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—On-demand broadcast is a promising data dissemination approach in mobile computing environments thanks to its adaptability and scalability for large-scale and dynamic workload. An important class of emerging data broadcast applications needs to monitor multiple time-varying data items continuously to be kept aware of the up-to-date information. This paper(More)
Data broadcast is a widely accepted data dissemination method for mobile computing systems. When data broadcast is used to deliver frequently updated data to mobile read-only transactions, we call it updates dissemination. Existed updates dissemination protocols are unsuitable for mobile real-time read-only transaction processing since they neglect the time(More)
In distributed computing systems, processes in different hosts take checkpoints to survive failures. For mobile computing systems, due to certain new characteristics such as mobility, low bandwidth, disconnection, low power consumption and limited memory, conventional distributed checkpointing schemes need to be reconsidered. In this paper, a novel(More)
Data management issues in mobile computing environments have got lots of concerns of relevant researchers. Among these research topics, data broadcast has been extensively investigated due to its advantages such as scalability and bandwidth effectiveness. While plenty of works have been done on this subject, it is still less touched when data broadcast is(More)
BACKGROUND The availability of reference intervals is essential for physicians to interpret laboratory results. Most of our laboratory reference intervals are derived from data on the foreign population. We have studied the reference intervals of 24 common laboratory biochemical tests in an apparently healthy adult Han population of Northern China. (More)
Locality Sensitive Hashing (LSH) is widely recognized as one of the most promising approaches to similarity search in high-dimensional spaces. Based on LSH, a considerable number of nearest neighbor search algorithms have been proposed in the past, with some of them having been used in many real-life applications. Apart from their demonstrated superior(More)
Real-time stream processing applications must be prepared to operate under overloaded conditions. Existing load shedding techniques are not suitable for processing real-time data streams because their tuple dropping policies may violate application deadlines in an uncontrolled way. We'd argue that a more precise load shedding model, e.g., the (m, k)(More)