Patents by Inventor Hakan KARDES

Hakan KARDES has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Graph-based organization entity resolution
    Patent number: 10572817
    Abstract: A novel entity resolution approach for the organization entity domain can be implemented in the MapReduce framework with low memory requirements so that it may scale to large scale datasets. A new clustering approach, sClust, significantly improves the recall of the pairwise classifier.
    Type: Grant
    Filed: March 19, 2015
    Date of Patent: February 25, 2020
    Assignee: PEOPLECONNECT, INC.
    Inventors: Hakan Kardes, Deepak Konidena, Siddharth Agrawal, Micah Huff, Ang Sun, Lin Chen, Andrew Kellberg, Xin Wang
  • Fast and scalable connected component computation
    Patent number: 10157429
    Abstract: Finding connected components in a graph is a well-known problem in a wide variety of application areas such as social network analysis, data mining, image processing, and etc. We present an efficient and scalable approach to find all the connected components in a given graph. We compare our approach with the state-of-the-art on a real-world graph. We also demonstrate the viability of our approach on a massive graph with ˜6B nodes and ˜92B edges on an 80-node Hadoop cluster. To the best of our knowledge, this is the largest graph publicly used in such an experiment.
    Type: Grant
    Filed: March 19, 2015
    Date of Patent: December 18, 2018
    Assignee: PeopleConnect, Inc.
    Inventors: Hakan Kardes, Siddharth Agrawal, Xin Wang, Ang Sun
  • FAST AND SCALABLE CONNECTED COMPONENT COMPUTATION
    Publication number: 20150269230
    Abstract: Finding connected components in a graph is a well-known problem in a wide variety of application areas such as social network analysis, data mining, image processing, and etc. We present an efficient and scalable approach to find all the connected components in a given graph. We compare our approach with the state-of-the-art on a real-world graph. We also demonstrate the viability of our approach on a massive graph with ˜6B nodes and ˜92B edges on an 80-node Hadoop cluster. To the best of our knowledge, this is the largest graph publicly used in such an experiment.
    Type: Application
    Filed: March 19, 2015
    Publication date: September 24, 2015
    Inventors: Hakan KARDES, Siddharth AGRAWAL, Xin WANG, Ang SUN
  • GRAPH-BASED ORGANIZATION ENTITY RESOLUTION
    Publication number: 20150269494
    Abstract: A novel entity resolution approach for the organization entity domain can be implemented in the MapReduce framework with low memory requirements so that it may scale to large scale datasets. A new clustering approach, sClust, significantly improves the recall of the pairwise classifier.
    Type: Application
    Filed: March 19, 2015
    Publication date: September 24, 2015
    Inventors: Hakan KARDES, Deepak KONIDENA, Siddharth AGRAWAL, Micah HUFF, Ang SUN, Lin CHEN, Andrew KELLBERG, Xin WANG