Shashi Shekhar - US grants

9818338
Tripathi, Anand
Zhang, Zhi-Li
University of Minnesota - Twin Cities

CISE Research Instrumentation: Research in Networked Information Systems

This research instrumentation enables research projects in:
- Scalable and QoS-Aware Multimedia Systems
- Agent-Based Distributed Computing
- High Performance Geographic Information Systems, and
- High Performance Data Mining

To support the aforementioned projects, this award contributes to the purchase, consisting mainly of a CISCO Catalyst 5505 network switch, two Sun Enterprise, and various Sun Ultra, by the Department of Computer
Science and Engineering at the University of Minnesota. This instrumentation will support research in new paradigms, system architectures, and algorithms for network computing involving distributed multimedia systems, mobile Internet agents, geographic information systems (GIS), and data mining and information search over the Internet. The research in distributed multimedia systems is investigating scalable server
architectures, specification of QoS measures, and mapping of QoS measures to resource allocation and scheduling decisions. The research in agent-based Internet computing focuses on programming abstractions and their underlying mechanisms for secure and robust agent-based computing. Several agent based applications are being investigated by this project, including a multimedia based active mail system. The research activity in GIS is investigating parallelization and other approaches for efficient execution of range query and map overlay operations. This research, together with the activities in distributed multimedia, is addressing QoS problems for the presentation of graphics and images resulting from GIS queries. The research in data mining is investigating parallel algorithms for large data sets obtained from various Internet sites. The synergy among the various activities of these projects is realized through several
focal-point applications. These applications are related to agent-based multimedia mail system, Digital Earth initiative, and the Sky Survey project.

EIA-9986042
George Karypis
University of Minnesota

CISE Research Instrumentation: Cluster Computing for Knowledge Discovery in Diverse Data Sets

The Department of Computer Science & Engineering at the University of Minnesota will purchase the following equipment which will be dedicated to support research in computer and information science and engineering. The requested instrumentation consists of 10 dual processor Dell 410 workstations with a total of 10GB of main memory, a CISCO Catalyst 4000 Gigabit switch, and a Hitachi 5750 E RAID system with a total of 360GB storage. This equipment will be used to form a computer cluster suited for data-and compute-intensive applications.

The equipment will be primarily used by four research projects which address research problems in the area of knowledge discovery, including in particular: data mining of genomic and scientific data sets, spatial data mining, next-generation recommender systems, and bid evaluation in multi-agent contracting. For several of these projects, the currently available equipment are either inadequate or obsolete for supporting their core needs. For other projects, the availability of this equipment will make a significant increment to their capabilities. The collective needs of these projects are for a tightly-coupled, high-performance, large-memory cluster of computers with a high-performance high-capacity storage server attached to it.

EIA 0224392
Terveen, Loren
Konstan, Joseph A.
Riedl, John
Shekhar, Shashi
University of Minnesota Twin Cities

Title: CISE RR: Being There: Mobile Devices for Community and Commerce

This project, building a mobile computing system, provides a set of mobile, connected, location-aware computing units consisting of Personal Digital Assistants (PDAs) and wearable displays with some input ability to allow experimentation to affect deployment on the types of interfaces, applications, and technologies needed as these devices become commonplace. Devices will carry out a wide range of experimentation, from tightly controlled experiments to broad field studies, from previously planned extensions of existing research to last-minute ideas created by student projects, and from small pilot studies to large deployment studies. The infrastructure supports research projects in human computer interaction (HCI), recommender systems, and spatial data management for environments where users are mobile; specifically,
Ubiquitous Peripheral Social Interaction (UPSI),
Location-Aware Recommender Systems, and
Spatial Data Management for Location Aware Mobile Computing.
The first project presumes availability of several wireless communication channels. Information, at the periphery of users' attention, is constantly streamed to the device and displayed as a scrolling "ticker." A user carries a pocket computer and wears a Networked Wrist Device linked by Bluetooth. This approach presents challenges in populating and managing the information model driving the interaction, designing the interface for the networked wrist display, coupling the wrist computer and PDA, and identifying the types of tasks for which this interaction paradigm is suitable. Using algorithmic and interface research, pilot tests, real-world experiments, and metrics, the second project looks into suitable applications among wired, disconnected, and wireless connected recommenders. Given local and temporal patterns of users, this project examines when it is appropriate to interrupt its user with a recommendation, how to present recommendations most effectively across multi-device interfaces, and how to compute recommendations when the user is disconnected from the network. The last project looks into the requirements of spatial data management systems for applications like mobile location based recommendation systems. The project gathers a trace-based workload from location aware recommendation systems to evaluate solutions for critical design decisions such as algorithms for map compression and predictive pre-fetching strategies to cache relevant segments of maps based on past spatial queries, scheduled meetings locations, and related information. The educational plan includes integration of the platform into courses and student involvement in summer internships.

Land cover characterization is essential for modeling, monitoring, and prediction of the terrestrial ecosystem and climate. Such characterizations are produced based largely on information extracted from satellite remote sensing measurements. However, much of the global and regional land cover consists of heterogeneous mixtures of different land cover types at varying spatial scales, while standard methods for remote sensing land cover characterization operate at a single scale and use a fixed set of categories. This research will develop an integrated statistical and computational framework that will produce land cover characterizations from remote sensing data that allow generalization in spatial resolution and categorical scale simultaneously. The result of this land cover characterization process will no longer be a land cover map, as a map implies a single spatial scale, but rather a land cover database, which can be queried in both traditional manners and ways currently unavailable. In essence, users will be free to select a range of spatial and categorical scales most appropriate for their needs. The key elements involved in the development of this framework include new contributions to the fields of geography and remote sensing, statistics, and computer science. At the foundation is a new class of multi-scale statistical models , called mixlets, from which there will result a new paradigm for representation and visualization of land cover categorization. In turn, these advances will be integrated with new developments in spatial database representations and spatial query systems.

Land cover characterizations, critical for many studies involving terrestrial ecosystems and climate, and the human impacts on the natural environment, are needed at many different spatial scales and by a variety of user communities. This project will develop a comprehensive framework and prototype system for producing such characterizations in a manner that adapts automatically to multiple scales of information using remote sensing and GIS data. Our research has direct impact in the fields of ecology, biology, geography, forestry and environmental sciences dealing with multiscale patterns and processes. More broadly, the development and availability of these tools will contribute significantly to the improved understanding within the scientific community, and ultimately in the community at large, of the complexity of land cover.

This award is jointly supported by the Division of Mathematical Sciences and the Directorate for Social, Behavioral, and Economic Sciences as part of the Mathematical Sciences Priority Area.

0414388 Hondzo This planning grant will address seven critical issues that need further definition and resolution for the full-scale development of a proposed NSF program called CLEANER (Collaborative Large-scale Engineering Analysis Network for Environmental Research). Special attention in this project will be paid to design and analysis of two alternative types of environmental field facilities (EFFs), which are viewed as the fundamental units of the CLEANER network. A type 1 EFF would be a regional environmental system that studies a range of interconnected environmental problems; a type 2 EFF would be focused on a single cross-cutting issue of national concern.

In 1960, Jane Goodall began the first long-term field study of the closest living relatives of humans, chimpanzees (Pan troglodytes), in Gombe National Park, Tanzania to describe their behavior by making extensive observations in their natural habitat. This study, which continues today, has made many contributions to understand chimpanzee behavior and human evolution, and has also inspired people around the world to study science and work toward wildlife conservation. Analysis of the complete observational dataset from Gombe and other field studies, such as the Kanyawara chimpanzee project, has the potential of providing new insights into many unanswered behavioral ecology questions, e.g. the influence of social relationships within the group on territorial behavior.

However, this observational paradigm is extremely labor-intensive and only a small part of the Gombe dataset has been analyzed so far. The goal of this project is to begin developing data analysis tools and techniques to reduce the time and effort required to analyze observation datasets. Expected results include a cartridge for mining concept patterns, a computationally efficient execution environment for concept pattern mining, and spatial semi-supervised learning algorithms to improve classification performance in creating maps. Expected results will not only benefit behavioral ecologists, but also contribute to research in many other spatio-temporal application domains, including location based services, transportation and epidemiology. Dissemination plans include development of instructional tools based on the Gombe data to motivate younger students to learn science and information technology as well as a workshop to increase collaboration between Computer Scientists and Behavioral Ecologists.

