# Graham Cormode, Summit US

## Graham Cormode, Summit, NJ US

Patent application number | Description | Published |
---|---|---|

20090132561 | LINK-BASED CLASSIFICATION OF GRAPH NODES - A method of labeling unlabeled nodes in a graph that represents objects that have an explicit structure between them. A computing device can use a labeling engine to labeled nodes in a graph that are labeled and can identify an unlabeled node in the graph that is structurally associated with the labeled nodes. The labeling engine can label the unlabeled node with the label of the labeled node based on the structural association between the unlabeled node and the labeled node. | 05-21-2009 |

20090153379 | System and Method for Encoding a Signal Using Compressed Sensor Measurements - 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. | 06-18-2009 |

20090172058 | Computing time-decayed aggregates under smooth decay functions - Aggregates are calculated from a data stream in which data is sent in a sequence of tuples, in which each tuple comprises an item identifier and a timestamp indicating when the tuple was transmitted. The tuples may arrive at a data receiver out-of-order, that is, the sequence in which the tuples arrive are not necessarily in the same sequence as their corresponding timestamps. In calculating aggregates, more recent data may be given more weight by a decay function which is a function of the timestamp associated with the tuple and the current time. The statistical characteristics of the tuples are summarized by a set of linear data summaries. The set of linear data summaries are generated such that only a single linear data summary falls between a set of boundaries calculated from the decay function and a set of timestamps. Aggregates are calculated from the set of linear data summaries | 07-02-2009 |

20090172059 | Computing time-decayed aggregates in data streams - Aggregates are calculated from a data stream in which data is sent in a sequence of tuples, in which each tuple comprises an item identifier and a timestamp indicating when the tuple was transmitted. The tuples may arrive out-of-order, that is, the sequence in which the tuples arrive are not necessarily in the sequence of their corresponding timestamps. In calculating aggregates, more recent data may be given more weight by multiplying each tuple by a decay function which is a function of the timestamp associated with the tuple and the current time. The tuples are recorded in a quantile-digest data structure. Aggregates are calculated from the data stored in the quantile-digest data structure. | 07-02-2009 |

20090292726 | System and Method for Identifying Hierarchical Heavy Hitters in Multi-Dimensional Data - 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. | 11-26-2009 |

20100153064 | Methods and Apparatus to Determine Statistical Dominance Point Descriptors for Multidimensional Data - Methods and apparatus to determine statistical dominance point descriptors for multidimensional data are disclosed. An example method disclosed herein comprises determining a first joint dominance value for a first data point in a multidimensional data set, data points in the multidimensional data set comprising multidimensional values, each dimension corresponding to a different measurement of a physical event, the first joint dominance value corresponding to a number of data points in the multidimensional data set dominated by the first data point in every dimension, determining a first skewness value for the first data point, the first skewness value corresponding to a size of a first dimension of the first data point relative to a combined size of all dimensions of the first data point, and combining the first joint dominance and first skewness values to determine a first statistical dominance point descriptor associated with the first data point. | 06-17-2010 |

20100153379 | System and Method for Generating Statistical Descriptors for a Data Stream - 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. | 06-17-2010 |

20100312872 | METHOD AND APPARATUS FOR MONITORING FUNCTIONS OF DISTRIBUTED DATA - This invention discloses continuous functional monitoring of distributed network activity using algorithms based on frequency moment calculations given by | 12-09-2010 |

20120066383 | METHOD AND APPARATUS FOR MONITORING FUNCTIONS OF DISTRIBUTED DATA - A method and system of monitoring computer network activity including determining a first phase frequency estimate, associated with a first frequency vector, determined in response to receiving first bits from a first plurality of remote computer network devices. The first bits received from the first plurality of remote devices in response to satisfying a first activity threshold. Also, determining a second phase frequency estimate associated with a second frequency vector and determined in response to receiving second bits from a second plurality of remote devices. The second bits received from the second plurality of remote devices in response to a second activity threshold being satisfied. The second phase frequency estimate determined in response to the first phase frequency estimate exceeding a global threshold. Further, providing a frequency moment F | 03-15-2012 |

20130054798 | METHOD AND APPARATUS FOR MONITORING FUNCTIONS OF DISTRIBUTED DATA - A method and system of monitoring computer network activity including determining a first phase frequency estimate, associated with a first frequency vector, determined in response to receiving first bits from a first plurality of remote computer network devices. The first bits received from the first plurality of remote devices in response to satisfying a first activity threshold. Also, determining a second phase frequency estimate associated with a second frequency vector and determined in response to receiving second bits from a second plurality of remote devices. The second bits received from the second plurality of remote devices in response to a second activity threshold being satisfied. The second phase frequency estimate determined in response to the first phase frequency estimate exceeding a global threshold. Further, providing a frequency moment F | 02-28-2013 |

20130173525 | METHODS AND APPARATUS TO CONSTRUCT HISTOGRAMS AND WAVELET SYNOPSES FOR PROBABILISTIC DATA - 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. | 07-04-2013 |