Leidos, Inc.

Matthew William Vahlberg

Thomas James Cannaliato

Joshua A Decker

Jonathan D Michel

Joshua Anders Wepman

Timothy Leigh Crowell

A system and method for learning and asserting what portions of a utility GIS network model are incorrect or flawed as they relate to real world conditions, and what the correct real world relationships are in the field is described. The system and method leverage available smart grid data to assess the quality of a primary (GIS) source data set; quality data renders derived analyzes across the utility valid, sound, and action worthy. The system and method utilize existing partially correct electrical network distribution model data and various non-specialized source data including smart meter, spatial, and customer information data collected from the network to test, validate and suggest corrections to the connectivity model. By forming putative ground truth assignments between utility components, the system tests the assumptions by examining the geospatial proximity and correlating voltage and event data over time to form refined hypothesis. These hypotheses are compared to the existing model and statistical tests are performed at a variety of confidence levels to propose a corrected network model to the user.

System and method for big data geographic information system discovery

A processing platform integrates ETL (extract, transform, and load), real time stream processing, and 'big data' data stores into a high performance analytic system that runs in a public or private cloud. The platform performs real time pre-storage enrichment of data records to form a single comprehensive record usable for analytics, searching and alerting. The platform further supports sharing of components and plug-ins and performs automatic scaling of resources based on real time resource monitoring and analysis.

Growing community of inventors

Severna Park, MD, United States of America

Matthew William Vahlberg