With the rising popularity of social media portals and networking applications, data is being generated in huge amounts on a daily basis. Depending upon the source of data, this data can be of different types, generated at different velocities and amounting to different volumes. The volume, variety and velocity of data characterize what can be referred to as ‘big data’.
This data can be analyzed and used for varied field-specific applications, making it immensely valuable for organizations and people. The data generated by social media portals can be analyzed to determine market trends for products and opinions on issues. Analysis of tweets for prediction of election results is a real-life example of how big data analytics is being used in the present day scenario.
In order to get value out of big data, it has to be stored and processed, which is a recurring challenge for data scientists and researchers all over the world. Many technologies like cloud computing and bio-inspired computing are used in conjunction with big data analytics to empower commercially viable solutions.
Another technology that has gained relevance in the big data context is Internet of Things (IoT). A network of embedded technology-enabled devices, which are connected and accessible using the Internet, communicates with the external environment, to generate data. Considering the enormous size of this network, the data hence collected will also be massive, making data storage one of the most profound challenges. Cloud is seen as an appropriate solution to solve this problem owing to the scalability and cost-effectiveness of solutions that it provides.
In order to derive value from the captured IoT data, the big data technologies used by organizations also need to be modified. For instance, IoT-linked devices are connected using WiFi or Bluetooth and communication between devices need to be performed using a well-defined protocol. Commonly used protocols for this purpose include Message Queue Telemetry Transport (MQTT) and the Mosquito. The Hadoop Ecosystem then performs the storage and processing of data.
Many existing applications and research fronts bring these technologies together to create solutions for real-world problems. However, the vision of any such solution is to infuse intelligence into existing systems and facilitate decision-making in, and automation of, the fundamental processes that exist in the system, making them ‘smart’. With that said, IoT applications promise to change lives, not just at system and administrative level, but also at the individual and personal level.
Smart homes and smart cities are popular big data-powered, IoT-enabled, cloud-supported applications. From making basic systems like home security and waste management, smart, at the home level, the realm of this field also encompasses bigger systems like traffic management, environmental monitoring and water distribution, in addition to many others. Industry specific applications like livestock management in farming, connected health systems for personalized medicine or drug designing purposes in healthcare and supply chain management for businesses, also exist.
While most of these applications affect the customers or users at the personal level, indirectly, some applications like wearable devices have a direct use for the customer at the personal level. The smart watches available in the market today are preliminary research outcomes of this field. The limited capabilities of these solutions are being worked upon to empower them for complex applications and personalized experiences. Considering the broad scope of this synergistic approach and the speed at which research is underway in this field, the day is not far when smart systems will rule every walk of human life.