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This paper describes initiatives at Marist College to develop a Game Concentration in the undergraduate Computer Science curriculum. These initiatives contemplate recommendations for existing courses as well as adoption of new courses. We also consider activities of the Association of Computing Machinery (ACM) in this area and opportunities for students(More)
Many of today's applications can benefit from the discovery of the most central entities in real-world networks. This paper presents a new technique that efficiently finds the k most central entities in terms of closeness centrality. Instead of computing the centrality of each entity independently, our technique shares intermediate results between(More)
From sensor networks to transportation infrastructure to social networks, we are awash in data. Many of these real-world networks tend to be large (“big data”) and dynamic, evolving over time. Their evolution can be modeled as a series of graphs. Traditional systems that store and analyze one graph at a time cannot effectively handle the complexity and(More)
Most real-world networks evolve over time. This evolution can be modeled as a series of graphs that represent a network at different points in time. Our G* system enables efficient storage and querying of these graph snapshots by taking advantage of the commonalities among them. We are extending G* for highly scalable and robust operation. This paper shows(More)
Evolving networks can be modeled as series of graphs that represent those networks at different points in time. Our G* system enables efficient storage and querying of these graph snapshots by taking advantage of their commonalities. In extending G* for scalable and robust operation, we found the classic challenges of data distribution and replication to be(More)
Most real-world networks (including financial, health, transportation, social, citation, and sensor networks) evolve over time. Their evolution can be modeled as a series of graph snapshots that represent those networks at different points in time. Our distributed dynamic graph database, G*, provided efficient cluster-based storage and querying of graph(More)
The world is full of evolving networks, many of which can be represented by a series of large graphs. Neither the current graph processing systems nor database systems can efficiently store and query these graphs due to their lack of support for managing multiple graphs and lack of essential graph querying capabilities. We propose to demonstrate our system,(More)