|By Tony Shan||
|November 11, 2012 11:11 AM EST||
- Modeling: First and foremost, a good model must be established to represent, capture, and ingest the large amount of data in a structured, unstructured, or semi-structured format. The nature of the data elements is a largely deciding factor for an appropriate data store.
- Algorithm: A sophisticated algorithm has to be developed to process the data in an optimized way. An easy-to-use coding method is needed to balance the local processing and global computation in a distributed fashion. For example, historical data can be tapped for generating valuable recommendations based on a user profile by means of the click-through rate and interest match metrics.
- Statistics: Statistical data analysis is becoming increasingly important, and open source packages like R make data mining more transparent. Growing commercial supports for R from the major vendors fuel the adoption, integration, and distribution of R.
- Semantics: Context-awareness is a must. Simple analysis is no longer sufficient for today's business. Complex analytics requires advanced techniques such as patterns and probabilistic reasoning. Vagueness is inevitable and got be dealt with properly to extract insights from massive data in a fuzzy way.
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Whether your IoT service is connecting cars, homes, appliances, wearable, cameras or other devices, one question hangs in the balance – how do you actually make money from this service? The ability to turn your IoT service into profit requires the ability to create a monetization strategy that is flexible, scalable and working for you in real-time. It must be a transparent, smoothly implemented strategy that all stakeholders – from customers to the board – will be able to understand and comprehe...
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