Lucent Technologies Inc.

Alcatel-lucent USA Inc.

At&t Intellectual Property I, L.p.

Alcatel Lucent

Yahoo! Inc.

Rutgers, the State University of New Jersey

Minos N Garofalakis

Rajeev R Rastogi

Graham Cormode

Sumit Ganguly

Amit Kumar

Abraham Silberschatz

Banu Rahime Ozden

Kyuseok Shim

Yuri J Breitbart

Jayavel Shanmugasundaram

Erik Nathan Vee

Wenfei Fan

Srinivasan Seshadri

Yannis E Ioannidis

Krishan Kumar Sabnani

Yigal Bejerano

Shanmugavelayutham Muthukrishnan

Rajeev Ramnarain Rastogi

Clifford Eric Martin

Johannes Gehrke

Ming Xiong

Michael Abraham Benedikt

Yuan Zhuge

Jagadish Prasad Samantarai

Ashwin Kumar V Machanavajjhala

Andrew Iain Shaw McGregor

Abhinandan Sujit Das

Rajagopal Baskaran

Chyr-Chong (Joseph) Ting

Chee-Yong Chan

Alin Dobra

Shivnath Babu

Antonios Deligiannakis

Aristides Gionis

Kaushik Chakrabarti

A grammar for mapping a first grouping of XML data into a second grouping of XML data and a method for accomplishing same to incorporate the first grouping into the second grouping. The grammar includes a first rule for computing a first child element attribute and a second rule for computing a second parent element attribute. The first rule and second rule vary according to a production of an element type of the first grouping. The element types include PCDATA, disjunctive, conjunctive and Kleene star, each having a unique rule set for defining inherited and synthesized attributes of the parent and child elements. The method includes the step of executing a mapping of a first grouping having at least one parent element and a set of corresponding child elements into a second grouping in accordance with the grammar rules based on the production of the element type.

Grammar and method for integrating XML data from multiple sources

A disclosed example method involves generating a plurality of wavelet coefficient quantities. Each wavelet coefficient quantity is generated based on items represented by probabilistic data. Each wavelet coefficient quantity represents different ones of the items by multiplying corresponding wavelet vectors. The example method also involves determining an error measure associated with each of the plurality of wavelet coefficient quantities, and selecting at least one of the plurality of wavelet coefficient quantities based on its associated error measure. The method also involves displaying parameter information associated with the one of the plurality of wavelet coefficient quantities to represent the probabilistic data.

Methods and apparatus to construct histogram and wavelet synopses for probabilistic data

Example methods and apparatus to construct histogram and wavelet synopses for probabilistic data are disclosed. A disclosed example method involves receiving probabilistic data associated with probability measures and generating a plurality of histograms based on the probabilistic data. Each histogram is generated based on items represented by the probabilistic data. In addition, each histogram is generated using a different quantity of buckets containing different ones of the items. An error measure associated with each of the plurality of histograms is determined and one of the plurality of histograms is selected based on its associated error measure. The method also involves displaying parameter information associated with the one of the plurality of histograms to represent the data.

Methods and apparatus for representing probabilistic data using a probabilistic histogram are disclosed. An example method comprises partitioning a plurality of ordered data items into a plurality of buckets, each of the data items capable of having a data value from a plurality of possible data values with a probability characterized by a respective individual probability distribution function (PDF), each bucket associated with a respective subset of the ordered data items bounded by a respective beginning data item and a respective ending data item, and determining a first representative PDF for a first bucket associated with a first subset of the ordered data items by partitioning the plurality of possible data values into a first plurality of representative data ranges and respective representative probabilities based on an error between the first representative PDF and a first plurality of individual PDFs characterizing the first subset of the ordered data items.

Methods and apparatus for representing probabilistic data using a probabilistic histogram

Novel, computationally efficient schemes for deterministic wavelet thresholding with the objective of optimizing maximum-error metrics are provided. An optimal low polynomial-time algorithm for one-dimensional wavelet thresholding based on a new dynamic-programming (DP) formulation is provided that can be employed to minimize the maximum relative or absolute error in the data reconstruction. Directly extending a one-dimensional DP algorithm to multi-dimensional wavelets results in a super-exponential increase in time complexity with the data dimensionality. Thus, novel, polynomial-time approximation schemes (with tunable approximation guarantees for the target maximum-error metric) for deterministic wavelet thresholding in multiple dimensions are also provided.

