Big data collection in IoT made effective
An estimated 20 billion devices will get connected to the Internet by 2020, producing an enormous amount of data. Accurate interpretation of information generated through IoT networks will open up new ways for data utilization in all business areas such as healthcare, travel and retail.
The increasing impact of IoT technology also poses a new challenge for effective data collection. It raises the stakes to produce better technology solutions that are capable of capturing and storing data produced in a network and then differentiating important data from the total.
How big data changes IoT data collection practices
For a modern company, information analysis is the key business foundational element which reaches much further than the statistics about users’ activities on a website. It includes tracking information that is being produced inside and outside a company.
To cope with the overwhelming volume of data in the Internet of Things, technological advances in rebuilding database infrastructure and data analysis principles are required. The traditional approach to data analysis (collecting information and pulling it through software) has to be modified in order for it to handle the vast amounts of incoming data.
To process vast amounts of data in IoT, advanced solutions for database structuring and data analysis practices are necessary.
Initial data analysis must be drawn closer to data origination sources. This means moving analysis away from an IoT network to avoid overloading the IoT network. Management of new data needs to move to applications that represent the network’s edge.
New database structure to ensure rapid data processing
Database restructuring will also require direct embedding of data analysis into IoT devices, making them responsible for collecting information and filtering for useful and redundant data, even though they will be running on different hardware with different user interfaces. Advanced database technology will manage structured and unstructured data while requiring a small amount of memory.
Data will be generated outside the IoT system and respond with geographical location, time and other kinds of unstructured monitoring data. After processing, this information will be “squeezed” and directed to the center of the IoT analysis network.
Big data will be produced outside the IoT network and will be sent to the network center only after structuring and squeezing.
This model will lower the workload on the analysis center by enabling multiple edge-based distributed systems to preprocess all incoming data. To perform information interpretation tasks, advanced database systems will be required to have built-in functions of self-tuning and system recovery.
These changes will make it possible to handle IoT data in a structured manner without time loss. Also, this will assist IoT technology to claim a stronger position in the market since it will be easier to foresee events and generate correct responses resulting from streaming information analysis in real time.
Currently, the database system development for IoT networks is taking a first step in conducting evaluation operations of the equipment in the network. The main goal is to learn to foresee potential failure and to avoid those situations, to ensure cost effectiveness, remove downtime occurrences and bring vital changes to the digital economy.
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