As for any other kind of algorithm, we want to design streaming algorithms that are fast and that use as little memory as possible. To support the data curators, we initiate a study of pan-private algorithms; roughly speaking, these algorithms retain their privacy properties even if their internal state becomes visible to an adversary. Furthermore, the input is accessed in a sequential fashion, therefore, can be viewed as a stream of data elements. Along the way we obtain new and improved bounds for some applications. In the streaming computational model, algorithms are restricted to use much less space than they would need to store the input. Sketching, streaming, and sub-linear space algorithms Piotr Indyk MIT (currently at Rice U) Data Streams •A data stream is a sequence of data that is too large to be stored in available memory •Examples: –Network traffic –Sensor networks –Approximate query optimization and answering in large In most models, these algorithms have access to limited memory (generally logarithmic in the size of and/or the maximum value in the stream). Afterwards, we begin to look at graph streaming algorithms. Google, a packet stream going through a router, or a stream of downloads over time made from some content delivery service. We already saw the 0th moment, which counts the number of distinct elements. An example could be a company like Facebook The semi-streaming model allows for nding a maximal matching (a 2-approximation for the maximum matching) using O~(n) space in a greedy manner. Algorithms in this model must process the input stream in the order it ar-rives while using only a limited amount memory. streaming algorithms to evaluate distributed graph applica-tion performance in terms of partitioning cost amortization. Our principal focus is on streaming algorithms, where each … Data stream model Here algorithms compute results by treating a graph as a stream of edges[9, 15]. ..... 30 8.2 Short Data Stream History . mean algorithms that use o(m) bit space, and by stream of edges, we mean a sequence of edges that is an arbitrary permutation of E. In addition to the space usage, we restrict the algorithms to have only O(1) passes over the stream and o(m) per-edge processing time. Streaming Algorithms for Data in Motion M. Hoffmann1, S. Muthukrishnan2⋆, and Rajeev Raman1 1 Department of Computer Science, University of Leicester, Leicester LE1 7RH, UK. A data streaming algorithm Atakes Sas input and computes some function fof stream S. Moreover, algorithm Ahas access the input in a “streaming fashion”, i.e. Finally, we study the impact of network sampling algorithms on the parameter estimation and performance evaluation of relational classification algorithms. Introduction to Streaming Algorithms Je M. Phillips September 21, 2013. pass) streaming algorithms for projective clustering prob-lems have a linear dependence on the product of kand d, and therefore, they tend to require (nd) space for when k= ( n). Network Router Internet Router I data per day: at least I Terabyte I packet takes 8 nanoseconds to pass through router I few million packets per second What statistics can we keep on … These algo-rithms make a constant or logarithmic number of passes over the edge stream and are restricted to using limited memory. 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