Value Shifting of Knowledge Management in the Age of Internet of Things (IoT) and Cloud Computing
Big Data analytics create knowledge that brings value to businesses. With the advent of Internet of Things (IoT) huge amount ofdata is generated from different sensing sources. This data is stored in the Cloud and analyzed with data analytics techniques to create knowledge that should add value to
their businesses. Currently, most of the data analytics is done in the Cloud. To process Big Data, a substantial high-performance computer (HPC) is needed. With Cloudservices, the end-user (e.g., smart building manager, smart grid operator, oil and gas operators, etc.) is provided with a Cloud application interface to input thedata, request data processing, get relevant input and pay for the resources utilized. However, the cost of using the Cloud depends on the computing resources consumed by the user. The question that chief information officers (CIOs) should ask do businesses get the knowledge quality only through the Cloud computing? Are there other ways to get more knowledge that could complement Cloud computing?
Edge computing will enable deep learning and real time data processing that makes artificial Intelligence (AI) a reality
One area that should not be ignored is the computing power in the sensors and the edge computer, which is a computer at the business site uses to aggregate data to be transferred to the cloud. There is indeed a shift of value from the HPC Cloud computing to more distributed data analytics that involves both the Cloud and the edge.Historically this shift of value was accompanied by the improvement of processing capabilities included in the system. For instance, In the 80s the shift of value from the hardware running with a basic operating system to applications running on advanced operating systems was sparked by the computing enhancement of the hardware.
Since the year 2000 the value shifted to the Internet and the world wide webas the compute power implemented in the network improved substantially. Now the value is shifting to cloud computing. With the integration of more computing in the edge and the advent of Artificial Intelligence, the value is now shifting to more distributed data analytics that utilizes both the Cloud and the edge.Given the computing power implemented in these sensors (especially smart phone), a lot of computation capabilities can be implemented at the edge (sensors and computers). While the current practice uses big data in the Cloud to do the analytics, there is a shift of values as sensors are becoming smarter. Data analytics take advantage of time locality and space locality to create knowledge.
Even though time locality tends to be preserved as it is captured and stored with data as meta-data, space locality tends to be lost if it is not carefully preserved. With the smart sensing and edge computing capabilities,local processing at the sensor itself and the edge computer can take advantage of both space and time locality and create knowledge that will give more insight. Edge computing will enable deep learning and real time data processing that makes artificial Intelligence (AI) a reality. The data generated create time locality and space locality thatcan create new knowledge. Adopting data analytics, that enables us to create new knowledge should take in consideration time locality principle and space locality principle in order to enable smart data analytics that combines both edge computing and Cloud computing.