Self-driving database management systems (DBMSs) are a new family of DBMSs that can optimize themselves for better performance without human intervention. Self-driving DBMSs use machine learning (ML) models that predict system behaviors and make planning decisions based on the workload the system sees. These ML models are trained using metrics produced by different components running inside the system. Self-driving DBMSs are a challenging environment for these models that require a significant amount of training data that must be representative of... [Read More]
In this DB group meeting, we are going to watch this presentation by Emery Berger on Mesh from PLDI 2019. This is legit. [Read More]
This talk gives an overview of Oracle NVM Direct, an open source implementation of a C API to simplify application development for byte addressable Non Volatile Memory. NVM Direct consists of two major components, namely: A precompiler that converts a source file containing C extensions for NVM to a standard C source file. A runtime library to implement NVM regions, heaps, locks, and transactions. Some of the functions are called by code inserted by the precompiler, and some are called... [Read More]
[DB Seminar] Fall 2019 DB Group: Transactions and Scalability in Cloud Databases—Can’t We Have Both?
In this DB group meeting, we are going to watch this presentation by the Amazon AWS group given at FAST 2019. [Read More]
Autonomous / Self-Driving Databases utilize machine learning techniques to eliminate the manual labor associated with database tuning, security, backups, updates, and other routine management tasks traditionally performed by DBAs. This talk will focus specifically on how we implement a self-performing database with Oracle’s Database In-Memory product to automatically tune for query optimization, memory management, and storage management and data tiering. We will first present Oracle’s Database In-Memory architecture and various features built for optimizing analytics and mixed workload performance, and... [Read More]
Tensors and tensor decompositions have been very popular and effective tools for analyzing multi-aspect data in a wide variety of fields, ranging from Psychology to Chemometrics, and from Signal Processing to Data Mining and Machine Learning. Using tensors in the era of big data presents us with a rich variety of applications, but also poses great challenges, especially when it comes to scalability and efficiency.In this talk, we will demonstrate the effectiveness of tensor decompositions as data analytic tools in... [Read More]