In this paper we present a recommendation service based on Neo4j graph data base which is applied in the context of smart cities application and leverages the potential of big data. The current work studies a modelling approach for user generated data combined with open big data and proceeds with the appropriate reference implementation and experimentation to validate recommendation services for innovative citizen- centric applications. Moreover, we study and validate performance issues of this Neo4j based recommendation service and evaluate it as a useful appliance for real-time big data application.
%0 Conference Paper
%1 8035080
%A Palaiokrassas, G.
%A Charlaftis, V.
%A Litke, A.
%A Varvarigou, T.
%B 2017 International Conference on High Performance Computing Simulation (HPCS)
%D 2017
%K neo4j
%P 217-223
%R 10.1109/HPCS.2017.41
%T Recommendation Service for Big Data Applications in Smart Cities
%U http://ieeexplore.ieee.org/document/8035080/
%X In this paper we present a recommendation service based on Neo4j graph data base which is applied in the context of smart cities application and leverages the potential of big data. The current work studies a modelling approach for user generated data combined with open big data and proceeds with the appropriate reference implementation and experimentation to validate recommendation services for innovative citizen- centric applications. Moreover, we study and validate performance issues of this Neo4j based recommendation service and evaluate it as a useful appliance for real-time big data application.
@inproceedings{8035080,
abstract = {In this paper we present a recommendation service based on Neo4j graph data base which is applied in the context of smart cities application and leverages the potential of big data. The current work studies a modelling approach for user generated data combined with open big data and proceeds with the appropriate reference implementation and experimentation to validate recommendation services for innovative citizen- centric applications. Moreover, we study and validate performance issues of this Neo4j based recommendation service and evaluate it as a useful appliance for real-time big data application.},
added-at = {2018-01-18T17:53:09.000+0100},
author = {Palaiokrassas, G. and Charlaftis, V. and Litke, A. and Varvarigou, T.},
biburl = {https://www.bibsonomy.org/bibtex/254030db2bef926cdb16dc68d060648f4/defeatnelly},
booktitle = {2017 International Conference on High Performance Computing Simulation (HPCS)},
doi = {10.1109/HPCS.2017.41},
interhash = {dc6e1588da91f1445ca3f1423a4f01bd},
intrahash = {54030db2bef926cdb16dc68d060648f4},
keywords = {neo4j},
month = {July},
pages = {217-223},
timestamp = {2018-01-18T17:53:09.000+0100},
title = {Recommendation Service for Big Data Applications in Smart Cities},
url = {http://ieeexplore.ieee.org/document/8035080/},
year = 2017
}