This IGERT training grant will bring together scholars of ecology, civil engineering, and the earth sciences to study the interplay between landscape changes and ecosystem processes across a wide range of spatial and temporal scales and across interfaces with an emphasis on non-equilibrium dynamics. Changes in the abiotic and biotic world have taken place over a wide range of temporal and spatial scales. In particular, human activities have greatly accelerated the rate at which the physical and biological world is perturbed through modifications in transport processes. Opportunities for graduate education and research spanning these disciplines and issues will be provided at the University of Minnesota research facilities at Itasca State Park and the National Center for Earth-surface Dynamics (NCED). Key education and training features are a one-year comprehensive, team-taught course that emphasizes data collection using modern instrumentation, data analysis, data interpretation, and model building across spatial and temporal scales and across interfaces. Collaborative projects, virtual seminars with international partners, ethics training and professional preparation will enhance this experience. The core training in the basic sciences and engineering will also include historical, social, and economic topics. This IGERT training program will recruit students from a broad spectrum and prepare them for an international and collaborative workforce. The broader impacts of this IGERT include partnerships with colleges and pre-graduate school internships to recruit from underrepresented groups. The partnership with NCED will provide opportunities for public outreach activities through collaborations with the Science Museum of Minnesota and the Minnesota Historical Society, such as development of teaching materials for K-12 based on historical records in Minnesota. IGERT is an NSF-wide program intended to meet the challenges of educating U.S. Ph.D. scientists and engineers with the interdisciplinary background, deep knowledge in a chosen discipline, and the technical, professional, and personal skills needed for the career demands of the future. The program is intended to catalyze a cultural change in graduate education by establishing innovative new models for graduate education and training in a fertile environment for collaborative research that transcends traditional disciplinary boundaries.

Proposal #: CNS 07-08604
PI(s): Tripathi, Anand
Mokbel; Mohamed F.; Shekhar, Shashi
Institution: University of Minnesota - Twin Cities
Minneapolis, MN 55455-5200
Title: IAD:Infrastructure for Research in Spatio-Temporal and Context-Aware Systems and Applications

This project, acquiring infrastructure for evaluating distributed event stream processing architecture, spatio-temporal data mining algorithms, and scalability of middleware architectures for supporting large scale context aware systems, responds to the increasing demand for applications that exploit the spatio-temporal context information to enrich and augment mobile users' computing environments. This demand is motivated by factors such as the need to provide pertinent resources and information to users based on their physical location context (e.g., yellow pages and location-based services), provide location based public safety services (e.g., enhanced 911 services), and assist seamless mobility of users across different spaces by dynamically adapting the applications on their mobile computing devices based on the location. Challenges in building such context-aware computing environments and applications stem from the high complexity in developing applications due to the need of integration of different kinds of technologies, context-based security and privacy requirements, need to identify required pertinent resources and information, and timely detection and querying of context information. The infrastructure contributes to address these challenges through research in
. Programming environments and middleware architectures for context-aware applications;
. Spatio-temporal query processing with privacy preserving techniques,
. Spatio-temporal data mining, profiling, and prefetching methods, and
. Agent-based distributed event data stream processing for context detection.
These synergistic projects use
. Context information in providing security and privacy to these applications,
. Spatio-temporal data mining for context-based adaptations,
. Continuous query processing for context-based information retrieval, and
. Programming abstractions and middleware architectures for rapid construction of these applications from their high level specifications.

Broader Impacts: The infrastructure advances knowledge and contributes to develop new software infrastructure for building context-aware applications. The project actively seeks and encourages participation of students from under-represented groups, facilitates students' theses, and collaborates with an IGERT initiative.

Context
The recent loss of lives, and traffic jams stretching for tens of miles as hurricanes Rita and Katrina approached the Gulf Coast demonstrate the difficulty of evacuating urban areas. Mass evacuations are among the most difficult problem areas in Transportation Science because they violate key assumptions underlying traditional theories, e.g., Wardrop equilibrium among selfish commuters. A key challenge in this domain is to develop an understanding of non-equilibrium traffic dynamics over transportation networks to aid in the design of emergency traffic management techniques. This is a formidable task due to the data-intensive nature of the problem, and the semantic gap between current database management systems and transportation science. The goal of this project is to research novel and scalable data management concepts in partnership with the development of novel transportation science models and theories to understand emergency traffic. New collaborative computer science research is proposed to probe innovative database concepts underlying network non-equilibrium dynamics data and queries.

