No description, website, or topics provided.
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.

README.md

Nanocubes: an in-memory data structure for spatiotemporal data cubes

Nanocubes are a fast data structure for in-memory data cubes developed at the Information Visualization department at AT&T Labs Research. Visualizations powered by nanocubes can be used to explore datasets with billions of elements at interactive rates in a web browser, and in some cases nanocubes uses sufficiently little memory that you can run a nanocube in a modern-day laptop.

About this branch

This branch (v4) contains a new implementation of Nanocubes in the C programming language. The goal with this new implementation was to get a much finer control in all aspects of the data structure and specially on its memory aspects (allocation, layout). In our original C++ template-based implementation of Nanocubes (up to version 3.3), we implemented the Nanocube data structure on top of C++ STL (standard library) and while this was a reasonable solution at the time, it had some important downsides: (1) complex serialization which made it hard to save/load Nanocube into files; (2) variations in the internal memory layout of a Nanocube based on the specific STL implementation we used.

Here is a link to the new API

Compiling on Linux or Mac

# Dependencies for Ubuntu 18.04
# sudo apt install build-essential curl unzip
#
# Dependencies for Mac OS X 10.13.4
# XCode

# get the v4 branch
curl -L -O https://github.com/laurolins/nanocube/archive/master.zip
unzip master.zip
cd nanocube-master

# modify INSTALL_DIR to point to another installation folder if needed
export INSTALL_DIR="$(pwd)/install"
./configure --with-polycover --prefix="$INSTALL_DIR"
make
make install

# Test if nanocubes is working
$INSTALL_DIR/bin/nanocube

# Add nanocube binaries to the PATH environment variable
export PATH="$INSTALL_DIR/bin":$PATH

Creating and serving a nanocube index

# create a nanocube index for the Chicago Crime dataset (small example included)
# Inputs: (1) CSV data file, (2) mapping file (data/crime50k.map)
# Output: (1) nanocube index called data/crime50k.nanocube
nanocube create <(gunzip -c data/crime50k.csv.gz) data/crime50k.map data/crime50k.nanocube

# serve the nanocube index just created on port 51234
nanocube serve 51234 crimes=data/crime50k.nanocube &

# test querying the schema of the index
curl "localhost:51234/schema()"

# test querying the number of indexed records
curl "localhost:51234/format('text');q(crimes)"

# test querying the number of records per crime type
curl "localhost:51234/format('text');q(crimes.b('type',dive(1),'name'))"

For more information on .map files go to mapping files

For more query examples go to API

Viewer

# If you need to install pip (e.g. on MacOS)
# python <(curl https://bootstrap.pypa.io/get-pip.py) --user
python -m pip install --user requests future

# Setup a web viewer on port 8000 for the crimes nanocube previously opened 
# on port 51234.
#
# Parameters:
#     -s         nanocube backend server (any http heachable machine)
#     --ncport   nanocube backend port
#     -p         port of the webviewer to be open in the localhost
#

nanocube_webconfig -s http://`hostname -f` --ncport 51234 -p 8000

Zoom into the Chicago region to see a heatmap of crimes.

image

Extra

For more advanced information follow this link: extra