Boa is a domain-specific language and infrastructure that eases mining software repositories. Boa's infrastructure leverages distributed computing techniques to execute queries against hundreds of thousands of software projects very efficiently.
Graph-based data processing algorithms impact a variety of application domains ranging from transportation networks, artificial intelligence systems, cellphone networks, social networks, and the Web. Nevertheless, the emergent big-data era poses key conceptual challenges: several existing graph-based methods used in practice exhibit unreasonably high running time; several other methods operate in the absence of correctness guarantees. These challenges severely imperil the safety and reliability of higher-level decision-making systems of which they are a part.
Creating a computational infrastructure to analyze the wealth of information contained in data repositories that scales well is difficult due to significant barriers in organizing, extracting and analyzing relevant data. Shared Data Science Infrastructures like Boa can be used to more efficiently process and parse data contained in large data repositories. The main features of Boa are inspired from existing languages for data intensive computing and can easily integrate data from biological data repositories.
Boat is a domain specific language on top of Hadoop to analyze transportation data.
A Cyberinfrastructure for Big Data Transportation Engineering has published in the Journal of Big Data Analytics in Transportation
A key problem in computational material science deals with understanding the effect of material distribution (i.e., microstructure) on material performance. The challenge we consider here is to synthesize microstructures with desired physical and chemical properties, given a finite number of microstructure images, evaluated based on the physical invariances that the microstructure exhibits.
Phase retrieval (PR), or 'signal recovery from phaseless measurements', is a problem that occurs in numerous signal/image acquisition domains, such as Fourier ptychography and sub-diffraction imaging, in which only the magnitude (intensity) of certain linear projections of the signal or image can be measured. While PR is a classical problem, in recent years there has been renewed interest in PR with the goal of developing provably correct and fast algorithms.
Software development is inherently incremental. Nowadays, many software companies adopt an agile process and a shorter release cycle, where software needs to be delivered faster but with quality assurances, and many versions of software may co-exist in the field and need to be maintained. While faster releases do not increase the user's tolerance of bugs, it is challenging to correctly introduce a change on top of existing programs.
In many software engineering environments, software developers would like to understand, test, debug and verify a relatively small fragment of code instead of the entire program. However, currently available program analysis and testing tools only work on programs that are whole, in the sense that they can be compiled and executed as a whole program.
With the rapid integration of massive amounts of data and new network devices, today's network infrastructures are being stretched to their limits. As a result, recent years have witnessed a critical need for developing fast-converging distributed stochastic network control and optimization algorithms to increase throughput and reduce delay.
Iowa DOT consumes data from multiple streams, including probe data from INRIX and weather data from Mesonet, which is stored to assist in smart decision making. The cumulative data size for the past 5 years of data can easily be in the range of 15–20 terabytes.
Despite access to unprecedented amount of data, decision makers are often restricted in their ability to explore these data sets. Under the present set up, a simple query, such as how many crashes happen during congested conditions, can’t be answered easily and requires a dedicated research project.
The research team will work closely with the Iowa Department of Transportation’s Office of Traffic Operations for input and feedback specific to connected autonomous vehicle (CAV) research, applications, data analytics, demonstrations, and discovery. This project will comprise multiple tasks, including research and support.
This work is focused to support Iowa DOTs vision of delivering a safe, reliable, and efficient transportation system by developing an AV-ready driving environment. The work focuses on priority areas for the DOT as part of an overall CAV focus.
The Federal Highway Administration estimates that a quarter of the congestion on U.S. roads is due to traffic incidents such as a crash, an overturned truck, or stalled vehicles. Congestion costs the commercial trucking industry $9.2 billion annually, and incidents have been shown to increase the risk of secondary crashes by 2.8 percent with every minute of congestion.
The objective of this project is to develop a prototype system that demonstrates the potential of using existing closed circuit TV (CCTV) cameras to detect and monitor traffic conflicts. The Iowa Department of Transportation (DOT) has deployed more than 365 cameras that are currently used for manual verification of events and road-conditions on the freeway system. These cameras are connected and can be accessed over the network.
Understanding the causal mechanisms underlying an observed phenomenon is one of the primary goals of science. The realization that statistical associations in themselves are insufficient for elucidating those mechanisms has led researchers to enrich traditional statistical analysis with techniques based on "causal inference". Most of the recent advances in the field, however, operate under overly optimistic assumptions, which are often not met in practical, large-scale situations. This project seeks to develop a sound and general causal inference theory to cover those situations.
This National Science Foundation award provides support for the Conference Board of the Mathematical Sciences Conference "Harmonic Analysis: Smooth and Non-smooth" to be held at Iowa State University on June 4-8, 2018. In recent years there has been an increasing interest in understanding the harmonic analysis of non-smooth geometries that are unlike the familiar smooth Euclidean geometry, such as when nearby points are not locally connected to each other.
The immense amount of data at hand in modern applications creates challenges in storing, processing, and mining the data. Furthermore, datasets often are distributed, so that no single data center has all relevant data available to analyze. New mathematical and statistical algorithms are needed for analyzing distributed databases, particularly datasets that consist of spatiotemporal data. This project aims to address challenges posed by such massive datasets in an effort to better understand human dynamics. This project has the potential to benefit society in several ways.