madesparq intro paper v0.1

Cryptogenic Bullion's MADEsparq Project: Made Alignment Data Exploration

Mercury Stills

[email protected]

https://plus.google.com/+MercuryStills/about

http://cryptogenicbullion.org/

November 12, 2013, version 0.1

Abstract

This paper introduces a new paradigm for utilizing the hash string outputs from the proof-of-work or proof-of-stake scheme of the virtual commodity, Cryptogenic Bullion (CB). This new paradigm, named the MADEsparq Project, aims to blur the defining lines between currency, technology, and data by using the CB block chain to mediate the mapping of data to create, and add value, to content and unstructured and semi-structured data. Over time, as more data is mapped onto the CB block chain from a vast spectrum of subjects and interests, chance occurrences for discovering valuable mathematical, structural, functional, or social relationships from the overlap and intersection of data mappings increase. Therefore, since the data mappings onto the CB block chain can overlap from separate knowledge-domains, there is the potential for new insights and discoveries from the collaboration of participants in the MADEsparq Project, whose specialties lie in vastly different subject matters, that might not have been possible before. Thus, the intrinsic value of the virtual commodity (ie. CB's hash chain) mediating the data mappings should increase over time.

Keywords: Cryptogenic Bullion, data mining, sequence alignment, random number recycling, symbolic dynamics, open collaboration, categorization, personalization, hash chain, fractal, fractal dimension, peer-to-peer network, technological monetary system, prediction markets, opinion aggregation, Bitcoin, Litecoin, ppcoin, Bitshares, Netcoin, Emunie, Ripple, decentralization, Keyhotee, Diaspora*, Tor Network, virtual currency, virtual commodity, graph theory, regex parsing, decision trees, pattern recognition, machine learning, probabilistic modeling, Twitter, hashtag, SPARQtag.

Mercury Stills, [email protected] 1Cryptogenic Bullion©, 2013. All Rights Reserved.The MADEsparq Project©, 2013. All Rights Reserved.

mailto:[email protected]

http://cryptogenicbullion.org/

https://plus.google.com/+MercuryStills/about


Introduction

(This paper assumes the reader is familiar with virtual currencies like Bitcoin and the mechanisms governing the Bitcoin system, like proof-of-work, hash chain, block chain, etc. Please refer online for more information: http://en.wikipedia.org/wiki/Bitcoin )

A hash chain is a successive application of a cryptographic hash function h(x) to a string of characters, y. The applications of hash chains are wide, especially with regards to securing private data. But thanks to the rise of the virtual currency, Bitcoin (Nakamato, 2008), the concept and understanding of a hash chain or cryptographic hash function is steadily becoming more prevalent within the public domain.

Bitcoin's proof-of-work scheme was partly influenced by Adam Back's Hashcash (Back, 2002). The Bitcoin block chain is basically a hash chain of hash chains. But the usage of 'block chain' and 'hash chain' will be used interchangeably in this paper. The hash strings of the Bitcoin block chain are composed of hexadecimal characters.

In its basic essence, the Bitcoin network is a lottery system, where many participants (called miners) calculate hash function outputs with the hope of finding a result with certain requirements (https://en.bitcoin.it/wiki/Proof_of_work), winning a reward of bitcoins if successful.

Not long after Bitcoin's release to the public, other virtual currencies emerged onto the scene. Many of these alt-coins (short for alternate crypto-coins) were either straight copies of Bitcoin, or they used a different cryptographic hash function to form the hash chain and govern the proof-of-work scheme. For example, the second largest virtual currency, Litecoin, according to coinmarketcap.com, uses a memory-hard, scrypt cryptographic hash function to govern its proof-of-work scheme.

Sunny King's ppcoin project (King, 2012) is a peer-to-peer virtual currency utilizing a hybrid scheme with the same proof-of-work method as Bitcoin (using the SHA256 cryptographic hash function to govern the hash chain) but with an additional novel scheme termed proof-of-stake, which would be the dominant network securing mechanism over the long-term. And not long after ppcoin was released, Novacoin released a hybrid proof-of-work/proof-of-stake scheme utilizing the scrypt cryptographic hash function to govern the proof-of-work.

In June of 2013, Cryptogenic Bullion (CB) was released to the public. It was basically a clone of Novacoin with a modification to its supply rate. One major difference between CB and Novacoin or ppcoin is that CB uses the size of the block chain (ie. hash chain) to decide the amount of the proof-of-work rewards, whereas Novacoin and ppcoin use the size of their network's hashing computation. For CB, as the block chain got longer the proof-of-work rewards got smaller, and miners possessed less power and influence over the network. For ppcoin and Novacoin, as the network's hashing computation increased, the proof-of-work rewards decreased, but miners are



https://en.bitcoin.it/wiki/Proof_of_work

http://en.wikipedia.org/wiki/Bitcoin

much more influential on the network's rewards over time. CB's reduced proof-of-work rewards were designed to be accelerated, further quickening the pace of the dominance of the proof-of-stake scheme securing the hash chain—for CB, the stake holders possess the most power (ie. people holding CB in their client-software wallets), not the miners.

