Skip to main content
SpringerLink
Log in

Data Management on New Hardware

7th International Workshop on Accelerating Data Analysis and Data Management Systems Using Modern Processor and Storage Architectures, ADMS 2016 and 4th International Workshop on In-Memory Data Management and Analytics, IMDM 2016, New Delhi, India, September 1, 2016, Revised Selected Papers

  • Conference proceedings
  • © 2017

Overview

Editors:
  1. Spyros Blanas

    1. Ohio State University, Columbus, USA

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  2. Rajesh Bordawekar

    1. IBM Thomas J Watson Research Center, Yorktown Heights, USA

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  3. Tirthankar Lahiri

    1. Oracle Cor., Redwood Shores, USA

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  4. Justin Levandoski

    1. Computer Science Department, Microsoft Corporation, Redmond, USA

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  5. Andrew Pavlo

    1. Carnegie Mellon University, Pittsburgh, USA

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 10195)

Part of the book sub series: Information Systems and Applications, incl. Internet/Web, and HCI (LNISA)

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (9 papers)

  1. Front Matter

    Pages I-VII
    Download chapter PDF

  2. Cache-Sensitive Skip List: Efficient Range Queries on Modern CPUs

    • Stefan Sprenger, Steffen Zeuch, Ulf Leser
    Pages 1-17

  3. Exploit Every Cycle: Vectorized Time Series Algorithms on Modern Commodity CPUs

    • Bo Tang, Man Lung Yiu, Yuhong Li, Leong Hou U
    Pages 18-39

  4. Compression-Aware In-Memory Query Processing: Vision, System Design and Beyond

    • Juliana Hildebrandt, Dirk Habich, Patrick Damme, Wolfgang Lehner
    Pages 40-56

  5. Overtaking CPU DBMSes with a GPU in Whole-Query Analytic Processing with Parallelism-Friendly Execution Plan Optimization

    • Adnan Agbaria, David Minor, Natan Peterfreund, Eyal Rozenberg, Ofer Rosenberg
    Pages 57-78

  6. To Copy or Not to Copy: Making In-Memory Databases Fast on Modern NICs

    • Aniraj Kesavan, Robert Ricci, Ryan Stutsman
    Pages 79-94

  7. DBMS Data Loading: An Analysis on Modern Hardware

    • Adam Dziedzic, Manos Karpathiotakis, Ioannis Alagiannis, Raja Appuswamy, Anastasia Ailamaki
    Pages 95-117

  8. Locality-Adaptive Parallel Hash Joins Using Hardware Transactional Memory

    • Anil Shanbhag, Holger Pirk, Sam Madden
    Pages 118-133

  9. SwingDB: An Embedded In-memory DBMS Enabling Instant Snapshot Sharing

    • Qingzhong Meng, Xuan Zhou, Shiping Chen, Shan Wang
    Pages 134-149

  10. Runtime Fragility in Main Memory

    • Endre Palatinus, Jens Dittrich
    Pages 150-165

  11. Back Matter

    Pages 167-167
    Download chapter PDF
Back to top

Other volumes

  1. Data Management on New Hardware

Keywords

About this book

This book contains selected papers from the 7th International Workshop on Accelerating Analytics and Data Management Systems Using Modern Processor and Storage Architectures, ADMS 2016, and the 4th International Workshop on In-Memory Data Management and Analytics, IMDM 2016, held in New Dehli, India, in September 2016. The joint Workshops were co-located with VLDB 2016. The 9 papers presented were carefully reviewed and selected from 18 submissions. They investigate opportunities in accelerating analytics/data management systems and workloads (including traditional OLTP, data warehousing/OLAP, ETL streaming/real-time, business analytics, and XML/RDF processing) running memory-only environments, using processors (e.g. commodity and specialized multi-core, GPUs and FPGAs, storage systems (e.g. storage-class memories like SSDs and phase-change memory), and hybrid programming models like CUDA, OpenCL, and Open ACC. The papers also explore the interplay between overall system design, core algorithms, query optimization strategies, programming approaches, performance modeling and evaluation, from the perspective of data management applications.

Editors and Affiliations

  • Ohio State University, Columbus, USA

    Spyros Blanas

  • IBM Thomas J Watson Research Center, Yorktown Heights, USA

    Rajesh Bordawekar

  • Oracle Cor., Redwood Shores, USA

    Tirthankar Lahiri

  • Computer Science Department, Microsoft Corporation, Redmond, USA

    Justin Levandoski

  • Carnegie Mellon University, Pittsburgh, USA

    Andrew Pavlo

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation