At&t Intellectual Property I, LP

At+t Corp.

Microsoft Technology Licensing, LLC

Edith Cohen

Haim Kaplan

Nicholas G Duffield

Carsten Lund

Mikkel Thorup

Balachander Krishnamurthy

Jennifer Lynn Rexford

Daniel R Delling

David Lee Applegate

Renato F Werneck

Oliver Spatscheck

Alexandre Gerber

Graham Cormode

Moises Goldszmidt

Andrew V Goldberg

Frederick True

David Dolan Lewis

Adam Hersh

Thomas Pajor

Nadav Grossaug

Uri Zwick

Fabian Fuchs

One or more all-distances sketches are generated for nodes in a graph. An all-distances sketch for a node includes a subset of the nodes of the graph, and a shortest distance between the node and each of the nodes in the subset of nodes. The generated all-distances sketches are used to estimate the closeness similarity of nodes. The estimated closeness similarity can be used for targeted advertising or for content item recommendation, for example.

Estimating similarity of nodes using all-distances sketches

Methods and systems for estimate derivation are described. In one embodiment, a query may be received with a predicate for sets over a collection of items. Associated samples associated with the query may be accessed. Items of an associated sample may be accessed from the collection of items. A determination of whether the predicate is an attribute-based selection from a union of at least some sets may be made. Available items of the particular associated sample may be selected from the items. Identified items may be identified among the available items in the associated sample that satisfy the predicate. An adjusted weight may be assigned to an item based on a weight of the item and a distribution of the associated samples. An estimate may be generated based on the adjusted weight of the identified items of the associated samples that satisfy the predicate. Additional methods and systems are disclosed.

Methods and systems to estimate query responses based on data set sketches

Historic inverse probability estimators are created for a graph based on all-distance sketches associated with each node of the graph. The historic inverse probability estimators include adjusted weights for each node. Graph operations such as closeness centrality are estimated using the historic inverse probability estimators. The historic inverse probability estimators can also be applied to element streams and can be used to estimate a number of unique elements in received in the element stream.

Performing graph operations using historic inverse probability estimators

A graph that includes multiple nodes and edges is received. Multiple instances of the graph are generated by randomly instantiating the edges according to either a binary independent cascade model or a randomized edge length independent cascade model. Where the binary independent cascade model is used, combined reachability sketches are generated for each node across all instances of the graph. Where the randomized edge length independent cascade model is used, combined all-distances sketches are generated for each node across all instances of the graph. Depending on which model is used, the combined reachability or all-distances sketches are used to estimate the influence of nodes in the graph or to estimate a subset of nodes from a graph of a specified size with a maximum influence using a greedy algorithm.

Estimating influence using sketches

Methods, apparatus, and articles of manufacture are disclosed to sample signed weighted updates. Example methods disclosed herein include identifying a first key associated with a data update obtained by sampling a stream of data received from a plurality of nodes in a network, the first key being representative of a first node in the plurality of nodes, adjusting a first value associated with the first key based on a weight associated with the data update, the weight being positive when the data update corresponds to opening of a new data connection with the first node, the weight being negative when the data update corresponds to closing of an existing data connection with the first node, and estimating a status of the first node based on the first value.

Methods and apparatus to sample data connections

A method and apparatus for processing top-k queries are disclosed. For example, the method receives a top-k query with a value for a number of samples and a value of a confidence parameter. The method samples in accordance to the number of samples, and determines a top-k weight of a sample top-k set. The method bounds the top-k weight in an interval having an upper bound and a lower bound such that the top-k weight is in the interval with a probability equal to one minus the value of the confidence parameter, and provides a response to the top-k query in accordance with the upper and lower bounds.

Method and apparatus for processing of top-K queries from samples

In accordance with aspects of the invention, 'low-level' caches can utilize source selection while non-authoritative sources can take advantage of rejuvenation to alleviate what the inventor refers to as 'age penalty' and thereby reduce validation traffic.

