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0. read ../README.txt and make sure you have "Prerequisites"

1. provide conf.xml file. As there is s sample conf file "conf.xml.sample", copy it to conf.xml
   and modify conf.xml appropriately.

   $ cp conf.xml.sample conf.xml
   $ vi conf.xml

2. build alike (in the parent directory of this directory)

   $ ant
   $ sbt clean package

3. change to this directory

   $ cd demo

4. download ukbench image file

   $ mkdir ukbench
   $ cd ukbench
   $ wget http://www.vis.uky.edu/~stewe/ukbench/ukbench.zip

5. unzip the zip file

   $ unzip ukbench.zip

6. remove existing intermediate and result files of demo

   # go back to demo directory
   $ cd ..
   $ ant clean

7. run extractor program

   # before running the program, desc directory needs to be created
   $ mkdir desc
   $ export PYTHONPATH=../src/python; python run_desc_extractor.py

   If you use Mac OS X and got "ImportError: No module named cv2" despite the fact that
   you've installed opencv +python27 via MacPorts, you may want to set your
   PYTHONPATH environment variable to fix this problem:

   # look for the place where cv2 module installed via MacPorts
   $ sudo port contents opencv|grep py
   $ export PYTHONPATH=$PYTHONPATH:/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages

   If you use Mac OS X and got "segmentation fault" while running above,
   your python that was already installed with shipment couldn't suite the version
   of the opencv+python27 you've installed via MacPorts. To use MacPorts's version
   of python27, check the directory python27 installed and set your PATH environment
   variable:

   $ which python
   # look for the place where python27 command installed via MacPorts
   $ sudo port contents python27|grep bin
   $ export PATH=/opt/local/Library/Frameworks/Python.framework/Versions/2.7/bin:$PATH
   # rerun the above
   $ python run_desc_extractor.py

8. if you want to run mahout on hadoop distributed environment, do the following,
   otherwise, go to 9.

   (1) install Hadoop, set HADOOP_HOME environment variable
   (2) copy $HADOOP_HOME/conf to demo/hadoop-conf
       $ cp -R $HADOOP_HOME/conf hadoop-conf
   (3) set JAVA_HOME and HADOOP_CLASSPATH in demo/hadoop-conf/hadoop-env.sh.
       HADOOP_CLASSPATH should include all jar files in lib of parent directory
       and demo/hadoop-conf directory
   (4) set core-site.xml, hdfs-site.xml and mapred-site.xml appropriately in
       demo/hadoop-conf directory
   (5) set HADOOP_CONF_DIR environment variable to demo/hadoop-conf
   (6) execute hadoop namenode -format and start-all.sh

9. run clustering and vector quantization programs. You can choose use of (1) Mahout or (2) Spark
   for clustering

   #
   # (1) If you prefer Mahout
   #
   $ ant piv
   # kmeans may take tens of minutes
   $ ant kmeans
   $ ant clusterdump

   #
   # (2) If you prefer Spark (um, it takes 4 and half hours on my MacBook)
   # IMPORTANT: You need to install Spark 1.0.1 beforehand
   #
   $ spark-submit --jars ../apache-alike-0.2.job --class org.apache.alike.KMeansClusteringExecutor ../target/scala-2.10/alike-spark_2.10-0.2.jar

   # After (1) or (2), do the following for vector quantization
   $ ant qv

10. goto Solr site, download Solr 4.9.0 or superior and unzip

   # Apache Solr web site and download solr-4.9.0.tgz or superior
   http://lucene.apache.org/solr/

   # unzip
   $ tar xvzf solr-4.9.0.tgz

11. startup Solr server

   $ cd solr-4.9.0/example
   $ java -Dsolr.solr.home=../../solrhome -jar start.jar

12. index demo vector quantization data

   $ ./post.sh solr-demo-data.xml

13. startup demo web server

   $ python demoserver.py

14. access to http://localhost:8080/ in your web browser and enjoy the demo!