We prepare two similar runs on each day for the convenience of people in different time zones. Note that all the time has been adjusted to your local time zone.
Hybrid Evaluation for Distributed Iterative Matrix Computation Zihao Chen (East China Normal University); Chen Xu (East China Normal University)*; Juan Soto (TU Berlin); Volker Markl (Technische Universität Berlin); Weining Qian (East China Normal University); Aoying Zhou (East China Normal University )
VSS: A Storage System for Video Analytics Brandon Haynes (Gray Systems Lab, Microsoft)*; Maureen Daum (University of Washington); Dong He (University of Washington); Amrita Mazumdar (University of Washington); Magdalena Balazinska (UW); Alvin Cheung (University of California, Berkeley); Luis Ceze (University of Washington and OctoML)
ARM-Net: Adaptive Relation Modeling Network for Structured Data Shaofeng Cai (National University of Singapore); Kaiping Zheng (National University of Singapore); Gang Chen (Zhejiang University); H. V. Jagadish (University of Michigan); Beng Chin Ooi (NUS)*; Meihui Zhang (Beijing Institute of Technology)
Scalable and Usable Relational Learning With Automatic Language Bias Jose Picado (Oregon State University); Arash Termehchy (Oregon State University)*; Alan Fern (Oregon State University); Sudhanshu Pathak (Oregon State University); Praveen Ilango (Oregon State University); John Davis (Oregon State University)
QuTE: Answering Quantity Queries from Web Tables Vinh Thinh Ho (Max Planck Institute for Informatics)*; Koninika Pal (Max Planck Institute for Informatics ); Gerhard Weikum (Max-Planck-Institut fur Informatik)
An In-Depth Benchmarking of Text-to-SQL Systems Orest Gkini (Athena Research Center); Theofilos Belmpas (Athena Research Center); Georgia Koutrika (Athena Research Center)*; Yannis Ioannidis (University of Athens)
Slot 1: The many faces of Entity Resolution, Matching, and Canonicalization
BEER: Blocking for Effective Entity Resolution Sainyam Galhotra (University of Massachusetts Amherst)*; Donatella Firmani (Roma Tre University); Barna Saha (University of California, Berkeley); Divesh Srivastava (AT&T Labs Research)
Joint Open Knowledge Base Canonicalization and Linking Yinan Liu (Nankai University)*; Wei Shen (Nankai University); Yuanfei Wang (Nankai University); Jianyong Wang (Tsinghua University); Zhenglu Yang (Nankai University); Xiaojie Yuan (Nankai Univeristy)
Reducing Ambiguity in Json Schema Discovery William Spoth (University at Buffalo)*; Oliver A Kennedy (University at Buffalo, SUNY); Ying Lu (Oracle); Beda Hammerschmidt (Oracle); Zhen Hua Liu (Oracle)
BullFrog: Online Schema Evolution via Lazy Evaluation Souvik Bhattacherjee (University of Maryland, College Park); GANG LIAO (UNIVERSITY OF MARYLAND); Michael Hicks (University of Maryland, College Park); Daniel J Abadi (UMD)*
Presenters: Yixiang Fang (The Chinese University of Hong Kong, Shenzhen); Kai Wang (University of New South Wales); Xuemin Lin (University of New South Wales); Wenjie Zhang (University of New South Wales) Abstract: With the advent of a wide spectrum of recent applications, querying heterogeneous information networks (HINs) has received a great deal of attention from both academic and industry societies. HINs involve objects (vertices) and links (edges) that are classified into multiple types; examples include bibliography networks, knowledge networks, and user-item networks in E-business. An important component of these HINs is the cohesive subgraph, or a subgraph containing vertices that are densely connected internally. Searching cohesive subgraphs over HINs has found many real applications, such as community search, event organization, and friend recommendation. Consequently, how to design effective cohesive subgraph models and how to efficiently search cohesive subgraphs on large HINs become important research topics in the era of big data. In this tutorial, we first highlight the importance of cohesive subgraph search over HINs in various applications and the unique challenges that need to be addressed. Subsequently, we conduct a thorough review of existing works of cohesive subgraph search over HINs. Then, we analyze and compare the models and solutions in these works. Finally, we point out new research directions. We believe that this tutorial not only helps researchers to have a better understanding of existing cohesive subgraph search models and solutions, but also provides them insights for future study.
