Graph databases methodology and tool supporting index/store versioning

Abstract

a b s t r a c tGraph 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 with inferential engines that materialize some knowledge as triples during indexing or querying. In these cases, deleting 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 a 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, this paper proposes a lifecycle methodology and a tool supporting versioning of indexes for RDF KB store. 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.

Assessing RDF Graph Databases for Smart City Services

“…This may be used to generalize/specialize about entities, to same-as, equivalence, transitive, symmetrical, etc. The inference may imply the materialization of triples in the phase of indexing [Bellini et al, 2015].…”

Km4City Smart City API: An Integrated Support for Mobility Services

“…This approach allows re-conciliating data and exploiting a coherent model to reduce the errors, integrating data representing the same concept and coming from different structures, operators, and sources. The usage of a multi-domain ontology allows the adoption of a model representing relationships of specialization among classes and relationships, aggregation, association, and similarity, that enable the inferential processes in the RDF Graph Database [26], [22]. Thus, the obtained knowledge base can be used for creating strategies for data quality improvement and for setting up algorithms and reasoning about the several aspects and services belonging to multiple domains.…”

“…As described in the previous section, Case (c) solution is the most suitable for providing smart services as those of SiiMobility, provided by mobility operators as routing, personal moving assistant, geolocalized suggestions, etc. Therefore, Sii-Mobility adopted a Case (c) solution for data aggregation and service production by exploiting and improving the Km4City Ontology (http://www.disit.org/km4city) [22], [26], as the main ontological model. According to READY4 SmartCities FP7 CSA project of the European Commission [38], Km4City is the most comprehensive smart city ontology at the state of the art (in terms of coverage), among the analyzed ontologies for smart cities.…”

“…• SPARQL Query: calls are directly performed on the RDF Store endpoint using the standard SPARQL query protocol (based on REST) using GET or POST requests with the query parameter containing the SPARQL query; • SPARQL Query with Inference: calls are directly performed on the RDF Store endpoint http://servicemap.disit.org/WebAppGrafo/sparql, using the standard SPARQL query protocol (based on REST) using GET or POST requests with the query parameter containing the SPARQL query, including inference aspects in the case of Virtuoso, or automatically exploiting the inference in the case of OWLIM [26] A similar request can be performed using the REST API http://servicemap.disit.org/WebAppGrafo/api/v1/?selection=ht tp://www.disit.org/km4city/resource/45e458d0c9e5bf53e34e7 e2fed73a6b4&categories=Service&maxDist=100&format=jso n, where selection indicates the service URI around which services should be located, categories indicates the kind of service to be located, maxDist indicates the max distance in meters and format indicates the return data format (JSON or HTML). The same query presented above can be performed as Smart City API based on Query ID.…”

Snap4City: A Scalable IOT/IOE Platform for Developing Smart City Applications

“…The world of ICT solutions for smart city is very wide encompassing services at different levels of complexity and coverage, from classical Open Data portals (CKAN [9], Open-DataSoft [19], ArcGIS and OpenData [2]) to sophisticate solutions that provide data aggregation and Smart City APIs facilitating the development of web and mobile applications (Km4City [5], City SDK [8]). The former solutions are mainly suitable for collecting and sharing open data files and their indexing on the basis of corresponding descriptive metadata.…”