590Q schedule - Google Drive


1	CSE590q: Reading Seminar in Databases
2	Mondays @ 1:30pm - 2:30 pm in the database lab (CSE 291) Topic: Graph Analytics
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4	Organizers	Leilani Battle
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6	Date	Topic	Presenter(s)	Paper	Slides	Notes
7	3/25/2024	Introduction	Leilani		slides
8	4/1/2024	Graph Processing Challenges + Benchmarks	(1) Ameya, (2) Suh Young	(1) The future is big graphs + (2) LDBC social network benchmark
9	4/8/2024	Challenges for Graph Analytics Users	(1) Nicole, (2) Vanshika	(1) Barriers to network exploration + (2) Knowledge graphs in practice
10	4/15/2024	Approximate Graph Processing	Kyle	GraphGuess (arXiv)
11	4/22/2024	Large Graph Processing on One Machine	Yihong	MiniGraph
12	4/29/2024	Graph Data Structures	Jiacheng + Dhruv Khanna	Sortledton
13	5/6/2024	Graph Machine Learning (Benchmarking Perspective)	Enhao + Joobee	Open Graph Benchmark
14	5/13/2024	Scalable Graph Analytics (Emphasis on ML Applications)	Guorui + Ritika Rajpal	Lotan
15	5/20/2024	Visual and Interactive Graph Querying	(1) Shivam (2) Junran	(1) ComBiNet + (2) Visually Appealing Property Graph Queries
16	5/27/2024	Memorial Day


1	CSE590q: Reading Seminar in Databases
2	Mondays @ 1:30pm - 2:30 pm in the database lab (CSE 291) or https://washington.zoom.us/j/99056417435 Topic: Knowledge Graphs
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4	Organizers	Dan Suciu
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6	Date	Topic	Presenter(s)	Paper	Slides	Notes
7	1/8/2024	Introduction	Dan	slides
8	1/15/2024	MLK Day
9	1/22/2024	Querying a KG	Yihong+Apoorva	SPARQL Autocompletion and Semantic Parsing	slides1 slides2
10	1/29/2024	Completeness of LLMs	Nicole+Karol	Will LLMs Replace Knowledge Graphs?
11	2/5/2024	KG Embeddings	Junran	A Survey of Approaches and Applications
12	2/12/2024	Query2Box	Moe	Reasoning over Knowledge Graphs in Vector Space Using Box Embeddings
13	2/19/2024	Presidents Day
14	2/26/2024	KB Completion	Kyle+ Raymond	BoxE: A Box Embedding Model for Knowledge Base Completion
15	3/4/2024	Relational Deep Learning	Jiacheng	Graph Representation Learning on Relational Databases and slides
16	3/11/2024	Neural DBs	Guorui+Eric	From Natural Language Processing to Neural Databases
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1	CSE590q: Reading Seminar in Databases
2	Mondays @ 1:30pm - 2:30 pm in the database lab (CSE 291) or https://washington.zoom.us/j/99056417435 Topic: LLMs and Data Management
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4	Organizers	Moe Kayali and Magda Balazinska
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6	Date	Topic	Presenter(s)	Paper	Notes
7	10/2/2023	What are Large Language Models?	Prof. Luke Zettlemoyer
8	10/9/2023	Experimenting with ChatGPT on data management problems	Guorui Xiao and Kyle Deeds	No paper. Start reading next week's paper, which is interesting but long
9	10/16/2023	What are LLMs good at?	Enhao	Sparks of Artificial General Intelligence: Early experiments with GPT-4
10	10/23/2023	What are some limitations of LLMs?	Yihong	Faith and Fate: Limits of Transformers on Compositionality
11	10/30/2023	LLMs for data discovery and exploration	Moe and Anthony	CHORUS: Foundation Models for Unified Data Discovery and Exploration
12	11/6/2023	LLMs for data wrangling	Dong and Diandre	Can Foundation Models Wrangle Your Data?
13	11/13/2023	LLMs for data integration	Junran and Will	Unicorn: {A} Unified Multi-tasking Model for Supporting Matching Tasks in Data Integration
14	11/20/2023	LLMs for data lakes	Rulin Shao and Oscar Liu	Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes.