But essentially, CB has the same structure as Bitcoin; the integrity of the monetary system and the transactions that occur on the network is secured by a hash chain.

One of the main criticisms toward the proof-of-work scheme is that many resources (eg. electricity) are wasted doing useless (hashing) work. Once the hash function is calculated and the result accepted as the winner, the hash string output just sits on the block chain...it is quite literally a hexadecimal string doing nothing itself, albeit oxymoronically with purpose.

CB aims to change the uselessness of the hash string outputs used to form the network's block chain (ie. the hash chain which holds all information of the transactions that occur on the CB network) by creating a paradigm, named the MADEsparq Project. Using the PySide Qt-python integrated framework for the initial client-software development, and also using a web portal with a robust application programming interface, the MADEsparq Project will mediate and offer functionality to participants and use of many computing tools like regular expressions or string parsing to map the hexadecimal characters composing the hash strings of CB's block chain to domain specific data (or vice versa). Thus, the hash chain securing the CB network also becomes a mapping to the contents of a distributed data store (http://en.wikipedia.org/wiki/Data_store) for unstructured or semi-structured data categorized by the participants. And participants will have the option of making their made mappings public, thus making the CB block chain a medium for open collaboration.

The core value to be understood from the MADEsparq Project is in how the hash chain can become a map image (or pre-image) to (or from) a vast store of data categorizations from a variety of knowledge-domains, therefore facilitating the overlap and intersection of structural, functional, mathematical, or even social relationships.

Note, nothing is getting stored on the CB block chain itself. Storage and mapping by the MADEsparq Project is a distinct and separate computation and storage space from the CB client wallet that maintains the CB block chain. But the MADEsparq Project will have a inherent development culture of analyzing the feasibility of migrating certain functionality from the MADEsparq client software to the CB client wallet. This will be decided by the community of developers who join the CB and MADEsparq Project over time. Herein lies the strength of being an open-source, open-collaboration project, both for CB and the MADEsparq Project.

The rest of this paper is organized as follows: 1) The fundamental concepts of the MADEsparq Project will be introduced. 2) A survey of examples for methods of data categorization and mapping will be given. 3) Some industry specific applications and examples. 4) Ideas of why the MADEsparq Project creates intrinsic value for CB over time and helps to strengthen CB's economic and monetary system will be discussed. 5) Synergy between MADEsparq Project



http://en.wikipedia.org/wiki/Data_store

development and CB's block chain. 6) The vision for the CB and MADEsparq Project global brand name and marketing plan will be discussed. 7) Concluding remarks.

Note that this paper is mainly for the laymen and many mathematical and programming details have been omitted. When details have been omitted, then it will be acknowledged that a future paper with more details is planned or in the process of being completed.

1 Fundamental Concepts of CB's MADEsparq Project

The amount of unstructured and semi-structured data is growing at an exponential rate. While browsing the internet, we are flooded with raw information, most of which goes unused or ignored, even if possibly useful, because we just don't have the means, time, or motivation to properly attend to the data encountered.

There are many companies just now starting to remedy our chaotic relationship with the massive amounts of data we encounter. And many of the origins for this data, like Twitter, Facebook, Google+, Youtube, or Netflix, are starting to provide better tools for categorizing and personalizing our experience with the data.

But ever since Edward Snowden's revealing of the NSA's practices (http://en.wikipedia.org/wiki/Edward_Snowden) and corporations' submission to its requests, we now know our anonymity and private data or information we choose to share with loved ones and friends is at the mercy of these corporations and organizations like the NSA. A peer-to-peer, distributed and decentralized paradigm for communicating and sharing our interests may strengthen our independence when browsing the internet, while also aiding our ability to be more productive and informed with the large amounts of data we encounter.

Cryptogenic Bullion (CB) is a decentralized, peer-to-peer virtual commodity (see below about the conscious decision to term CB as a 'virtual commodity' and not a 'virtural currency'). The hash chain (aka. The block chain), is public and open, and the information from the block chain is accessible by anyone. A decentralized, peer-to-peer network already exists within the structure of the CB system. We just need to create the distributed paradigm.

One of the main ideas underlying MADEsparq is a mapping. The forms of how to represent a mapping will be supported by a variety of data formats (eg. CSV, JSON, XML, vector notation, arrow notation, etc.). But regardless of the data format and contents, the final task with any data categorization is to find some defined mapping to hexadecimal characters and strings so that when mapped, the data categorization will be represented by some substring of a hash string, a whole hash string, or many hash strings of the block chain, or possibly the whole block chain (see DNA sequence alignment below). There will be many examples and applications described below. But a simple example of a mapping is as follows:



http://en.wikipedia.org/wiki/Edward_Snowden

We can map words to hexadecimal characters and strings:

the = 0a = 1dog = 2car = 3cat = 4my = 5shoe = 6ate = 7playful = 8nice = 9boy = agirl = bred = czebra = dhit = edone = fdo = 00does = 01how = 02why = 03

Note that the words 'do', 'does', 'how', and 'why' have been mapped to hexadecimal strings of length two.