Presenters: Marcelo Arenas (PUC Chile); Claudio Gutierrez (Universidad de Chile, Chile); Juan Sequeda (data.world) Abstract: Graphs have become the best way we know of representing knowledge. The computing community has investigated and developed the support for managing graphs by means of digital technology. Graph databases and Knowledge graphs surface as the most successful solutions to this program. This tutorial will provide a conceptual map of the data management tasks underlying these developments, paying particular attention to data models and query languages for graphs.
Speaker: Wang-Chiew Tan (Facebook AI) Abstract: We are witnessing the widespread adoption of deep learning techniques as avant-garde solutions to different computational problems in recent years. In data integration, the use of deep learning techniques has helped establish several state-of-the-art results in long standing problems, including information extraction, entity matching, data cleaning, and table understanding. In this talk, I will reflect on the strengths of deep learning and how that has helped move forward the needle in data integration. I will also discuss a few challenges associated with solutions based on deep learning techniques and describe some opportunities for the data management community.
Bao: Making Learned Query Optimization Practical Ryan C Marcus (MIT)*; Parimarjan Negi (MIT CSAIL); Hongzi Mao (MIT CSAIL); Nesime Tatbul (Intel Labs and MIT); Mohammad Alizadeh (MIT CSAIL); Tim Kraska (MIT)
SIA: Optimizing Queries using Learned Predicates Qi Zhou (Georgia Institute of Technology)*; Joy Arulraj (Georgia Tech); Shamkant Navathe (Georgia Institute of Technology); William Harris (Galois Inc); jinpeng wu (Alibaba)
Steering Query Optimizers: A Practical Take on Big Data Workloads Parimarjan Negi (MIT CSAIL)*; Matteo Interlandi (Microsoft); Ryan Marcus (MIT CSAIL); Mohammad Alizadeh (Massachusetts Institute of Technology); Tim Kraska (MIT); Marc Friedman (Microsoft); Alekh Jindal (Microsoft)
MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems Lin Ma (Carnegie Mellon University)*; William Zhang (Carnegie Mellon University); Jie Jiao (Carnegie Mellon University); Wuwen Wang (Carnegie Mellon University); Matthew Butrovich (Carnegie Mellon University); Wan Shen Lim (Carnegie Mellon University); Prashanth Menon (Carnegie Mellon Universiy); Andrew Pavlo (Carnegie Mellon University)
Attaining Workload Scalability and Strong Consistency for Replicated Databases with Hihooi Michael Georgiou (Cyprus University of Technology); Michael Panayiotou (Cyprus University of Technology); Lambros Odysseos (Cyprus University of Technology); Aristodemos Paphitis (Cyprus University of Technology); Michael Sirivianos (Cyprus University of Technology); Herodotos Herodotou (Cyprus University of Technology)*
TardisDB: Extending SQL to Support Versioning Maximilian E Schüle (Technical University of Munich)*; Josef Schmeißer (Technical University of Munich); Thomas Blum (TUM); Alfons Kemper (TUM); Thomas Neumann (TUM)
Rethink the Scan in MVCC Databases Jongbin Kim (Hanyang University); Kihwang Kim (Hanyang University); Hyunsoo Cho (Hanyang University); Jaeseon Yu (Hanyang University); Sooyong Kang (Hanyang University); Hyungsoo Jung (Hanyang University)*
Blockchains vs. Distributed Databases: Dichotomy and Fusion Pingcheng Ruan (National University of Singapore); Tien Tuan Anh Dinh (Singapore University of Technology and Design); Dumitrel Loghin (National University of Singapore); Meihui Zhang (Beijing Institute of Technology)*; Gang Chen (Zhejiang University); Qian Lin (ByteDance); Beng Chin Ooi (NUS)
Slot 2: Panel on Blockchain and the Database research community Mo Sadoghi, Hank Korth, Anh Dinh, Mohammad Amini, Amr El Abbadi, Divy Agrawal, Jeeta Chacko, Ruben Mayer
Do the Rich Get Richer? Fairness Analysis for Blockchain Incentives YUMING HUANG (National University of Singapore); Jing Tang (National University of Singapore)*; Qianhao Cong (National University of Singapore); Andrew Lim (National University of Singapore); Jianliang Xu (Hong Kong Baptist University)
A Byzantine Fault Tolerant Storage for Permissioned Blockchain Xiaodong Qi (East China Normal University)*; Zhihao Chen (East China Normal University); Zhao Zhang (East China Normal University); Cheqing Jin (East China Normal University); Aoying Zhou (East China Normal University ); Haizhen Zhuo (Ant Group); Quangqing Xu (Ant Group)
