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Opioid (e.g., heroin and morphine) addiction has become one of the largest and deadliest epidemics in the United States. To combat such deadly epidemic, there is an urgent need for novel tools and methodologies to gain new insights into the behavioral processes of opioid addiction and treatment. In this paper, we design and develop an intelligent system named iOPU to automate the detection of opioid...
This paper proposes the GraphF abstraction which exploits Adaptive Radix Tree for efficient graph indexing with lower storage cost. Leveraging the GraphF abstraction, we implement a separate graph computation engine on Spark. Experiments showed that on average GraphF outperforms GraphX and PowerGraph by up to 8.1X and 3.6X separately in execution time both for real world and for synthetic graphs....
Data clustering is usually time-consuming since it by default needs to iteratively aggregate and process large volume of data. Approximate aggregation based on sample provides fast and quality ensured results. In this paper, we propose to leverage approximation techniques to data clustering to obtain the trade-off between clustering efficiency and result quality, along with online accuracy estimation...
DHTs are scalable, self-organizing, and adaptive to underlying topology changes, thus being a promising infrastructure for realizing autonomic communications in distributed systems. To provide the above advantages, however, DHTs sacrifice flexibility, that is, all messages are routed by using a common algorithm in a DHT on the assumption that all participant nodes are homogeneous. In practice, nodes...
DHTs are scalable, self-organizing, and adaptive to underlying topology changes, thus being a promising infrastructure for realizing efficient Web service discovery. Range queries play an important role in service discovery, and in recent years a number of DHT-based range query schemes have been proposed. However, most of them suffer from high query delay and high processing cost. This paper presents...
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