We can see that in searching along the hash string of block one (omitting leading zeroes),

Block 1: c98ca5027576dcdf662cedd271db881f5fe6e781a220bdd08e11da1a094

the substring of length two, starting at the third position, 8c, represents the expression “playful red”, which is the first syntactically correct statement while conducting a brute force search along the hash string, even if it doesn't make much sense.

And perhaps a linguist would conduct a much more complex and optimized search along the Cryptogenic Bullion block chain for when a sequence of hexadecimal characters which have been mapped to the above set of data (ie. words) represents a statement that is both syntactically as well as semantically correct. (Much research in computational linguistics is concerned with finding statistical or rule-based modeling of natural languages: http://en.wikipedia.org/wiki/Computational_linguistics)

To give a more thorough explanation, we'll start with one example for using MADEsparq to search for some necessity or interest:



http://en.wikipedia.org/wiki/Computational_linguistics

Assume a user named Jack just saw the movie, Pi, by Darren Aronofsky (http://www.pithemovie.com). And since Jack enjoyed the movie so much, he was curious if there are movies that are similar in style, but not necessarily similar in plot or mathematical undertones underlying the drama. Perhaps a movie similar in style but more artsy and less math stuff. The 'similar movies' links on movie websites proved to be unsatisfying to his tastes. Jack can use MADEsparq to query other users' categorization of films to help find a movie similar to Pi which might be better aligned with his tastes.

Users of MADEsparq can be divided into two groups: DL or DJ. A DL ('data listener') is a user who is querying (or searching, or filtering) for data because of some necessity or interest, and a DJ ('data jockey') is a user who provides (or categorizes, or personalizes, or maps) data based on their own specialty or interests. 'Data categorization' and 'data mapping' will often be used interchangeably and will mean the same thing, but as will be explained below, all mappings are categorizations but not all categorizations are mappings. In other words, not all data categorizations will have a representation as a string of hexadecimal characters on the block chain.

Before continuing, here are some bullet points about the structure and corporate governance of MADEsparq.

● The main governing entity will be named 'MADEsparq Project'.

● The MADEsparq Project will be a not-for-profit organization. At the time of writing this paper, registration was in the process of being filed with the state of North Carolina, USA.

● MADEsparq client software will be an open-source and open-collaboration project under the MIT License.

● There are two MADEsparq mediums for data listening/broadcasting: MADEsparq.org and the peer-to-peer MADEsparq client software (see future development projects below).

● MADEsparq.org will build an open and accessible corpus of DJ 'broadcasts' based on agreed upon standards, which can be searched by anyone, without the need to register with the website. MADEsparq.org will also be similar to a social-network where users can register a page and publish data mappings—this can be done anonymously or a personal profile can be filled out and made public to other MADEsparq participants. The structure and format of MADEsparq.org will have some similarities to wikipedia.org, but the method of publishing or broadcasting data will be quite different. More of this difference below.

● MADEsparq's client software will build and maintain a library of search algorithms and data libraries over time. The MADEsparq Project will decide which libraries, search, and SPARQtag (see below) standards will get approved and accepted with MADEsparq.org and the MADEsparq peer-to-peer client software. The format and structure for approving standards will be similar in format to the Apache Software Foundation.



http://www.pithemovie.com/

● MADEsparq.org and the MADEsparq client software will initially only support Cryptogenic Bullion's block chain. This can change and it is dependent on the major virtual currency exchanges to support Cryptogenic Bullion trading (BTC-e, Okcoin, MCXnow, Bter, Vircurex, Crypto-trade, etc.). Moreover, a partnership of some kind with the developer team of other major virtual currencies can be formed which would garner MADEsparq support in exchange for additional developer support. (see below for more about future plans on global branding and marketing)

Data Jockey

There are a few ways to be a Data Jockey (DJ) and 'broadcast' or 'publish' a data mapping or data categorization ('broadcast' and 'publish' will be used interchangeably—certain kinds of data or their categorizations will decide the context for which term to use).

One way, which will most likely be the dominant way to be a DJ, is by creating a MADEsparq subdomain to an existing website. So, for example, if your website is cryptogenicbullion.org, then a MADEsparq subdomain would be madesparq.cryptogenicbullion.org (or made. cryptogenicbullion.org or sparq.cryptogenicbullion.org, it is a matter of preference). And this MADEsparq subdomain can be another website (using HTML, Javascript, etc); it can be flashy or a plain block of text. Or the contents of the MADEsparq subdomain can be rendered XML or JSON. Perhaps the MADEsparq subdomain is sophisticated enough to provide a robust XML or JSON (or possibly an RSS) application programming interface (API). It needs to be stressed that to increase the chance for mass adoption of the MADEsparq Project, as many options as possible need to be made available so to decrease barriers to entry.

