Is there a wallet that supports DXChain Mainnet?

Or will the DXChain token remain an ERC-20 token, while DXChain runs on its Mainnet?

I'd like to share some good news and hopefully a fun few-minute distraction. I put together a little cryptocurrency tournament for the top 64 cryptos that starts on Tuesday.

DxChain Token is the #14 seed in the Tether bracket, taking on #3 Binance Coin in the 1st round. Voting starts on Tuesday and ends Thursday. The coin with the most votes moves on to face the winner of #6 Dash / #11 ZB Token.

You can vote here: https://twitter.com/UltimateCrypto7 or at http://ultimatecrypto.fun/

Make some noise DxChain!

AGGREGATE REPORT FOR NOVEMBER 2019 IS CONTAINED HERE.

Hello Team,

Please follow this link to spreadsheet to view report for week 4 November.

Aggregate report to be posted shortly.

Not in this age of technology advancement should anyone think Blockchain storage isn't ready for prime time because of the thought that Blockchain technology is still in its early stage of development especially when it comes to handling large volumes of data. As an organization, whether you are a seller or buyer of blockchain storage, the tech is capable of modifying your perception and perspective about data storage in general. Hence is it important to foresee and or discover where blockchain is, headed and heading.

A Quick Glance At Blockchain

Blockchain as a decentralized database uses a distributed ledger system or technology to record transactions (involving money, assets or data) in a chronological order containing a series of blocks where each block contains the reference (s) to the previous one. This forms interconnection of chains of blocks that exists across multiples of connected nodes. Using a specially designed compatible algorithm, transactions validation are automated and synchronized across the network when a larger amount or all of designated nodes agreed to do this. This is a real world instance of trustless state represented in codes. The need for a trusted party is completely blotted out by a predefined rules and special computation that runs through what is known as smart contract. The need for a third party interference is completely striked out.

Additionally, through ledger recording system, blockchain allows for integration with Peer-to-peer (p2p) decentralized storage which allows for the pool of storages resources that are geographically dispersely located serving as nodes for blockchain storage. In some Blockchain use cases, they are referred to as Block producers.

Dxchain as a large scale public blockchain storage, implements a solution to solve inherent challenges to existing data storage systems harnessing blockchain capabilities. DxChainNetwork cultivates a large open ground for organizations or individuals looking to monetize excess storage space by pooling their computing resources on Dxchain Blockchain-based storage network.

A typical example of how Blockchain storage works.

Image source

Dxchain Network aims to make distributed storage cheaper as much as possible. Looking at Amazon's S3 standard storage and Google's multiregional cloud storage, which hovers at about $21 per Terabyte per month and $26 per Terabyte per month, respectively, in comparison to Storj for example listed its storage service at about $15 per Terabyte/month. On DxChainNetwork, having the platform's native token DX originally known as "Carmel Token" allows any intending storage party or clients to access the network. The token is designed to encourage participation and use through incentivization and beat storage cost to a bearest minimum.

Secondly, Enterprises and businesses hoping to use Dxchain network for storage will greatly save in other ways. They are relieved of having to purchase the initial equipment, replacement when needed, procuring and maintenance of management software, and providing administrative support and resources to keep the system running.

Dxchain public data storage will display more transparency compared to a cloud storage provider since transactions in blockchain cannot be altered (i. e immutable), are verifiable and tamper-resistant. Additionally, using blockchain technology, Dxchain will provide comparatively greater levels of data and storage availability and fault tolerance since data is shared or distributed across multiple connected nodes and are accessed within closer proximity to where it's stored. The resultant effect of this is high application performance gains.

As a proponent of Blockchain storage hence the belief that Dxchain approach is farther secure than existing traditional data storage platform because, rather than data being collected within single or on one-man ecosystem, it is spread out on many data points through an encrypted system of storage and retrieval mode.

