Think Pudding

Description

Using a triple store to store and query spek output from candidate smasher. Determine which candidates are acceptable using causal pathways.

Use

Nebula Tripple Store

1. Upload data to S3 Bucket

2. Spin up Nebula cluster stack with bastion host

   aws cloudformation create-stack --stack-name $STACK_NAME\
     --capabilities "CAPABILITY_IAM" \
     --template-body file://nept_stack_cf.yaml \
     --parameters '[{"ParameterKey":"Env","ParameterValue":"test"},'\
     '{"ParameterKey":"DbInstanceType","ParameterValue":"db.r4.xlarge"},'\
     '{"ParameterKey":"KeyName","ParameterValue":"aws-growls-useast-1"}]'

3. Assign IAM ROLE to cluster

   aws neptune add-role-to-db-cluster \
     --role-arn ${NeptuneLoadFromS3IAMRoleArn} \
     --db-cluster-identifier ${DBClusterId}

4. Configure Bastion Host

   ansible-playbook -i ${BastionIp}, -u ec2-user bastion_play.yml

5. Login to Bastion Host and Load Data

   ssh ec2-user@${BastionIp} scripts/neptune_load.sh

Fuseki Tripple Store

1. Start in memory fuseki that allows for updates

   ${FUSEKI_HOME}/fuseki-server --mem --update /ds 1> fuseki.out 2>&1 &

2. Input example spek

   ./insert_spek.sh

3. Run ISR update to identify acceptable candidates

   ./update_isr.sh

4. Run query to get results

   ./query_isr.sh

Requirements

• fuseki

License

Creative Commons 3.0