Garofalakis
Minos Garofalakis, Chania -Crete GR
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20100153328 | 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. | 06-17-2010 |
20100185515 | ALLOCATION OF INTERNET ADVERTISING INVENTORY - A method and system for allocating inventory in an Internet environment is provided. A method employed by the system may include generating an inventory pool that represents a number of impressions deliverable to all users, then determining, from multiple past orders for booking impressions, a hierarchy of parameters utilized to target users and a number of impressions deliverable to users characterized by the parameters. The inventory pool may then be partitioned into multiple inventory pools according to the hierarchy, where each inventory pool represents a number of impressions deliverable to users characterized by parameters associated with the inventory pool. The hierarchy of pools may then be stored to a database. | 07-22-2010 |
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 |
Minos Garofalakis, Chania GR
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20100082428 | DISPLAY ADVERTISING INVENTORY ESTIMATION - Example embodiments described herein may relate to estimating inventory for a display advertising system utilized, for example, in Web-based advertising. | 04-01-2010 |
20110145223 | METHODS AND APPARATUS FOR REPRESENTING PROBABILISTIC DATA USING A PROBABILISTIC HISTOGRAM - 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. | 06-16-2011 |
Minos Garofalakis, San Francisco, CA US
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20090112846 | SYSTEM AND/OR METHOD FOR PROCESSING EVENTS - 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. | 04-30-2009 |
20090125502 | SYSTEM AND METHODS FOR GENERATING DIVERSIFIED VERTICAL SEARCH LISTINGS - A method of generating a diversified vertical search results listing, including listing attribute values related to search criteria and their frequency of occurrence to create a plurality of listings; creating a plurality of interval bands based on the plurality of listings; generating a random diversity score for each listing over a substantially uniform distribution within each of the plurality of bands; and sorting a set of search results for diversified listing in response to a user searching for the search criteria according to the diversity score of each listing. | 05-14-2009 |
20090204703 | AUTOMATED DOCUMENT CLASSIFIER TUNING - Subject matter disclosed herein relates to document classification and/or automated document classifier tuning. | 08-13-2009 |
20100036865 | Method For Generating Score-Optimal R-Trees - A method of constructing a score-optimal R-tree to support top-k stabbing queries over a set of scored intervals generates a constraint graph from the set, and determines over each node in the constraint graph that has no other nodes pointing to it the node with the smallest left endpoint; for each of these nodes, the associated interval is added to the tree and the node is removed from the constraint graph. | 02-11-2010 |
Minos N. Garofalakis, Morristown, NJ US
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20100115350 | DETERMINISTIC WAVELET THRESHOLDING FOR GENERAL-ERROR METRICS - 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. | 05-06-2010 |