Data Rodeo: A Data Analytics Environment for the Central Texas Region
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2016 D-STOP Symposium ("Smart Cities") session by CTR's Jen Duthie. Get symposium details: http://ctr.utexas.edu/research/d-stop/education/annual-symposium/
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My presentation to the August 2014 ABP RhD "Dialogue" peer-talks series, "Examples of Collaborative Data, and Free and Open Source Software, of interest to Urban Researchers”.
5. big data vs it stki - pini cohen
The document discusses big data and how it differs from traditional IT approaches. It defines big data using the four V's - volume, velocity, variety, and variability. Technologies used for big data like Hadoop, MapReduce, and NoSQL databases are outlined. Differences between big data infrastructure and traditional IT infrastructure and BI are explored. Examples of how Orbitz and the DoD use big data are provided. The business value of big data analytics is discussed as enabling new types of analysis and insights not previously possible.
Open data and community engagement
A presentation I gave to the 100% Digital Brazil conference about open data, data.gov.au, GovHack and Gov as an API. Enjoy!
Open Data Initiatives
This document discusses open data initiatives and provides guidance on starting an open data program. It defines open data as government data that is freely available in a machine-readable format for public use and reuse. Making data open means it is technically accessible in standard formats and legally open with an open license allowing commercial and non-commercial use. Open data programs aim for transparency, citizen engagement and business support. The document lists examples of open data from different cities and recommends choosing a initial data set, applying an open license, formatting it for audiences, and creating a central portal for discovery. It also discusses using hackathons to support open data and the need for open data policies, resources, and sustainability plans.
Dotted Eyes - Open Software, Standards and Data
Dotted Eyes is a UK-based spatial solutions provider with over 20 years of experience. They take a solution-led approach, focusing on open software, standards and data to provide tailored solutions that best meet customers' requirements. Case studies show how open solutions can help keep transport maps up to date for events and provide a cost-effective hosted application for contractor data analysis.
Deployment strategies of Open Data Node focused mainly on pilots (2015-May)
This document discusses enabling open data management in public institutions through a pilot deployment of the COMSODE Open Data Node. It describes the benefits of open data for public administrations, private sectors, and civil society. Public institutions are invited to participate in the pilot program and will receive assistance from COMSODE in categorizing their data, setting up processes to publish select data through the Open Data Node tools, and getting trained on using the platform.
Geospatial Intelligence Middle East 2013_Big Data_Steven Ramage
Some initial considerations and discussion points around geospatial big data. Location adds context and relevance. Need to consider a number of V factors including Value.
Open source for customer analytics
Presented at "Customer Insight & Analytics Conference" in London 2-3 December 2015. Introductory level, 30 mins duration.
Datapedia Analysis Report
https://www.youtube.com/watch?v=nvlHJgRE3pU
Won ITAC Graduation Projects Competition, ITAC ID: GP2015.R10.75
A web application that analyze big volumes of product reviews, social networks posts and tweets related to a given product. Then, present these results of this big data analytical job in a user friendly, understandable, and easily interpreted manner that can be used by different customers for different purposes.
Technologies used:
1- Hadoop
2- Hadoop Streaming
3- R Statistical
4- PHP
5- Google Charts API
02 a holistic approach to big data
The document outlines an agenda for a presentation on big data. It discusses key topics like the state of big data adoption, a holistic approach to big data, five high value use cases, technical components, and the future of big data and cloud. The presentation aims to provide an overview of big data and how organizations can take a comprehensive approach to leveraging their data assets.
Boost your data analytics with open data and public news content
Get guidance through the gigantic sea of freely available Open Data and learn how it can empower you analysis of any kind of sources.
This webinar is a live demo of news and data analytics, based on rich links within big knowledge graphs. It will show you how to:
Build ranking reports (e.g for people and organisations)
View topics linked implicitly (e.g. daughter companies, key personnel, products …)
Draw trend lines
Extend your analytics with additional data sources
Towards the Open Geospatial Web (eurogeographics edition)
A further iteration of the talk given in Trinidad and Finland. This one goes in to creation of geospatial information in addition to the sharing.