Deterministic wavelet thresholding for general-error metrics

The subject matter disclosed herein relates to processing information regarding events. In one particular example, a stabbing query may be formulated in response to an event. One or more sets are associated with and/or mapped to nodes of a tree.

System and/or method for processing events

The first fast solution to the problem of tracking wavelet representations of one-dimensional and multi-dimensional data streams based on a stream synopsis, the Group-Count Sketch (GCS) is provided. By imposing a hierarchical structure of groups over the data and applying the GCS, our algorithms can quickly recover the most important wavelet coefficients with guaranteed accuracy. A tradeoff between query time and update time is established, by varying the hierarchical structure of groups, allowing the right balance to be found for specific data streams. Experimental analysis confirmed this tradeoff, and showed that all the methods significantly outperformed previously known methods in terms of both update time and query time, while maintaining a high level of accuracy.

Fast approximate wavelet tracking on streams

A method and system for answering set-expression cardinality queries while lowering data communication costs by utilizing a coordinator site to provide global knowledge of the distribution of certain frequently occurring stream elements to significantly reduce the transmission of element state information to the central site and, optionally, capturing the semantics of the input set expression in a Boolean logic formula and using models of the formula to determine whether an element state change at a remote site can affect the set expression result.

Distributed set-expression cardinality estimation

Subject matter disclosed herein relates to document classification and/or automated document classifier tuning. In an example embodiment, a document received from a user computing platform in an online database stored in a memory of a server computing platform may be classified based, at least in part, on a training set. Also for an example embodiment, the training set may be modified based, at least in part, on statistics gathered from user browsing behavior.

Automated document classifier tuning including training set adaptive to user browsing behavior

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 method of distributed approximate query tracking relies on tracking general-purpose randomized sketch summaries of local streams at remote sites along with concise prediction models of local site behavior in order to produce highly communication-efficient and space/time-efficient solutions. A powerful approximate query tracking framework readily incorporates several complex analysis queries, including distributed join and multi-join aggregates and approximate wavelet representations, thus giving the first known low-overhead tracking solution for such queries in the distributed-streams model.

Method for distributed tracking of approximate join size and related summaries

The invention provides methods and systems for summarizing multiple continuous update streams such that an approximate answer to a query over one or more of the continuous update streams (such as a Query requiring a join operation followed by a duplicate elimination step) may be rapidly provided. The systems and methods use multiple (parallel) Join Distinct (JD) Sketch data structures corresponding to hash buckets of at least one initial attribute.

Method for distinct count estimation over joins of continuous update stream

A distinct-count estimate is obtained in a guaranteed small footprint using a two level hash, distinct count sketch. A first hash fills the first-level hash buckets with an exponentially decreasing number of data-elements. These are then uniformly hashed to an array of second-level-hash tables, and have an associated total-element counter and bit-location counters. These counters are used to identify singletons and so provide a distinct-sample and a distinct-count. An estimate of the total distinct-count is obtained by dividing by the distinct-count by the probability of mapping a data-element to that bucket. An estimate of the total distinct-source frequencies of destination address can be found in a similar fashion. By further associating the distinct-count sketch with a list of singletons, a total singleton count and a heap containing the destination addresses ordered by their distinct-source frequencies, a tracking distinct-count sketch may be formed that has considerably improved query time.

Streaming algorithms for robust, real-time detection of DDoS attacks

A method of estimating set-expression cardinalities over data streams with guaranteed small maintenance time per data-element update. The method only examines each data element once and uses a limited amount of memory. The time-efficient stream synopsis extends 2-level hash-sketches by randomly, but uniformly, pre-hashing data-elements prior to logarithmically hashing them to a first-level hash-table. This generates a set of independent 2-level hash-sketches. The set-union cardinality can be estimated by determining the smallest hash-bucket index j at which only a predetermined fraction of the b hash-buckets has a non-empty union |A&#x222a;B|. Once a set-union cardinality is estimated, general set-expression cardinalities may be estimated by counting witness elements for the set-expression, i.e., those first-level hash-buckets that are both a singleton for the set-expression and a set-union singleton. The set-expression cardinality is the set-union cardinality times the number of witness elements divided by the number of hash-buckets.

Growing community of inventors

Morristown, NJ, United States of America

Minos N Garofalakis