[Spring 2024] Beyond SQL: Dataframes in the Database (Devin Petersohn)
Dataframes are popular tools for interacting with and exploring data, but they are not as well understood nor as deeply studied as databases. Python's pandas. and Apache Spark are two of the most popular dataframes in use by data practitioners, but even these are extremely different from each other in terms of guarantees and user expectations. In this talk, we will explore these differences and take a deep dive into pandas-like dataframes with a theoretical lens, exploring the dataframe data... Read More
[Spring 2024] Manufacturing AI Applications (Anthony Tomasic)
Developing AI applications is costly and difficult and recent trends have only intensified these challenges. Developers use a bottom-up approach, focusing on the nitty-gritty of integration and infrastructure, which leads to a complex "blob" of code. Changes to this blob are risky due to the intricate web of dependencies. Fort Alto has fundamentally rethought the application development process with a groundbreaking approach that redefines how AI applications are built. (i) Separate application semantics from the infrastructure code. (ii) Split application... Read More
PhD Defense: On Embedding Database Management System Logic in Operating Systems via Restricted Programming Environments (Matt Butrovich)
The rise in computer storage and network performance means that disk I/O and network communication are often no longer bottlenecks in database management systems (DBMSs). Instead, the overheads associated with operating system (OS) services (e.g., system calls, thread scheduling, and data movement from kernel-space) limit query processing responsiveness. User-space applications can elide these overheads with a kernel-bypass design. However, extracting benefits from kernel-bypass frameworks is challenging, and the libraries are incompatible with standard deployment and debugging tools. This thesis presents... Read More
[Spring 2024] Towards a Systematic Framework for Index Structure Design (Dong Xie)
Index structures are at the database management systems' core to facilitate efficient data access. Due to the constant changes in application requirements and hardware trends, people are going through exhaustive and painstaking work designing/tailoring new index structures to catch up. In this talk, I will show a vision of a systematic index structure design framework that will allow index designers to focus on data layout design and query algorithms without worrying about support for other practical features (update and concurrency)... Read More
[Spring 2024] Embedding Database Logic in the Operating System Is Finally a Good Idea (Matt Butrovich)
The rise in computer storage and network performance means that disk I/O and network communication are often no longer bottlenecks in database management systems (DBMSs). Instead, the overheads associated with operating system (OS) services (e.g., system calls, thread scheduling, and data movement from kernel-space) limit query processing responsiveness. To avoid these overheads, user-space applications prioritizing performance over simplicity can elide these software layers with a kernel-bypass design. However, extracting benefits from kernel-bypass frameworks is challenging, and the libraries are incompatible... Read More
[Winter 2023] Survey and Evaluation of Database Management System Extensibility (Abi Kim)
Database management system (DBMS) extensibility is a feature which enables users to extend the DBMS with user software. However, the DBMS extensibility environment is fraught with perils, and DBMS developers have to resort to unspecified methods of developing extensions, including copying core DBMS source code and casing between different versions of the DBMS. Extending a DBMS to support new functionality is challenging due to the tight coupling between the system's internal components. This thesis studies and evaluates the design of... Read More
[ML⇄DB 2023] Chroma: Vector Database Straight From the Tenderloin! (Jeff Huber)
This talk is part of the ML⇄DB Seminar Series. Zoom Link: https://cmu.zoom.us/j/91461275681 (Passcode 177332) Read More
[ML⇄DB 2023] pgvector: Stylish Hierarchical Navigable Small World Indexes! (Jonathan Katz)
This talk is part of the ML⇄DB Seminar Series. Zoom Link: https://cmu.zoom.us/j/91461275681 (Passcode 177332) Read More
[ML⇄DB 2023] Alibaba: Domain Knowledge Augmented AI for Databases (Jian Tan)
One goal of applying AI for databases is to make the systems easier to use, e.g., natural language to SQL conversion (NL2SQL), and more efficient to operate, e.g., DevOps root cause analysis (RCA). Although scaling up general models and datasets with less hand engineering have achieved unprecedented successes in various applications, we argue that utilizing domain knowledge to augment AI for databases can provide an efficient and effective solution. Specifically, we use two production systems, SQL Bridge (NL2SQL) and ShapleyIQ... Read More
[ML⇄DB 2023] Milvus: A Purpose-Built Vector Data Management System! (Li Liu)
This talk is part of the ML⇄DB Seminar Series. Zoom Link: https://cmu.zoom.us/j/91461275681 (Passcode 177332) Read More