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Insights Product Engineering

Move to technology-driven smart policing

Leverage predictive analytics to reduce crimes and burglaries by 30%

Today, the crime rates in most parts of the world are high, despite taking necessary measures. Reports by FBI reveals, “3.9% increase in the estimated number of violent crimes and a 2.6% decrease in the estimated number of property crimes when compared to 2014.” Due to this, the police forces globally are under tremendous pressure to leverage technologies such as predictive analytics, to draw insights from the vast complex data for fighting the crimes. It not only helps in preventing robberies and burglaries but also aids in better utilization of the limited police resources.

Fig. Predicting crime by applying analytics on data feeds from various sources

As per studies conducted by the University of California, crime in any area follows the same pattern as the earthquake aftershocks. It is difficult to predict an earthquake, but once it happens, the following ones are quite easy to predict. The same is applicable when it comes to crimes in any geographical area. Combinational analysis of the past crime data and other influencing parameters help in predicting the location, time, and category of crime.


With the increasing crime rates, globally the police forces are under tremendous pressure to leverage technologies such as predictive analytics to draw insights from the vast complex data for fighting crimes.

    Authors:
  • Avaiarasi S, Director – Delivery (IoT)
  • Kamakya C, Project Manager – IoT
  • Sarvagya Nayak, Business Analyst – Insights
Categories
Product Engineering

Deliver high-quality entertainment services at high speed and with flawless quality

Automate the end-to-end compatibility testing and rollout steps to deliver a seamless viewing experience across multiple digital platforms and form factors

A series of technological advancements has completely changed the way people consume video content. Compared to earlier days, when a television set was the primary source to consume videos, today’s consumers have many other options – smart TV, streaming box/stick, gaming consoles, DVR, set-top box, tablet, computer, mobile, etc. A recent ComScore OTT state report clearly shows the growing penetration of different digital devices among U.S households.


To deliver a seamless viewing experience, service providers need to ensure video compatibility across a broad range of device types, operating systems, browsers, and network types.

The end-users now have the flexibility to watch their preferred videos on any digital platform of their choice without being much concerned about the supporting operating systems, browsers, and network connectivity.

But if we look from the lens of service providers, delivering video service faster and with high quality has become much more complicated. It requires them to ensure feature compatibility with a broad range of device types with different operating systems, browsers, and network connectivity. This requires a humongous amount of testing in the background. And as digital-savvy users expect feature updates at lightning speed, service providers cannot afford to spend much time testing and rolling out services.

This mandates service providers to technically upgrade their way of working, the testing process, and existing release platforms.

Categories
Product Engineering

Use AI to Bolster your Network Capacity Planning decisions

The Content Delivery Network (CDN) market is poised to explode as content consumption gains more momentum. This calls for an efficiency-focused approach towards CDN capacity planning.

As per a Cisco report, the annual global IP traffic has already crossed the zettabyte (ZB) threshold. To cope with the increased content consumption by users, more supply chains should be established along with a reliable and scalable infrastructure. This puts a lot more pressure on the Content Delivery Networks (CDNs), which forms a well-established global backbone for content delivery.

For service providers, it becomes vital to take an efficiency-focused approach towards CDN capacity planning. This means satisfying the future capacity requirements without increasing the total cost of ownership.

The legacy manual way of capacity planning uses basic statistical tools to collect data and set a static threshold on capacity requirements. Such manual planning typically does not analyze the network in a holistic manner and produces a final proposal with a “one rule fits all” approach. However, this approach is inefficient in today’s scenario where consumer behavior changes very dynamically. Manual planning is also prone to human error, so the outcome might deviate from time to time, wasting a substantial number of resources and time. The service providers often run out of capacity due to increased data consumption and changes in the consumption patterns, which are not identified correctly during capacity planning.


To satisfy the customer demands in a timely fashion, it is necessary to have a modern capacity planning strategy.

Network planners need to confront these challenges before it impacts the customer experience. Leveraging Artificial Intelligence (AI) can significantly improve network capacity planning, thereby improving the end-user experience and reducing the total cost of ownership.