Skip to main content
Scalable Euclidean Embedding for Big Data
Kno.e.sis Publications
  • Zohreh S. Alavi
  • Sagar Sharma, Wright State University - Main Campus
  • Lu Zhou, Wright State University - Main Campus
  • Keke Chen, Wright State University - Main Campus
Document Type
Conference Proceeding
Publication Date
Euclidean embedding algorithms transform data defined in an arbitrary metric space to the Euclidean space, which is critical to many visualization techniques. At big-data scale, these algorithms need to be scalable to massive dataparallel infrastructures. Designing such scalable algorithms and understanding the factors affecting the algorithms are important research problems for visually analyzing big data. We propose a framework that extends the existing Euclidean embedding algorithms to scalable ones. Specifically, it decomposes an existing algorithm into naturally parallel components and non-parallelizable components. Then, data parallel implementations such as MapReduce and data reduction techniques are applied to the two categories of components, respectively. We show that this can be possibly done for a collection of embedding algorithms. Extensive experiments are conducted to understand the important factors in these scalable algorithms: scalability, time cost, and the effect of data reduction to result quality. The result on sample algorithms: FastMap-MR and LMDS-MR shows that with the proposed approach the derived algorithms can preserve result quality well, while achieving desirable scalability.

Presented at the 8th IEEE International Conference on Cloud Computing, New York, NY, June 27-July 2, 2015.

Citation Information
Zohreh S. Alavi, Sagar Sharma, Lu Zhou and Keke Chen. "Scalable Euclidean Embedding for Big Data" (2015)
Available at: