News & Events

Hardware Accelerated Database Lectures – Seminar Series (Fall 2018)

The CMU Database group is holding a semester-long seminar series with the leading developers of DBMSs designed for modern hardware (e.g., GPUs, FPGAs, ASICs). The Hardware Accelerated Database Lectures is designed to showcase some of the newer technologies available for data-intensive applications. Each speaker will present the implementation details of their respective systems and examples of the technical challenges that they faced when working with real-world customers. The list of confirmed speakers are: Sep 6 - Nima Negahban (Kinetica) Sep Read More

[Hardware Accelerated Databases] Jake Wheat (SQream)

This talk will present SQream's journey to building an analytics data warehouse powered by GPUs. SQream DB is an SQL data warehouse designed for larger than main-memory datasets (up to petabytes). It's an on-disk database that combines novel ideas and algorithms to rapidly analyze trillions of rows with the help of high-throughput GPUs. We will explore some of SQream's ideas and approaches to developing its analytics database – from simple prototype and tech demos, to a fully functional data warehouse Read More

[Hardware Accelerated Databases] Nima Negahban (Kinetica)

Widespread digital transformation and the explosion of the Internet of Things has led to a dramatic increase in the volume, complexity and unpredictability of data. This has created a need for converged solutions that require a new combination of processing capabilities that are not met by traditional OLTP and OLAP implementation approaches. The challenge set by modern enterprise is to create a scalable converged platform that can simultaneously manage millions of mutations, power high throughput key lookup, do high resolution Read More

[DB Seminar] Fall 2018: Andy Pavlo

Andy will regale everyone with stories from the summer and CMU-DB research plans for the next year. Read More

Molham Aref (Relational AI)

In this talk, I will summarize our work on in-database solvers for relational machine learning and artificial intelligence. We implement a declarative query language that offers support for model (or instance) finding. The capability is used to express predictive and prescriptive analytics. The presentation gives an overview of the platform and the language. In particular, it focuses on the use of algebraic (e.g. semi-rings) and combinatorial structure (e.g. query width) for asymptotic improvements of gradient descent based solvers used in Read More

[Hardware Accelerated Databases] Todd Mostak (MapD)

In this talk Todd Mostak, Founder and CEO of MapD Technologies, will speak about the technical underpinnings of MapD, an open-source GPU-accelerated SQL engine and visual analytics platform that enables querying and visualization of large datasets (tens of billions of records) in milliseconds, without indexing or or other forms of pre-computation. While some of this performance can be attributed to the system’s use of GPUs, a large part is also due to a focus around extracting the full performance and Read More

Pat Helland (Salesforce)

Applications have had an interesting evolution as they have moved into the distributed and scalable world. Similarly, storage and its cousin databases have changed side by side with applications. Many times, the semantics, performance, and failure models of storage and applications do a subtle dance as they change in support of changing business requirements and environmental challenges. Adding scale to the mix has really stirred things up. This article looks at some of these issues and their impact on systems. Read More

[DB Seminar] Summer 2018: Nikos R. Katsipoulakis

The increasing need for real-time data processing has triggered the rapid evolution of distributed Stream Processing Engines (dSPEs). In a dSPE, data are processed as soon as they become available and queries execute continuously. Low operational cost and timely processing can become a challenge for a dSPE, considering the volatile and uncharted nature of input streams. This calls for adaptable dSPEs, which can react to fluctuations in processing demands. In this talk, I will present work done on dSPEs’ adaptability, Read More

Carnegie Mellon Database Group Wins 2018 SIGMOD Best Paper Award

Houston, TX — When the dice are down on the pass and you are coming up with sevens, that's when you know your research is truly on point. No need for squabbling, no need for additional rolls. You got it done right the first time. The Carnegie Mellon Database Group is pleased to announce that their latest paper SuRF: Practical Range Query Filtering with Fast Succinct Tries has won the 2018 SIGMOD Best Paper Award. The paper's lead author author Read More

Prof. Christos Faloutsos wins PAKDD Distinguished Contributions Award

Prof. Christos Faloutsos attracted the PAKDD Distinguished Contribution Award, in the upcoming PAKDD 2018, in Melbourne, Australia. Quote from the conference web site: The Steering Committee considers it an honor to award the PAKDD Distinguished Contributions Award for 2018 to Professor Christos Faloutsos, for his many seminal contributions to the field of data mining, including time series matching, network analysis, graph computation, and their scalability. Especially notable is his highly successful program in showing how strong mathematical results can be Read More