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.
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.
OmniSci is designed for big data but for custom computations the data needs to be retrieved to a client machine. This puts a ceiling on data sizes based on both network speed and local hardware resources. What if we could send the algorithm to the data instead?
This talk presents the Remote Backend Compiler (RBC), a package that affords OmniSci database capabilities to execute functions written in Python inside SQL queries. Our approach uses Numba, a high performance Python compiler, to generate fast low level machine code of user defined functions. We will learn more about how the RBC connects open source technologies, like NumPy and Numba, to the OmniSci database. Lastly, we conclude with a demonstration using an applied example of the Black-Scholes financial model. This will highlight how to leverage the newly added User-Defined Functions and User-Defined Table Functions capabilities with OmniSci.
Altair is a lovely tool that lets you build up complex interactive charts in Python. Ibis is also a lovely tool that lets you use a Pandas-like API to compose SQL expressions in OmniSci and other backends. By tying them together you can use the familiar syntax of Pandas, combined with the expressive power of Vega and Vega Lite, to visualize large amounts of data stored in OmniSci. This talk will walk through a number of examples of using this pipeline and then go through how it works.
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.
Eliot Eshelman, VP HPC Initiatives, Microway & Michael McCracken, Federal Sales Engineer, OmniSci
Today's agencies face a huge challenge: they have an avalanche of data from billions of sources but struggle to deliver information dominance at the speed of thought.
During this webinar, the team from OmniSci and Microway will show how GPU-accelerated analytics solve today's data analytic challenges for government and enable agencies to:
- Combine visual analytics and geo-visualization
- Attack problems previously considered too large, too complex or too time consuming
- Eliminate downsampling, pre-aggregation, and partial visibility
- Convert millions and billions of records of data into better, more actionable information
- Fuse data from multiple data providers
- Reduce costs and the power necessary to complete the mission
To show these capabilities for all government & agency customers, we will analyze flight traffic patterns during the COVID-19 pandemic as an example unclassified dataset.
With the OmniSci GPU-accelerated analytics platform and Microway’s WhisperStation, we will trace insight from the trajectory of flight patterns during stay-at-home orders, reopening of airports and commercial airline flights, and commercial airline flight destinations and size of planes using data from multiple data providers fused under a single pane of glass. These same capabilities can translate to your mission and unique data.
:During this session we’ll explore the spread of the COVID-19 global pandemic by analyzing and visualizing billions of rows of records with near-zero latency. Adam Edelman, Federal Solutions Engineer at OmniSci, will explain OmniSci’s GPU-accelerated analytics platform before providing a demonstration of its powerful capabilities.
Learn how massive data sets of anonymized data can be leveraged for contact tracing, understanding population movement, and identifying trends around specific points of interest.
Learn how to build cohorts to optimize analytic queries and rapidly spot new pandemic hotspots.
Learn how OmniSci bookends data science workflows by integrating with popular data science tools.
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.
Cecile Texenas du Montcel, Advanced Compute & Solutions Business Development Manager, HP
n a world of emerging technologies such as 5G and IOT, the data volume is exploding; 80% generated at the edge in conjunction with phenomenal GPU processing power enhancement. Shifting intelligence to the point data is created opens a new world of possibilities: saving cost, enabling real time access & increasing security. HP Z workstations are the perfect way to access data sets and run models locally in combination with OmniSci solutions.
Eric Kontargyris, Principal Solutions Consultant, OmniSci
With the emergence of 5G, Digital Service Providers (DSPs) need to transform their business assurance solutions to support the dynamism and complexity that 5G offers. Business Assurance must adopt artificial intelligence, machine learning and Open API’s for the 5G verticals’ digital ecosystems to support the DSP’s business goals of improved revenue and customer experience beyond the traditional value chain. This demo will demonstrate how to use AI to assure 5G services, including 5G slicing, perform as planned with API-based use cases including customer experience, churn-management and fraud-management.
View historical flight data such as delays and other activity from almost 3 decades, and see which airlines got you there on time. Only OmniSci makes this demanding level of flight data processing possible.
Mike Flaxman, Spatial Data Science Practice Lead, OmniSci
The 5G telco networks of the future are a significant capital investment, requiring 10x as many towers. Knowing where to efficiently and cost-effectively place these femto, pico, and micro cell towers is critical to telco carriers. Too few towers means poor cell reception and costly customer churn. Too many towers cuts into your bottom line.
In this webinar, experience GPU-accelerated analytics through a real-life 5G network planning demo.
Learn how OmniSci can be used to combine RF mapping and demographic data sets at scale using service quality and NPV measures to optimize both cost-effectiveness and performance.
Venkat Krishnamurthy, VP of Product Management, OmniSci
This webinar will discuss OmniSci’s vision of pioneering modern hardware and software to allow for data insights at the speed of curiosity. One catalyst for this effort is through the open data science stack of today that is clearly a de-facto platform for experimentation. Through our shared vision and partnership with Intel, we are working to advance and unify the worlds of Data Science with traditional analytics on modern hardware. We will discuss the relationship between data science applications and data discovery along with the collaboration between OmniSci and Intel to advance innovation within this ecosystem.
Mike Flaxman, Spatial Data Science Lead, OmniSci & Adam Edelam, Federal Solutions, OmniSci
Finding purposely-hidden nuclear sites is hard. But new tools and datasets allow analysts to interactively explore huge geotemporal datasets. OmniSci has recently partnered with the Center for Nonproliferation Studies (CNS) and Planet to demonstrate how daily satellite imagery, machine learning for feature extraction, and interactive analytics can help make the world safer. CNS continually assesses potential nuclear missile production sites. It has found that in North Korea these are often hidden at the ends of new mountain roads. How can we turn this insight into actionable data?
OmniSci’s GPU database technology lets us combine several factors into a suitability model considering roads and their relationships to terrain. We leveraged an amazing new machine learning product from Planet - a monthly road change dataset at 5 meter resolution. We combined this with absolute elevation, percent slope and topographic position. Since there are less than 20 known sites, we elected to use a “human in the loop” process to empower analysts to assess the parameters of known sites semi-manually, and then to search for similar sites across the full country. This allowed us to discover hundreds of potential new sites, which CNS plans to further explore and then monitor.
Amdocs, Verizon Ventures, Reliance Jio, Charter Communications
Recording from the TM Forum Virtual Leadership Summit on 7/1/2020
This summit will discuss how CSPs can leverage their largest datasets, and the predictive power of artificial intelligence (AI) and machine learning (ML), to design-in greater resilience to their network and business.
Black swan events like the current pandemic reinforce the necessity of strong resiliency, and we’ll show examples of how CSPs are combining multiple sources of big data to dynamically assign resources, respond to changes, and handle major network planning and improvement projects like 5G.
We will also show how improving the business resilience directly impacts other critical facets of the business, including customer churn, and business assurance.