Intellectual Merit
These approaches to time-variant graphs and spatio-temporal database support for flow networks significantly differ from the traditional approaches in the database literature. Th project is expected to create innovations in the following areas: 1) graph-aggregates, a novel representation of time-varying graphs, 2) database support for flow network operations, e.g. min-cut, and max-flow, 3) the proposed database concepts will be designed and evaluated in collaboration with domain scientists and professionals using grand challenge problems (e.g., emergency traffic management) and datasets (e.g. large urban evacuation scenarios, population distributions, and flow networks). The hope is to significantly enhance scientists' ability to understand and manage non-equilibrium network behavior, not only in Transportation Science, but also in many other important domains including logistics, telecommunication networks, electric power grids, and distribution networks for gas, water, etc.

Broader Impact
Teaching materials (e.g., slides, software prototypes) to facilitate incorporation of research results in courses and classroom activities will be prepared. This project will broaden the participation of underrepresented groups at many levels: one PI has a track record of participation in summer institutes involving undergraduate students from historically black colleges and universities. If successful, the results wil benefit society by reducing evacuation time, which may save lives and reduce exposure of vulnerable populations in the face of man-made and/or natural disasters.

Understanding Climate Change: A Data Driven Approach

Climate change is the defining environmental challenge now facing our planet. Whether it is an increase in the frequency or intensity of hurricanes, rising sea levels, droughts, floods, or extreme temperatures and severe weather, the social, economic, and environmental consequences are great as the resource-stressed planet nears 7 billion inhabitants later this century. Yet there is considerable uncertainty as to the social and environmental impacts because the predictive potential of numerical models of the earth system is limited. These models are incapable of addressing important questions relating to food security, water resources, biodiversity, mortality, and other socio-economic issues over relevant time and spatial scales.

Climate model development has contributed small and incremental improvements; however, extensive modeling gains have not been forthcoming. Modeling limitations have hampered efforts at providing information on climate change impacts and adaptation and mitigation strategies. A new and transformative approach is required to improve prediction of the potential impacts on human welfare. Data driven methods that have been highly successful in other facets of the computational sciences are now being used in the environmental sciences with success. This Expedition project will significantly advance key challenges in climate change science developing exciting and innovative new data driven approaches that take advantage of the wealth of climate and ecosystem data now available from satellite and ground-based sensors, the observational record for atmospheric, oceanic, and terrestrial processes, and physics-based climate model simulations.

To realize this ambitious goal, novel methodologies appropriate to climate change science will be developed in four broad areas of data-intensive computer science: relationship mining, complex networks, predictive modeling, and high performance computing. Analysis and discovery approaches will be cognizant of climate and ecosystem data characteristics, such as non-stationarity, nonlinear processes, multi-scale nature, low-frequency variability, long-range spatial dependence, and long-memory temporal processes such as teleconnections. These innovative new approaches will be used to better understand the complex nature of the earth system and the mechanisms contributing to such climate change phenomena as hurricane frequency and intensity in the tropical Atlantic, precipitation regime shifts in the ecologically sensitive African Sahel or the Southern Great Plains, and the propensity for extreme weather events that weaken our infrastructure and result in environmental disasters with economic losses in excess of $100 billion per year in the U.S. alone.

Assessments of climate change impacts, which are useful for stakeholders and policymakers, depend critically on regional and decadal scale projections of climate extremes. Thus, climate scientists often need to develop qualitative inferences about inadequately predicted climate extremes based on insights from observations (e.g., increase in hurricane intensity) or conceptual understanding (e.g., relation of wildfires to regional warming or drying and hurricanes to sea surface temperatures). These urgent societal priorities offer fertile grounds for knowledge discovery approaches. In particular, qualitative inferences on climate extremes and impacts may be transformed into quantitative predictive insights based on a combination of hypothesis-guided data analysis and relatively hypothesis-free, yet data-guided discovery processes.

A primary focus of this Expedition project will be on uncertainty reduction, which can bring the complementary or supplementary skills of physics-based models together with data-guided insights regarding complex climate processes. The systematic evaluation of climate models and their component processes, as well as uncertainty assessments at regional and decadal scales is a fundamental problem that will be addressed. The ability to translate gains in the predictive skills of climate variables to improvements in impact assessments and attributions is a critical requirement for informing policymakers. Novel methodologies will be developed to gain actionable insights from disparate impacts-related datasets as well as for causal attribution or root-cause analysis.

This research will be conducted in close collaboration with the climate science community and will complement insights obtained from physics-based climate models. Improved understanding of salient atmospheric processes will be provided to those contributing to the development and improvement of climate models with the goal of improving predictability. The approaches and formalisms developed in this research are expected to be applicable to a broad range of scientific and engineering problems, which use model simulations to analyze physical processes. This project will also contribute to efforts in education, diversity, community engagement, and dissemination of tools and computer and atmospheric science findings.