15	11/27/2023	LLMS for DB applications	Jiacheng	SEED: Simple, Efficient, and Effective Data Management via
16	12/4/2023	LLMs for code synthesis for query processing	Nicole	CodexDB: Synthesizing Code for Qery Processing from Natural Language Instructions using GPT-3 Codex
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3	In 2022-2023, we re-purposed CSE590Q toward an interdisciplinary "Data Science Lab" seminar
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5	Fall 2022	https://docs.google.com/spreadsheets/d/e/2PACX-1vRB0x5H8hgXu3alLbijQttQ2gltswhrSqBzWTqLEDbEikEVKMdMGqm7ov7GwiuRl38sWIHalucWs0N7/pubhtml
6	Winter 2023	https://docs.google.com/spreadsheets/u/1/d/e/2PACX-1vTUyehJ5jVG7fd_Iw_xtOfop3CyvOryaUHLwyfN3V4ZSiYVOMWy6TlGVPSaqUBJ18_T8ewdg4HIhXab/pubhtml
7	Spring 2023	https://docs.google.com/spreadsheets/u/1/d/e/2PACX-1vTUyehJ5jVG7fd_Iw_xtOfop3CyvOryaUHLwyfN3V4ZSiYVOMWy6TlGVPSaqUBJ18_T8ewdg4HIhXab/pubhtml


1	CSE590q: Reading Seminar in Databases
2	Mondays @ 10am - 11am Topic: upcoming papers in SIGMOD/PODS/VLDB
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4	Date	Topic	Presenter(s)	Paper	Slides	Notes
5	4/4/2022	Intro (10'), Query2Box	Anton	https://cs.stanford.edu/people/jure/pubs/query2box-iclr20.pdf	https://docs.google.com/presentation/d/12NKWM30cajB-tcxxcbJzx-qULe3Y6aqYTprEEiIgluc/edit#slide=id.p
6	4/11/2022	Selective Data Acquisition in the Wild for Model Charging	Jieyu	http://da.qcri.org/ntang/pubs/autodata.pdf	https://docs.google.com/presentation/d/1X6IihGvxS9RIn3tagXB2lZfsvWFSircJI1wDpV5I7mM/edit?usp=sharing
7	4/18/2022	Zeus: Efficiently Localizing Actions in Videos using Reinforcement Learning	Enhao	https://arxiv.org/pdf/2104.06142.pdf	https://docs.google.com/presentation/d/11NGXhQbPIH69-niBUlHaLCemky3AhuChIxNCn3QGgH0/edit?usp=sharing
8	4/25/2022	CHEF: A Cheap and Fast Pipeline for Iteratively Cleaning Label Uncertainties	Cheng-Yu	https://arxiv.org/pdf/2107.08588.pdf	https://docs.google.com/presentation/d/1zhM47aFLHxZZlfeyaDp5396If-5JhGdtsUb1Vpjw5EQ/edit?usp=sharing
9	5/2/2022		Jeff & Esteban
10	5/9/2022	LearnedSQLGen: Constraint-aware SQL Generation using Reinforcement Learning	Dong & Junran	https://dbgroup.cs.tsinghua.edu.cn/ligl/papers/sigmod2022-sqlgen.pdf
11	5/16/2022	User-Defined Operators: Efficiently Integrating Custom Algorithms into Modern Databases	Moe & Maureen	https://vldb.org/pvldb/vol15/p1119-sichert.pdf	https://docs.google.com/presentation/d/1L_RrrzxiCA0klmtzTqSx9rWjmiNVfiXbItHXYO4hHss/edit?usp=sharing
12	5/23/2022	CANCELLED	NO 590Q
13	5/30/2022	MEMORIAL DAY	NO 590Q
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1	CSE590q: Reading Seminar in Databases
2	Mondays @ 1:30pm Topic: Video Analytics
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4	Date	Topic	Presenter(s)	Paper	Slides	Notes	Slides
5	1/3/2022	Intro (by zoom: see the uwdb calendar)	Magda
6	1/10/2022	NO MEETING
7	1/17/2022	MLK Day -- will makeup at a later date TBD
8	1/24/2022	Basic events	Maureen	BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics
9	1/28/2022	Friday (makeup in 591D) Trajectory queries	Enhao	OTIF: Efficient Tracker Pre-processing over Large Video Datasets
10	1/31/2022	Storage	Kyle	VStore: A Data Store for Analytics on Large Videos
11	2/7/2022	Edge computing	Dong	Server-Driven Video Streaming for Deep Learning Inference
12	2/14/2022	Cloud/streaming	Eric	SVE: Distributed Video Processing at Facebook Scale
13	2/21/2022	Holiday: NO MEETING
14	2/25/2022	Friday (makeup in 591D)	Esteban will give a 591D talk (registered in the 591D spreadsheet)
15	2/28/2022	Data drift	Jeffrey	ODIN: Automated Drift Detection and Recovery in Video Analytics
16	3/7/2022	Cloud computing	Aamir	Llama: A Heterogeneous & Serverless Framework for Auto-Tuning Video Analytics Pipelines
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18	Readings
19		Basic events (specialized NNs): BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics
20		Trajectory queries: OTIF: Efficient Tracker Pre-processing over Large Video Datasets
21		Complex events: Caesar: Cross-camera Complex Activity Recognition
22		Storage: VStore: A Data Store for Analytics on Large Videos
23		Edge computing: Server-Driven Video Streaming for Deep Learning Inference
24		Cloud computing: Llama: A Heterogeneous & Serverless Framework for Auto-Tuning Video Analytics Pipelines
25		Cloud/streaming: SVE: Distributed Video Processing at Facebook Scale
26		Data drift: ODIN: Automated Drift Detection and Recovery in Video Analytics
27	Contact:
28		Enhao Zhang	enhaoz@cs.washington.edu
29		Maureen Daum	mdaum@cs.washington.edu
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1	CSE590q: Reading Seminar in Databases
2	Mondays @ 11:00am until 4/12, 9:00am from 4/19; by zoom Topic: Discuss upcoming papers in SIGMOD/PODS/VLDB/MLSYS 2021
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4	Date	Topic	Presenter(s)	Paper	Slides	Notes	Slides
5	4/5/2021	Organizational
6	4/12/2021	Invited Talk	Aaron Elmore	CrocodileDB: Resource Efficient Database Execution
7	4/19/2021	Dong's Quals Talk	Dong He	DeepEverest: Accelerating Declarative Top-K Queries for Deep Neural Network Interpretation
8	4/26/2021	Distributed Computing Invariants	Kyle	Interactive Checks for Coordination Avoidance
9	5/3/2021	something from VLDB	Moe + Remy	From natural language processing to neural databases
10	5/10/2021	SQL Procedures Performance Analysis	Anton	Procedural Extensions of SQL: Understanding their usage in the wild
11	5/17/2021		Enhao + Maureen	Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics
12	5/24/2021	Invited Talk	Tim Kraska	TBA
13	5/31/2021	Holiday	Holiday
14	6/7/2021	NO SEMINAR
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18	Resources:
