Reading list

In no particular order,

Building Blocks for Exploratory Data Analysis Tools (dead link, lost to the sands of time).
http://www.eecs.berkeley.edu/~alspaugh/papers/alspaugh-idea2014-final.pdf (dead link, lost to the sands of time).
Netflix Recommendations: Beyond the 5 stars.

Statisticians invent data science

Data Cubes

Gray et al., Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Harinarayan et al., Implementing Data Cubes Efficiently
Sismanis et al., Dwarf: Shrinking the Petacube
Liu et al., imMens: Real-time Visual Querying of Big Data
Lins el al., Nanocubes for Real-Time Exploration of Spatiotemporal Datasets

Streaming/Sampling

Hellerstein et al., Online Aggregation
Spark
BlinkDB
Blais et al., Rapid Sampling for Visualizations with Ordering Guarantees
Trust me, I’m partially right
Sample-Oriented Task-Driven Visualizations

Additional reading

Approximating population distributions by carefully-chosen samples:

Phillips, ε-Samples for Kernels
Zheng et al., Quality and Efficiency in Kernel Density Estimates for Large Data
Zheng and Phillips, L∞ Error and Bandwidth Selection for Kernel Density Estimates of Large Data

Vis Systems

The Case for Data Visualization Management Systems

Books

Tukey’s Exploratory Data Analysis
Gutierrez’s Data Scientists at Work