@article{Endler2019_4,
	title = {Editorial of the 2019 Workshop on Very Large Internet of Things (VLIoT)},
	author = {Markus Endler and Sven Groppe},
	journal = {{OJIOT}},
	volume = {5},
	number = {1},
	pages = {1--5},
	year = {2019},
	url = {http://nbn-resolving.de/urn:nbn:de:101:1-2019092919330960165487}
}

@Article{OJWT_2018v5i1n06_Babayigit,
        author    = {Sinan Babayigit and Sven Groppe},
        title     = {Webpage Ranking Analysis of Various Search Engines with Special Focus on Country-Specific Search},
        journal   = {Open Journal of Web Technologies (OJWT)},
        year      = {2018},
        volume    = {5},
        number    = {1},
        pages     = {44--64},
        issn      = {2199-188X},
        note      = {Special Issue: Proceedings of the International Workshop on Web Data Processing \& Reasoning (WDPAR 2018) in conjunction with the 41st German Conference on Artificial Intelligence (KI) in Berlin, Germany.},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2018093019303617104000},
        urn       = {urn:nbn:de:101:1-2018093019303617104000},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {In order to attract many visitors to their own website, it is extremely important for website developers that their webpage is one of the best ranked webpages of search engines. As a rule, search engine operators do not disclose their exact ranking algorithm, so that website developers usually have only vague ideas about which measures have particularly positive influences on the webpage ranking. Conversely, we ask the question: "What are the properties of the best ranked webpages?" For this purpose, we perform a detailed analysis, in which we compare the properties of the best ranked webpages with the worse ranked webpages. Furthermore, we compare countryspecific differences.}
}

@inproceedings{Blochwitz2018_15,
	title = {Hardware-Accelerated Index Construction for Semantic Web},
	author = {Christopher Blochwitz and Julian Wolff and Mladen Berekovic and Dennis Heinrich and Sven Groppe and Jan Moritz Joseph and Thilo Pionteck},
	booktitle = {International Conference on Field-Programmable Technology ({FPT}),
               Naha, Okinawa, Japan},
	year = {2018},
	url = {https://doi.org/10.1109/FPT.2018.00053}
}

@ARTICLE{Werner2015,
  author = {Stefan Werner and Dennis Heinrich and Marc Stelzner and Volker Linnemann
	and Thilo Pionteck and Sven Groppe},
  title = {Accelerated join evaluation in Semantic Web databases by using FPGAs},
  journal = {Concurrency and Computation: Practice and Experience},
   year = {2015},
  volume =    {28},
  number =    {7},
  pages =     {2031--2051},
  month = {May 18},
  doi = {http://dx.doi.org/10.1002/cpe.3502}
}

@Article{OJDB_2016v3i1n02_Werner,
        author    = {Stefan Werner and Dennis Heinrich and Sven Groppe and Christopher Blochwitz and Thilo Pionteck},
        title     = {Runtime Adaptive Hybrid Query Engine based on FPGAs},
        journal   = {Open Journal of Databases (OJDB)},
        issn      = {2199-3459},
        year      = {2016},
        volume    = {3},
        number    = {1},
        pages     = {21--41},
        url       = {http://www.ronpub.com/publications/OJDB_2016v3i1n02_Werner.pdf},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {This paper presents the fully integrated hardware-accelerated query engine for large-scale datasets in the context of Semantic Web databases. As queries are typically unknown at design time, a static approach is not feasible and not flexible to cover a wide range of queries at system runtime. Therefore, we introduce a runtime reconfigurable accelerator based on a Field Programmable Gate Array (FPGA), which transparently incorporates with the freely available Semantic Web database LUPOSDATE. At system runtime, the proposed approach dynamically generates an optimized hardware accelerator in terms of an FPGA configuration for each individual query and transparently retrieves the query result to be displayed to the user. During hardware-accelerated execution the host supplies triple data to the FPGA and retrieves the results from the FPGA via PCIe interface. The benefits and limitations are evaluated on large-scale synthetic datasets with up to 260 million triples as well as the widely known Billion Triples Challenge.}
    }