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

Nanocubes are a fast datastructure 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.

Releases

Number	Description
2.1	Javascript front-end, CSV Loading, Bug Fixes
2.0	New feature-rich querying API
1.0	Original release with a simple querying API

Compiling latest release

Prerequisites The nanocubes server is written in C++ 11 (gcc >=4.7.2). You'll need a recent version of boost (>=1.48) and the GNU build system.

Run the following commands to compile nanocubes on your linux/mac system.

$ wget https://github.com/laurolins/nanocube/archive/2.1.zip
$ unzip 2.1.zip
$ cd nanocube-2.1
$ ./bootstrap
$ ./configure
$ make

If a recent version of gcc is not the default, you can run configure with the specfic recent version of gcc in your system. For example

$ CXX=g++-4.8 ./configure

If your system has tcmalloc installed, we suggest linking nanocubes with it. For example

$ CXX=g++-4.8 ./configure LIBS=<path-to-tcmalloc>/libtcmalloc_minimal.a

Loading a CSV file into a nanocube

This procedure assumes your machine is not running anything on ports 8000 and 29512

1. Installing Pandas in a separate python env (Optional)

    $ wget http://pypi.python.org/packages/source/v/virtualenv/virtualenv-1.11.4.tar.gz
    $ tar xfz virtualenv-1.11.4.tar.gz
    $ python virtualenv-1.11.4/virtualenv.py  myPy
    
    # activate the virtualenv, type "deactivate" to disable the env
    $ source myPy/bin/activate
    $ pip install argparse numpy pandas
   

2. Start a web server in the "web" directory and send it to background

    $ cd web
    $ python -m SimpleHTTPServer &
   

3. Run the script and pipe it to the nanocubes server using the included example dataset (Chicago Crime)

    $ cd ../scripts
    $ python csv2Nanocube.py --catcol='Primary Type' crime50k.csv | NANOCUBE_BIN=../src  ../src/ncserve --rf=100000 --threads=100
   

This should be it. Point your browser to http://localhost:8000 for the viewer

    Parameters for csv2Nanocube.py
    --catcol='Column names of categorical variable'
    --latcol='Column names of latitude'
    --loncol='Column names of longitude'
    --countcol='Column names of the count'
    --timecol='Column names of the time variable'
    --timebinsize='time bin size in seconds(s) minutes(m) hours(h) days(D)
    --sep='Delimiter of Columns'
    e.g. 1D/30m/60s'

Further Details

For a better understanding on how to ingest data into nanocubes and how to query nanocubes follow this link. For larger datasets or if you want more flexibility on ingesting/querying data using nanocubes the CSV loading method illustrated above might not be the most efficient way to go.

Asking for help

IRC: #nanocubes channel on irc.freenode.net

mailing list: http://mailman.nanocubes.net/mailman/listinfo/nanocubes-discuss_mailman.nanocubes.net