19		SIGMOD accepted papers	?
20		PODS accepted papers	https://2021.sigmod.org/pods_list.shtml
21		VLDB accepted papers	https://dblp.uni-trier.de/db/journals/pvldb/pvldb14.html
22		MLSys accpeted papers	https://proceedings.mlsys.org/paper/2021
23		MLSys top 5 paper awards	https://twitter.com/AlexGDimakis/status/1378808982576451588
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1	CSE590q: Reading Seminar in Databases
2	Mondays @ 1:30 by zoom Topic: Embeddings of Structured Data
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4	Date	Topic	Presenter(s)	Paper	Slides	Notes	Slides
5	10/5/2020	Introduction	Alex	Neural Word Embedding as Implicit Matrix Factorization	https://drive.google.com/file/d/1-QJNWwXlgaaKcTwWKkAZj_Vt3bLLiIUN/view?usp=sharing
6	10/12/2020	Introduction (cont'd)	Dan	A Theory of Vector Embeddings of Structured Data, PODS'2020	Martin's slides
7	10/19/2020	TRANSE (KB embedding)	Vlad	Translating embeddings for modeling multi-relational data, NIPS'2013		see also	https://drive.google.com/file/d/1in2XlZU0f-wTyhteZT59CgjMFDh8ajLQ/view?usp=sharing
8	10/26/2020	Survey on graph embeddings	Maureen	Representation learning on graphs: methods and applications, DEBUL'2017		see also the node2vec paper below	https://drive.google.com/file/d/1y8SN2YnBN_Nsp8zDnYF6_3JalbA_rRHd/view?usp=sharing
9	11/2/2020	Termite	Enhao	Termite: a system for tunneling through heterogeneous data			https://docs.google.com/presentation/d/163yZoMrWokhj6lJ6I_x7nTC8exQ4s6mtDgpeGda5UuU/edit?usp=sharing
10	11/9/2020	Application to Database Integration	Dong	Creating Embeddings of Heterogeneous Relational Datasets for Data Integration Tasks, SIGMOD'2020	slides
11	11/16/2020	Application to Fairness	Kyle	Operationalizing Individual Fairness with Pairwise Fair Representations, VLDB'2020
12	11/23/2020	GNN v.s 1-WL	Moe	Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks, AAAI'2019	Christopher's slides
13	11/30/2020	Connection between GNN and Logic	Remy	The Logical Expressiveness of Graph Neural Networks, ICLR'2020
14	12/7/2020	Discussion: lessons learned	Everyone	Meeting notes
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18	Other readings
19		Survey on graph kernels		A survey on graph kernels, 2019
20		Survey on kb embeddings		Knowledge Graph Embedding: A Survey of Approaches and Applications. TKDE'2017
21		Geometry of graphs		The geometry of graphs and some of its algorithmic applications. Combinatorica, 1995
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23		Database applications		Exploiting Latent Information in Relational Databases via Word Embedding, CIDR'2019
24				Using word embedding to enable semantic queries in relational databases, DEEM'2017	First application of embedding to databases
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26		the node2vec paper		node2vec: Scalable feature learning for net- works. KDD'2016
27		RESCAL (KB embedding)		A three-way model for collective learning on multi-relational data, ICML'2011		Perhaps experiment with the tool
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29				Deep sets	see also other papers by Ravanbakhsh
30				Embeddings of E/R's
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32				Graph Representation Learning (book by Hamilton)
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1
2	(Mondays @ 1:30 in DB lab, CSE 291 (Allen Center), unless noted otherwise) Topic: Readings from SIGMOD/VLDB/PODS
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4	Date	Topic	Presenter(s)	Paper 1	Paper 2	Paper 3?
