[PDL/SDI] Fall 2019: Shasank Chavan (Oracle)

The classic dilemma faced by a data architect is whether to use multiple single-purpose data management solutions for different use cases, or a single monolithic data management solution for all use cases. We will present the pros and cons of these approaches, and then show how Oracle’s machine learning enhanced Autonomous Database enables a converged data management solution with in-memory computing, one that combines the benefits of both approaches without the drawbacks. Read More

[DB Seminar] Summer 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

[DB Seminar] Fall 2019 DB Group: Huzaifa Abbasi

Huzaifa will present this paper in this meeting: Title: Scalable Garbage Collection for In-Memory MVCC Systems, VLDB 2020 Read More

[DB Seminar] Fall 2019 DB Group: Gustavo Angulo

Gus will present this paper in this meeting: Title: How I Learned to Stop Worrying and Love Re-optimization, Arxiv 2019 Read More

[DB Seminar] Fall 2019 DB Group: Shasank Chavan (Oracle)

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

Fall 2019: Vagelis Papalexakis (UC Riverside)

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

[DB Seminar] Fall 2019 DB Group: Dongsheng Yang

Dongsheng will present this paper in this meeting: Title: An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning: SIGMOD 2019 Read More

[DB Seminar] Fall 2019 DB Group: Matt Butrovich

Matt will present this paper in this meeting: Title: Adapting TPC-C Benchmark to Measure Performance of Multi-Document Transactions in MongoDB: VLDB 2019 Read More

PhD Defense: Huanchen Zhang

The growing cost gap between DRAM and storage together with increasing database sizes means that database management systems (DBMSs) now operate with a lower memory to storage size ratio than before. On the other hand, modern DBMSs rely on in-memory search trees (e.g., indexes and filters) to achieve high throughput and low latency. These search trees, however, consume a large portion of the total memory available to the DBMS. This dissertation seeks to address the challenge of building compact yet Read More

[DB Seminar] Fall 2019 DB Group: Lily Liu

Lily will present this paper in this meeting: Title: Native Store Extension for SAP HANAConference: VLDB 2019 Read More