It is very difficult for one system architecture to cater to these use... Read more

  • Hydra: A Data System for Large Multi-Model Deep Learning

    Kabir Nagrecha (UC San Diego) • Oct 27, 2021
    Recent advances in deep learning (DL) architectures have improved model quality in a variety of domains, but have come at the expense of a substantial... Read more

  • Accelerating Analytic Queries on Oracle In-Memory Database

    Dr. Weiwei Gong (Oracle) • Oct 18, 2021
    Oracle In-Memory database was first released in 2014, with its unique dual-format architecture, Oracle Database In-Memory transparently accelerates analytics queries by orders of magnitude, and... Read more

  • Self-Driving Database Management Systems: Forecasting, Modeling, And Planning

    Dr. Lin Ma (CMU) • Oct 6, 2021
    Database management systems (DBMSs) are an important part of modern data-driven applications. However, they are notoriously difficult to deploy and administer because they have many... Read more

  • Programmatically Building & Managing Training Data with Snorkel

    Dr. Alex Ratner (UW-Seattle and Snorkel AI) • Dec 4, 2020
    One of the key bottlenecks in building machine learning systems is creating and managing the massive training datasets that today’s models require. In this talk,... Read more

  • Responsible Data Management

    Dr. Julia Stoyanovich (NYU) • Nov 20, 2020
    The need for responsible data management intensifies with the growing impact of data on society. One central locus of the societal impact of data are... Read more

  • Systems for Human Data Interaction

    Dr. Eugene Wu (Columbia University) • Nov 13, 2020
    The rapid democratization of data has placed its access and analysis in the hands of the entire population. While the advances in rapid and large-scale... Read more

  • Elements of Learning Systems

    Dr. Tianqi Chen (CMU and OctoML) • Nov 6, 2020
    Data, models, and computing are the three pillars that enable machine learning to solve real-world problems at scale. Making progress on these three domains requires... Read more

  • Interpretable Data Analysis with Explanations and Causality

    Dr. Sudeepa Roy (Duke University) • Oct 30, 2020
    In current times, data is considered synonymous with knowledge, profit, power, and entertainment, requiring development of new techniques to extract useful information and insights from... Read more

  • The Socio-Technical Phenomena of Data Integration and Knowledge Graph

    Dr. Juan Sequeda (Data.World) • Oct 23, 2020
    Data Integration has been an active area of computer science research for over two decades. A modern manifestation is as Knowledge Graphs which integrates not... Read more

  • An Exabyte scale global data infrastructure for CMS@LHC

    Prof. Frank Wuerthwein (UCSD Physics and HDSI) • Oct 16, 2020
    The science program at the Large Hadron Collider at CERN is preparing to produce, distribute, and access an Exabyte of new data per year starting... Read more

  • Grouped Learning: Group-By Machine Learning Model Selection Workloads

    Side Li (UCSD CSE) • Oct 9, 2020
    ML practitioners routinely build separate models for data subsets based on some specified attribute(s), e.g., one model per state. We call this practice “ML over... Read more

  • Vista: An End-to-end Declarative Transfer Learning System for Multimodal Analytics with Deep Neural Networks

    Advitya Gemawat (UCSD HDSI) • Oct 9, 2020
    Scalable systems for ML are largely siloed into dataflow systems for structured data and DL systems for unstructured data. This gap has left workloads that... Read more

  • Cerebro: A Layered Data Platform for Scalable Deep Learning

    Yuhao Zhang and Supun Nakandala (UCSD CSE) • Oct 2, 2020
    Deep learning (DL) is gaining popularity across myriad domains due to the new ubiquity of unstructured data, tools such as TensorFlow, and easier access to... Read more

  • Panorama: A Data System for Unbounded Vocabulary Querying over Video

    Yuhao Zhang • Jun 3, 2020
    Deep convolutional neural networks (CNNs) achieve state-of-the-art accuracy for many computer vision tasks. But using them for video monitoring applications incurs high computational cost and... Read more

  • SpeakQL: Towards Speech-driven Multimodal Querying of Structured Data

    Vraj Shah • Jun 3, 2020
    Speech-driven querying is becoming popular in new device environments such as smartphones, tablets, and even conversational assistants. However, such querying is largely restricted to natural... Read more

  • Towards Scalable Hybrid Stores: Constraint-Based Rewriting to the Rescue

    Rana Alotaibi • May 27, 2020
    Big data applications increasingly involve diverse datasets, conforming to different data models. Such datasets are routinely hosted in heterogeneous stores, each capable of handling one... Read more

  • Vista: Declarative Feature Transfer from Deep CNNs at Scale

    Supun Nakandala • May 20, 2020
    Scalable systems for machine learning (ML) are largely siloed into dataflow systems for structured data and deep learning systems for unstructured data. This gap has... Read more

  • Knowledge Graph Use Cases at Intuit

    Jay Yu (Intuit) • May 20, 2020
    Intuit, the leading financial software/service company behind TurboTax, Mint and Quickbooks, is embarking on a multi-year transformational journey into an AI-driven Expert Platform to help... Read more

  • From Data to Models and Back: Experiences from Google's Production ML Pipelines

    Alkis Polyzotis (Google Research) • May 13, 2020
    Building a good ML model requires good input data. Conversely, debugging a model inevitably involves data debugging and understanding. In this talk, I will present... Read more

  • Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML

    Carlo Curino and Kostas Karanasos (Microsoft Jim Gray Systems Lab) • May 6, 2020
    Machine learning (ML) has proven itself in high-value web applications such as search ranking and is emerging as a powerful tool in a much broader... Read more

  • Analysis of data-driven workflows

    Prof. Victor Vianu • Apr 15, 2020
    Software systems centered around databases have become pervasive in a wide variety of applications, including health-care management, e-commerce, business processes, scientific workflows, and e-government. Such... Read more

  • Automatic Verification of Database-powered Workflows

    Allessandro Gianola (Visitng PhD student from Free University of Bolzano) • Feb 21, 2020
    During the last two decades, a huge body of research has been dedicated to the challenging problem of reconciling data and process management within contemporary... Read more






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