Keynote 1: Interactive Scalable Visualizations for Data Discoveries and Interpretable AI Polo Chau
RawVis: A System for Efficient In-situ Visual Analytics Stavros Maroulis (Research Center ATHENA)*; Nikos Bikakis (Athena); George Papastefanatos (ATHENA Research Center); Panos Vassiliadis (University of Ioannina); Yannis Vassiliou (NTUA)
ExDRa: Exploratory Data Science on Federated Raw Data Sebastian Baunsgaard (Graz University of Technology); Matthias Boehm (Graz University of Technology)*; Ankit Chaudhary (TU Berlin); Behrouz Derakhshan (DFKI); Stefan Geißelsöder (Siemens AG); Philipp Marian Grulich (Technische Universität Berlin); Michael Hildebrand (Siemens AG); Kevin Innerebner (Graz University of Technology); Volker Markl (Technische Universität Berlin); Claus Neubauer (Siemens AG); Sarah Osterburg (Siemens AG); Olga Ovcharenko (Graz University of Technology); Sergey Redyuk (TU Berlin); Tobias Rieger (Graz University of Technology); Alireza Rezaei Mahdiraji (DFKI); Sebastian Benjamin Wrede (Know-Center GmbH); Steffen Zeuch (Humboldt Universität zu Berlin)
DataPrep.EDA: Task-Centric Exploratory Data Analysis for Statistical Modeling in Python Jinglin Peng (Simon Fraser University); Weiyuan Wu (Simon Fraser University)*; Brandon Lockhart (Simon Fraser University); Song Bian (The Chinese University of Hong Kong); Jing Nathan Yan (Cornell University); Linghao Xu (Simon Fraser University); Zhixuan Chi (Simon Fraser University); Jeffrey M Rzeszotarski (Cornell University); Jiannan Wang (Simon Fraser University)
Interactive Search for One of the Top-k Weicheng Wang (Hong Kong University of Science and Technology)*; Raymond Chi-Wing Wong (Hong Kong University of Science and Technology); Min Xie (Shenzhen Institute of Computing Sciences )
An Ecosystem of Applications for Modeling Political Violence Aline Bessa (New York University); Vito D'Orazio (University of Texas at Dallas)*; Sonia Castelo (New York University); Mike Shoemate (Harvard University); Aécio Santos (New York University); Juliana Freire (New York University); Remi Rampin (NYU)
Slot 3: Semistructured/Unstructured Data Exploration
Keynote 2: Natural Language Exploration with Relational Databases in Chatbot Wook-Shin Han
Presenters: Alberto Lerner (University of Friborug, Switzerland); Philippe Bonnet (IT Univ Copenhagen, Denmark) Abstract: The Solid-State Drive (SSD) landscape is in constant evolution. For years, this evolution was hidden behind the unchanging abstractions of block devices and POSIX I/O. However, these abstractions have become problematic. They hinder performance and no longer reduce software complexity. Such a state of affairs impacts the database community in at least two ways.
First, using SSDs through legacy interfaces that hide internal mechanisms invariably results in erratic performance. The blame often goes to SSDs' notoriously expensive garbage collection. In truth, several other complex processes result in non-linear effects in terms of latency and bandwidth. In this tutorial, we describe these processes and how they are implemented in modern devices. This knowledge will help system designers better choose SSDs and shape database workloads to match their performance characteristics.
Second, the inadequacy of the traditional I/O abstractions opens up an entire research field focused on the co-design of SSD and database management systems (DBMS). Such research aims at devising mechanisms and policies coupling the storage manager of a DBMS and SSD internals: e.g., placing an SSD FTL (its "brains") under the control of an application, changing SSD subsytems in response to the workload, or executing logic within a SSD on a database's behalf. In this tutorial, we describe the research opportunities and challenges through this continuum of DBMS/SSD co-design techniques, and present platforms supporting their simulation and prototyping.
We believe that those two areas---a more seamless integration of Database and Storage, and the study of SSD variations adapted to Database computations---are central to the development of the next generation of Database Systems. This (opinionated) survey will equip both researchers and practitioners alike to enter the field.
