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

  • Spyros Blanas
  • Rajesh Bordawekar
  • Tirthankar Lahiri
  • Justin Levandoski
  • Andrew Pavlo
Conference proceedings IMDM 2016, ADMS 2016
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10195)

Table of contents

  1. Front Matter
    Pages I-VII
  2. Stefan Sprenger, Steffen Zeuch, Ulf Leser
    Pages 1-17
  3. Bo Tang, Man Lung Yiu, Yuhong Li, Leong Hou U
    Pages 18-39
  4. Juliana Hildebrandt, Dirk Habich, Patrick Damme, Wolfgang Lehner
    Pages 40-56
  5. Adnan Agbaria, David Minor, Natan Peterfreund, Eyal Rozenberg, Ofer Rosenberg
    Pages 57-78
  6. Aniraj Kesavan, Robert Ricci, Ryan Stutsman
    Pages 79-94
  7. Adam Dziedzic, Manos Karpathiotakis, Ioannis Alagiannis, Raja Appuswamy, Anastasia Ailamaki
    Pages 95-117
  8. Anil Shanbhag, Holger Pirk, Sam Madden
    Pages 118-133
  9. Qingzhong Meng, Xuan Zhou, Shiping Chen, Shan Wang
    Pages 134-149
  10. Endre Palatinus, Jens Dittrich
    Pages 150-165
  11. Back Matter
    Pages 167-167

About these proceedings

Introduction

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.

Keywords

data management systems database query processing database transaction processing main memory engines memory management data layouts database management system engines in-memory databases index structures information systems main memory main-memory databases robust query processing scientific databases tuple reconstruction

Editors and affiliations

  • Spyros Blanas
    • 1
  • Rajesh Bordawekar
    • 2
  • Tirthankar Lahiri
    • 3
  • Justin Levandoski
    • 4
  • Andrew Pavlo
    • 5
  1. 1.Ohio State UniversityColumbusUSA
  2. 2.IBM Thomas J Watson Research CenterYorktown HeightsUSA
  3. 3.Oracle Cor.Redwood ShoresUSA
  4. 4.Computer Science DepartmentMicrosoft CorporationRedmondUSA
  5. 5.Carnegie Mellon UniversityPittsburghUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-56111-0
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-56110-3
  • Online ISBN 978-3-319-56111-0
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book