5	3/30/2020	Organization
6	4/6/2020	Answering (Unions of) Conjunctive Queries using Random Access and Random-Order Enumeration	Remy	Answering (Unions of) Conjunctive Queries using Random Access and Random-Order Enumeration
7	4/13/2020	MIRIS: Fast Object Track Queries in Video	Dong	MIRIS: Fast Object Track Queries in Video
8	4/20/2020	Pattern Functional Dependencies for Data Cleaning	Chenglong	Pattern Functional Dependencies for Data Cleaning	Slides: https://docs.google.com/presentation/d/1x-2pl3pSD9TMFrCo-0vfOn9BOeLGx31s3yBybSykrKw/edit?usp=sharing
9	4/27/2020	Joins on Samples	Walter	Join on Samples: A Theoretical Guide for Practitioners
10	5/4/2020	Maureen Quals Talk -- moved to May 14	canceled
11	5/11/2020	Moe+Xinyue
12	5/18/2020		cong	Qd-tree: Learning Data Layouts for Big Data Analytics
13	5/25/2020	Memorial day
14	6/1/2020	practice talk (1:30-3:30) or watch/discuss videos	Cong+Guna
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19	Additional Material
20		Answering (Unions of) Conjunctive Queries using Random Access and Random-Order Enumeration
21		Join on Samples: A Theoretical Guide for Practitioners
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23		Pattern Functional Dependencies for Data Cleaning
24		A Relational Matrix Algebra and its Implementation in a Column Store (SIGMOD 2020)
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26		VLDB papers:	https://dblp.uni-trier.de/db/journals/pvldb/pvldb13.html
27		PODS papers:	https://research.cs.wisc.edu/dbworld/messages/2020-03/1583732547.html
28		SIGMOD papers:	???
29		MIT DSAIL SIGMOD papers:	http://dsail.csail.mit.edu/index.php/publications-new/	(only from the MIT group)
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31		Fusion paper in SIGMOD'2020
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41		Causal approach to fairness	Interventional Fairness: Causal Database Repair for Algorithmic Fairness


1
2	(Mondays @ 1:30 in DB lab, CSE 291 (Allen Center), unless noted otherwise) Topic: Responsible Data Management
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4	Date	Topic	Presenter(s)	Paper 1	Paper 2	Paper 3?
5	1/13/2020	Introduction	Dan	based on lecture1 of the NYU course (see below)
6	1/20/2020	HOLIDAY
7	1/27/2020	Sources of unfairness	Khang+Maureen	Blog: How big data is unfair	COMPAS Story, ProPublica
8	2/3/2020	Statistical definitions of discrimination	Sahil	Fairness Definitions Explained	Survey (working paper)
9	2/10/2020	Impossibility results	Jack+Alex	Fair Prediction with Disparate Impact	Inherent Trade-Offs in the Fair Determination of Risk Scores
10	2/17/2020	HOLIDAY
11	2/24/2020	Critique of ML algorithms	Josh	The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning
12	2/28/2020	Principles of Interpretability	Mike (Friday)	Towards a Rigorous Science of Interpretable Machine Learning	The Mythos of Model Interpretability
13	3/2/2020	Two approaches for explanation	Remy	"Why Should I Trust You?"	Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
14	3/9/2020	Building Interpretable models	Jonathan	Please Stop Explaining Black Box Models	An Interpretable Model
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16	Additional Material
17		Must watch (if you haven't already)	Capuchin video
18		Introduction	NYU Course on Reponsible Data Science
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20		Other seminars	Diversity in Big Data: A Review. Marina Drosou, H.V. Jagadish, Evaggelia Pitoura, Julia Stoyanovich
21			Semantics derived automatically from language corpora contain human-like biases. Aylin Caliskan, Joanna J. Bryson, Arvind Narayanan
22			The frontiers of fairness in machine learning. A Chouldechova, A Roth
23			Algorithmic decision making and the cost of fairness
24			Equality of Opportunity in Supervised Learning
25			Algorithmic decision making and the cost of fairness
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28		Journalistic Investigations	Minority Neighborhoods Pay Higher Car Insurance Premiums Than White Areas With the Same Risk. ProPublica. 2017.
29			ProPublica Seeks Source Code for New York City’s Disputed DNA Software. ProPublica. 2017.
30			Inside the Algorithm That Tries to Predict Gun Violence in Chicago. New York Times. 2017.
31			What Happens When An Algorithm Cuts Your Health Care. The Verge. 2018.
32			Aggression Detectors: The Unproven, Invasive Surveillance Technology Schools Are Using to Monitor Students. ProPublica. 2019