Hybrid Evaluation for Distributed Iterative Matrix Computation Zihao Chen (East China Normal University); Chen Xu (East China Normal University)*; Juan Soto (TU Berlin); Volker Markl (Technische Universität Berlin); Weining Qian (East China Normal University); Aoying Zhou (East China Normal University )
VSS: A Storage System for Video Analytics Brandon Haynes (Gray Systems Lab, Microsoft)*; Maureen Daum (University of Washington); Dong He (University of Washington); Amrita Mazumdar (University of Washington); Magdalena Balazinska (UW); Alvin Cheung (University of California, Berkeley); Luis Ceze (University of Washington and OctoML)
ARM-Net: Adaptive Relation Modeling Network for Structured Data Shaofeng Cai (National University of Singapore); Kaiping Zheng (National University of Singapore); Gang Chen (Zhejiang University); H. V. Jagadish (University of Michigan); Beng Chin Ooi (NUS)*; Meihui Zhang (Beijing Institute of Technology)
Scalable and Usable Relational Learning With Automatic Language Bias Jose Picado (Oregon State University); Arash Termehchy (Oregon State University)*; Alan Fern (Oregon State University); Sudhanshu Pathak (Oregon State University); Praveen Ilango (Oregon State University); John Davis (Oregon State University)
QuTE: Answering Quantity Queries from Web Tables Vinh Thinh Ho (Max Planck Institute for Informatics)*; Koninika Pal (Max Planck Institute for Informatics ); Gerhard Weikum (Max-Planck-Institut fur Informatik)
An In-Depth Benchmarking of Text-to-SQL Systems Orest Gkini (Athena Research Center); Theofilos Belmpas (Athena Research Center); Georgia Koutrika (Athena Research Center)*; Yannis Ioannidis (University of Athens)
Slot 1: The many faces of Entity Resolution, Matching, and Canonicalization
BEER: Blocking for Effective Entity Resolution Sainyam Galhotra (University of Massachusetts Amherst)*; Donatella Firmani (Roma Tre University); Barna Saha (University of California, Berkeley); Divesh Srivastava (AT&T Labs Research)
Joint Open Knowledge Base Canonicalization and Linking Yinan Liu (Nankai University)*; Wei Shen (Nankai University); Yuanfei Wang (Nankai University); Jianyong Wang (Tsinghua University); Zhenglu Yang (Nankai University); Xiaojie Yuan (Nankai Univeristy)
Reducing Ambiguity in Json Schema Discovery William Spoth (University at Buffalo)*; Oliver A Kennedy (University at Buffalo, SUNY); Ying Lu (Oracle); Beda Hammerschmidt (Oracle); Zhen Hua Liu (Oracle)
BullFrog: Online Schema Evolution via Lazy Evaluation Souvik Bhattacherjee (University of Maryland, College Park); GANG LIAO (UNIVERSITY OF MARYLAND); Michael Hicks (University of Maryland, College Park); Daniel J Abadi (UMD)*
Presenters: Yixiang Fang (The Chinese University of Hong Kong, Shenzhen); Kai Wang (University of New South Wales); Xuemin Lin (University of New South Wales); Wenjie Zhang (University of New South Wales) Abstract: With the advent of a wide spectrum of recent applications, querying heterogeneous information networks (HINs) has received a great deal of attention from both academic and industry societies. HINs involve objects (vertices) and links (edges) that are classified into multiple types; examples include bibliography networks, knowledge networks, and user-item networks in E-business. An important component of these HINs is the cohesive subgraph, or a subgraph containing vertices that are densely connected internally. Searching cohesive subgraphs over HINs has found many real applications, such as community search, event organization, and friend recommendation. Consequently, how to design effective cohesive subgraph models and how to efficiently search cohesive subgraphs on large HINs become important research topics in the era of big data. In this tutorial, we first highlight the importance of cohesive subgraph search over HINs in various applications and the unique challenges that need to be addressed. Subsequently, we conduct a thorough review of existing works of cohesive subgraph search over HINs. Then, we analyze and compare the models and solutions in these works. Finally, we point out new research directions. We believe that this tutorial not only helps researchers to have a better understanding of existing cohesive subgraph search models and solutions, but also provides them insights for future study.
