Partner Perspective

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.

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.

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.

Recording from the TM Forum Virtual Leadership Summit on 7/1/2020

From the TM Forum Virtual Leadership Summit on 7/1/2020

Recording from the TM Forum Virtual Leadership Summit on 7/1/2020

Recording from the TM Forum Virtual Leadership Summit on 7/1/2020

Topics Covered:
- What is needed to remove the scale and performance bottlenecks, and design for resiliency?
- What are the data science workflows and ML tools that unlock the value of predictive analytics?
- Demo: Hong Kong Network Traffic: How to combine multiple data sources, along with AI and ML, to improve network management during sudden changes in usage patterns.
- How do CSPs get started? What does a typical implementation plan look like?

Recording from the TM Forum Virtual Leadership Summit on 7/1/2020

Recording from TM Forum Virtual Leadership Summit on 7/1/2021

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.

From the TMForum Virtual Leadership Summit on 7/1/2020

Topics:

The unique benefits of 5G is expected to unlock $1.4tn of value in eight key industries by 2030. This is based on models generated from 200+ interviews and surveys with industry executives. What steps should operators take to deliver these benefits? What business and organisational transformations must occur to unlock this opportunity?

1) As we’ve seen earlier, the big data revolution has helped operators design-in resiliency , but it has also made it necessary for business leaders to invest in technologies that can handle the scale and performance of their largest datasets. Only decision makers with the best and most informed understanding of their data can set the standard for their business’s success. What are still the biggest barriers to leveraging your data? Where are your current tools struggling to meet your expectations?
2) “We simply can’t get hold of the data” Are data rules and regulations hampering CSPs usage of data? What is the opportunity to leverage the telecom data for your B2B business?
3) We’ve seen a sharp rise in operators adding predictive analytics, using AI and ML, into their data strategy to reduce costs, make faster, better decisions, and create new products or services to meet customers’ changing needs. Where is your organization in the adoption of predictive analytics and what kinds of benefits have you seen from leveraging the technology?

Introduction section to the TM Virtual Leadership Summit on 7/1/2020

Topics:
- Welcome & Introduction
- What is the definition and measure of network and business resiliency?
- What is the state of research in this area? How do black swan events reinforce the need?
- What types of data do CSPs have that give them insight into that resilience?
- Why have scale and performance become the critical bottleneck to improving resilience?
- What role does predictive analytics play, and more specifically what is the business value that AI/ML brings?
- Assessing the resilience of your networks: survey results
- Open poll for attendees: what are your challenges? What are you missing to address them?

Telco markets worldwide are highly competitive. Consumers have more options than ever to satisfy their telecommunications needs. This means providers are competing harder, and paying closer attention to critical factors like cost of customer acquisition, average revenue per unit (ARPU), and customer churn. The smartest providers understand the tight relationship between these issues and their massive, and growing, stockpiles of data.

Fortunately, there are new technologies that can handle billions of rows of data, joined across multiple datasets, with millisecond filtering and visualization times. With OmniSci, massive customer data is an asset instead of a barrier.

In this webinar, you will learn:
→ How OmniSci can query billions of rows of data, create interactive dashboards, literally at the speed of your curiosity
→ With OmnSci you can bring multiple cohorts of data together in one easy to interpret geospatial display
→ How this analysis capability, along with AI and ML, can unlock insights previously hidden, and allow us to predict the future and drive mitigation before it affects your networks or your customers.

Data and analytics have played a central role in the first wave of the global response to COVID-19. From the earliest days of the pandemic, we’ve had up-to-date reports of case counts, fatalities and recoveries gathered by industrious volunteers everywhere. See how OmniSci and our partners at AWS, Safegraph, Veraset, and X-Mode are using anonymized, data-driven methods to contribute to relief efforts at a national scale for the next phase of the COVID-19 response efforts.

Harvard’s Center for Geographic Analysis was established in 2006 to support research and teaching across all disciplines in the University as they relate to geospatial technology and methods. They recently launched an initiative that has made the OmniSci platform available to all researchers at Harvard, utilizing the university’s slurm HPC cluster. In this talk, Devika and Ben will share why OmniSci is relevant to their work, the kinds of challenges facing researchers in general, and some of the projects that are specifically benefitting from OmniSci at Harvard. They will also explain how they got OmniSci running within their slurm cluster, and share the scripts they’ve developed to make it possible for researchers to get an OmniSci instance, for large-scale research, with a few simple clicks.

Charter Communications is using Data Science to enhance Systems/Network Operations. Charter will present their evolution to a “Data-First” operations model. Q&A will follow, as well as a live demo of some of Charter’s Dashboard/Tools.

New data shows more than 100 billion opioid pills were distributed nationwide from 2006-2014. Experience the power of accelerated analytics to unlock and visualize socially-impactful insights about a national public health crisis.

We heard your feedback, and the Immerse SQL Editor has been much improved for OmniSci Release 5.2. Today, you can do more in the SQL Editor by running selected queries, incorporating query snippets, and re-running previous SQL statements. Text highlighting, query logging, and sample queries make data analysis easier and faster. In this session we will take a closer look at SQL Editor in Immerse 5.2

On from Saul Shanabrook's great session introducing the value of tools like Ibis, Altair, Vega and the ibis-vega-transform, we'll dive in a little deeper to see how we can use these tools to explore multiple large datasets with minimal forklifting. Looking at github data again, we'll pull in additional data from disparate sources into an integrated visualization inside JupyterLab to provide more context. We'll show how Ibis provides a seamless analytical API layer across sources, and with particularly fast sources like OmniSci, this opens up a whole world of exploratory analysis.

At Quansight, we’ve been working closely with OmniSci to create interfaces with interactive scientific Python computing technologies. Throughout this work we have built healthy and sustainable relationships with open source communities that complement the abilities of OmniSci. In this talk, we’ll explore these abilities to describe, query, and visualize data in OmniSci databases in Python, and interface with a wider scientific computing community. It will highlight Quansight’s efforts to bring to OmniSci data and visualizations to interactive scientific computing workflows in Jupyter. Further we’ll acknowledge the benefits of working with open source communities building open standards and technology.