Graph Databases Lifecycle Methodology and Tool, RDF building, indexing and versioning

Graph databases are taking place in many different applications: smart city, smart cloud, smart education, etc. In most cases, the applications imply the creation of ontologies and the integration of a large set of knowledge to build a knowledge base as an RDF KB store, with ontologies, static data, historical data and real time data. Most of the RDF stores are endowed of inferential engines that materialize some knowledge as triples during indexing or querying. In these cases, the delete of concepts may imply the removal and change of many triples, especially if the triples are those modeling the ontological part of the knowledge base, or are referred by many other concepts. For these solutions, the graph database versioning feature is not provided at level of the RDF stores tool, and it is quite complex and time consuming to be addressed as black box approach. In most cases the indexing is time consuming process, and the rebuilding of the KB may imply manually edited long scripts that are error prone. Therefore, in order to solve these kinds of problems, a lifecycle methodology and a tool supporting versioning of indexes for RDF KB store is proposed. The solution proposed has been developed on the basis of a number of knowledge oriented projects as Sii-Mobility (smart city), RESOLUTE (smart city risk assessment), ICARO (smart cloud). Results are reported in terms of time saving and reliability.

Figure 1.   RDF Index Buidling Monitor

For the RDF Index generation the RDF Index Manager produces a script according to the index descriptor and the RDF store target. The script is structured in the following steps: (i) setup of script, (ii) initialization of RDF store, (iii) bulk uploading of triples into the store, (iv) RDF store finalization, (v) create eventual additional indexes as textual indexes, geographical indexes that need additional database commands, and (vi) update index building status. In most cases, the RDF store rebuilt by indexing is time consuming, and may imply manually edited long scripts that are error prone. In order to solve this kind of problem, in this paper, a lifecycle methodology and our RIM tool for RDF KB store versioning is proposed. The results have shown that saving time up to the  95% depending on the number of triples, files and cases to be indexed. 

last changes 07-06-2015

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