33			What a Report from Germany Teaches Us About Investigating Algorithms. Columbia Journalism Review. 2019.
34			Keep Track Of Who Facebook Thinks You Know With This Nifty Tool. Gizmodo. 2018
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36		Northpointe's response to ProPublica	COMPAS Risk Scales: Demonstrating Accuracy Equity and Predictive Parity
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40		Causal approach to fairness	Interventional Fairness: Causal Database Repair for Algorithmic Fairness


1
2	(Mondays @ 3:30 in DB lab, CSE 291 (Allen Center), unless noted otherwise) Topic: Learned Database Systems
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4	Date	Time/Room	Presenter(s)	Paper(s)		Other notes
5	9/25/2019	1:30, ECE025	Tom	SIGMOD'19 tutorial part 1	better slides	joint w 590s
6	9/30/2019	3:30, ECE025	Tom	SIGMOD'19 tutorial part 2		joint w 590s
7	10/2/2019	1:30, ECE025	Remy	SageDB: A Learned Database System, CIDR’2019		joint w 590s
8	10/7/2019	3:30, ECE025	Ruqian, Lequn	The Case for Learned Index Structures, SIGMOD’2018	FITing-Tree: A Data-aware Index Structure, SIGMOD’2019	joint w 590s	see also Neumann's blog
9	10/14/2019	3:30, AC291	Cong	SkinnerDB, SIGMOD’2019
10	10/21/2019	3:30, AC291	Jonathan & Kang	Neo: A Learned Query Optimizer, VLDB’2019	Hands-Free Query Optimizer through Deep Learning, CIDR’2019
11	10/28/2019	3:30, AC291	Shrainik Jain	Flexible Operator Embeddings via Deep Learning	Database-Agnostic Workload Management. CIDR 2019
12	11/4/2019	3:30, AC291	Maureen & Alex	AI Meets AI: Index Recommendations, SIGMOD’2019
13	11/11/2019	3:30, AC291		Veterans' Day (no classes)
14	11/13/2019	1:30, ECE025	Walter & Matt	A Model for Learned Bloom Filters, NeurIPS, 2018	Meta-Learning Neural Bloom Filters, ICML’2019	joint w 590s
15	11/18/2019	3:30, AC291	Moe	Learning to Optimize Join Queries With DL	Plan-Structured Deep Neural Network Models for Query Performance Prediction, VLDB'2019
16	11/25/2019	3:30, AC291	Jack &	Learned Cardinalities, CIDR’2019	Estimating Cardinalities with Deep Sketches, SIGMOD’2019	see also
17	12/2/2019	3:30, AC291		Approximate Query Processing using Deep Models	CANCELED
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21	Notes
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1
2	(Mondays @ 2:30 in DB lab, CSE 405, unless noted otherwise) Topic:
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4	Date	Presenter(s)	Paper	Other notes
5	4/1/2019
6	4/8/2019	Moe, Maureen	XIangyao's papers: 1000 cores, TicToc
7	4/15/2019	Shana	Ranked Enumeration of Minimal Triangulations
8	4/22/2019	Jonathan	ShrinkWrap: Efficient SQL Query Processing in Differentially Private Data Federations.
9	4/29/2019	Walter	DIFF: A Relational Interface for Large-Scale Data Explanation
10	5/6/2019	Laurel	DeepBase
11	5/13/2019	CANCELED
12	5/20/2019	TBD	TBD
13	5/27/2019	Memorial day
14	6/3/2019	Brandon	HetExchange: Encapsulating heterogeneous CPU-GPU parallelism in JIT compiled engines
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18	Notes	we read papers from SIGMOD/PODS/VLDB'2019
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20		VLDB'2019	https://dblp.uni-trier.de/db/journals/pvldb/pvldb12.html
21		SIGMOD'2019	http://sigmod2019.org/sigmod_research_papers_list
22		PODS'2019	https://sigmod2019.org/pods_research_papers
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25	VLDB
26	PODS		On the Enumeration Complexity of Unions of Conjunctive Queries


1	Data Management Reading Group CSE 590Q - Winter 2019
2	(Mondays @ 2:30 in DB lab, CSE 405, unless noted otherwise) Topic:
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4	Date	Presenter(s)	Paper	Other notes
5	1/7/2019	Introduction
6	1/14/2019	CANCELED
7	1/18/2019 (Stolen from 591D)	Walter	In-Database Learning with Sparse Tensors
8	1/21/2019	MLK day
9	1/28/2019	Guna	Transactions in relaxed memory architectures
10	2/4/2019	Laurel	Ten years test of time: The Chase Revisited (read the 2008 paper)
11	2/11/2019	Maureen	SQL's Three-Valued Logic and Certain Answers. ICDT 2015: 94-109
12	2/18/2019	Presidents day
13	2/25/2019	Maureen	SQL's Three-Valued Logic and Certain Answers. ICDT 2015: 94-109
14	3/4/2019	Cong+Remyi	Two-variable logic on data trees and XML reasoning + Synthesizing coupling proofs of differential privacy.	Cong slides
15	3/11/2019	Shana	Enumerating with constant delay the answers to a query. ICDT 2013	slides
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19	NOTES
20	Suggested papers for this quarter. Feel free to browse the proceedins and select some other paper.
21	PODS	https://dblp.uni-trier.de/db/conf/pods/pods2018.html
22			Ten years test of time: The Chase Revisited (read the 2008 paper)	read the 2008 paper, which is a classic
23			Abo Khamis, Ngo, Nguyen, Olteanu, Schleich: In-Database Learning with Sparse Tensors	dense, but very thorrough treatment of the interaction between DB and ML. Olteanu's 2016 SIGMOD paper could be a good warmup.
24	POPL	https://dblp.uni-trier.de/db/journals/pacmpl/pacmpl2.html
25			Transactions in relaxed memory architectures	theoretic model for reasoning about transactions
26			Synthesizing coupling proofs of differential privacy.	lots of math
27			Unifying analytic and statically-typed quasiquotes.	for scala fans (from Christoph Koch)
28	Others
29		Leonid Libkin:	SQL's Three-Valued Logic and Certain Answers. ICDT 2015: 94-109	see also TODS 2016
30		Andrea Cerone, Alexey Gotsman	Analysing Snapshot Isolation, J. ACM 2018	there is an earlier conference paper, could be easier read
31		Rachid Guerraoui, Jingjing Wang:	How Fast can a Distributed Transaction Commit?
32		Luc Segoufin:	Enumerating with constant delay the answers to a query. ICDT 2013	really cool and fundamental problem: we want to "stream" query answers, and guarantee constant dealy between each answer, when can we do that? Read both papers. Note that there are other papers by Segoufin on FO queries.