Advanced Analytics and Machine Learning with Data Virtualization
Watch full webinar here: https://bit.ly/32c6TnG
Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python and Scala put advanced techniques at the fingertips of the data scientists. However, these data scientists spent most of their time looking for the right data and massaging it into a usable format. Data virtualization offers a new alternative to address these issues in a more efficient and agile way.
Attend this webinar and learn:
- How data virtualization can accelerate data acquisition and massaging, providing the data scientist with a powerful tool to complement their practice
- How popular tools from the data science ecosystem: Spark, Python, Zeppelin, Jupyter, etc. integrate with Denodo
- How you can use the Denodo Platform with large data volumes in an efficient way
- About the success McCormick has had as a result of seasoning the Machine Learning and Blockchain Landscape with data virtualization
Similar to Data Rodeo: A Data Analytics Environment for the Central Texas Region (20)
Open Data Portals: 9 Solutions and How they Compare
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[Srijan Wednesday Webinar] Leveraging the OGD Platform and Visualization Engine
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Geospatial Intelligence Middle East 2013_Big Data_Steven Ramage
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Advanced Analytics and Machine Learning with Data Virtualization
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More from Center for Transportation Research - UT Austin
Flying with SAVES
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/
Regret of Queueing Bandits
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/
Advances in Millimeter Wave for V2X
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/
Collaborative Sensing and Heterogeneous Networking Leveraging Vehicular Fleets
Collaborative Sensing and Heterogeneous Networking Leveraging Vehicular FleetsCenter for Transportation Research - UT Austin
This document discusses ongoing research projects related to collaborative sensing and heterogeneous networking leveraging vehicular fleets. Specifically, it discusses:
1) How increased cluster density of vehicles improves overall data rates and reduces variability in individual user rates.
2) Modeling what collaborative sensing systems can "see" or be aware of in obstructed environments and how coverage benefits scale with increased penetration of collaborative vehicles.
3) Developing optimal information sharing policies to maximize situational awareness for autonomous nodes in resource-constrained network environments.Collaborative Sensing for Automated Vehicles
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/
Statistical Inference Using Stochastic Gradient Descent
Statistical Inference Using Stochastic Gradient DescentCenter for Transportation Research - UT Austin
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/CAV/Mixed Transportation Modeling
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/
Real-time Signal Control and Traffic Stability / Improved Models for Managed ...
Real-time Signal Control and Traffic Stability / Improved Models for Managed ...Center for Transportation Research - UT Austin
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/Sharing Novel Data Sources to Promote Innovation Through Collaboration: Case ...
Sharing Novel Data Sources to Promote Innovation Through Collaboration: Case ...Center for Transportation Research - UT Austin
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/UT SAVES: Situation Aware Vehicular Engineering Systems
UT SAVES: Situation Aware Vehicular Engineering SystemsCenter for Transportation Research - UT Austin
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/Regret of Queueing Bandits
Online platforms are emerging as a powerful mechanism for matching resources to requests. In the setting of freight, the requests arrive from shippers, who have a diverse collection of goods. The resources are supplied by shippers (trucks), and have various physical constraints (driver’s route preferences, carrying capacity, geographic preferences, etc.). Online platforms are emerging that (a) learn the characteristics of shippers and carriers, and (b) efficiently match goods to trucks based on such learning.
Our project will develop algorithms for such online resource allocation. This is a challenging problem, due to the complexity of the learning tasks. Such algorithms can have considerable impact on efficiently using trucking resources.
Sharing Novel Data Sources to Promote Innovation through Collaboration: Case ...