Using standard HTML (or XML) tag and meta-tag practices, a MADEsparq subdomain will tag a set of categorized data (ie. the text contents) based on agreed upon MADEsparq standards (to be finalized later, after there is enough feedback from the MADEsparq community)—if the data can be represented by ASCII characters in a text file then it can be categorized. And a DJ can publish her categorization for MADEsparq listeners by simply having this subdomain available and open, just like any other website. The MADEsparq subdomain needs to be registered on MADEsparq.org. Once registered, the MADEsparq.org API (or web-crawler engine) will fetch the data categorization from the MADEsparq subdomain, as well as the methods used to categorize/personalize the data (more about methods for categorization below). Then the categorization will be published and made available for data listeners through MADEsparq.org as well as the client software.

Moreover, DLs will have the option of 'subscribing' to a DJ's MADEsparq subdomain or just to a particular mapping within that DJ's MADEsparq subdomain, similar to a RSS feed (http://en.wikipedia.org/wiki/RSS). It will also be possible to 'subscribe' to a section of the CB block chain and receive alerts when new data has been mapped to that section. It is with this publishing/subscribing framework that the MADEsparq Project, with the CB block chain as a kind



http://en.wikipedia.org/wiki/RSS

of mapping mediator, facilitates the exchange of customized, categorized, and even personalized data, while also governing a distributed system for disseminating content.

Another way to be a DJ is by directly uploading files of a data categorization to MADEsparq.org, thus one can be a DJ without the need of hosting a website.

And lastly, a DJ will be able to publish a data categorization through the MADEsparq peer-to-peer client software. This will be done by uploading a text file to the client software (see Data Mapping Template below).

Again, to emphasize, anyone can be a DJ, any specialty or interest or hobbyist or expert or novice. And there may be new ways for DJs to publish their data categorizations in the future. For instance, MADEsparq is incorporating the Git revision control system as a method for DJs and DLs to share data and/or their mappings onto the block chain.

Data Listener

A data listener can search for data categorizations by going to MADEsparq.org and using the web interface to enter her search. Or a data listener can download the client software and use the client interface to enter her search.

Moreover, we hope other search engines and social networks, centralized and decentralized (eg. Diaspora*), will adopt this standard and build their corpus of MADEsparq DJ categorizations and offer their own search options. And hopefully being an open source, not-for-profit organization will help to make it easier for other corporate entities to accept the MADEsparq Project into their future business plans and thus to instigate a race-to-arms for data categorization and personalization based on and mediated by mappings to the Cryptogenic Bullion block chain, consequently increasing awareness of Cryptogenic Bullion and virtual currencies as a whole. But more of this below when global brand name and marketing as well as future projects are discussed.

Note that data listening also includes the mechanism of utilizing the inherent randomness of the Cryptogenic Bullion block chain for one's own needs. As shown below, being a DL includes the activities of recycling the random hexadecimal characters of the block chain to model probabilities based on the DL's own set of needs or defined rules (using programming scripts to model decision trees, or using the default modeling tools of the MADE sparq Project). And results from such probability models can in turn be mapped and aligned onto the Cryptogenic Bullion block chain, perhaps on the very hash strings used for random input. Therefore, the mapping would be categorized as an endomorphism (http://en.wikipedia.org/wiki/Endomorphism) by the MADEsparq Project, along with possibly other categorizations (ie. categorized by a participant or by the search algorithms incessantly executed by the MADEsparq Project development team). Also note that the contents or reasons for the DL's use of the Cryptogenic Bullion block chain to model probabilities, whether it be for



http://en.wikipedia.org/wiki/Endomorphism

video game modeling or DNA mutation modeling, is irrelevant.

Back to Jack's Movie Search

Returning to our example from above. Recall Jack was interested in finding a movie similar to Pi, with the same visual style but less math stuff and more artsy. So Jack would be taking the role of 'data listener' when using the MADEsparq Project for his search.

Below are bullet points of descriptions for several possibilities of how his search could be conducted and satisfied. And as the MADEsparq Project grows, then so shall the possibilities for conducting searches like this one:

● A keyword search for 'Movie' and 'Pi' would give results of participant movie lists, each being categorized based on that participant's own defined rules. Some of the categorizations have been mapped and aligned with segments of the block chain, each with a percentage value signifying the level of accuracy with matching a string of hexadecimal characters of the block chain, starting at some location on the block chain (eg. The hash string of block number 2, at position 12). The MADEsparq Project has set a minimum accuracy of 70% for mappings to be qualified for storage into the MADEsparq Project corpus, but this will most likely change.

● Jack could enter a search on the MADEsparq.org web interface based on quantifiers and qualifiers, like so: 'similar to: Pi' + 'more: art' + 'less: math' + 'equal: visual style'.