To encourage more US-based participation in geo-spatial research, this support will enable 20 promising graduate and undergraduate students in computer science the necessary financial support to attend one of the field's premier forums, the International Symposium on Spatial and Temporal Databases (SSTD), to be held this year in Minneapolis August 24-26. The biennial event showcases cutting-edge research spanning a vast array of topics and attracts researchers, developers, and end users around the world from academia, industry, and government. The engagement and subsequent education of the next generation of scientists and engineers working with spatial and temporal data will have a significant impact on the lives of ordinary people everywhere. In addition to improvements in vehicular and various forms of public transportation, security mechanisms featuring unprecedented sophistication will be made possible by enhanced intelligence gathering via spatial analysis of satellite imagery, and macroscopic ecological health will be more accurately modeled with more informative uses of spatio-temporal data. Promoting this conference participation by U.S. based graduate and undergraduate students is important for helping the United States to maintain a major foothold in the exciting products, services and ideas to come out of the field of spatial and temporal data.

Terra Populus: A Global Population/Environment Data Network (TerraPop) will develop organizational and technical infrastructure that will integrate, preserve, and disseminate data describing changes in the human population and environment over time. A plethora of high-quality environmental and population datasets are available, but they are widely dispersed, have incompatible or inadequate metadata, and have incompatible geographic identifiers. The project will enable researchers to identify and merge data from heterogeneous sources to study the relationships between human behavior and the natural world.

TerraPop will focus on four specific kinds of data: (1) census and survey microdata describing the characteristics of individuals and their families and households; (2) aggregate census and survey data, describing the characteristics of places, including aggregate population characteristics, land use, and land cover; (3) remote-sensing data describing land cover and other environmental characteristics; and (4) climate data describing temperature, precipitation, and other climate-related variables. All four data types have an important temporal dimension; most of the data span the past five decades, and some sources reach back to the nineteenth century. TerraPop will make these data interoperable across time and space, disseminate them to the public and to multiple research communities, and preserve them for future generations.

Understanding of interactions between population and the environment has been hampered by the dearth of internationally comparable data. This infrastructure will expand the quality and quantity of such data while making them highly interoperable and easily accessible. Population data closely integrated with data on the environment will allow us to describe the unfolding transformation of human and ecological systems. Data on the human population are crucial for understanding changes in the Earth?s biological and climate processes; equally important, data on climate and land provide essential tools for understanding the impact of environmental change on human behavior. By creating a framework for locating, analyzing, and visualizing the world's population and environment in time and space, TerraPop will provide unprecedented opportunities for investigating the agents of change, assessing their implications for human society and the environment, and developing policies to meet future challenges. The data collection and its analysis tools will contribute to education and public understanding. It will allow teachers to integrate research and teaching, bringing the excitement of discovery into the classroom from primary school to graduate school. More broadly, TerraPop will be a model for the sustainable expansion, maintenance, and improvement of a global data resource.

The goal of this research project is to design and develop highly efficient spatial and spatio-temporal database systems on flash memory storage. More specifically, this project conducts research, develops requisite scientific knowledge, builds software infrastructure, and integrates teaching activities with research for four specific aims: (1) Supporting efficient spatial indexing on flash memory, which includes data- and space-partitioning index structures, (2) Supporting efficient spatial query processing and optimization, which includes supporting various forms of the spatial join operation and query cost estimation, (3) Supporting spatio-temporal indexing and querying, which includes extending the spatial index data structures and the query processor to support the high update frequency common in spatio-temporal applications, and (4) Exploiting a storage hierarchy of flash and magnetic disks where spatial and spatio-temporal indexing, query processing, and optimization can exploit the full potential of both storage media. Besides its impact on industry, this project will have significant broader impact across multiple segments of society that include enhancing productivity, graduate and undergraduate student education, curriculum development, and tutorial presentations. Publications, technical reports, open-source software, and experimental data from this research are disseminated via the project web site (http://www.cs.umn.edu/~mokbel/flash).

