TALLINN, ESTONIA / ACCESSWIRE / August 16, 2018 / With the advent of the "big data" era, the importance of data in all walks of life has been greatly enhanced, so the data collection, storage and analysis have become an industry direction which is desiderated to be promoted. And existing infrastructures have become increasingly difficult to cope with the requirements of more than 3.6 billion connected people and the massive amounts of data generated in the process. The server storage of nowadays and the framework which provides data access are both expensive and inefficient. In the era of increasing geometrical number of connected devices, data access efficiency, storage cost and performance stability, etc., are facing great challenges.
Today's central intermediary - the server storage, and the framework which provides data access, are very expensive but also very inefficient. Meanwhile, data center consumes between 1.5% and 2.0% of global electricity, and grows at a rate of 60% per year. With the accelerated integration of the Internet and industry at this stage, the volume of global data will show an exponential upward trend. As early as 2010, the scale of global data has reached the level of "ZB" (1ZB = 1024TB). And through IDC's estimation, the total amount of global data information can be doubled every two years or so. Only a year later, in 2011, the total amount of data created and replicated worldwide exceeded 1.8ZB. Nowadays, owing to the rise of the concepts of "Internet +" and smart cities, etc., the world has gradually realized the valuable application value of data. According to the prediction of IDC, global data usage will reach 40ZB in 2020, and such a large amount of data is equivalent to about 42.9 billion 1TB hard disk storage space.
However, the most pressing challenge in the industry today is the issue of security and integrity. Security aims at the security of data storage and the privacy of data, while the integrity is mainly for the compatibility of data interaction and the continuity of data. Especially in the AI industry, stable data storage is the foundation of the AI industry. The important technique of the AI industry is to make the robots learn and complete through a large amount of data information so as to make it more intelligent. Therefore, the stability of data storage can affect the normal operation of the entire AI framework. If the storage system encounters some problems such as data loss and downtime, etc., it will have a fatal impact on AI products. Secondly, because the performance of the data storage has a great impact on the efficiency of the whole AI and the reading speed of the storage system is up to the efficiency of data storage and extraction, so it directly limits the maximum efficiency of AI. Thirdly, the scalability of the data storage system will determine the scalability of the entire AI. Therefore, a stable storage system is crucial to the development of artificial intelligence applications. And AI has extremely high requirements on the processing technique of data storage. Data is one of the raw materials for the development of AI. As an intangible asset, any organization that wishes to get involved in AI is increasing its importance.
However, at this stage the data collection and storage technique still fails to solve the following pain points.
1. Data Security
Regardless of how the data storage industry develops, data security is always a key issue that cannot be ignored. It can be divided into two parts, one is the problem of data storage time, and the other is the problem of data loss and theft. Many big data stores in the fields of business, law and medical, etc. all involve the issue of storage time, which require storage periods ranging from a few years to decades. As the foundation of big data analysis, historical data undoubtedly puts higher requirements on data storage time. It is the key to ensure the validity and availability of data in the long-term phase. At present, data theft and data trafficking have formed a complete industry chain, people pay more attention to the security of data. In areas of finance, healthcare and government, there is a clear demand for data security. Big data analysis often requires a comprehensive reference of multiple types of related data, and in the past, there is no mixed data access. Therefore, big data storage and processing gradually present some new security issues.
2. Data Validity Discrimination
In the context of big data, the data scale and file quantity are increasing substantially, and the management of metadata accumulated at the file system level becomes a difficulty. In addition to the continuous upgrade of storage capacity, the identification of data validity has become an urgent demand of the current market. The most efficient solution is to identify data and filter invalid data to effectively reduce the demand of data storage capacity and solve the problems of system delay.
3. Slow Speed of Data Processing
The requirements for immediacy are high in the application and coordination of data, so more and more big data application environments require higher IOPS performance, and the widespread popularity of virtual servers comes up with severe challenges to processing performance and processing efficiency.