● Jack could have created his own basic categorization and included other movies to help give more context for search. For example, Jack could have defined a two-dimensional graph (ie. a cartesian plane), with the horizontal and vertical axes ranging from one to ten. Each extreme of the horizontal axis would be art and math, respectively. And similarly for the vertical extremes, comedy and drama, respectively. The coordinates of the origin of this graph would be (5.5, 5.5). And Jack could define all movies on his list to be at some position on this defined graph. For example, Jack defined Pi to be positioned at the location (8, 10). And finally, Jack could position where he thinks his desired movie would be located on this graph, for example at (2, 10).

● Similar to the previous bullet point. Jack could have created a n-dimensional graph, each axes representing some categorization of extremes, for instance: art/math, comedy/drama, visual style/musical style, romance/violence, realism/fantasy. (Note, the extremes don't have to be conventional extremes, they could be subjectively defined.)

● Jack could have used some other structure, like a n-ary tree, with a defined inherent hierarchy for his categorization search (see SPARQtag below). As long as Jack has a well-defined parsing algorithm for the string representing the list of movies in his categorization, then other participants of the MADEsparq Project can collaborate with



him. (This is for advanced participants of the MADEsparq Project)

Brief Overview and Description of Jack's Possible Search Results

Below are bullet points of possible search results for Jack's search. All results are the result of some DJ categorization and/or mapping.

● A keyword match will return a list of url links to MADEsparq subdomains that have included Pi in their data (ie. list of movies). The layout of such a search results page has yet to be finalized, but it is planned that a brief description of each categorization will be included next to each URL, along with mapping location on the CB block chain if available.

● Perhaps Jack has narrowed his search to only search the MADEsparq DJs who are considered movie specialists. These specialists can be movie portal sites that have a MADEsparq subdomain or movie bloggers with a MADEsparq subdomain, etc. And there's even a small (stubborn) DVD movie merchant who's MADEsparq subdoman is one of the only tools to keep his online store relevant, but luckily this DVD merchant is quite knowledgeable about movies and is quite skillful with categorizing data. So she's able to maintain a steady following and spark her DVD sales every once in awhile.

● Maybe Jack uses his graph categorization as a search query. And lo' and behold, there are dozens of other participants using the same graph for categorization, except that the defined movies' locations on the graph are slightly different. But in one categorization, a movie by the name of Stay, starring Ryan Gosling is near the location where he thinks his desired film should be located (ie. at location coordinates (2, 10) ).

● One search result was from an advanced DJ. This advanced DJ used a complex, self-written script and published her results on her MADEsparq subdomain. This script categorized a list of movies of which Pi was included, using regex, interval sampling (see below) and neural networks (http://en.wikipedia.org/wiki/Artificial_neural_network) using the inherent randomness of the hexadecimal characters of each hash string of the block chain. A basic explanation of how the script worked is this: starting at the first hash string of CB's block chain, the script would iterate over each hexadecimal character and would consequently be tested against a series of If/Then statements. And depending on which statements were true or false, the script would move the regex cursor along the block chain in a certain direction, for example to the next hash string, or five blocks ahead and five positions into that hash string, etc. Thus, every movie on the DJ's list was categorized by some position on the block chain. But the final result of the script was a average coordinate from all results that were run on the first 5000 hash strings of CB's block chain. Pi had a final coordinate of (100.3, 22.7), which represent 100.3 blocks up and 22.7 character positions into that block's hash string (these are averages, so they usually won't be integers). Movies that were closest to each other were said to have



http://en.wikipedia.org/wiki/Artificial_neural_network

many similar characteristics. The two closest movies were The Fountain, by the same director, Darren Aronofsky, and again, Stay. (See info on k-nearest neighbors algorithm for more about the underlying statistical methods used: http://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm)

SPARQtag

Twitter gave birth to microblogging and the hash tag (http://en.wikipedia.org/wiki/Hashtag, no pun intended with hash chain). And similarly, MADEsparq will have a SPARQtag©. The SPARQtag will be a way for DJs and DLs to broadcast and microblog their interests or queries, or to direct attention to a certain section of the block chain, or to a specific mapping onto the block chain. The standards and syntax for using the SPARQtag have yet to be finalized, but the conventions will be very similar to a string representing a breadth-first-traversal of a tree (http://en.wikipedia.org/wiki/Breadth-first_search).

But one of the main initiatives of The MADEsparq Project is to integrate with many new internet technologies specializing in decentralization and privacy, and not just the public behemoths like Google, Twitter, or Facebook. A few companies and their technology which The MADEsparq Project plans to integrate with are listed below. But it must be stressed that the MADEsparq Project is a community developed project and participants can choose whatever social-network or microblogging platform they desire:

● Bitmessage is a 'decentralized, encrypted, peer-to-peer, trustless communications protocol that can be used by one person to send encrypted messages to another person, or to multiple subscribers (http://en.wikipedia.org/wiki/Bitmessage).

