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Multi-dimensional online tracking

机译:多维在线跟踪

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We propose and study a new class of online problems, which we call online tracking. Suppose an observer, say Alice, observes a multi-valued function f: Z+ → Zd overtime in an online fashion, i.e., she only sees f(t) for t ≤ tnow where tnow is the current time. She would like to keep a tracker, say Bob, informed of the current value of f at all times. Under this setting, Alice could send new values of f to Bob from time to time, so that the current value of f is always within a distance of Δ to the last value received by Bob. We give competitive online algorithms whose communication costs are compared with the optimal offline algorithm that knows the entire f in advance. We also consider variations of the problem where Alice is allowed to send 'predictions' to Bob, to further reduce communication for well-behaved functions. These online trackingproblems have a variety of application ranging from sensor monitoring, location-based services, to publish/subscribe systems.
机译:我们提出并研究了一类新的在线问题,我们称之为在线跟踪。假设观察者(例如爱丽丝(Alice))以在线方式观察了一个多值函数f:Z +→Zd超时,即,她仅看到t(tnow)的f(t),其中tnow是当前时间。鲍勃说,她想随时随地了解f的当前值。在此设置下,爱丽丝可以不时将新的f值发送给Bob,这样f的当前值始终与Bob收到的最后一个值之间的距离为Δ。我们提供了具有竞争力的在线算法,该算法的通信成本与预先知道整个f的最优离线算法相比。我们还考虑了允许Alice向Bob发送“预测”的问题的变体,以进一步减少行为良好的功能的交流。这些在线跟踪问题的应用范围很广,从传感器监视,基于位置的服务到发布/订阅系统。

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