At+t Corp.

At&t Intellectual Property I, L.p.

Rutgers, the State University of New Jersey

Alcatel-lucent USA Inc.

At&t Wireless Services, Inc.

Cingular Wireless Ii, LLC

Shanmugavelayutham Muthukrishnan

Martin Joel Strauss

Graham Cormode

Nikolaos Koudas

Piotr Indyk

Anna C Gilbert

Ioannis Kotidis

Divesh Srivastava

Philip Korn

Oliver Spatscheck

Theodore Donald Johnson

Hosagrahar Visvesvaraya Jagadish

Vladislav Shkapenyuk

Simon D Byers

Sudipto Guha

Nathan Hamilton

James F Parkyn

Minos N Garofalakis

Rajeev Ramnarain Rastogi

Sudipto Cuha

Zhiyuan Chen

Phillip Korn

Martin J Struass

A method maintains a multidimensional histogram for a data array having a data array size, the method having a processing time substantially less than proportional to the data array size. The method involves receiving a data update that indicates a change to data in the data array; with the data update, updating an intermediate data structure having a size substantially smaller than the data array size, so that the updated intermediate data structure remains an at-least-approximate representation of the data in the data array as changed by the data update; collecting a number of substantially-largest-coefficient linear combinations of then-current data, the number being small compared with the data array size; and forming the multidimensional histogram as a histogram to an intermediate data array re synthesized from the collected linear combinations.

Space- and time-efficient management and summarization of data using intermediate summary structure and hierarchical multidimensional histogram

A method maintains a multidimensional histogram for a data array having a data array size, the method having a processing time substantially less than proportional to the data array size. The method involves () receiving a data update that indicates a change to data in the data array; () with the data update, updating an intermediate data structure having a size substantially smaller than the data array size, so that the updated intermediate data structure remains an at-least-approximate representation of the data in the data array as changed by the data update; () collecting a number of substantially-largest-coefficient linear combinations of then-current data, the number being small compared with the data array size; and () forming the multidimensional histogram as a histogram to an intermediate data array re synthesized from the collected linear combinations. Another method prepares a multidimensional histogram for a data array, the data array characterized by a data array size and including data, the method having an execution time proportional to the data array size and using an amount of storage space substantially smaller than the data array size. The method involves () receiving data from the data array; () transforming the data into linear combinations of data items, or of approximations of the linear combinations of data items; () collecting a moderate number of substantially-largest-coefficient linear combinations of the data; and () forming the multidimensional histogram as a histogram to the collected linear combinations.

A system, method and computer-readable medium provide for assigning sampling methods to each input stream for arbitrary query sets in a data stream management system. The method embodiment comprises splitting all query nodes in a query directed acyclic graph (DAG) having multiple parent nodes into sets of independent nodes having a single parent, computing a grouping set for every node in each set of independent nodes, reconciling each parent node with each child node in each set of independent node, reconciling between multiple child nodes that share a parent node and generating a final grouping set for at least one node describing how to sample an input stream for that node.

Query-aware sampling of data streams

Described is a system and method for receiving a data stream of multi-dimensional items, collecting a sample of the data stream having a predetermined number of items and dividing the sample into a plurality of subsamples, each subsample corresponding to a single dimension of each of the predetermined number of items. A query is then executed on a particular item in at least two of the subsamples to generate data for the corresponding subsample. This data is combined into a single value.

System and method for generating statistical descriptors for a data stream

A system and method are provided for summarizing dynamic data from distributed sources through the use of histograms. In particular, the method comprises receiving a first data signal at a first location, determining a first array sketch of the first data signal, and constructing a first output histogram from the first array sketch and a first robust histogram via a first hybrid histogram. Array sketches of a number of data signals may be calculated, and added to yield a single vector sum. The histogram is constructed from the vector sum. In that way, the vector sum may be analyzed without revealing the individual data signals that form the basis of the sum.