Cache validation using smart source selection in a data network

A method for producing a summary A of data points in an unaggregated data stream wherein the data points are in the form of weighted keys (a, w) where a is a key and w is a weight, and the summary is a sample of k keys a with adjusted weights w. A first reservoir L includes keys having adjusted weights which are additions of weights of individual data points of included keys and a second reservoir T includes keys having adjusted weights which are each equal to a threshold value &#x3c4; whose value is adjusted based upon tests of new data points arriving in the data stream. The summary combines the keys and adjusted weights of the first reservoir L with the keys and adjusted weights of the second reservoir T to form the sample representing the data stream upon which further analysis may be performed. The method proceeds by first merging new data points in the stream into the reservoir L until the reservoir contains k different keys and thereafter applying a series of tests to new arriving data points to determine what keys and weights are to be added to or removed the reservoirs L and T to provide a summary with a variance that approaches the minimum possible for aggregated data sets. The method is composable, can be applied to high speed data streams such as those found on the Internet, and can be implemented efficiently.

Method for summarizing data in unaggregated data streams

Certain exemplary embodiments can provide a method, which can comprise automatically storing and computing a sketch of a dataset that supports an automatically determined estimator of properties of a dataset. The dataset can be related to any population. For example, the dataset can comprise data flows through a network node (e.g., a router), sales data, and/or marketing data, etc. The estimator can be based upon a sketch of the dataset.

Systems, devices, and/or methods for managing data

Certain exemplary embodiments can provide a method, which can comprise automatically storing a sketch of a dataset that supports automatic determination of an estimator of properties of a dataset. The automatic determination can be based upon computed adjusted weights to the items included in a sketch of the dataset. The adjusted weights can be used to compute estimates on the weight of any subpopulation of the items in the dataset that is specified using a selection predicate. We propose the rank conditioning, the subset conditioning, and/or a Markov-chain based method to compute these adjusted weights. We also provide a method that provides upper and lower confidence bounds on the size of a subpopulation.

Systems, devices, and/or methods for determining dataset estimators

A request is received at a resource server for a first resource, the request accompanied by a proxy filter. A second resource is identified based on the proxy filter and based on a relationship between the first resource and the second resource. The first resource and information regarding the second resource is provided to a network interface for communication to a proxy server.

Method and apparatus for improving end to end performance of a data network

A method and apparatus for provide highly efficient traffic routing for a wide range of possible traffic matrices (TM) in an intra-domain network. That routing optimally balances the traffic loads over a range of traffic matrices so as to minimize the deviation for any particular traffic matrix from the optimal routing. Such a routing provides a guaranteed performance ratio against the best possible network routing. The invention utilizes a method of optimally configuring a traffic network based on solving a linear program to obtain the optimal routing, and then configuring the routing on the network accordingly.

Method and apparatus for efficient routing of variable traffic

The present invention relates to a method of obtaining a generic sample of an input stream. The method is designated as VAROPT. The method comprises receiving an input stream of items arriving one at a time, and maintaining a sample S of items i. The sample S has a capacity for at most k items i. The sample S is filled with k items i. An nitem i is received. It is determined whether the nitem i should be included in sample S. If the nitem i is included in sample S, then a previously included item i is dropped from sample S. The determination is made based on weights of items without distinguishing between previously included items i and the nitem i. The determination is implemented thereby updating weights of items i in sample S. The method is repeated until no more items are received.

Variance-optimal sampling-based estimation of subset sums

Methods and apparatus to bound network traffic estimation error for multistage measurement sampling and aggregation are disclosed. An example method disclosed herein comprises determining a hierarchical sampling topology representative of multiple data sampling and aggregation stages, the hierarchical sampling topology comprising a plurality of nodes connected by a plurality of edges, each node corresponding to at least one of a data source and a data aggregation operation, and each edge corresponding to a data sampling operation characterized by a generalized sampling threshold, selecting a first generalized sampling threshold from a set of generalized sampling thresholds associated with a respective set of edges originating at a respective set of descendent nodes of a target node undergoing network traffic estimation, and transforming a measured sample of network traffic into a confidence interval for a network traffic estimate associated with the target node using the first generalized sampling threshold and an error parameter.

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Berkeley Heights, NJ, United States of America

Edith Cohen