Mike Flaxman, Spatial Data Science Lead, OmniSci & Adam Edelam, Federal Solutions, OmniSci
Today’s geospatial professionals are increasingly called upon to deliver content in the form of interactive web apps. This can be challenging with traditional architectures, both because of the inflexibility of conventional template-based approaches, and due to major performance issues when delivering large geotemporal data across the web. OmniSci is a GPU database with built-in high performance geographic rendering (capable of handling literally billions of features). OmniSci’s Immerse web application allows end users to build or customize a wide range of dashboards themselves. This workshop will present some typical GEOINT applications delivered as interactive dashboards, and will drill down into the construction of one of them in detail. Attendees will learn all three levels of a modern web stack. We will start by reviewing the capabilities of OmniSci Immerse. Next, we will dig into the capabilities of OmniSQL, which uses postGIS-like syntax to expose powerful geoprocessing tools. Lastly, we will walk through Jupyter Notebook integration, and show how Python and the pymapd library can be used to construct powerful workflows.
1. What is GPU analytics and why should I care? How can I get it?
2. How to create powerful geotemporal dashboards in Immerse
3. Using omniSQL to handle geotemporal data at scale
4. Leverage Jupyter notebook integration to optimize data pipelines supporting interactive use cases
Jon Stresing, Account Manager DoD, NVIDA & David Goodwin, Director DoD, OmniSci
The presence of extraordinary amounts of data, to train on, to learn from, and to explore, represents a golden age of computing. But beneath this incredible opportunity lies a massive challenge. Traditional CPU compute cannot keep pace with the growth in data, and as a result, even the most sophisticated organizations are unable to unlock its value. There is a new approach to computing and analytics that solves this yawning gap, involving the application of GPU compute from NVIDIA and analytical software from OmniSci Join OmniSci and NVIDIA to learn about GPUs, GPUs AI/ML, and how you can use NVIDIA and OmniSci for big data analytics and data science initiatives.
Herfini Haryono, VP Vertical Industry, OmniSci & Jared Ritter, Sr. Director Analytics & Automation, Charter Communications
As one of the earliest adopters and collectors of big data, Telcos are now leading the world in addressing the challenges that come from having lots of data, but oftentimes too few insights. Some of those challenges include:
- Multiple Sources of Data
- Lengthy ETL Processes
- Limited Data Science Teams
- Growing Data Science Teams
- Multiple Service Offerings
In this talk, Charter Communications and OmniSci will show how they are addressing these challenges for the telecom industry. We’ll demonstrate using GPUs and OmniSci’s accelerated analytics platform for fast queries and joins of multiple sources of data, interactive visualizations of spatiotemporal data, and integration with the latest AI/ML tools for running complete data science pipelines without the need for a large team of data scientists.
Dr. Mike Flaxman, Spatial Data Science Lead, OmniSci
5G Is on its way to consumer markets in the coming years. 5G network infrastructure is expected to completely revolutionize network connectivity. With proper 5G network planning and optimization, telecommunications companies will be able to deliver better customer experiences, solve complex problems, and move their business forward. 5G and big data go hand-in-hand. Experts predict 5G data usage could increase by 10-14 times current figures. This astronomical influx of 5G data creates an incredible opportunity for telco companies to explore real-time 5G insights and leverage 5G data analytics for 5G network optimization that gives your customers fast and consistent network connectivity at all times.
Abhishek Damera, Data Scientist, Product Management & Dr. Mike Flaxman, Spatial Data Science Practice Lead, OmniSci
Undercounting within the US National Census has always been a significant issue for specific populations, such as those with complex family structures. With the rise of Covid-19 and a shortened counting period, this year’s census is probably the most challenging to date. How can data science and new datasets help?
We found that modern machine learning techniques can provide the basis for much-improved census undercount prediction. We also found that the addition of GPS data and point of interest data increased model accuracy and provided further insight into behavioral factors beyond demographics which significantly affect undercount.
Tony Fast, Developer Advocate & Kim Pevey, Software Engineer, Quansight
Holoviz is a framework for visualization and application development that encourages annotating data to generate rich interactive visualizations and dashboards. Holoviz provides interfaces to multiple plotting backends in python including Bokeh and Matplotlib. This talk, will demonstrate brand new functionality showing how Omnisci, through the ibis framework, can now plot data using hvplot or holoviews. We’ll demonstrate these novel integrations with Omnisci in a series of computational notebooks that visualize different projections of Omnisci data, and deploy them as applications.
Abhishek Damera, Data Scientist & Dr. Mike Flaxman, Spatial Data Science Practice Lead, OmniSci
Human mobility patterns have been studied for millennia and are of obvious interest to transportation infrastructure and retail planners among others. It is only in the last few years that consumer GPS and cell phones have allowed observations of large samples over wide areas and long periods of time. Yet the mechanisms used by these services vary widely and should be expect by default to contain demographic sampling biases. We have conflated these GPS-derived spatial event stream data sources with traditional census block group data in order to quantify and calibrate them. We have used these adjustments to produce the first known map of US populations at an hourly basis before and during the COVID-19 epidemic. In order to protect individual privacy, we have deployed an explicit aggregation system using variable-sized hexagonal bins. Nonetheless, our maps represent much higher resolution data than conventionally available, ranging from 50m in dense urban areas to 1km in non density rural areas. Because sampling along roads is much higher than elsewhere, we are able to provide traffic estimates at high spatial precision nationally. In addition to overall population density, we have used overnight device dwell times in residential areas to infer the hourly demographic characteristics of areas based on device trajectories.
Mike Hinchey, Solutions Architect, OmniSci & Dr. Mike Flaxman, Spatial Data Science Practice Lead, OmniSci
As employees go back to work in offices in some countries, employers can use Network and Wifi connection logs to assess risk of disease transmission for high-traffic areas and proximity to individuals that are found to be infected.
In this overview talk, we'll showcase how Data Science workflows are a key part of the OmniSci platform, since the launch last year. We will showcase new integrations of OmniSci within the open PyData ecosystem, new foundational capabilities within the open source OmniSci engine useful for data science, and discuss joint work with our collaborators at Intel and Quansight, as well as some exciting future plans
In this session you will learn how to operationalize the insights gained through data exploration. We will walk the audience through a logistics use-case where modern AI techniques applied to the right data can accelerate optimization plans by many orders of magnitude and unlock new sources of revenue.