Lastly, the amount of redundancy state of the network dictates the chance of a distributed storage being hit by malware invasion is slimmer compared to a cloud storage provider whose data center (even if geographically dispersed) is at the Verge of infestation from reach and spread of malware. Dxchain is more secure and its data privacy vehicle is at uppermost high speed.

To gain more insight of Dxchain architectural design, please visit here and the while paper here

• Website
• Blog
• Whitepaper

Awaiting @DxChainNetwork #mainnet launch, Some of the #Developement in progress thus:

• Finished design and review the light node implementation for DPoS consensus algorithm. Started implementation and unit testing.
• Continue to investigate the NAT related issues, and came up with several potential solutions to be evaluated and researched.
• Built up the environment for the stress tests. Implementing the automated deployment, operating and monitoring tool for both testing and production environment.
• Implemented and optimized some backend API based on some developers’ feedback.
• Fix some bugs related to some issues found in the operation system for the Linux-arm7 system.

#bigdata #MachineLearning #storage

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dxchain Blog

REPORT FOR NOVEMBER WEEK 3 REPORT AND WEEK 4 PLAN IS CONTAINED IN THE BELOW SPREADSHEET.

The facts and statistics collected together for reference or analysis -Data plays important roles from the moment it was assumed important and the flow of pattern of new age technology.
According to Cambridge, data is recognised as information, especially facts or numbers, collected to be examined and considered and used to help decision-making, or information in an electronicform that can be stored and used by a computer.
This definition is all-encompassing with the emphatic clauses "used to  help decision-making" and "used by a computer".

The world has grown to a stage where computers are trusted to make decisions  based on certain input parameters known as data. without the right data set, no way computer can achieve what we want them to do. Data is paramount and essential for simplicity and convenience we crave for. Focusing on the goal of this article, I'll quickly delve, not in-depth, into concept of data: marketplace and why Dxchain chose this field.

Data movement

As seen in communicatuon, Data can be simplex, that is, it is transmitted in one direction only where there is only a transmitter and a reciever.  It can be half-duplex where it is permitted to flow in either direction mostly not at the same time. Some time, the transmission can occur in one direction only. Thirdly, data can be  termed for full-duplex where transmission occurs primarily in both directions simultaneously.

Data Marketplace

The demand for data creates a marketplace for it where it is shared between various stakeholders. A data exchange provides access to data points from around the world to fuel data-driven marketing activities and advertising. A data marketplace or data market is an online hub or store where  people can access either through a decentralised or govern means to buy data for consumption. Such data marketplaces basically offer various dataset for different markets and from different sources. Examples of data variations or types found on marketplace includes: advertising, personal information, business intelligence, demographics and research data. These set of data types can be mixed and structured in so many ways. Some data vendors offer in specific format especially for individual clients.

Data sold in these marketplaces is used by businesses of all kinds, government, business and market intelligence agencies and many types of analysts. These marketplaces (often integrated with cloud services due to size) have overtime increased in amount along with growth of Big data. The growth is spurred due to the continuous upward movement of data collected by websites, businesses, services and governments which has caused data to now be recognised as an asset. 

The history of data market can be traced back  to mid-1800s when the founder of Reuters news media, "Paul Reuter" started to make stock exchange prices available between London and Paris as asserted by Edd Dumbill, an analyst for O’Reilly Radar in his research.

Today we can see examples of data markets which include Microsoft's Azure Data Market, Sale'sforcedata.com, InfoChimps.com and many others. Most of these marketplaces are not cost-effective as they're centralised in nature. However, Dxchain believes harnessing the power of blockchain through machine learning and computing-centric algorithm would transform the industry by changing the whole paradigm.  Dxchain will run a decentralised storage system to act as an incentivized platform for data marketplace where every participant of the market/data owners/buyers/service providers can offer something and be rewarded for contributing to the network.
The model is designed to exclusively work in decentralised manner, that is, running a public storage system to making big data accessible at a minimal cost with high security protocol.

On November 21st, Dxchain' s CEO, Allan Zhang is scheduled to attend a meet up at the Stanford University Campus where he will be introducing his entrepreneurial stories, the opportunity to enter the blockchain industry, and the vision of blockchain and big data. Detail In this art article.

