Download Insight Explorer

[Jonathon Burnside]

Thismay be a dumb question, but is there a way to return an image's size (Length and Width) in Cognex insight explorer spreadsheet? I am somewhat familiar with this software and was hoping to use some form of GetMaxRow() on AquireImage(), but this does not work. I've spent an hour searching through the help docs and can't seem to find what I'm looking for. There has to be an easy function for returning the image resolution (row and col), right?

Relying on huge carbon data sets as a measuring stick, cities as diverse as New York, Berlin, Oslo, and Rio de Janeiro have committed to reducing their carbon footprint by 80% within the next 30 years. But many small and midsize cities lack the resources to gather data such as building emissions, making it hard to set firm carbon commitments of their own.

The Environmental Insights Explorer (EIE), a new online tool created by Google in collaboration with the Global Covenant of Mayors for Climate & Energy (GCoM), is designed to help level the playing field for smaller cities, amplify the emissions insights of big cities, and ultimately accelerate the transition to a low-carbon future.

Developed by the Google Earth Outreach team, EIE analyzes Google Maps data to provide rich insights into our surroundings. EIE pairs this information with third-party data and standard greenhouse gas (GHG) emissions factors, deriving carbon estimates and reduction potential for cities around the world. With EIE, data sets that once required on-site measurements can now be assessed virtually, reducing the barriers that prevent cities from taking action.

EIE is designed to simplify data gathering, helping cities supplement their current data inventories with a few clicks. With more complete inventories, cities have more accurate baselines from which to build policy and measure progress. Even cities with advanced data inventories will find EIE data valuable in augmenting or confirming their latest carbon footprint analyses.

Statistics from the general to the specific are available, including the percentage of various emissions types, the time period from which the data was culled, and key assumptions. The site also links to other critical information, such as ways to reduce emissions. Users can modify or customize emissions factors to play out specific scenarios.

As with other Google information tools, the methodology used to source, aggregate, and distill the EIE data sets can be reviewed on the site. We also initiated a rigorous quality-assurance process months before launch.

Since I got only 8 out of 103 metric types got mapped to the WIndows server I added an Event Rule to map the host to maximum metrics as mentioned in the URL, Create Event Rule to Bind Metric Event to host Cis

After doing so , I created an Anomaly model Testing For only Tomcat related metrics.and saw the results having no scores and even the insights explorer says No data to display.

I even tried restarting the Tomcat service in my windows server being monitored but Im not able to see the metric data , even the Grey Dots that would indicate the metrics being tracked.

MetricBase is the TimeSeries database. It's where the metrics, baselines and anomalies are stored. As a timeseries database, MetricBase allows to query timeseries data selecting various types of aggregations and manipulations (avg, min, max, deviations, counts, median and many others), it also allows touse triggers on metrics to start flows in FlowDesigner/IntegrationHUB and execute actions as a result of that trigger.

I don't know if things work differently with dev instances but the normal procedure to get MetricBase provisioned and connected to an instance is by requesting the plugin enablement on the Hi portal.

Many aspect of OI are driven by jobs. Learning is done by a job called "Operational Intelligence - Metric Learner job". Anomaly detection happens on the MID server so that detected anomalies can be quickly sent to the instance without having to wait for the metrics to be shipped and stored.

The main challenge facing media agencies is to forecast the key performances of DOOH campaigns in a Private Marketplace (PMP) setting, which includes important metrics like impression volumes, average CPMs, and frame locations. The goal is to deliver precise predictions for these campaign metrics across multiple publishers and points of sales.

You can share your campaign to clients and planners in read-only mode so they can explore data and forecasts beforehand. Demonstrate insights and increase your conversions. Benefit from:

Our advanced forecasting tool delivers accurate predictions, powered by our proprietary in-house machine learning algorithm. This cutting-edge technology dynamically calculates impressions and CPM on a per-frame basis for every hour of the week. Adjust your targeting parameters and witness real-time updates as your forecasts adapt instantly to the changes, providing you with unparalleled control and precision in your decision-making process.

Our 5 steps process places data at the heart of buying with an advanced foundation of audience and segments. It is easy to justify your DOOH activation campaign by building segments with different publishers, POS, demographics, frames and so on.

Get reports about your business to make faster and better decisions driven by artificial intelligence. Our data analytics cloud platform consolidates data from various Avaloq and third-party sources and allows you to access insights in the way you need. The pre-trained models consolidate data across all business areas into management or financial reports to help you understand key metrics such as sales efficiency. Leverage automation across other Avaloq products to answer routine client requests, select important news or find new prospects, and make your advisors more efficient.

Reduce the time required to access insights from multiple data sources. Avaloq Insight Explorer provides out-of-the-box data analytics capabilities for wealth management and private banking. Automatically generate near real-time insights across your whole organization so you can monitor the performance of advisors, client churn risk, or product penetration rates. Our pre-defined models allow you to get started in days and streamline your management, financial and compliance reporting.

Achieve transformation and growth faster with a complete front-to-back wealth technology solution. Access world-leading core banking and powerful investment management technology with out-of-the-box integration to reduce complexity.

With the apparent advantages of using data-driven insights to develop their business, many wealth managers are beyond the point of just thinking about data science. This report helps wealth managers with examples of tangible AI use cases and explains how the initial challenges in data science projects can be overcome.

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City transportation is crucial to connecting residents to education, employment and essential services. At the same time, the transportation sector is where global greenhouse gas (GHG) emissions are rising the quickest.

The need for action is now, and we need to rise to the challenge quickly. Google technology is unlocking our ability to generate climate-related insights and impact at a global scale. Here are a few of the latest ways we're using AI and Google Maps data in EIE.

Using AI, our systems analyze transportation trends in a city by mode, helping local governments take stock of their progress in tackling GHG emissions. GHG inventory processes traditionally take months and multiple data sources to compile, and are now streamlined, allowing government staff to reduce the cost and personnel burden of reporting.

Modeling transportation flows is complex. With EIE, cities, states, regional policymakers, consultants can better understand the impact sustainable changes are making on global greenhouse gas emissions.

ICLEI USA provided technical expertise to review city-level greenhouse gas (GHG) inventory data provided by Google Environmental Insights Explorer, a modeling tool which uses unique Google data sources to produce GHG inventory estimates for building and transportation sectors. The insights pair actual measurements of activity and infrastructure with advanced machine learning techniques to understand how people use transportation, inform factors for scaling, and account for efficiency changes in emissions estimates.

Back from a recent trip to the Netherlands, Sustainability Analyst Kimberley Pavier travelled around the country to visit several semiconductor manufacturers to assess their environmental footprint and ESG efforts.

I recently returned from a European tour of semiconductor manufacturers, an all-important technology sector that is enabling generative AI, which is still very much in its infancy, with an ever-increasing number of use cases and solutions leveraging its capabilities. Being able to see the machines at work, and being on-the-ground, meeting with people always proves invaluable to gain and share additional insight is imperative to navigating this dynamic, highly competitive space.

The need for ever-increasing processing power, connectivity and intelligence is driving the shift to chips made from silicon to a diverse range of novel materials, one being silicon carbide (SiC), notably in electric vehicles, renewable energy and industrial applications. Focusing on semis, the improvement in chip performance comes with a downside. SiC chip production can potentially treble CO2 emissions compared to silicon wafers, as the process requires higher temperatures and tougher chemicals, as well as more time, at lower efficiencies.

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