Vijay V. Raghavan - US grants

A retrieval system model that leads to an integrated approach in which both local and global feedback naturally blend into a unified process is proposed. The novelty of this approach is that the relevance feedback of a particular query instance as well as the accumulated knolwdge from past queries are directly related to the performance of the present query. This model represents a cluster-based approach to information retrieval. The cluster-based approach is developed around a clustering technique that captures the users concept of closeness between documents. Since the clustering technique to support the retrieval process is developed. The principal advantage of the proposed model is that the retrieval performance can be directly influenced by the optimization criterion employed during clustering. The significance of our approach lies in the fact that document representation and the measure of similarity among doucments do not have to be prespecified in an ad hoc manner. Instead such design alternatives can be dictated by cluster-scope of application than just information retrieval.

I/UCRC for Visual and Decision Informatics (CVDI)

1160958 University of Louisiana at Lafayette; Raghavan
1160960 Drexel University; Hu

The Center for Visual Decision Informatics (CVDI) will develop new visual and analytic methods that leverage modern computer hardware and software, and develop analysis and discovery tools that can be applied to complex data-based decision-making process. The University of Louisiana at Lafayette (UL) and Drexel University (DU) are collaborating to establish the proposed center, with UL as the lead institution.

The proposed center will bring together analytic, visual, and perceptual techniques in order to support decision makers in industry and government by advancing the state-of-the-art in the research fields of Information, Visualization, Visual Analytics, and Automated Analysis. The proposed center's research agenda will be driven by the applications of information integration and decision support systems, 3D content creation and management in decision informatics, immersive visualizations, and human computer interaction technologies for training and education of workforce. To ensure that the research activities are transformative, software prototypes and frameworks developed through research will be tested in several application contexts by close collaboration with industry partners.

The proposed center activities will enhance the international competitiveness of the American industry in the problems and opportunities of leveraging data for evidence-based decision support. The center will help its potential members achieve higher productivity gains through the adopted data-driven decision making process, and will also impact the broader American industry through the publication and dissemination of the center's research results. Both universities are committed to including underrepresented groups and have active plans for addressing diversity. The center also intends to promote, catalyze, and accelerate the commercialization of technology innovations.

This project, designing and developing an instrument that can support visual analytics on high volume, high velocity data streams, aims to offer an easy to use software interface for researchers to develop visual analytics applications that need a combination of stream processing, deep analytics, and visualization capabilities. The instrument provides the computational capacity and tight interconnection of systems to handle both real-time in-memory stream system processing and complex analytics, along with dedicated visualization processing. The instrument under development:
- Offers a customized computational system design expected to offer up to an order of magnitude performance over existing systems offered by commercial hardware vendors of business intelligent solutions.
- Advances knowledge and understanding in building highly-scalable big data platforms for decision makers or deals with big data generated from internet of things, and dynamic social networks that represent transportation routes, paths of disease outbreaks, social community networks, financial transactions, and recommendation graphs.
- Supports data mining and analytical needs of the Center for Visual and Decision Informatics (CVDI) and specific research projects to develop visual techniques on sensor data streams, and builds upon the experimental cloud infrastructure established in the Center for Advanced Computing Studies (CACS) Lab for InterNet Computing (LINC).

The real-time stream processing and analytics system comprises tightly interconnected processors with Remote Memory Access (RDMA) capabilities, low-latency Solid State Drive (SSD) with Infiniband Interconnect to support distributed in-memory data stream pre-processing, and analytics. The deep analytics nodes comprise data nodes that can handle efficient batch processing and the visualization processing node with high-end graphics cards to support visualization of massive data sets and advanced visualization venues. Supporting knowledge discovery and decision making requires a powerful computational infrastructure; without it, visual analytics on large complex dynamic graphs constructed from networks of sensors, mobile phones, social networks cannot be realized.

The instrument supports applications in many areas such as: disaster response, public safety, public health, cyber security, ecommerce and financial sectors. GENI (Global Environment for Network Innovations)-based connectivity to Lousiana's Optical Network Initiative (LONI) and Internet2 facilitates partnerships with researchers across other campuses nationwide and the US Ignite Community. The instrument will serve and be used by students for big data research and education. Utilizing the Louis Stokes-LA Alliance for Minority Participation (LS-LAMP), the project also facilitates participation of underrepresented students, thus increasing more participation in STEM areas. Furthermore, the instrument enables productivity for many researchers and educators.

This award will help the University of Louisiana at Lafayette transition its IUCRC Center for Visual and Decision Informatics (CVDI) into Phase 2 operation for the next 5 years. At the start of Phase 2, the CVDI IUCRC is expanding from three sites to 5 sites, with the addition of two new sites, and University of Louisiana at Lafayette will serve as the managing lead site of the Center for this phase. The Center seeks to continue its record of sustained accomplishments in terms of strengthening and growing industry partnerships, delivering innovation through intellectual property and publications, expanding research competencies through increased faculty participation, and broadening educational experience of students through participation in industry funded research.

The research program of the Center focuses on developing next-generation visual and decision support tools and techniques that enable decision makers to fundamentally improve the way an organization's information is interpreted and exploited. CVDI's area of research is industry-relevant, attracts membership and has the potential to improve US competitiveness in this area. The center intends to broaden participation through center engagement in university programs. Through participation in the Louis Stokes Louisiana Alliance for Minority Participation Program, of which the PI is the co-coordinator, this site will introduce undergraduate minority students to industry-driven research. CVDI will recruit students from neighboring Historically Black Colleges and Universities to join our graduate programs. The site will also participate in university outreach programs for K-12 students, such as the annual Engineering and Technology Expo Week and the Science Day.