33			Constant Delay Enumeration for Conjunctive Queries. SIGMOD Record, 2015
34		Martin Grohe, Stephan Kreutzer, Sebastian Siebertz:	Deciding First-Order Properties of Nowhere Dense Graphs, JACM 2017	To discuss in TWO seminars. Main takeawy is that we can process queries much more efficiently if the database is "nowhere dense", but what does no-where dense mean?
35		Mikolaj Bojanczyk, Claire David, Anca Muscholl, Thomas Schwentick, Luc Segoufin:	Two-variable logic on data trees and XML reasoning, PODS 2006	a deep, celebrated paper. Quite relevant today for JSon/Protobuf data, and languages like SQL++ etc. Should be easy and fun to read at the high level, much harder in detail. Check the journal version too
36		Martin Grohe	Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks, Corr 2018


1	Data Management Reading Group CSE 590Q - Fall 2018
2	(Mondays @ 2:30 in DB lab, CSE 405, unless noted otherwise) Topic: Selected papers from DB + PL conferences
3
4		Presenter(s)	Paper	Other notes
5	10/1/2018	Maaz	MixT: a language for mixing consistency in geodistributed transactions.
6	10/8/2018	Shumo	Leonid Libkin: Certain Answers Meet Zero-One Laws.
7	10/15/2018	Laurel and Maureen	Kraska: The Case for Learned Index Structures
8	10/22/2018	SIGMOD deadline
9	10/29/2018	Guna	Bootstrapping Privacy Compliance in Big Data Systems
10	11/5/2018	Practice Talks (Colloqium)	Swap with 591d
11	11/12/2018	Veterans Holiday
12	11/19/2018	Shrainik	Selecting Subexpressions to Materialize at Datacenter Scale
13	11/26/2018	cong	SuRF: Practical Range Query Filtering with Fast Succinct Tries.
14	12/3/2018	Batya	Querying Probabilistic Preferences in Databases
15
16
17
18	NOTES
19	Edit link
20	Suggested papers for this quarter. Feel free to browse the proceedins and select some other paper.
21	SIGMOD	https://dblp.uni-trier.de/db/conf/sigmod/sigmod2018.html
22			Pavlo: SuRF: Practical Range Query Filtering with Fast Succinct Tries.	best paper award
23			Kraska: The Case for Learned Index Structures	the controversial paper...
24			Neumann: Adaptive Optimization of Very Large Join Queries	1000 joins
25	PODS	https://dblp.uni-trier.de/db/conf/pods/pods2018.html
26			Yufei Tao: Entity Matching with Active Monotone Classification	best paper award; more ML than DB
27			Ten years test of time: The Chase Revisited (read the 2008 paper)	read the 2008 paper, which is a classic
28			Leonid Libkin: Certain Answers Meet Zero-One Laws.	lighweight and sweet (if you like probabilities and 0/1 laws)
29			Abo Khamis, Ngo, Nguyen, Olteanu, Schleich: In-Database Learning with Sparse Tensors	dense, but very thorrough treatment of the interaction between DB and ML. Olteanu's 2016 SIGMOD paper could be a good warmup.
30	VLDB	https://dblp.uni-trier.de/db/journals/pvldb/pvldb11.html
31			Snorkel: Rapid Training Data Creation with Weak Supervision	a must read...
32			Relaxed Operator Fusion for In-Memory Databases: Making Compilation, Vectorization, and Prefetching Work Together At Last
33			Analyzing the Impact of System Architecture on the Scalability of OLTP Engines for High-Contention Workloads.
34			Contention-Aware Lock Scheduling for Transactional Databases.
35			Quickstep: A Data Platform Based on the Scaling-Up Approach.
36			Selecting Subexpressions to Materialize at Datacenter Scale	uses ILP to choose which expression to materialize
37			Plan Stitch: Harnessing the Best of Many Plans
38			You Say 'What', I Hear 'Where' and 'Why'? (Mis-)Interpreting SQL to Derive Fine-Grained Provenance.
39			Exploiting Coroutines to Attack the "Killer Nanoseconds".	how bad are pointers really?
40	PLDI	https://dblp.org/db/conf/pldi/pldi2018
41			MixT: a language for mixing consistency in geodistributed transactions.
42			Spatial: a language and compiler for application accelerators.	for the hardware inclined
43	POPL	https://dblp.uni-trier.de/db/journals/pacmpl/pacmpl2.html
44			Transactions in relaxed memory architectures	theoretic model for reasoning about transactions
45			Synthesizing coupling proofs of differential privacy.	lots of math
46			Unifying analytic and statically-typed quasiquotes.	for scala fans (from Christoph Koch)


1	Data Management Reading Group CSE 590Q - Fall 2017
2	(Mondays @ 2:30 in DB lab, CSE 405, unless noted otherwise) Topic: Deep Learning Meets DB
3
4		Presenter(s)	Paper	Other notes
5	10/2/2017	Tomer	Database Meets Deep Learning: Challenges and Opportunities (SIGMOD Record 2016)
6	10/9/2017	Brandon	Optasia: A Relational Platform for Efficient Large-Scale Video Analytics (SoCC 16)
7	10/16/2017	Walter, Parmita	Cardinality estimation using neural networks (CASCON '15)
8	10/23/2017	Shana, Maaz	SystemML: Declarative Machine Learning on Spark	Slides!