Increasingly, location-aware datasets are of a size, variety, and update rate that exceed the capability of spatial computing technologies. This project addresses the emerging challenges posed by such datasets, which sometimes are also referred to as Spatial Big Data (SBD). SBD examples include trajectories of cell-phones and GPS devices, temporally detailed (TD) road maps, vehicle engine measurements, etc. SBD has the potential to transform society. A recent McKinsey Global Institute report estimates that personal location data could save consumers hundreds of billions of dollars annually by 2020 by helping vehicles avoid congestion via next generation routing services such as eco-routing. Eco-routing may leverage various forms of SBD to compare routes by fuel consumption or greenhouse gas (GHG) emissions rather than total distance or travel-time.

To develop next-generation eco-routing services, this project innovates in three areas. Frist, Lagrangian Xgraphs, a novel concept in computer science, is explored at conceptual, logical and physical database levels to model traveler's frame of reference, a major departure from traditional binary relationship (e.g., adjacency) graphs. Second, it probes the concept of route-collections, and scalable algorithms for finding route-collections. For example, to identify a route-collection over all possible start-times of a given time-interval, the project explores a critical time point approach which divides a given time-interval into a set of disjoint sub-intervals of stationary-rankings among alternative routes. The approach is not only novel but also very important for the field. Critical time points may become a vital component of dynamic programming (DP) solutions, which would need reconsideration in the face of emerging temporally detailed SBD that violate DP assumptions about stationary ranking of alternate solutions. Third, to address the increasing diversity of SBD methods, algorithm-ensembles and flexible architectures that allow rapid integration of new data sources and routing algorithms are developed.

The proposed work serves national goals for energy independence and sustainability by laying the ground work for eco-routing and other travel-related services that reduce fuel consumption and greenhouse gas emissions. By increasing the availability of SBD, the project also enhances the research infrastructure for other researchers. Educational activities include curriculum development and training of students in the emerging area of SBD and Eco-routing. Result dissemination is planned via publication in relevant peer-reviewed conferences and journals. More details are available on the project website (www.spatial.cs.umn.edu/eco-routing/).

In coming decades, the world population is projected to grow significantly increasing the demand for food, water, energy, and other resources. Furthermore, these resource challenges may be amplified due to climate change, urbanization, and the interdependent and interconnected nature of food, energy, and other resources. Furthermore, these resource challenges may be amplified due to environmental changes, urbanization, and the interdependent and interconnected nature of food, energy and water (FEW) systems, which were traditionally analyzed and planned independently. Such piece-meal approaches (e.g., bio-fuels) to solving problems in one system (e.g., energy) have led to unanticipated problems (e.g., increase in food prices) in other systems. The goal of the nexus FEW security approach is to reduce such surprises by understanding, appreciating and visualizing the interconnections and interdependencies in the FEW system of systems at local, regional and global levels. However, the nexus approach for sustainable management of global resources faces significant challenges due to differences in data collection protocols, data representation standards, access to complete data and data analysis tools. In addition, the FEW system of systems provides major challenges and opportunities for novel data science research. Although data science analysis methods extensively applied to large and complicated systems, such as social networks, data science efforts in complex physical systems (e.g., system of FEW systems) have been far more meager. Given FEW systems' rich data-driven history, there is a tremendous opportunity to systematically integrate novel large-scale data analysis methods with the physical, experiential, process oriented, and even conceptual knowledge that the broad climate, water, and energy research communities have developed. In addition, data science methods need to account for dependence between models, variables, locations and seasons (of food, energy and water systems) to reduce the risk of yielding misleading results.

There is a tremendous need to significantly advance data science and realize the promise of the nexus approach to meet societal challenges in the face of population growth, urbanization and climate change. The proposed workshop will gather thought leaders from both data science and the relevant areas of system of food, water, and energy systems. This workshop will use both FEW nexus pull and data science technology push discussions to identify data science challenges in understanding, appreciating and visualizing the FEW systems. The first two successive sessions will explore these opposing directions. The second day will identify FEW inspired data science grand challenges in a synthesis session. Specifically, the goal of this workshop will be to create a vision of how data driven methods could make a significant contribution to understanding interactions between FEW systems and what research is needed to realize that vision. This proposal provides a detailed schedule of milestone and tasks including this team's resume for leading visioning workshops. The proposed workshop will facilitate and enable interdisciplinary partnerships between data scientists and FEW nexus researchers from academia, industry and federal agencies to develop innovative, interdisciplinary research approaches enhancing the understanding, appreciation, and visualization of the interactions between FEW systems. It has potential to formulate next generation data science research agenda towards better understanding, appreciation and visualization of the interactions and interdependencies among FEW systems. Workshop report will be included in reading lists of graduate courses on data science in Ph.D. to integrate the results in education. The report will also be used in professional graduate data science degrees for workforce training. A key goal will be to diversify participation across career stages, under-represented groups, geographies, and disciplines (e.g., machine learning, data mining, geo-spatial analytics, and nexus of food, water and energy systems).