● Keyhotee by Invictus Innovations Incorporated. (http://invictus-innovations.com/)

● Diaspora*, a decentralized social-network (https://diasporafoundation.org/)

● The Tor Project; Tor is a network of virtual tunnels that allows people and groups to improve their privacy and security on the Internet. (https://www.torproject.org/)

So, returning to our example, Jack may broadcast a SPARQtag that may look something like this:

/**Pi/>Aronofsky/>Style/Visual/>Theme/less math/more art



https://diasporafoundation.org/

http://invictus-innovations.com/

http://en.wikipedia.org/wiki/Bitmessage

http://en.wikipedia.org/wiki/Breadth-first_search

http://en.wikipedia.org/wiki/Hashtag

http://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm

http://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm

which represents a kind of hierarchy that would be parsed as follows, with greatest emphasis for search being at the top of the hierarchy:

PiAronofsky

Style → VisualTheme → less math → more art

Jack can then await friends on his social network or followers on his microblog network to reply with suggestions.

Note that every SPARQtag starts with the three characters /** and every new row after the top row is denoted with />. If a participant wants to define more than one categorization for the same row then she can use parenthesis, like so:

/**Pi/>Aronofsky/>(Style/Visual/>Theme/less math/more art)

which defines the following hierarchical categorization:

PiAronofsky

Style → Visual | Theme → less math → more art

Note the '|' character in the bottom row of the categorization, which separates the two 'Style' and 'Theme' subsets.

If the SPARQtag were to be integrated with Twitter hashtags, then perhaps the hashtag would be qualified with a literal 'SPARQtag' at the front, like so:

#SPARQtag/**Pi/>Aronofsky/>Style/Visual/>Theme/less math/more art

It is acknowledged that such things in the social-network world grow organically and therefore no set standards have been defined regarding the combination of hashtags and SPARQtags.

In its essence, a SPARQtag defines a graph (http://en.wikipedia.org/wiki/Graph_(abstract_data_type)) or lattice structure (http://en.wikipedia.org/wiki/Lattice_(order)#Examples ) with an inherent hierarchy. And more dividing characters like '/>' will be defined as part of the SPARQtag standard in the future, depending on community feedback and participant needs, so that more complex structures can be represented with a SPARQtag.

Results—All Mappings are Categorizations but not all Categorizations Will Have a Mapping



http://en.wikipedia.org/wiki/Lattice_(order)#Examples

http://en.wikipedia.org/wiki/Graph_(abstract_data_type)

Ultimately, a data categorization with a result means that there is a section or perhaps the whole CB block chain that has been mapped to the data set using some method. The result can be a single number, representing the hash string of the block number to where the data set is mapped, or the results of a data categorization can be much more complex and descriptive. It is up to the DJ to properly format and comment on her data categorization, to make it as user friendly as possible. This will be a matter of personal style and preference. But there are minimal standards for sharing a data categorization. And although the standards have not been finalized, it is discussed in the next section.

Data Mapping Template—The (tentative) Common Format For All Data Categorizations

(This section is labeled as tentative since the standards have not been finalized.)

The current template for MADEsparq websites (ie. websites with a madesparq subdomain) is as follows:

---Optional Keyword headers

MethodsOptional Keywords{ some method variables here}

DataOptional Keywords{data set here or a URL to download data here}

Method Definitions if not part of current MADEsparq standardsOptional Keywords{method functions and/or regex or string parsing functions here}

Results{locations of sections of the CB block chain and/or a description of results and/or a URL to download results here, IF results exist}---If using HTML attribute conventions, this same template would make use of the 'id' or 'class' attributes:

<html><div class='CATEGORIZATION_1'>

<div class='METHODS'><div class='KEYWORDS'>



global keywords here</div><div id='METHOD_1'>

<div id='KEYWORDS_1'>keywords to override global keywords or to apply specifically to METHOD_1

</div>METHOD_1_VAR1_VAR2

</div>

<div id='METHOD_2'>METHOD_2_VAR1_VAR2

</div></div>

<div class='DATA'><div id='DATA_1'>

Data in some format like CSV, or vector notation here</div>

</div>

<div class='METHOD_DEFS'><div id='METHOD_2_DEF'

Method2 definition here</div>

</div></div></html>

No results exist (ie. the data categorization has not been mapped to the CB block chain), so no RESULTS block has been filled.

Note that there is no definition for METHOD1. This is an example to show that it is because it is a standard method in the MADEsparq library and therefore not necessary.

If a DJ were to use the client software to download a categorization, then the same template should be used. But the data format does not have to be in HTML. It can be in JSON, XML, or blocks can be defined by white space. There will be an optional header variable which can be a keyword identifier to signify what data format the text file is using. This is when a user cannot change file extensions to match the data format of the file extension (ie. file.xml for XML, file.csv for CSV, etc.). For example, if the DJ is uploading a text file using the JSON format, then the first line would be MADE_DATA_JSON. If using white space, then the first line would be MADE_DATA_WHITE (white space standards are still being decided upon and finalized).



Note that the MADEsparq Project is implementing the use of keyword variables in such a manner so to be as expressive as possible. The aim is so that the non-technical user (ie. those without programming experience) will have as mild a learning-curve as possible, especially with regards to being a DJ. But they are not required. Participants with programming experience can use whatever methods they're used to using.

