Automated Asset Management in Data Centers
CAKE researchers at Florida Atlantic University developed an innovative solution for visual asset identification using visual features of an image. Visual features of asset images are computed using complex mathematical methods. These visual features are used to identify and match asset images. A database with visual features of asset images was built for every distinct asset that is typically present in large data centers. Assets needing identification are captured using a camera on a mobile device. The device then extracts and transmits the visual features to the server for matching and asset information retrieval. This breakthrough, an optimized version of visual feature extraction and comparison methods, was developed to improve matching accuracy and reduce computational complexity of feature extraction as well as matching. This innovation introduced methods to prioritize and reduce the number of visual features used to identify and match asset images. This reduction in complexity enables efficient asset management solution on mobile devices. This work represents an improvement over previous state-of-the-art technology because it introduces simplified asset management tools based on visual features of assets. This innovative asset management system allows IT personnel to assess the state of computing assets by just pointing a mobile device camera at the asset.
Economic Impact: The advantage of this process is that it enables immediate identification of problematic assets using real-time operational data from the assets without having to explicitly and manually logging into the asset management system. This leads to reductions in data centers’ operational costs by using relatively inexpensive portable devices, such as mobile phones and tablets to minimize human errors, while improving productivity and reducing downtime.
System for Reducing Hospital Readmissions
Researchers at CAKE, in partnership with Soren Technology, developed a system for reducing hospital readmissions. The system integrates several telemedicine, patient care coordination, and clinical decision support systems to identify patients at high risk for re-admission. This is all based on data mining and a statistical analysis engine. The current system focuses on the readmission issues related to COPD. Predictive modeling of readmission is a complex effort. This work represents an improvement over previous state of the art because it enables comprehensive autonomous statistical analyses based on the mining of patient data using a unique process/algorithm. The clinical decision support system developed at CAKE is designed to predict hospital readmission risk for COPD using electronic health records (EHR) information. The COPD clinical decision support system is based on predictive analytics using structured and unstructured patient data to develop a readmission risk profile for a patient being discharged after an initial COPD related hospital admission.
Economic impact: This technology will have significant economic impacts through cost savings associated with reduction in COPD hospital readmissions. However, even the greater potential economic impact could be realized as the solution is expanded to include other diseases and chronic medical conditions.
Distributed Cloud Computing: 3-D Visualization Services for Climate Data on Demand
This breakthrough results from very successful collaborations involving two I/UCRCs, the Center for Advanced Knowledge Enablement (CAKE) at Florida International University (FIU) and Florida Atlantic University (FAU) and the Center for Hybrid Multicore Productivity Research (CHMPR) at the University of Maryland, Baltimore County (UMBC). This breakthrough work makes it possible to deliver a decade of 3-D animated visualizations of spectral infrared (IR) satellite radiance data from instruments on Aqua. These animations use 3-D to show the vertical structure of a decade of global and regional temperature trends occurring at the surface and lower troposphere. In addition, the algorithms developed by CHMPR have been providing CAKE with 3-D temperature profiles that specify the thermal structure around hurricanes in order to improve their landfall prediction. CAKE and CHMPR have implemented a distributed cloud computing web-based service, called SOAR. This service incorporates visualization as a public service available on a multi-core IBM-based server cluster. This system provides researchers and students with the ability to select regional and chronological periods and automatically transform IR orbital satellite data into spherical grid arrays of 3-D temperature profiles for viewing the continuous changing thermal structure of the atmosphere.
Economic impact: Fundamental Decadal Data Records are highly desired products recommended by the National Academy of Science/National Research Council. The SOAR distributed cloud computing web-based service enhances NASA’s ACCESS program by providing fundamental brightness temperature records. This can go a long way towards improving scientific and public understanding of the nature of global and regional climate change. As a result, everyone can be better positioned to design policies and actions for mitigating negative climate impacts on the economy, which could include billions of dollars of property value lost to sea-level rise and billions of dollars of insurable losses due to increases in extreme weather-related disasters.