Presenters: Marcelo Arenas (PUC Chile); Claudio Gutierrez (Universidad de Chile, Chile); Juan Sequeda (data.world) Abstract: Graphs have become the best way we know of representing knowledge. The computing community has investigated and developed the support for managing graphs by means of digital technology. Graph databases and Knowledge graphs surface as the most successful solutions to this program. This tutorial will provide a conceptual map of the data management tasks underlying these developments, paying particular attention to data models and query languages for graphs.
Speaker: Wang-Chiew Tan (Facebook AI) Abstract: We are witnessing the widespread adoption of deep learning techniques as avant-garde solutions to different computational problems in recent years. In data integration, the use of deep learning techniques has helped establish several state-of-the-art results in long standing problems, including information extraction, entity matching, data cleaning, and table understanding. In this talk, I will reflect on the strengths of deep learning and how that has helped move forward the needle in data integration. I will also discuss a few challenges associated with solutions based on deep learning techniques and describe some opportunities for the data management community.
Bao: Making Learned Query Optimization Practical Ryan C Marcus (MIT)*; Parimarjan Negi (MIT CSAIL); Hongzi Mao (MIT CSAIL); Nesime Tatbul (Intel Labs and MIT); Mohammad Alizadeh (MIT CSAIL); Tim Kraska (MIT)
SIA: Optimizing Queries using Learned Predicates Qi Zhou (Georgia Institute of Technology)*; Joy Arulraj (Georgia Tech); Shamkant Navathe (Georgia Institute of Technology); William Harris (Galois Inc); jinpeng wu (Alibaba)
Steering Query Optimizers: A Practical Take on Big Data Workloads Parimarjan Negi (MIT CSAIL)*; Matteo Interlandi (Microsoft); Ryan Marcus (MIT CSAIL); Mohammad Alizadeh (Massachusetts Institute of Technology); Tim Kraska (MIT); Marc Friedman (Microsoft); Alekh Jindal (Microsoft)
MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems Lin Ma (Carnegie Mellon University)*; William Zhang (Carnegie Mellon University); Jie Jiao (Carnegie Mellon University); Wuwen Wang (Carnegie Mellon University); Matthew Butrovich (Carnegie Mellon University); Wan Shen Lim (Carnegie Mellon University); Prashanth Menon (Carnegie Mellon Universiy); Andrew Pavlo (Carnegie Mellon University)
Attaining Workload Scalability and Strong Consistency for Replicated Databases with Hihooi Michael Georgiou (Cyprus University of Technology); Michael Panayiotou (Cyprus University of Technology); Lambros Odysseos (Cyprus University of Technology); Aristodemos Paphitis (Cyprus University of Technology); Michael Sirivianos (Cyprus University of Technology); Herodotos Herodotou (Cyprus University of Technology)*
TardisDB: Extending SQL to Support Versioning Maximilian E Schüle (Technical University of Munich)*; Josef Schmeißer (Technical University of Munich); Thomas Blum (TUM); Alfons Kemper (TUM); Thomas Neumann (TUM)
Rethink the Scan in MVCC Databases Jongbin Kim (Hanyang University); Kihwang Kim (Hanyang University); Hyunsoo Cho (Hanyang University); Jaeseon Yu (Hanyang University); Sooyong Kang (Hanyang University); Hyungsoo Jung (Hanyang University)*
Blockchains vs. Distributed Databases: Dichotomy and Fusion Pingcheng Ruan (National University of Singapore); Tien Tuan Anh Dinh (Singapore University of Technology and Design); Dumitrel Loghin (National University of Singapore); Meihui Zhang (Beijing Institute of Technology)*; Gang Chen (Zhejiang University); Qian Lin (ByteDance); Beng Chin Ooi (NUS)
Slot 2: Panel on Blockchain and the Database research community Mo Sadoghi, Hank Korth, Anh Dinh, Mohammad Amini, Amr El Abbadi, Divy Agrawal, Jeeta Chacko, Ruben Mayer
Do the Rich Get Richer? Fairness Analysis for Blockchain Incentives YUMING HUANG (National University of Singapore); Jing Tang (National University of Singapore)*; Qianhao Cong (National University of Singapore); Andrew Lim (National University of Singapore); Jianliang Xu (Hong Kong Baptist University)
A Byzantine Fault Tolerant Storage for Permissioned Blockchain Xiaodong Qi (East China Normal University)*; Zhihao Chen (East China Normal University); Zhao Zhang (East China Normal University); Cheqing Jin (East China Normal University); Aoying Zhou (East China Normal University ); Haizhen Zhuo (Ant Group); Quangqing Xu (Ant Group)