9	10/30/2017	cancelled for SIGMOD 11/2 deadline?
10	11/6/2017
11	11/13/2017	Jenny, Shrainik	Resource Management with Deep Reinforcement Learning (HotNets 2016)
12	11/20/2017	Helga	Quals
13	11/27/2017	Visitor
14	12/4/2017	Guna, Laurel	TensorFlow: A System for Large-Scale Machine Learning (OSDI 2016) AND/OR Final wrap up Discussion	Slides
15
16		Colin Evans?	Ian Covert?
17		Dominik
18	NOTES
19	Edit link
20	Suggested papers for this quarter:
21				Type
22	Database Meets Deep Learning: Challenges and Opportunities (SIGMOD Record 2016)			Gen
23	Cardinality estimation using neural networks (CASCON '15)			Using ML for DB
24	Resource Management with Deep Reinforcement Learning (HotNets 2016)			Using ML for Sys
25	NoScope: Optimizing Neural Network Queries over Video at Scale (VLDB 17)			ML Sys
26	Optasia: A Relational Platform for Efficient Large-Scale Video Analytics (SoCC 16)			ML Sys
27	Effective deep learning-based multi-modal retrieval (VLDB Journal 2016)			Using ML for IR
28	Project Adam: Building an Efficient and Scalable Deep Learning Training System (OSDI 2014)			ML Sys
29	Database Integration Using Neural Networks: Implementation and Experiences (2000) -- maybe? can't seem to find a copy			Using ML for DB
30	PyTorch : Case Study			ML Sys
31	White paper on Tensor Flow			ML Sys
32	Invite DL practitioners - small panel discussion? ask about their workflow, current data management hacks, empirical research etc.			Informal
33	SystemML	Shana


1	Joint DB & Systems Seminar CSE 590Q - Winter 2017
2	(Mondays @ 2:30 in DB lab, CSE 405, unless noted otherwise) Topic: transactions!
3
4		Presenter(s)	Paper	Other notes
5
6	1/9/2017	Laurel and Tomer	Spanner: Google’s Globally-Distributed Database (OSDI'12)
7	1/16/2017	MLK Day
8	1/23/2017	Maaz and Walter	An Evaluation of Distributed Concurrency Control (VLDB17) - Rachael Harding, Dana Van Aken, Andrew Pavlo, Michael Stonebraker, VLDB 17
9	1/30/2017	Guna and Brandon	High Performance Transactions via Early Write Visibility (VLDB 17)- Jose Faleiro, Daniel Abadi, Joseph Hellerstein
10	2/6/2017	snow day
11	2/13/2017	Cong and Shrainik	Speedy Transactions in Multicore In-Memory Databases (SOSP'13) - Stephen Tu, Wenting Zheng, Eddie Kohler, Barbara Liskov, and Samuel Madden
12	2/20/2017	Presidents Day
13	2/27/2017	Shumo and Helga	Transaction Healing: Scaling Optimistic ConcurrencyControl on Multicores (SIGMOD'16) - Yingjun Wu, Chee-Yong Chan, Kian-Lee Tan
14	2/27/2017	Jenny and Parmita	TicToc: Time Traveling Optimistic Concurrency Control (SIGMOD'16) - Xiangyao Yu, Andrew Pavlo, Daniel Sanchez. Srinivas Devadas
15	3/6/2017	Jialin/Ellis	Eris
16	(moved to 3/10 591D)	Jingjing and Ryan	Coordination Avoidance in Database Systems (VLDB'15) - Peter Bailis, Alan Fekete, Michael J. Franklin, Ali Ghodsi, Joseph M. Hellerstein, Ion Stoica.
17
18	NOTES
19	Suggested papers for this quarter:
20	1 (Laurel, Tomer)	Spanner: Google’s Globally-Distributed Database (OSDI'12)
21	2 (Maaz, Walter)	An Evaluation of Distributed Concurrency Control (VLDB17) - Rachael Harding, Dana Van Aken, Andrew Pavlo, Michael Stonebraker, VLDB 17 http://www.vldb.org/pvldb/vol10/p553-harding.pdf
22	3 (Guna, Brandon)	High Performance Transactions via Early Write Visibility (VLDB 17)- Jose Faleiro, Daniel Abadi, Joseph Hellerstein http://www.vldb.org/pvldb/vol10/p613-faleiro.pdf
23	5a (Jenny, Parmita)	TicToc: Time Traveling Optimistic Concurrency Control (SIGMOD'16) - Xiangyao Yu, Andrew Pavlo, Daniel Sanchez. Srinivas Devadas
24	4 (Cong, Shrainik)	Speedy Transactions in Multicore In-Memory Databases (SOSP'13) - Stephen Tu, Wenting Zheng, Eddie Kohler†, Barbara Liskov, and Samuel Madden
25	5b (Shumo, Helga)	Transaction Healing: Scaling Optimistic ConcurrencyControl on Multicores (SIGMOD'16) - Yingjun Wu, Chee-Yong Chan, Kian-Lee Tan
26	6 (Jingjing, Ryan)	Coordination Avoidance in Database Systems (VLDB'15) - Peter Bailis, Alan Fekete, Michael J. Franklin, Ali Ghodsi, Joseph M. Hellerstein, Ion Stoica.