Method and apparatus for using histograms to produce data summaries

A system, method and computer-readable medium are disclosed for identifying representative data using sketches. The method embodiment comprises generating a plurality of vectors from a data set, modifying each of the vectors of the plurality of vectors and selecting one of the plurality of generated vectors according to a comparison of a summed distance between a modified vector associated with the selected generated vector and remaining modified vectors. Modifying the generated vectors may involve reduced each generated vector to a lower dimensional vector. The summed distance then represents a summed distance between the lower dimensional vector and remaining lower dimensional vectors.

System, method and computer-readable medium for providing pattern matching

Described is a system and method for receiving a signal for transmission and encoding the signal into a plurality of linear projections representing the signal. The encoding includes defining a transform matrix. The transform matrix being defined by processing the signal using a macroseparation matrix, processing the signal using a microseparation matrix and processing the signal using an estimation vector.

System and method for encoding a signal using compressed sensor measurements

The invention comprises a method and apparatus for determining a rank of a query value. Specifically, the method comprises receiving a rank query request, determining, for each of the at least one remote monitor, a predicted lower-bound rank value and upper-bound rank value, wherein the predicted lower-bound rank value and upper-bound rank value are determined according to at least one respective prediction model used by each of the at least one remote monitor to compute the at least one local quantile summary, computing a predicted average rank value for each of the at least one remote monitor using the at least one predicted lower-bound rank value and the at least one predicted upper-bound rank value associated with the respective at least one remote monitor, and computing the rank of the query value using the at least one predicted average rank value associated with the respective at least one remote monitor.

Method and apparatus for globally approximating quantiles in a distributed monitoring environment

A system and method are provided for summarizing dynamic data from distributed sources through the use of wavelets. The method comprises receiving a first data signal at a first location, where the first data signal is dynamic, determining a first array sketch of the first data signal and constructing a first wavelet representation by manipulating the first array sketch with a B-term wavelet expansion to produce a first representation. The method further comprises receiving a second data signal at a second location, where the second data signal is dynamic and where the second location is distinct from the first location, determining a second array sketch of the second data signal, and constructing a second wavelet representation by manipulating the second array sketch with a B-term wavelet expansion to produce a second representation. In one embodiment, the method further comprises obtaining first and second array sketches from first and second locations respectively, and constructing a wavelet representation of a linear combination of the first and second array sketches. In one embodiment, the expansion is done using a Haar wavelet.

Method and apparatus for using wavelets to produce data summaries

A method including receiving a plurality of elements of a data stream, storing a multi-dimensional data structure in a memory, said multi-dimensional data structure storing the plurality of elements as a hierarchy of nodes, each node having a frequency count corresponding to the number of elements stored therein, comparing the frequency count of each node to a threshold value based on a total number of the elements stored in the nodes and identifying each node for which the frequency count is at least as great as the threshold value as a hierarchical heavy hitter (HHH) node and propagating the frequency count of each non-HHH nodes to its corresponding parent nodes.

System and method for identifying hierarchical heavy hitters in a multidimensional environment

Described is a system and method for receiving a plurality of linear projections representing a signal and decoding the plurality of linear projections to produce a reconstruction of the signal. The decoding includes defining a transform matrix and reconstructing coefficients and transforming a dictionary into a modified dictionary for a vector of real numbers. The transforming the transform matrix is performed through a macroseparation matrix, a microseparation matrix, and an estimation vector.

System and method for decoding a signal using compressed sensor measurements

Certain exemplary embodiments provide a method comprising a plurality of activities, comprising: automatically: receiving a signal; and via a plurality of iterations, determining, for the received signal, a provable near-optimal B-term representation formed from a linear combination of a plurality of elements selected from a predetermined redundant &#x3bc;-coherent dictionary, said determined near-optimal B-term representation corresponding to an error that is a minimum of a set of errors corresponding to all possible B-term representations associated with the predetermined redundant &#x3bc;-coherent dictionary.

Growing community of inventors

Washington, DC, United States of America

Shanmugavelayutham Muthukrishnan