This infrastructure project responds to the urgent need of managing and analyzing big spatial and spatio-temporal data. Such data is continuously produced from various devices including smart phones, space telescopes, and medical devices. The project goes beyond the idea of supporting spatial and spatio-temporal data using general purpose big data engines, by developing specialized spatial and spatio-temporal big data systems and algorithms. The hardware infrastructure acquired in this project, including new architectures such as Graphical Processing Units (GPU) and Solid State Drives (SSD), is used to study and develop spatial and spatio-temporal big data systems and techniques for various software and hardware platforms.

The software platforms studied range from traditional parallel database systems to state-of-the-art distributed computing platforms. Hardware platforms include large clusters of distributed computed nodes of commodity machines, GPUs, and SSDs. Spatial and spatio-temporal big data techniques investigated include indexing, querying, and map rendering. The project encapsulates its developed techniques in publicly available open-source free software. In addition to use within the University of Minnesota, the acquired hardware infrastructure is made accessible to developers, practitioners, and researchers worldwide through a wide set of publicly available web services for spatial and spatio-temporal big data.

This project will investigate a smart urban infrastructure systems framework for advancing access and wellbeing in cities. With transformative new infrastructures (e.g., smart electricity grid, urban farms) on the horizon, this research will provide new perspectives on how the future spatial deployment of these new infrastructures in cities will shape wellbeing, health, and the environmental sustainability of outcomes in the different areas of cities. The project advances basic research in multiple disciplines including environmental and civil engineering, computer science, urban planning and public policy. It will create a unique public database, establish citizen science protocols, and advance the science of smart sustainable urban systems through knowledge co-production with cities engaged in infrastructure planning. The project will engage in educational activities through interdisciplinary training for graduate students and professionals in urban planning, policy and sustainability. Furthermore, a strong component of citizen science engagement is involved through K-12 teachers and students, particularly in schools with underrepresented populations.

Environmental sustainability, human health and wellbeing outcomes in cities are significantly shaped by key physical infrastructure provisions of water, energy, food, shelter, transportation-communications, sanitation waste management and public spaces, as well as their interactions with the social, environmental and urban form parameters. The investigators will conduct an interdisciplinary, community-engaged research project in the cities of Minneapolis, St Paul, and Tallahassee. The research will engage four themes: (a) Develop the first comprehensive fine-scale intra-urban database of over 100 social-ecological-health and well-being parameters via novel citizen science/crowdsourcing campaigns using low cost sensors; (b) Develop advanced computational algorithms to uncover hotspots and spatial correlations in the data and evaluate data-driven as well as discipline-inspired access and wellbeing hypotheses; (c) Using outcomes from (a) and (b) develop connected multi-infrastructure futures scenario models with new infrastructures through shared scenario visioning exercises, and evaluate policy learning and value of information; (d) Focus on education and workforce development for middle-high schoolers, graduate students and sustainability professionals. Outcomes from this research will be useful for informing citizens and policymakers about smart infrastructure transition being planned in cities.

Transportation accounts for over a quarter of U.S. energy use and greenhouse gases as well as hundreds of thousands of premature deaths annually from toxic emissions such as Nitrous oxides. Therefore, reducing harmful vehicle emissions and energy consumption are important goals for our society and transportation science. A key challenge is the limited understanding of emissions and energy-consumption during real-world driving. This project investigates the potential of emerging vehicle big data to further the understanding of emissions and energy consumption during real-world driving. Currently underutilized by vehicle manufacturers and regulatory agencies, vehicle big data details emissions and energy use at high frequency and spatial resolution. It has rich information to help identify patterns of unacceptably high emissions or energy use as well as associated vehicle properties or road features. Such patterns will be used to improve prediction of emissions and energy use during real-world driving. In doing so, the research will lead to improved vehicle design and operation practices to reduce future emissions and energy use to save lives by improving air-quality and dampening climate change. It will also improve education through a creative eco-driving challenge to maximize distance travelled for a fixed energy (or emission) budget in a driving simulator environment.

