Schedule

(01/19) Introduction

• Slides

(01/24) Python

• Slides
• Notebook: Download, View

(01/26) Python

• Slides
• Notebook: Download, View

(01/31) Relational Databases

• Slides
• References:
  • Database Systems Concepts, Silberschatz, Korth, and Sudarshan

(02/02) Relational Databases

• Slides
• References:
  • Database Systems Concepts, Silberschatz, Korth, and Sudarshan

(02/07) Structured Data

• Slides
• Notebooks:
  • Lecture Examples: Download, View
  • PyData Ch. 4

(02/09) Data and Pandas

• Slides
• Notebook: Download, View, Data

(02/14) Data Wrangling

• Slides
• Notebook: Download, View

(02/16) Data Wrangling

• Slides
• Reading: Wrangler: Interactive Visual Specification of Data Transformation Scripts, S. Kandel et al., 2011
• References:
  • Original Wrangler Demo
  • Potter’s wheel: An interactive data cleaning system, V. Raman and J. M. Hellerstein, 2001.
  • Proactive Wrangling: Mixed-Initiative End-User Programming of Data Transformation Scripts, P. J. Guo et al., 2011
  • Predictive Interaction for Data Transformation, J. Heer et al., 2015
  • Trifacta Wrangler
  • Self-Service Data Preparation: Research to Practice, J. M. Hellerstein et al., 2018

(02/21) Data Cleaning

• Slides
• References:
  • HoloClean: Holistic Data Repairs with Probabilistic Inference, T. Rekatsinas et al., 2017

(02/28) Data Transformation

• Slides
• Reading:
  • Foofah: Transforming Data By Example, Z. Jin et al., 2017
• References:
  • Spreadsheet Table Transformations from Examples, W. R. Harris and S. Gulwani, 2011.
  • Transform-Data-by-Example (TDE): An Extensible Search Engine for Data Transformations, Y. He et al., 2018
• PyData Notebook (Ch. 7)

(03/02) Data Transformation

• Slides
• Reading:
  • Tidy Data, H. Wickham, 2014
• Notebook: Download, View

(03/07) Data Integration

• Slides
• References:
  • Principles of Data Integration, AH Doan et al., 2012

(03/09) Data Fusion

• Slides
• Reading: Dong et al.
• References:
  • Entity Resolution, L. Getoor and A. Machanavajjhala, 2012
  • Record Linkage, P. Christen, 2019
  • Data Fusion—Resolving Data Conflicts in Integration, X. L. Dong and F. Naumann
  • Slides: Integrating Conflicting Data:The Role of Source Dependence, X. L. Dong, 2009

(03/21) Data Curation

• Slides
• Reading:
  • The FAIR Guiding Principles for scientific data management and stewardship, M. D. Wilkinson et al., 2016.
• References:
  • Big Data, Little Data, No Data: Scholarship in the Networked World, (eBook available through NIU Library) C. L. Borgman, 2015.
  • Big Data, Little Data, or No Data, C. L. Borgman, 2018.

(03/23) Data Citation

• Slides
• References:
  • Mandated data archiving greatly improves access to research data, T. Vines et al., 2013
  • The Availability of Research Data Declines Rapidly with Article Age, T. Vines et al., 2014
  • Who Shares? Who Doesn’t? Factors Associated with Openly Archiving Raw Research Data, H. Piwowar, 2011
  • The Evolution of Data Citation: From Principles to Implementation, M. Altman and M. Crosas, 2013
  • Why data citation is a computational problem, P. Buneman et al., 2016.
  • How to cite curated databases and how to make them citable, P. Buneman, 2006.

(03/28) Scalable Databases

• Slides
• Reading:
  • Cassandra - A Decentralized Structured Storage System, A. Lakshman and P. Malik, 2009.
• References:
  • Introduction to Apache Cassandra, R. Stupp
  • Introduction to Cassandra, G. Atil

(03/30) Scalable Databases

• Slides
• Reading:
  • Spanner: Google’s Globally-Distributed Database, J. C. Corbett et al., 2012.
• References:
  • NewSQL, A. Pavlo, 2012.
  • F1: A Distributed SQL Database That Scales, J. Shute et al., 2013.
  • Spanner, TrueTime & The CAP Theorem, E. Brewer, 2017.

(04/04) Graph Data

• Slides
• Reading:
  • A Comparison of Current Graph Database Models, R. Angles, 2012.
• References:
  • Survey of Graph Database Models, R. Angles and C. Gutierrez, 2008.
  • An Introduction to Graph Data Management, R. Angles and C. Gutierrez, 2017
  • Graph Databases, D. Lembo and R. Rosati, 2015
  • Introduction to Graph Databases, M. De Marzi, 2012
  • The Ubiquity of Large Graphs and Surprising Challenges of Graph Processing, S. Sahu et al., 2017
  • The Future Is Big Graphs: A Community View on Graph Processing Systems, S. Sakr et al., 2021
  • The (sorry) State of Graph Database Systems, P. Boncz, 2022

(04/11) Databases and Visualization

• Slides
• Reading:
  • imMens: Real-time Visual Querying of Big Data, Z. Liu et al., 2013
• References:
  • Scalable Visualization, J. Heer, 2020
  • Falcon: Balancing Interactive Latency and Resolution Sensitivity for Scalable Linked Visualizations, D. Moritz et al., 2019

(04/13) Spatial Data

• Slides
• Reading:
  • Nanocubes for Real-Time Exploration of Spatiotemporal Datasets, L. Lins et al., 2013.
• References:
  • Data Cubes, J. Han, M. Kamber, and J. Pei, 2011.
  • Dynamic prefetching of data tiles for interactive visualization, L. Battle et al., 2016.

(04/18) Time Series Data

• Slides

(04/20) Provenance

• Slides
• Reading:
  • Provenance for Computational Tasks: A Survey, J. Freire et al., 2008
• Notebook: Download, View

(04/25) Provenance

• Slides
• Reading:
  • Reproducibility Using VisTrails, J. Freire et al., 2013.
• References:
  • Provenance in Databases: Why, How, and Where, J. Cheney et al., 2007
  • Provenance in Databases, A. Amarilli, 2019.

(04/27) Reproducibility

• Slides
• Reading:
  • Repeatability in computer systems research, C. Collberg and T. A. Proebsting, 2016.
• References:
  • Repeatability and Benefaction in Computer Systems Research, C. Collberg et al., 2015.
  • Reproducible Research in Computational Science, R. D. Peng, 2011.
  • Ten Simple Rules for Reproducible Computational Research, G. K. Sandve et al., 2013.
  • Computational Reproducibility: State-of-the-Art, Challenges, and Database Research Opportunities, J. Freire et al., 2012.
  • A Large-scale Study about Quality and Reproducibility of Jupyter Notebooks, J. F. Pimentel, 2019.

(05/02) Databases and Machine Learning

• Slides
• Reading:
  • SageDB: A Learned Database System, T. Kraska et al., 2019
• References:
  • Learning and Self-Designing Data Structures (SIGMOD Tutorial), S. Idreos and T. Kraska: Part 1 and Part 2
  • Benchmarking Learned Indexes, R. Marcus et al., 2021
  • Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads, J. Ding et al., 2021
  • Bao: Making Learned Query Optimization Practical, R. Marcus et al., 2021

(05/04) Review