RedisGraph GraphBLAS Enabled Graph Database

RedisGraph is a Redis module developed by Redis Labs to add graph database functionality to the Redis database. RedisGraph represents connected data as adjacency matrices. By representing the data as sparse matrices and employing the power of GraphBLAS (a highly optimized library for sparse matrix operations), RedisGraph delivers a fast and efficient way to store, manage and process graphs. Initial benchmarks indicate that RedisGraph is significantly faster than comparable graph databases.Comment: Accepted to IEEE IPDPS 2019 GrAPL worksho

Graph Database

This project will review the new technology of graph databases. Graph databases, which model data using nodes and relationships, utilize a different paradigm than the rows and columns of relational databases. The main goals of this project are to provide the basic background information on graph database technology and then use this knowledge to convert an RDBMS into a GDBMS. The RDBMS used will be the sample Accounts Payable (AP) relational database used in the Murach SQL 2012 book. The following will be accomplished: Explore graph database versus relational for querying and updating the Accounts Payable database. Review Cypher (Neo4j graph query language) and run CRUD queries against the AP graph database. Show step by step instructions to convert the Murach SQL 2012 Accounts Payable database into the graph database

Towards Persistent Storage and Retrieval of Domain Models using Graph Database Technology

We employ graph database technology to persistently store and retrieve robot domain models.Comment: Presented at DSLRob 2015 (arXiv:1601.00877

Healthcare Referral Graph Database

Medical providers in the USA are identified by a sequence of numbers called the NPI, which is unique per provider. This data is publicly available from http://nppes.viva-it.com/NPI_Files.html. Under federal law (FOIA act), providers are required to release data of patient referrals from one provider to another. This data is publicly available from https://questions.cms.gov/faq.php?id=5005&faqId=7977 (Credit to Docgraph). This graph database was built in Neo4j, with data from the NPI registry (May 2014 distribution) as the set of nodes with properties, and with data from physician referrals (2012-2013 distribution) as the set of edges. The original node dataset source (May 2014 distribution): http://nppes.viva-it.com/NPI_Files.html. Data headers/documentation is included with the data. For the database, we chose to focus on only a few node attributes. These include NPI as the index, organization vs individual medical provider, practice city and state, and specialty (represented by taxonomy codes, see codebook). The original edge dataset source (2012-2014 distribution): https://questions.cms.gov/faq.php?id=5005&faqId=7977. The edge data has three columns: source of referral, destination of referral, and quantity of patients referred. Source and destination of referral are represented with NPI