We study the continuous evaluation of conjunctive triple pattern queries over RDF data stored in distributed hash tables. In a continuous query scenario network nodes subscribe with long-standing queries and receive answers whenever RDF triples satisfying their queries are published. We present two novel query processing algorithms for this scenario and analyze their properties formally. Our performance goal is to have algorithms that scale to large amounts of RDF data, distribute the storage and query processing load evenly and incur as little network traffic as possible. We discuss the various performance tradeoffs that occur through a detailed experimental evaluation of the proposed algorithms.