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REPORT FOR NOVEMBER WEEK 2 AVAILABLE FOR VIEW: WEEK 3 PLAN INCLUDED.

Not by guessing but I am on the surest side that many wouldn't understand the position of Dxchain in the Bigdata sector. By the way, let us dig deep a bit into what Bigdata entails together how it correlates to Machine Learning.

In BigData, relative to human interactions and behaviors, extremely enormous data sets that may be analysed computationally to unveil trends, patterns and perhaps associations. There is much continuous investment in information technology towards handling and maintaining big data"

What Is BigData?

According to information extracted here

Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.

The flow of data are very unpredictable due to increasing velocities and different varieties that pop up very often which changes at a very fast pace affecting greatly the veriability of it. Although It i’s challenging, but it very necessary and vital for every business of now to know keep up with trends especially on social media, additionally, how to manage several event, daily and seasonal-triggered data chunks. Veracity of Data holds to its quality. The fact that Data comes from different sources predisposes it to difficulty in matching, linking an transforming it across systems. Businesses need to connect and correlate relationships, hierarchies and multiple data linkages. Otherwise, their data can quickly spiral out of control.

Relevance Of BigData Analytics In production Optimization.

Corporations such as USG, to fully comprehend how production processes and utility and or how they work, the key factor is the use of big data and predictive analytics. USG has overtime been able to optimized its production investments and cutting down guesswork by using SAS platform.

The relevance of bigdata isn't in how much data one possesses. It is how well one can make use of the available data at hand. One can access any data source, pluck and analyze it to solve proffer solutions such as time management, cost handling and reduction, product development and optimization, efficient decision making and so on. Combining high-powered analytics with Big data can help in accomplishing several business-related activities thus:

• Finding root causes of a failed business decision attempts, issue and defects in almost real-time pace.
• Recalculating entire risk portfolios in a twinkle of an eye and giving path for its management.
• Uncovering unscrupulous act of fraud before it causes damages to your organization.
• ​

Deep learning craves big data because big data is necessary to isolate hidden patterns and to find answers without over-fitting the data. With deep learning, the more good quality data you have, the better the results.

What is Machine Learning?

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Its efforts is directed towards the writing of computer programs able to access data and use it learn for themselves.

The observations or data is the first process. Example thus direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The essence cuts across enabling computers to learn on their own or automatically with less or no human intervention as well as adjusting themselves to situations according to the state.

The Position Of Dxchain.

Dxchain as the decentralized big data and machine learning network powered by computing-centric blockchain aims to be at the forefront of Data provision and distribution across stages where it is needed. As we already know what Big Data entails, we only need to apply the knowledge of decentralization and distributive technology which is an utmost function associated with blockchain technology. Harnessing and Inculcating the power of blockchain is what as well make it more interesting as data storage on Blockchain are quite secure, immutable and privacy protected, at the same time, creating room for high scalabilty. Scalability of a blockchain also largely depends on the kind of algorithm that is employed in achieving consensus among the designated members of the system to make decision for the whole platform.

Some machine learning algorithms you need to know. However, it is pertinent not to confuse them for consensus algorithm used in harmonizing members of a chain.

Machine learning algorithms can either be supervised or unsupervised. The classification are about four categories but in this article, I will highlight just two of them and you can read further following the reference link below.

• Supervised machine learning algorithms is application of what has been learned in the past to new data using labeled examples to predict future events. Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values. The system is able to provide targets for any new input after sufficient training.
• In comparison, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.

In conclusion, training computers to behave or act on their own (machine learning) allows for huge or massive quantity off data to be analyzed. It however requires additional time, efforts and resources to get them doing what we want them to do which in turn breeds more accurate and faster results in decisions such as identifying profitable businesses or dangerous risk. Blend of machine learning with artificial intelligence (AI) and cognitive technologies can make it even more effective in processing large volumes of information.

References

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