The goal of this project is to build next-generation spatio-temporal informatics (STI) tools to analyze emerging vehicle big data such as on-board diagnostics data to further the understanding of real-world emissions and energy consumption. The specific aims are to explore a set of concepts and develop a set of spatio-temporal informatics tools to: (a) provide a mapping between the concepts in transportation science and current informatics methods, (b) conveniently represent common patterns of interest to transportation scientists and practitioners, (c) efficiently mine novel, useful and interesting spatio-temporal patterns from vehicle big data, (d) use mined patterns to improve the physical science models of real-world vehicle emissions and energy use, and (e) integrate research results in education via eco-driving activities. The project will advance STI knowledge and understanding in multiple ways. For example, it will probe new algorithms to detect statistically-significant linear hotspots of high emissions or energy inefficiency even if these are not along shortest paths by considering simple paths in a transportation network. Furthermore, it will design new strategies to efficiently mine spatio-temporal co-occurrence patterns even when those are not prominent globally over the entire road network. The project will broaden STI's focus from simple GPS-trajectory data to multi-attributed trajectory data such as vehicle on-board diagnostics data with hundreds of physical variables and constraints. It will also enrich current laboratory and test-track focused transportation science by improving understanding of real-world energy-use, emissions, and physical science models used to predict these factors.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

This project builds on a prior Midwest Big Data Hub effort. In 2015 stakeholders in the Midwest region of the United States formed a consortium of partners and working groups called the Midwest Big Data Hub (MBDH). MBDH aimed to help member organizations working in Big Data coordinate current activities and launch new collaborative projects. The project included stakeholders in the twelve states of the Midwest Census region (Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin) and six leading universities that support hundreds of researchers, technologists, and students. This hub provides a basis for collaboration and outreach that increases the potential for benefitting society.

The current award is a collaboration among five academic sites (Indiana University, Iowa State University, UIUC/NCSA, the University of Michigan, the University of North Dakota, and the University of Minnesota - Twin Cities). The project focuses on priority areas that are important to the region and can also be influential on the national stage.
- The five thematic areas of focus, and the institutional partner leading that thematic area, are: Digital Agriculture (led by Iowa State); Smart, Connected, and Resilient Communities (Indiana University); Water Quality (University of Minnesota); Advanced Materials and Manufacturing (UIUC); and Health and Biomedicine (University of Michigan).
- Three cross-cutting areas that are emphasized across the project are: data science education and workforce development; cyberinfrastructure, data access and use; and communication and community development.
The priority areas have regional relevance and also have the prospect for integration into societal contexts at the national level. The overall goal is to enable the use of existing and emerging cyberinfrastructure and best practices to improve access to and use of data. The project plans to reach out to the Midwest community at large and to connect people, resources, and organizations. Ties to Big Data Hubs in three other regions provide a means to advance knowledge across these fields at the national level.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

The COVID-19 pandemic has impacted public health with a large number of mortalities and ravaged the economy by increasing unemployment to a historically high level. The goal of this project is to investigate the potential for novel spatiotemporal big data to assist in identifying COVID-19 related geographic patterns, such as locations where groups of people visit for long, overlapping times, and travel to and from hotspots. Such patterns are of great interest to policy-makers and public health researchers, but are difficult to find in traditional mobility datasets such as infrequent travel surveys and urban highway traffic data. Example spatiotemporal big data include privacy-protected aggregated location traces of mobile devices that have recently been opened for COVID-19 research. If successful, the results will inform disease spread models and policy-interventions to save lives and reopen the economy safely.

This project is expected to result in multiple data science and computer science innovations. First, it will define and quantify hangout-venues, a novel spatiotemporal pattern family modeling the places with many overlapping long visits. Examples include full-service dine-in restaurants which have many long visits, but not limited-service restaurants which mostly have short visits. Second, it will probe new interest measures to not only distinguish between patterns (e.g., full-service restaurants) and non-patterns (e.g., limited-service restaurants) but also support the design of computationally efficient algorithms based on properties such as anti-monotone. Third, it will design novel and scalable algorithms for analyzing the large (tens of terabytes) dataset for hangout-venues. Fourth, it will investigate the impact of selection bias and noise from differential privacy schemes. The results have the potential to transform data science knowledge with novel pattern families (e.g., hangout-venue) and improve the understanding of the impact of selection bias and noise added by differential privacy schemes on pattern mining methods and their results. Furthermore, the project will co-produce knowledge in close collaboration with public health researchers and policymakers. The results have the potential to transform the understanding of the public mobility for modeling disease transmission dynamics by leveraging the emerging spatiotemporal big data.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.