27	7 (Jialin and Ellis)	Eris: Coordination-Free Consistent Transactions using Network Multi-Sequencing (in progress) - Jialin Li, Ellis Michael, Dan R. K. Ports
28
29
30
31
32
33
34	No	'Cause I'm strong enough: reasoning about consistency choices in distributed systems (POPL 16) Alexey Gotsman, Hongseok Yang, Carla Ferreira, Mahsa Najafzadeh, and Marc Shapiro
35	No	Declarative Programming over Eventually Consistent Data Stores (PLDI'15) - KC Sivaramakrishnan, Gowtham Kaki, Suresh Jagannathan
36	No	Transaction chains: achieving serializability with low latencyin geo-distributed storage systems (SOSP'13) - Yang Zhang, Russell Power, Siyuan Zhou, Yair Sovran, Marcos K. Aguilera, Jinyang Li
37		Serializable Isolation for Snapshot Databases (SIGMOD'08) - Michael J. Cahill, Uwe Rohm, Alan Fekete


1	Data Management Reading Group CSE 590Q - Fall 2016
2	(Mondays @ 2:30 in DB lab unless noted otherwise) Topic: potpourri!
3
4		Presenter(s)	Paper	Other notes
5
6	10/3/2016		Organizational meeting
7	10/10/2016	Shumo and Walter	Paper 2
8	10/17/2016	Dom and Laurel	Paper 1a and 1b
9	10/24/2016	Parmita and Jay	Paper 3
10	10/31/2016	Shrainik	Paper 7
11	11/7/2016	Ryan	Paper 8
12	11/14/2016	Brandon and Tomer	Paper 5
13	11/21/2016	Shana and Guna	Paper 9
14	11/28/2016	Jingjing and Jenny	Paper 4
15	12/5/2016	Cong and Maaz	Paper 6	Shana quals talk @ 11am today
16
17	NOTES
18	Papers for this quarter:
19	A. Approximate Query Processing:
20	1	Bolin Ding, Silu Huang, Surajit Chaudhuri, Kaushik Chakrabarti, Chi Wang: Sample + Seek: Approximating Aggregates with Distribution Precision Guarantee. SIGMOD Conference 2016: 679-694
21		Srikanth Kandula, Anil Shanbhag, Aleksandar Vitorovic, Matthaios Olma, Robert Grandl, Surajit Chaudhuri, Bolin Ding: Quickr: Lazily Approximating Complex AdHoc Queries in BigData Clusters. SIGMOD Conference 2016: 631-646
22
23	2	Feifei Li, Bin Wu, Ke Yi, Zhuoyue Zhao: Wander Join: Online Aggregation via Random Walks. SIGMOD Conference 2016: 615-629
24		Walter is interested in wander join
25	3	Kai Zeng, Sameer Agarwal, Ion Stoica: iOLAP: Managing Uncertainty for Efficient Incremental OLAP. SIGMOD Conference 2016: 1347-1361
26
27	B. Databases on New Hardware:
28
29	4	Rethinking Database Algorithms for Phase Change Memory, CIDR 2011
30
31	5	Let's Talk About Storage & Recovery Methods for Non-Volatile Memory Database Systems, SIGMOD 2015
32		Brandon says "dibs" :)
33	6	Q100: the architecture and design of a database processing unit, ASPLOS 2014
34
35	C. Feature Selection on Normalized Data:
36
37	7	Arun Kumar, Jeffrey F. Naughton, Jignesh M. Patel, Xiaojin Zhu: To Join or Not to Join?: Thinking Twice about Joins before Feature Selection. SIGMOD Conference 2016: 19-34
38		Shrainik is interested in this
39	8	Maximilian Schleich, Dan Olteanu, Radu Ciucanu: Learning Linear Regression Models over Factorized Joins. SIGMOD Conference 2016: 3-18
40
41	9	Srinivas M. Aji, Robert J. McEliece: The generalized distributive law. IEEE Trans. Information Theory 46(2): 325-343 (2000) Mahmoud Abo Khamis, Hung Q. Ngo, Atri Rudra: FAQ: Questions Asked Frequently. PODS 2016: 13-28
42		^----< Shana is interested in these papers


1	Data Management Reading Group CSE 590Q - Spring 2016
2	Tentative Schedule (Mondays @ 4:30 in DB lab unless noted otherwise) Topic: Cool demos of recent DB systems / tools / theorems
3
4		Speaker name	Demo	Other notes
5
6	3/28/2016			Some people absent...
7	4/4/2016	Laurel / Dominik	BlinkDB or some other summary system
8	4/11/2016	Tianqi	Portable and Scalable Machine Learning on Cloud,Lessons and Thoughts
9	4/18/2016	Parmita	Azure Data-lake and related analytics services from Azure Cloud
10	4/25/2016	Ryan / Jingjing	DeepDream
11	5/2/2016	Brandon Haynes / Shana H	TileDB / GenomicsDB	Slides
12	5/9/2016	Sven Dorkenwald	Tensorflow
13	5/16/2016	(swapped with 590Q)
14	5/20/2016 (Friday)	Helga	Jepsen (breaking distributed databases)
15	5/23/2016	Jenny	Data Visualization with HoloLens
16	5/30/2016	(memorial day)
17
18	NOTES
19
20
21
22
23