In the era of the Internet of Things (IoT), enormous amount of sensing devices collect and/or generate various sensory data over time for a wide range of fields and applications. Based on the nature of the application, these devices result in big or fast/real time data streams. The analytics technique on the subject matter used to discover new information, anticipate future predictions and make decisions on important issues makes IoT technology valuable for both the business world and the quality of everyday life. In this study, first of all, the concept of IoT and its architecture and relation with big and streaming data are emphasized. Information discovery process applied to the IoT streaming data is investigated and deep learning frameworks covered by this process are described comparatively. Finally, the most commonly used tools for analyzing IoT stream data are introduced and their characteristics are revealed.
Data Mining algorithms
• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB Raspberry pi/arduino,other hardware components (please call) • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL
₹10000 (INR)
2018