pub test 1

@inproceedings{9006016,

title = {Single-cell regulatory network inference and clustering from high-dimensional sequencing data},

author = {A G Vrahatis and G N Dimitrakopoulos and S K Tasoulis and S V Georgakopoulos and V P Plagianakos},

doi = {10.1109/BigData47090.2019.9006016},

year  = {2019},

date = {2019-01-01},

booktitle = {2019 IEEE International Conference on Big Data (Big Data)},

pages = {2782-2789},

keywords = {Big Data},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/2740908.2742825,

title = {Kvasir: Seamless Integration of Latent Semantic Analysis-Based Content Provision into Web Browsing},

author = {Liang Wang and Sotiris Tasoulis and Teemu Roos and Jussi Kangasharju},

url = {https://doi.org/10.1145/2740908.2742825},

doi = {10.1145/2740908.2742825},

isbn = {9781450334730},

year  = {2015},

date = {2015-01-01},

booktitle = {Proceedings of the 24th International Conference on World Wide Web},

pages = {251–254},

publisher = {Association for Computing Machinery},

address = {Florence, Italy},

series = {WWW '15 Companion},

abstract = {The Internet is overloading its users with excessive information flows, so that effective content-based filtering becomes crucial in improving user experience and work efficiency. We build Kvasir, a semantic recommendation system, atop latent semantic analysis and other state-of-art technologies to seamlessly integrate an automated and proactive content provision service into web browsing. We utilize the power of Apache Spark to scale up Kvasir to a practical Internet service. Herein we present the architecture of Kvasir, along with our solutions to the technical challenges in the actual system implementation.},

keywords = {Big Data},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{9006016,

title = {Single-cell regulatory network inference and clustering from high-dimensional sequencing data},

author = {A G Vrahatis and G N Dimitrakopoulos and S K Tasoulis and S V Georgakopoulos and V P Plagianakos},

doi = {10.1109/BigData47090.2019.9006016},

year  = {2019},

date = {2019-01-01},

booktitle = {2019 IEEE International Conference on Big Data (Big Data)},

pages = {2782-2789},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/2740908.2742825,

title = {Kvasir: Seamless Integration of Latent Semantic Analysis-Based Content Provision into Web Browsing},

author = {Liang Wang and Sotiris Tasoulis and Teemu Roos and Jussi Kangasharju},

url = {https://doi.org/10.1145/2740908.2742825},

doi = {10.1145/2740908.2742825},

isbn = {9781450334730},

year  = {2015},

date = {2015-01-01},

booktitle = {Proceedings of the 24th International Conference on World Wide Web},

pages = {251–254},

publisher = {Association for Computing Machinery},

address = {Florence, Italy},

series = {WWW '15 Companion},

abstract = {The Internet is overloading its users with excessive information flows, so that effective content-based filtering becomes crucial in improving user experience and work efficiency. We build Kvasir, a semantic recommendation system, atop latent semantic analysis and other state-of-art technologies to seamlessly integrate an automated and proactive content provision service into web browsing. We utilize the power of Apache Spark to scale up Kvasir to a practical Internet service. Herein we present the architecture of Kvasir, along with our solutions to the technical challenges in the actual system implementation.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}