Sharing Novel Data Sources to Promote Innovation through Collaboration: Case ...Center for Transportation Research - UT Austin
Through this project, the research team will leverage the computing resources and expertise at UT to develop a “data discovery environment” for transportation data to aid decision-making. Many efforts focus on leveraging transportation data to help travelers make decisions, but less thought has gone into a framework for using big data to help transportation agency staff and decision makers. The team will start by building the DDE for the Central Texas region, in collaboration with the local MPO, the City of Austin, and the local transit agency. Initially, the project will focus on creating more meaning from existing data sources, and as the project progresses, it will grow to include more novel data sources and methods. The data platform will be web-based and part of the research includes not only building the tool but developing appropriate protocols for access and governance.CAV/Mixed Transportation Modeling
This document discusses modeling strategies for autonomous and connected vehicles. It proposes modifying traditional four-step transportation models to account for autonomous vehicle adoption rates and different trip types. Autonomous vehicle passenger car equivalents and flow ratios are modeled based on vehicle speed, market penetration, and other factors. The document also describes plans for a 4G deployment test bed to demonstrate connected vehicle technologies on managed lanes in Dallas-Fort Worth and Virginia.
Collaborative Sensing for Automated Vehicles
Advanced driver assistance systems (ADAS) are a key technology for improving road safety. But both current and proposed ADAS are limited in important ways. Vision- and lidar-based ADAS performs poorly in heavy rain, snow, or fog. Lack of vehicle situational awareness due to these sensing limitations will unfortunately be the cause of many accidents, including fatalities, for connected and automated vehicles in the years to come. The goal of this research is to develop and test a sensing strategy with robust perception: No blind spots, applicable to all driveable environments, and available in all weather conditions. We believe there are three key requirements for collaborative all-weather sensing:
– Precise vehicle positioning within a common reference frame
– Decimeter-accurate vision and radar mapping
– A means of quantifying the benefits of collaborative sensing
Advances in Millimeter Wave for V2X
Vehicular radar and communication are the two primary means of using radio frequency (RF) signals in transportation systems. Automotive radars provide high-resolution sensing using proprietary waveforms in millimeter wave (mmWave) bands and vehicular communications allow vehicles to exchange safety messages or raw sensor data. Both the techniques can be used for applications such as forward collision warning, cooperative adaptive cruise control, and pre-crash applications.
Statistical Inference Using Stochastic Gradient Descent
Statistical Inference Using Stochastic Gradient DescentCenter for Transportation Research - UT Austin
Many areas of machine learning and data mining focus on point estimates of key parameters. In transportation, however, the inherent variance, and, critically, the need to understand the limits of that variance and the impact it may have, have long been understood to be important. Indeed, variance and other risk measures that capture the cost of the spread around the mean, are critical factors in understanding how people act. Thus they are critical for prediction, as well as for purposes of long term planning, where controlling risk may be equally important to controlling the mean (the point estimate).
There has been tremendous progress on large scale optimization techniques to enable the solution of large scale machine learning and data analytics problems. Stochastic Gradient Descent and its variants is probably the most-used large-scale optimization technique for learning. This has not yet seen an impact on the problem of statistical inference — namely, obtaining distributional information that might allow us to control the variance and hence the risk of certain solutions.Status of two projects: Real-time Signal Control and Traffic Stability; Impro...
Status of two projects: Real-time Signal Control and Traffic Stability; Impro...Center for Transportation Research - UT Austin
Investigation and findings on reservation-based intersections and managed lanes
Real-Time Signal Control and Traffic Stability
Congestion on urban arterials is largely centered around intersection control. Traditional traffic signal schemes are limited in their ability to adapt in real time to traffic conditions or by their ability to coordinate with each other to ensure adequate performance. Specifically, there is a tension between adaptivity (as with actuated signals) and coordination through pre-timed signals (signal progression). We propose to investigate whether routing protocols in telecommunications networks can be applied to resolve these problems. Specifically, the backpressure algorithm of Tassiulas & Emphremides (1992) can ensure system stability through decentralized control under relatively weak regularity conditions. It is as yet unknown whether this algorithm can be adapted to traffic signal systems, and if so, what modifications are needed. Traffic systems differ in several significant ways from telecommunication networks: each intersection approach has relatively few queues (lanes) that must be shared among traffic to various definitions. First-in, first-out constraints lead to head-of-line blocking effects, traffic waves move at a much slower speed than data packets, and traffic queues are tightly limited by physical space (finite buffers). Determining whether (and how) the backpressure concept can be adapted to traffic networks requires significant research, and has the potential to dramatically improve signal performance.
