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Graph analytics on social networks, Web data, and communication networks has been widely used in a plethora of applications. Many graph analytics algorithms are based on breadth-first search (BFS) graph traversal, which is not only time-consuming for large datasets but also involves much redundant computation when executed multiple times from different(More)
Understanding who is investing in real estate, and the patterns of their investments, is critical both for assessing the need for, and the effects of, policy interventions by governments, lenders, and non-profit community development organizations. If we knew more about patterns of ownership, for example, we could target buildings that seem to "produce"(More)
From tweets to urban data sets, there has been an explosion in the volume of textual data that is associated with both temporal and spatial components. Efficiently evaluating queries over these data is challenging. Previous approaches have focused on the spatial aspect. Some used separate indices for space and text, thus incurring the overhead of storing(More)
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