4. High Cost of Data Storage
Whether it is a hardware device or a software platform for data storage, it takes a lot of time and money for enterprises to build their own data storage system from scratch. At present, in order to help users reduce building cost, more and more storage products are available in pure software form, which can be directly installed on existing, general or off-the-shelf hardware devices, but obviously, the enterprises which have a high demand for big data cannot accept this form. The problem in the data storage market is how to complete the input and save of data with the fastest time and the least budget under the premise of software and hardware.
We saw many losses and inconveniences caused by these problems, so we established Cyberange.
Cyberange is a basic chain for data collection and storage. It aims to offer a large block of data storage space and cross-chain technique for storage technique by combining all the blocks with blockchain technique and Artificial Intelligence technique.
As a public chain designed for data collection and storage, Cybrange is a blockchain infrastructure which is similar to the operating system designed for data managers. Cyberange AS storage system is a decentralized file data storage system, which aims to provide permanent, stable and secure storage services for each individual or group in the big data waves, and is connected to the browsing function of any web browser to achieve the retrieval management of accessible online data.
The Cyberange AS system provides basic services for platform users who hold the CRAT tokens based on the Star File System (IPFS) and Cyberang Message. The owners of the block storage data space are motivated to manage and share their own data resources. IPFS, as the most reliable method for permanent decentralization of saving and sharing files, is a distributed protocol for content addressable, versioned, peer-to-peer hypermedia. Therefore, we chose IPFS as the design basis of our public chain, and extended and upgraded it based on the project features.
In the CM data ecosystem, there are three roles of users: data uploader (data owner), data demander and data integrator. The unique point is that the identity of each user is not fixed, but he or she changes with his or her own needs. As the cornerstone of the entire ecosystem, the data uploader is the largest, obtaining Token incentives by uploading first-hand data and actively sharing its own data can also make the overall data storage ecology in a state of information flow, which helps to maintain the validity of data and promoting data update. Data demanders play a role in promoting the entire ecosystem. By continuously proposing data requirements and analysis programs, data volume is increasing and data analysis capabilities are constantly improving; data integration is the key in the ecosystem. A small number of integrated parties can systematically and completely classify and sort out existing data, and with the emerging techniques such as AI and so on, it can also effectively maintain the continuous operation of the CM data ecosystem.
Faced with the problem of data storage and slow analysis under the background of big data, Cyberange has sharply grasped the market demand, and constructively increased the AI sector in the data collection and storage public chain model. With the accuracy of artificial intelligence and even data classification and screening, it can greatly improve data storage capacity and improve analysis speed. Similarly, the rapid learning ability and stylization of artificial intelligence also make it more universal market application value, so it is extremely feasible to upgrade the existing data storage public chain model with the help of AI.
Cyberange performs the analysis of data storage by combining blockchain and AI techniques. Blockchain storage technique enables accurate recording, authentication, and execution, while artificial intelligence helps make decision, evaluate, and understand certain patterns and data sets, ultimately resulting in autonomous interactions. Artificial intelligence and blockchain require data sharing, and distributed databases emphasize the importance of sharing data between multiple clients on a particular network. Similarly, artificial intelligence relies on big data, especially the data sharing. The more open data available for analysis, the more predictable and evaluated the machine will be, and the more reliable algorithms generated. In addition, security is also an important advantage. When dealing with high-value transactions on a blockchain network, there is a strong demand for network security. This can be implemented through existing agreements. For artificial intelligence, machine autonomy also requires high security to reduce the likelihood of catastrophic events.
Cyberange has the confidence and ability to build a basic public chain for all industries and provide storage space, technique and analytical services, and build a fair, transparent, secure and efficient information data ecosystem. Then it will promote the long-term healthy development of the entire data storage analysis industry and improve the data management concept in the era of big data.
Contact:
Dalio 3206678586@qq.com
SOURCE: Cyberange