Improved Models for Managed Lane Operations
Managed lanes (ML) are increasingly being considered as a tool to mitigate congestion on highways with limited areas for capacity expansion. Managed lanes are dynamically priced based on the congestion level, and can be set either with the objective of maximum utilization (e.g., a public operator) or profit maximization (e.g., a private operator). Optimization models for determining these pricing policies make restrictive assumptions about the layout of these corridors (often a single entrance and exit) or knowledge of traveler characteristics on behalf of the modeler (e.g., distribution of willingness to pay). Developing new models to address these issues would allow for better utilization of these facilities.SAVES general overview
Professor Robert W. Heath Jr. is the director of UT SAVES (Situation-Aware Vehicular Engineering Systems), which combines expertise in wireless communications, signal processing, and transportation research. UT SAVES collaborates with automotive companies like Honda R&D Americas on projects involving sensing, communication, and analytics for applications such as automated driving. Membership provides access to UT SAVES research and facilities, including graduate research assistants and experimental capabilities in areas like millimeter wave communication and sensor fusion. Current research projects focus on cooperative sensing, vehicle-to-everything communication, and applying 5G cellular networks to driving assistance technologies.
D-STOP Overview April 2018
The Business Advisory Council meeting covered the following topics in 3 sentences or less:
The meeting covered updates on education and workforce development programs at the Engineering Education and Research Center including summer internships and distinguished lectures. Research updates were provided on 30 completed projects and 18 ongoing projects covering topics like connected corridors and autonomous vehicles. New proposed research was presented on topics such as video data analytics, traffic signal optimization, and modeling willingness to share trips in autonomous vehicles.
Managing Mobility during Design-Build Highway Construction: Successes and Les...
Managing Mobility during Design-Build Highway Construction: Successes and Les...Center for Transportation Research - UT Austin
The document discusses managing mobility during the design-build reconstruction of the Dallas Horseshoe highway interchange project. It describes the project's high traffic volumes and constraints. It highlights the contractor's successes in maintaining access and maximizing work during limited closures. It stresses the importance of collaboration between the agency and contractor in developing traffic control plans and finding solutions to difficult situations.More from Center for Transportation Research - UT Austin (20)
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GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
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Introduction of Cybersecurity with OSS at Code Europe 2024
Introduction of Cybersecurity with OSS at Code Europe 2024
Data Rodeo: A Data Analytics Environment for the Central Texas Region
- 1. A Data Analytics Environment for the Central Texas Region Dr. Jen Duthie, PE D-STOP Symposium April 2016 DATA RODEO
- 2. Access to data & tools Innovation & collaboration Data-centric research & learning Enable Smart Cities Goals
- 3. Public release of select data Open Portals • City • State • OpenStreetMapPublished/crowd-sourced data Public Sector Agencies Private Sector • App developers • Communications • OEMs AgencyData RegionalData Map image: screenshot from http://www.openstreetmap.org , TMC: Source: http://www.moxa.com ; Full link at http://bit.ly/1qfBJrd D-STOP + TACC • Storage • Analytics • Research • Education Community • Open Portals • OpenStreetMap Rodeo - Framework
- 4. Open: access data, access code Scalable: individuals, cities, regions Replicable: works here, works there Empowering: easy access, new insights Guiding principles
- 5. Server Side Leveraging TACC @ UT Hosted in the cloud Geospatial versioning (e.g., GeoGig) Conversion for common data types (i.e., ETL) Free and open source components DATA RODEO Client Side Edit via free tools (e.g., iD, QGIS) Edit via proprietary tools (e.g., ArcGIS) Analyze via custom tools Visualize via data dashboard Access via public API
- 7. Lower barrier to entry for transportation modeling and analytics Test predictive models against reality Enable coordination for regional planning Data-driven policies RODEO-Enabled innovations
Editor's Notes
- Guiding principles Archive transportation-related data from multiple sources (private & public) Provide easy and intuitive access to data & data analysis tools through a web framework Enable use of advanced modeling tools & computational resources through framework Promote “reproducible science” & faster technology transfer – Enable research
- Check these.. Do we need a vision statement?