Keynote 1: Interactive Scalable Visualizations for Data Discoveries and Interpretable AI Polo Chau
RawVis: A System for Efficient In-situ Visual Analytics Stavros Maroulis (Research Center ATHENA)*; Nikos Bikakis (Athena); George Papastefanatos (ATHENA Research Center); Panos Vassiliadis (University of Ioannina); Yannis Vassiliou (NTUA)
ExDRa: Exploratory Data Science on Federated Raw Data Sebastian Baunsgaard (Graz University of Technology); Matthias Boehm (Graz University of Technology)*; Ankit Chaudhary (TU Berlin); Behrouz Derakhshan (DFKI); Stefan Geißelsöder (Siemens AG); Philipp Marian Grulich (Technische Universität Berlin); Michael Hildebrand (Siemens AG); Kevin Innerebner (Graz University of Technology); Volker Markl (Technische Universität Berlin); Claus Neubauer (Siemens AG); Sarah Osterburg (Siemens AG); Olga Ovcharenko (Graz University of Technology); Sergey Redyuk (TU Berlin); Tobias Rieger (Graz University of Technology); Alireza Rezaei Mahdiraji (DFKI); Sebastian Benjamin Wrede (Know-Center GmbH); Steffen Zeuch (Humboldt Universität zu Berlin)
DataPrep.EDA: Task-Centric Exploratory Data Analysis for Statistical Modeling in Python Jinglin Peng (Simon Fraser University); Weiyuan Wu (Simon Fraser University)*; Brandon Lockhart (Simon Fraser University); Song Bian (The Chinese University of Hong Kong); Jing Nathan Yan (Cornell University); Linghao Xu (Simon Fraser University); Zhixuan Chi (Simon Fraser University); Jeffrey M Rzeszotarski (Cornell University); Jiannan Wang (Simon Fraser University)
Interactive Search for One of the Top-k Weicheng Wang (Hong Kong University of Science and Technology)*; Raymond Chi-Wing Wong (Hong Kong University of Science and Technology); Min Xie (Shenzhen Institute of Computing Sciences )
An Ecosystem of Applications for Modeling Political Violence Aline Bessa (New York University); Vito D'Orazio (University of Texas at Dallas)*; Sonia Castelo (New York University); Mike Shoemate (Harvard University); Aécio Santos (New York University); Juliana Freire (New York University); Remi Rampin (NYU)
Slot 3: Semistructured/Unstructured Data Exploration
Keynote 2: Natural Language Exploration with Relational Databases in Chatbot Wook-Shin Han
Presenters: Alberto Lerner (University of Friborug, Switzerland); Philippe Bonnet (IT Univ Copenhagen, Denmark) Abstract: The Solid-State Drive (SSD) landscape is in constant evolution. For years, this evolution was hidden behind the unchanging abstractions of block devices and POSIX I/O. However, these abstractions have become problematic. They hinder performance and no longer reduce software complexity. Such a state of affairs impacts the database community in at least two ways.
First, using SSDs through legacy interfaces that hide internal mechanisms invariably results in erratic performance. The blame often goes to SSDs' notoriously expensive garbage collection. In truth, several other complex processes result in non-linear effects in terms of latency and bandwidth. In this tutorial, we describe these processes and how they are implemented in modern devices. This knowledge will help system designers better choose SSDs and shape database workloads to match their performance characteristics.
Second, the inadequacy of the traditional I/O abstractions opens up an entire research field focused on the co-design of SSD and database management systems (DBMS). Such research aims at devising mechanisms and policies coupling the storage manager of a DBMS and SSD internals: e.g., placing an SSD FTL (its "brains") under the control of an application, changing SSD subsytems in response to the workload, or executing logic within a SSD on a database's behalf. In this tutorial, we describe the research opportunities and challenges through this continuum of DBMS/SSD co-design techniques, and present platforms supporting their simulation and prototyping.
We believe that those two areas---a more seamless integration of Database and Storage, and the study of SSD variations adapted to Database computations---are central to the development of the next generation of Database Systems. This (opinionated) survey will equip both researchers and practitioners alike to enter the field.