An example data template using a text file uploaded in the JSON format would be:

---MADE_DATA_JSON

{“CATEGORIZATION_1”: {

“METHODS”: “KEYWORDS”: “global, keywords, here”,“METHOD_1”:{

“KEYWORDS_1”: “keywords, to, override, global, keywords, or, to, apply, to, METHOD_1”

“METHOD_1_VAR”:”METHOD_1_VAR1_VAR2”}“METHOD_2”:{

“METHOD_2_VAR”: ”METHOD_2_VAR1_VAR2”}

“DATA”:“DATA_1”: “Data in some format like CSV, or vector notation

here”

“METHOD_DEFS”: “METHOD_2_DEF”: “Method2 definition here”

}}

---

And similarly for XML. Here is a nice example page from json.org that shows how the same data can be represented by both JSON and XML: http://json.org/example. But essentially, everything in the MADEsparq framework can depend on the keyword standards, so that the information parsed from the MADEsparq subdomains is interpreted properly afterwards.

The keyword header variable MADE_DATA_JSON (known as the data format header) notifies the MADEsparq interpreter or user who is downloading the data mapping that the format will be



http://json.org/example

in JSON. The scheme for the data format header is MADE_DATA_(data format in all capitalized letters).

More about what these elements and keywords of the data template mean below. But there will be future papers released soon after this introductory paper with much more details about all facets of DJing and DLing using the MADEsparq framework and agreed upon standards.

2 A Survey of Examples for Methods of Data Categorization and Mapping

Modeling Probabilities by Interval Sampling

It is possible to use the hexadecimal characters of the block chain to model probabilities, by assigning said probabilities to regex matches of characters or strings that lie within defined intervals. For example, lets use the hash string from the first block of Cryptogenic Bullion's block chain (omitting leading zeroes):


And for a trivial example, a model of the probability of a coin flip (½) could be defined as follows:

Heads: ½ = [0, 7] , Tales: ½ = [8, F]

Thus, using a trivial regex search along the CB block chain, we can map a Heads or Tales value to each hexadecimal character of the block chain. Or, depending on your needs, the randomness of the block chain can trigger a Heads or Tales value which could be used in some application (see Portfolio Theory, Modeling Decision Trees, and Event Driven Apps below).

And by the law of large numbers (http://en.wikipedia.org/wiki/Law_of_large_numbers), this model should be a fairly accurate model of a coin flip the more characters of the block chain are tested to lie in either interval, heads or tales (there are over 10 million hexadecimal characters composing the blocks' hash strings...this does not include the transaction hash strings). Indeed, just using the first block to test how many heads are flipped, the result is 27/59 or 0.46.

But also using the second block (omitting leading zeroes):

Block 2: 46d4130771ae7be87b41dcac7a99e9134c881818631e24c954c66eaf01d

The results become 57/118 or 0.48.

But once the intuition of modeling probabilities is developed, one quickly sees that anything with a defined probability can be assigned to an interval using a variety of options, like, for example, the inherent lexicographical property (http://en.wikipedia.org/wiki/Lexicographical_order) of hexadecimal strings (for strings of length 6):



http://en.wikipedia.org/wiki/Lexicographical_order

http://en.wikipedia.org/wiki/Law_of_large_numbers

Heads: ½ = [000000 , 777777] , Tales: ½ = [777778, ffffff] (*)

Or taking the power set of a hexadecimal substring of some length and applying the lexicographical property to each string of the power set, with each length (from the strings of the power set) having their own probability assignment like (*) above. (see Fantasy Sports Modeling below for an application)

Portfolio Theory, Modeling Decision Trees, and Event Driven Apps

From the Modern Portfolio Theory (MPT) Wiki (http://en.wikipedia.org/wiki/Modern_portfolio_theory):

“Modern Portfolio Theory is a mathematical formulation of the concept of diversification in investing, with the aim of selecting a collection of investment assets that has collectively lower risk than any individual asset. This is possible, intuitively speaking, because different types of assets often change in value in opposite ways.[2] For example, to the extent prices in the stock market move differently from prices in the bond market, a collection of both types of assets can in theory face lower overall risk than either individually.”

It is possible to apply MPT-type ideas to modeling probabilities with the MADEsparq Project. For example, a participant can assign intervals to model the (opinion) of probabilities of a change in value of a stock or bond (or any asset) over some defined time.

Say, for instance, a participant has the opinion that for every substring along the block chain of length two, the following assignments for a change in value for Apple Inc's stock (ticker symbol AAPL) have been assigned, which are based on the participants understanding of current market dynamics:

-3% → [(00),(00)]-2% → [(01),(03)]-1% → [(04), (09)]0% → [(0a),(9f)]+1% → [(a0),(dd)]+2% → [(de),(ef)]+3% → [(ef),(ff)]

Only one 2-string ('00') will decrease Apple's stock price by 3% while two 2-strings ('ef' and 'ff') will increase Apple's stock by 3%.

