@article {1655,
	title = {Gavel: A Fast and Easy-to-Use Plain Data Representation for Software-defined Networks},
	journal = {IEEE Transactions on Network and Service Management},
	volume = {16},
	year = {2019},
	month = {03/2019},
	pages = {606-617},
	abstract = {<p>In Software-defined Networking (SDN), high-level abstractions typically offer a useful means to avoids writing network applications and policies on lower levels. However, abstractions are typically developed for a specific use case, which in turn results in an abundance of existing abstractions for different networking tasks. As a consequence orchestrating these abstractions to implement a common network policy becomes an arduous task. To address this challenge, plain data representations of the network and its control infrastructure have been proposed recently, which offer programmable ad-hoc abstractions to administrators. However, these frameworks suffer from quite complex programming requirements and impractical performance in terms of latency, as they are based on relational database engines. In this work, we address these shortcomings by introducing Gavel, an SDN controller that at its heart facilitates a plain data representation based on a graph database. By exploiting the native graph support of the database engine, Gavel significantly eases application and policy writing. Additionally, we show by experimental evaluation of several typical applications on multiple different topologies that Gavel offers significant performance improvements over state-of-the-art solutions.</p>
},
	keywords = {Controller, Database, Engines, Graph, Libraries, Programming, Relational databases, Software-defined Networks., Task analysis, Writing},
	issn = {1932-4537},
	doi = {10.1109/TNSM.2019.2903440},
	author = {Osamah. L. Barakat and David Koll and Xiaoming Fu}
}
@article {1630,
	title = {Understanding the behavioral differences between american and german users: A data-driven study},
	journal = {Big Data Mining and Analytics},
	volume = {1},
	year = {2018},
	month = {December},
	pages = {284-296},
	abstract = {<p>Given that the USA and Germany are the most populous countries in North America and Western Europe, understanding the behavioral differences between American and German users of online social networks is essential. In this work, we conduct a data-driven study based on the Yelp Open Dataset. We demonstrate the behavioral characteristics of both American and German users from different aspects, i.e., social connectivity, review styles, and spatiotemporal patterns. In addition, we construct a classification model to accurately recognize American and German users according to the behavioral data. Our model achieves high classification performance with an F1-score of 0.891 and AUC of 0.949.</p>
},
	keywords = {American users, behavioral characteristics, behavioral data, behavioral difference, behavioral differences, Big Data, Business, Cultural differences, data-driven study, Europe, German users, learning (artificial intelligence), machine learning, online social networks, pattern classification, review styles, social aspects of automation, social connectivity, Social network services, social networking (online), spatiotemporal patterns, Spatiotemporal phenomena, Urban areas, Yelp},
	issn = {2096-0654},
	doi = {10.26599/BDMA.2018.9020024},
	author = {Chenxi Yang and Yang Chen and Qingyuan Gong and Xinlei He and Yu Xiao and Yuhuan Huang and Xiaoming Fu}
}
