SuRF: Practical Range Query Filtering with Fast Succinct Tries
SuRF: Practical Range Query Filtering with Fast Succinct Tries Huanchen Zhang Carnegie Mellon University [email protected] Hyeontaek Lim Carnegie Mellon University [email protected] Viktor Leis TU München [email protected] David G. Andersen Carnegie Mellon University [email protected] Michael Kaminsky Intel Labs [email protected] Kimberly Keeton Hewlett Packard Enterprise [email protected]
SIGMOD 2018 Accepted Papers
Here is the list of accepted papers for SIGMOD 2018. I’ve attached links to PDFs to some of the papers of my interest if they are already available on the web. The 2018 ACM SIGMOD/PODS Conference: Houston, USA - SIGMOD Accepted Research Papers Fonduer: Knowledge Base Construction from Richly Formatted Data Sen Wu (Stanford University); Luke Hsiao (Stanford… Research PapersCold Filter: A Meta-Frame
Building a Bw-Tree Takes More Than Just Buzz Words
Building a Bw-Tree Takes More Than Just Buzz Words Ziqi Wang Carnegie Mellon University [email protected] Andrew Pavlo Carnegie Mellon University [email protected] Hyeontaek Lim Carnegie Mellon University [email protected] Viktor Leis TU München [email protected] Huanchen Zhang Carnegie Mellon University [email protected] Michael Kaminsky Intel Labs [email protected] David G. Andersen Carnegie
Tuning Your DBMS Automatically with Machine Learning | Amazon Web Services
AWS Machine Learning Blog Tuning Your DBMS Automatically with Machine Learning This is a guest post by Dana Van Aken, Andy Pavlo, and Geoff Gordon of Carnegie Mellon University. This project demonstrates how academic researchers can leverage our AWS Cloud Credits for Research Program to support their scientific breakthroughs. Database management systems (DBMSs) are the most important component of
SIGMOD accepted papers « SIGMOD/PODS 2017
SIGMOD '17- Proceedings of the 2017 ACM International Conference on Management of Data Full Citation in the ACM Digital Library SESSION: Keynote Session - Grand Challenges in Data Management: Transactions The Next 700 Transaction Processing Engines Anastasia Ailamaki What Are We Doing With Our Lives?: Nobody Cares About Our Concurrency Control Research Andrew Pavlo SESSION: SIGMOD Session 1. Concu
OLTP Through the Looking Glass, and What We Found There
OLTP Through the Looking Glass, and What We Found There Stavros Harizopoulos HP Labs Palo Alto, CA stavr[email protected] Michael Stonebraker Samuel Madden Daniel J. Abadi Yale University New Haven, CT [email protected] Massachusetts Institute of Technology Cambridge, MA {madden, stonebraker}@csail.mit.edu ABSTRACT Online Transaction Processing (OLTP) databases include a suite of features — disk-resident B-
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Data Series Management: The Road to Big Sequence Analytics
https://dl.acm.org/doi/10.1145/3035918.3056101
https://dl.acm.org/citation.cfm?id=3035918.3064029
Druid: a real-time analytical data store
Approximate Query Processing: No Silver Bullet
Spanner: Becoming a SQL System
LittleTable: A Time-Series Database and Its Uses
https://dl.acm.org/citation.cfm?id=2737784
https://15721.courses.cs.cmu.edu/spring2016/papers/p1493-polychroniou.pdf
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