The document proposes a system called HEART for storing and processing large-scale RDF data using HBase and Hadoop. It aims to provide scalability and support for various queries while addressing the challenges of sparse RDF data. HEART will be one of the Apache incubator projects, featuring components like a data loader, storage manager, query processor, and an extended SPARQL query language.

1. Highly Extensible & Available RDF Table: Heart Edward J. Yoon (edwardyoon@apache.org), Hyunsik Choi (hyunsik.choi@gmail.com)

2. Background RDF and Graph Databases have gained increasing attention from both academic and industrial areas. Most of the RDF and Graph Databases are likely to be very large as follows: web data, biological data, and social Networks There is no product for large-scale RDF data.

3. Proposal A system to store and process Large-Scale RDF. Our system is based on distributed frameworks, Hbase and Hadoop. Hbase and Hadoop are Apache Projects

4. The Main Goals Scalability for large-scale data To support various queries

5. Why is RDF on Hbase? RDF data is very sparse (when RDF are denomalized for each same subject) Hbase well support very sparse data. Column-based storage Various compression techniques

6. Architecture Heart consists of components as follows: Heart Data Loader Heart Storage Manager Heart Query Processor Heart Query Language Heart Data Materializer

7. Heart Data Loader For bulk insertion Load RDF data from a very large RDF or TURTLE file Organizes the graph data into a Hbase table

8. Heart Storage Manager Concern with how to partition graphs (i.e., triples) in terms of their topological characteristics. Determine which clients store the given partitioned graphs. Give locality between adjacent subjects

9. Heart Query Processor Executes RDF queries on large-scale RDF data in a Hbase table SPQRQL query -> Query Parser -> Query Optimizer -> Execution

10. Heart Query Language Based on SPARQL (from W3C) But, we intend to extend SPARQL in order to support more various graph queries.

11. Heart Data Materializer Generate index data for RDF Sub-graph Query Result in efficient query processing We have to design an efficient index scheme.

12. Summary We propose Heart to store and process large-scale RDF data on Hbase. The main goals are to provide scalability and various queries. It will be one of the Apache Incubator Projects.