Big data refers to the use of predictive analytics, user behavior analytics, and other advanced data analytics tools to extract from big data instead of the size of the data set. These analytic tools can help businesses find trends in customer behavior, prevent medical emergencies, prevent crime, among many others.
Phoenix Global intervention
Many users are still limited by what they can achieve with their big data analytics tools. For example, epidemiologists, scientists, medical practitioners, governments, etc., often face difficulties analyzing large data sets using the present infrastructure. This problem arises due to the different formats these data come in, e.g., internet search records, health records, climate, and geographic data, Internet of Things (IoT) devices, etc.
Blockchain technology’s immutability, decentralization, and security make it a shoo-in when combined with big data. Phoenix Global Oracle Big Data Service is Phoenix’s big data service solution created and incorporated to perform big data analytic operations faster and more efficiently. This system will be integrated into Phoenix Global’s blockchain to offer better services for businesses across different industries and use cases.
Benefits of Phoenix Oracle Big Data
Some of the functions of Phoenix Global Oracle Big Data Service are outlined below –
- Increased data integrity and more transparent data audit ensured by multiple nodes.
- The information transferred through the blockchain is not exposed to security risks associated with sending information across different siloed systems.
- It prevents malicious activities because of its decentralized nature. No single entity can generate enough computing power to alter data within the system.
- Predictive analysis on the blockchain is more straightforward and accessible to even smaller-scale users because they don’t have to generate computing power by themselves.
- The Phoenix blockchain detects frauds and irregularities in real-time, compared to other systems that depend on historical data from already completed transactions.
- Data is not controlled by any single entity, making it easily accessible to every user.
- The absence of physical infrastructures and routine equipment maintenance makes Phoenix Global Oracle Big Data Service more cost-effective.
Phoenix Oracle main components
Phoenix Global Oracle’s release is closer than ever, and it is expected to have a significant effect on node operators and the entire platform. The Oracle service will serve other enterprise applications requiring real-time off-chain data feeds.
Phoenix Oracle has three main components that node operators play significant roles in:
Data Source Connections — These are data connectors and APIs that connect to off-chain data sources and feeds.
On-Chain Contract Execution — These are the middleware protocols that respond to data queries from interacting applications and smart contracts.
Phoenix Oracle Client — This is the primary node client that acts as the fundamental bridge between on-chain and off-chain environments.
The Oracle Node Network interacts with users on a transactional model. Nodes provide data to smart contracts and dApps that request them in exchange for fees.
The Phoenix Oracle was utilized in the next wave of enterprise pilots to further test performance, stability, and security. The existing plan proposes a baseline monthly or nodal reward for Oracle Operators with data transaction fees as add-ons.
Conclusion
Even though the application of blockchain technology in big data services is still relatively new, it already looks promising. There are already instances where decentralized blockchains have been used to collect and transfer data, for example, Streamr, Endor Protocol, ReBloc, Storj, to mention a few. However, the analytic and predictive tools that comprise big data are yet to be fully optimized using blockchain technology.
Phoenix Global Oracle Big Data Service leverages the best qualities of blockchain technology to provide all the utilities mentioned above and more through the Phoenix blockchain.
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