Facility: CASCADE - Center for Assured and Scalable Data Engineering

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Function: Data Management/Analysis

Department: School of Computing, Informatics, and Decision Systems Engineering
College/Unit: IRA A. FULTON SCHOOLS OF ENGINEERING

Description:

ASU's Center for Assured and Scalable Data Engineering (CASCADE) provides the following data management, analysis, and assurance services to ASU community and industrial partners:

  • processing big data, streaming data, IoT, and other big data issues,
  • fusing and analyzing unstructured/multimedia, semistructured, and structured  data,
  • methods for extracting information from data,
  • data mining, machine learning, and visual analytics methods for knowledge discovery,
  • data security, integrity, and privacy,
  • data acquisition support to address specific research questions, and
  • developing novel tool and architectures for reliable and timely data-drivendecision making. 
The type of services provided by CASCADE includes
  • consulting and big data expertise,
  • software development and testing,
  • summer and long-term internship programs,
  • technology showcases and white papers, and
  • training, certificate programs, and developer workshops.

CASCADE also provides access to a diverse spectrum of high-performance data computing platforms for development, evaluation, and testing.

Link to More Details:
http://cascade.asu.edu

Facility People

Resources (1)

GEARS An Infrastructure for Energy-Efficient Big Data Research on Heterogeneous and Dynamic Data

GEARS (an enerGy-Efficient big-datA Research System) is a heterogeneous computing and storage resource based on cohesively co-designed software and hardware components. It enables a variety of important studies on heterogeneous and dynamic data and advances the scientific knowledge in data-driven disciplines. The hardware of GEARS includes a cluster of data nodes equipped with heterogeneous processors and storage devices and fine-grained power management capability. The software is developed upon widely-used big data frameworks to support unified programming across CPUs, GPUs, and FPGAs, and transparent data access across a deep storage hierarchy integrating DRAM, NVM, SSD, and HDD.

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