October 201719 To complement passive probing, we apply minimal load to eliminate latency due to event correlations. This dynamic process is also very useful for active machine learning.NetWhere's temporal-spatial rules engine monitors location streams and triggers action based on business rules. A generic application of this capability is location-based alerts.NetProbe and NetWhere enable MNOs to be situation-aware across their entire network; for example, to detect and predict road traffic congestions. NetProbe is truly a fabric for "smart pipes".QWhy JSpectrum?We understand return-on-investments comes with solving real problems. We help our customers to traverse the steep learning curve by using the latest technology to solve business problems affordably and with minimal risk. QCite us a real-life example as corroboration to the capabilities you have mentioned?One of our customers significantly evolved their geo capability from location-based advertising and geo-fencing to creating a series of successful big data analytic business subsidiaries. By adopting JSpectrum platform evolution and with only incremental investment each of these business units is now very profitable.QGive us a picture of your roadmap ahead. Any enhancements, or new strategic alliances that might be of interest to our readers?Always agile in adopting latest open source frameworks, we have built our fast data infrastructure. We have also developed precise mobile positioning sever using hybrid techniques. We are now adding automatic rule-generation and anomaly detection based on unsupervised training.As for the second part of the question, we are looking for partners who possess domain knowledge within various sectors including railway and airports. Our solutions are particularly applicable to organizations with a role to play in various smart cities initiatives. Using middleware and data abstraction, we can collaborate without the need for system integration.We are innovating non-stop. Using our real-time processing experience of fast data, we are developing a new breed "situation awareness" platform. By combining deep reinforced learning, pattern recognitions, fusing extraneous data sources such as breaking news or open data feeds and more importantly IoT data streams flowing through the mobile network, our platform will be able to support holistic anomaly detection and automatic "what if" scenarios for decision support. With our product vision, a network will be able to evolve from being a dumb pipe to a smarter pipe and eventually have distributed intelligence like that of the octopuses.
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