The Instrument will directly support our current NSF fundamental multi-institutional research project III: Large: Collaborative Research: Moving Objects Databases for Exploration of Virtual and Real Environments led by this P.I. Rishe at FIU. Florida International University, the University of Illinois at Chicago, Brown University, and Northwestern University are transforming the fields of computational transportation and mobile sensing by developing a universal high-performance model for information processing and fusion in mobile environment, providing a collaborative integration between the real and virtual worlds [http://cake.fiu.edu/MOD]. This model enables querying and visualization of moving objects data (MOD) and their relationship to static and dynamic geospatial data. Expected results include: balancing the processing of location-based data streams into MOD servers with efficient processing of visualization-related queries; determining optimal distribution of queries/tasks among multiple regional servers; maximizing the scalability of prediction techniques in terms of efficient management of objects' data and queries; modeling data uncertainty; coupling map generalization with trajectories' data reduction when zooming across different scales; resolving issues of privacy and security; and enabling semantic querying.

The Cloud and Autonomic Computing Center at the University of Florida [http://www.nsfcac.org/] (see letter) will utilize the Instrument's system logs and performance metrics in refining its research on Autonomic Resource Management.

The Instrument will also enable the research of research Dr. Ming Zhao's team at FIU on high-performance real-time virtualization architectures aimed at eliminating the overhead from virtualization and achieving the same level of performance and responsiveness for computing as typical lightweight OSes employed in high-performance computing (HPC).

The team of Dr. Abraham Kandel [http://www.cse.usf.edu/people/faculty/abraham_kandel/], who is now FIU faculty, will utilize the Instrument to foster their research on the empirical and theoretical investigation of Fuzzy Stigmergic Computational Techniques inspired by Nature.

The team of Dr. Tao Li [http://users.cis.fiu.edu/~taoli/research-project.html] at FIU will utilize the Instrument to establish a comprehensive framework for large-scale data mining from multiple information sources. The framework focuses on unsupervised learning and semi-supervised learning and is able to perform fusion at all different levels including feature integration, semantic integration, and intermediate integration.

With the explosive growth of the volume and complexity of document data, it has become a necessity to semantically understand documents and deliver meaningful information to users. Areas dealing with these problems are crossing data mining, information retrieval, and machine learning.

The Instrument's vast repository will facilitate research on Big Data by these co-PIs and colelagues, expanding the knowledge Big Data representation and modeling [Agrawal et al. 2010; Agrawal et al. 2011; Cuzzocrea et al. 2011; Dutta et al. 2011; Fox and Hendler 2011; Hadoop 2009; Han et al. 2011; Hanlon et al. 2011; Meijer and Bierman 2011; Sakr et al. 2011].

References Cited

[MOD] Collaborative Research: Moving Objects Databases for Exploration of Virtual and Real Environments http://cake.fiu.edu/MOD

[ADA10] D. Agrawal, S. Das, and A. Abbadi. "Big data and cloud computing: new wine or just new bottles?." Proceedings of the VLDB Endowment 3, no. 1-2 (2010): 1647-1648.

[ADA11] D. Agrawal, S. Das, and A. Abbadi. "Big data and cloud computing: current state and future opportunities." In Proceedings of the 14th International Conference on Extending Database Technology, pp. 530-533. ACM, 2011.

[CSD11] A. Cuzzocrea, I. Song, and K. Davis. "Analytics over large-scale multidimensional data: the big data revolution!." In Proceedings of the ACM 14th international workshop on Data Warehousing and OLAP, pp. 101-104. ACM, 2011.

[DKP11] H. Dutta, A. Kamil, M. Pooleery, S. Sethumadhavan, and J. Demme. "Distributed Storage of Large-Scale Multidimensional Electroencephalogram Data Using Hadoop and HBase." In Grid and Cloud Database Management, pp. 331-347. Springer Berlin Heidelberg, 2011.

[FH11] P. Fox, and J. Hendler. "Changing the equation on scientific data visualization." Science (Washington) 331, no. 6018 (2011): 705-708.

[TAH+09] The Apache Hadoop Project. http://hadoop.apache.org/core/, 2009.

[HHL+11] J. Han, E. Haihong, G. Le, and J. Du, Survey on NoSQL Database, The 6th International Conference on Pervasive Computing and Applications, pp. 363-366, October 26-28, 2011

[HDM+11] M.R. Hanlon, R. Dooley, S. Mock, M. Dahan, P. Nuthulapati, and P. Hurley, Benefits of NoSQL Databases for Portals & Science Gateways, Proceedings of the 2011 TeraGrid Conference: Extreme Digital Discovery, Article No. 36, Salt Lake City, Utah, USA, 2011.

[MB11] E. Meijer and G. Bierman. "A co-relational model of data for large shared data banks." Communications of the ACM 54, no. 4 (2011): 49-58.

[SLB+11] S. Sakr, A. Lin, D.M. Batista, and M. Alomari, A Survey of Large Scale Data Management Approaches in Cloud Environments, IEEE Communications Surveys &Tutorials, vol. 13, no. 3, pp. 311-336, Third Quarter 2011.