流图计算与一个有用的顾问
Streaming Graph Computations with a Helpful Advisor
摘要：
出于将工作外包给商业云计算服务的趋势，我们考虑流模式的变化，其中流算法可以由可以向数据流提供注释的强大助手来辅助。我们扩展了以前的工作，这样的{ \em注释模型}考虑一些图流问题。没有注释，流算法的图形问题一般需要大量的内存，我们表明，对于许多标准的问题，包括所有的图形问题，可以表示与完全么模整数规划公式，只有一个恒定数量的散列值需要单通算法给定线性大小的注释。我们还获得了一个协议，实现\textit{optimal}之间的权衡注释长度和内存使用的矩阵向量乘法，这一结果有助于最近的研究趋势，在流模型的数值线性代数。
英文摘要：
Motivated by the trend to outsource work to commercial cloud computing services, we consider a variation of the streaming paradigm where a streaming algorithm can be assisted by a powerful helper that can provide annotations to the data stream. We extend previous work on such {\em annotation models} by considering a number of graph streaming problems. Without annotations, streaming algorithms for graph problems generally require significant memory; we show that for many standard problems, including all graph problems that can be expressed with totally unimodular integer programming formulations, only a constant number of hash values are needed for single-pass algorithms given linear-sized annotations. We also obtain a protocol achieving \textit{optimal} tradeoffs between annotation length and memory usage for matrix-vector multiplication; this result contributes to a trend of recent research on numerical linear algebra in streaming models.
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