• Business Intelligence Search Engine specialized in geospatial data analysis

    NetDB Business Intelligence Search Engine specialized in geospatial data analysis

    The idea with NetDB is to create an opportunity to measure, collect and analyze an ever-increasing variety of behavioral statistics, cross-correlation of this data could empower business intelligence users to uncover and operationalize the insights hidden within Internet of Things (IoT) data. NetDB makes it possible to intelligently listen in real-time and then use analytics to see the distinctive patterns in massive streams of IoT data.

    NetDB has opened new avenues for business intelligence market. The ability to display data using an appropriate visualization is essential to providing insights to business intelligence users. For data with a geographical dimension, geo-spatial views can often be most appropriate. Wider adoption of geo-spatial analytic visualizations in companies has hampered because of the challenge of acquiring mapping and geospatial data, of integrating mapping applications into existing BI products, and of managing these deployments.


    NetDB is self-service software with real-time scanners across countries, querying information to all sensor and IoT data wherever it exists globally(vendor name, device name, brand,version, etc), and then propagate the data into multiple platforms in an analytical ecosystem. Data is propagated to our Big Data Platform Apache Lucene in JSON format, to be analyzed at scale with our Search Engine Client in NodeJs.

    Data Analytics: 

    The Scan Analysis section is for gathering insight from the results gained from the primary and secondary scans, such as statistical information, and geolocation information. Our scan analysis stage uses geographical IP address information, retrieved from regional internet registries (RIR).

    Indexed Data Value:

    We are not indexing locals, stores or places. We are only indexing devices, “things” of “Internet of Things”, even if this “thing” or device can be moving like a smartphone we will track his geospatial location dimension but also time dimension, so we can even know how device moves.

    Commercial Potential

    ¿Why Netdb is the next wave in Business Information Intelligence? We have information that nobody has but the most important we have a human-friendly search engine to do queries with infinity operator and dimensions.We are going to sell data, bulk data(cost based on bulk size and queries computational cost), and also self-hosted solutions. We know our indexed data belongs to most Fortune 500 companies.

    Primary Dimensions for querying: 
    • Time 
    • Geo Spatial (by country, city, latitude, longitude, etc)
    • Device Information(product name, product version, etc.

    Case Study

    For this examples It is important to differentiate, that we are not geo-locating locals or stores, we are locating final customer devices in their home, office or anywhere “IoT device” are. So when a client makes a query, the system will find for “Product/Device” information.

    The Spatio-Temporal Prediction (STP) technique can be used to predict how an area is likely to change over time.

    Which competitors vendors are located or being consumed in a specific geo-spatial area?

    • How much has been growing my product in a determinate city since determinate time? 
    • How much has been penetrated my competitor in a determinate city? 
    • Heatmap of the lowest consumed product areas of my product in a determinate country ?
    • Let’s predict where are sales focused of our competitor
    • Let’s see where are the cities lowest products usage of our competitor?
    • Which are emerging competitors and where are they geospatial located?
    • Emerging demographic trends
    • Predict customer patterns
    • Outperform your competitors with Geospatial Analytics
    • Identifying new customer markets
    • Target marketing, expansion planning, mergers & acquisitions
    • A retailer that needs to plan for store expansion can leverage geospatial analysis to understand areas where the supply of product is predicted to be lower than the demand. This would allow the retailer to be strategic about opening new stores over the next several years.
    • Healthcare organizations will be able to analyze spatial and time data to predict movements of disease outbreaks over time and adequately prepare for potential epidemics before they occur.
    • Insurers can incorporate geospatial information into their risk calculations to optimize pricing for known risk factors such as flood, hurricane, earthquake, and avoid high concentrations of customers in those areas.
    • Police departments can study past geospatial data to see where crimes occurred and understand where and when future crimes are most likely to happen, enabling them to increase patrols in those areas.

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