So, assume the stock price starts at 450, then for every two-character substrings along CB's block chain, the stock's price is incremented or decremented (in percentage terms) depending on which interval the two-character substring lies in. Using the first block to represent one trading day:



http://en.wikipedia.org/wiki/Bond_market

http://en.wikipedia.org/wiki/Stock_market

http://en.wikipedia.org/wiki/Modern_portfolio_theory#cite_note-2

http://en.wikipedia.org/wiki/Diversification_(finance)

http://en.wikipedia.org/wiki/Modern_portfolio_theory


And iterating over every 2-string. For example, iterating over the first four 2-strings 'c9', '98', '8c', 'ca' changes Apple's stock price by +1%, 0%, 0%, +1%, respectively, so that Apple's stock price after the fourth 2-string is 459.05. Iterating over the whole hash string, we can see that this probability assignment models Apple's stock for one day's trading to close at $592.87, which is a gain of about 32%. So either this model assignment is taking into consideration some positive influence, or it is just an extremely optimistic model. Below is a Python script a DJ could have used to execute the model and then publish the results in the RESULTS block in her MADEsparq subdomain:

#!usr/bin/python

#This code uses the hash string of Cryptogenic Bullion's first block #of its block chain and applies the following interval assignment #to each iteration of substrings of length 2 to model Apple's stock price#which has an initial price of 450.

#Interval assignments to model probability# -3% → [(00),(00)]# -2% → [(01),(03)]# -1% → [(04), (09)]# 0% → [(0a),(9f)]# +1% → [(a0),(dd)]# +2% → [(de),(ef)]# +3% → [(ef),(ff)]

block = 'c98ca5027576dcdf662cedd271db881f5fe6e781a220bdd08e11da1a094'

aapl = 450print "block is %s" % (block)raw_input("Continue? (Press enter...)")str = ''for i in range(58):

str = str + block[i] + block[i+1]print "str is %s" % (str)raw_input("Continue? (Press enter...)")if str == '00':

print "taking -3%"raw_input("Continue? (Press enter...)")aapl = aapl * 0.97

elif str > '01':if str <= '03':



print "taking -2%"raw_input("Continue? (Press enter...)")aapl = aapl * 0.98

elif str <= '09':print "taking -1%"raw_input("Continue? (Press enter...)")aapl = aapl * 0.97

elif str <= '9f':print "unchanged"raw_input("Continue? (Press enter...)")pass #unchanged--do nothing

elif str <= 'dd':print "adding +1%"raw_input("Continue? (Press enter...)")aapl = aapl * 1.01

elif str <= 'ef':print "adding +2%"raw_input("Continue? (Press enter...)")aapl = aapl * 1.02

else:print "adding +3%"raw_input("Continue? (Press enter...)")aapl = aapl * 1.03

print "current price of aapl is %s" % (aapl)raw_input("Continue? (Press enter...)")str = '' #clear string

# print final price of aaplprint "final price of AAPL is: %s" % (aapl)raw_input("Continue? (Press enter...)")

But a stock's price doesn't change without affecting its own future price. For example, any huge spike in price usually triggers selling since some holders of the stock would choose to take profits. Thus, a participant can model decision trees in the form of If/Then trees to reassign probabilities if certain requirements are met. Using the above Apple Inc. example, there can be a reassignment of probabilities to increase the chance of the stock price decreasing (ie. increase the size of the intervals for negative percentage change values and decrease either the unchanged interval or the positive change intervals or both) whenever the stock price increases, say, by at least 10%. More information regarding standards for modeling decision trees will be given in the future once enough community feedback has been received.

Note that modeling If/Then or decision trees can trigger events based on some stored variable value that changes, like the stock price in the example above, or it can be based on regex



matches along the block chain. When this is the case it is termed 'event driven modeling' or 'event driven apps' within the MADEsparq framework (see video game analysis and practice below).

Opinion Aggregation, Colored Coins, and Prediction Markets

Next generation altcoins are on the horizon (Netcoin, http://www.netcoin.io/, Bitshares, http://invictus-innovations.com/bitshares2). And the bitcoin developers have expressed plans for a project, named MasterCoin, to create new altcoins in Bitcoin's block chain (http://www.coindesk.com/mastercoin-to-create-new-altcoins-in-bitcoins-block-chain/). Moreover, there is talk of using feedback mechanisms in bitcoin's block chain to create prediction markets or to model a physical asset's price (eg. gold, silver, etc.). But no one is talking about helping people make more informed investing decisions by offering new analytical tools. There exist back-testing tools, but at a cost, or perhaps a requirement, like having a brokerage account with the firm offering the back-testing tools. Given some of the examples shown above, the MADEsparq Project can be a source for new analytical tools for back-testing, therefore helping people make more informed decisions about their finances.

Moreover, with the inherent opinion aggregation of the MADEsparq Project, it might be possible to create prediction markets based on the data from the MADEsparq Project as a whole. Given the public nature of the MADEsparq Project, both the public and private sector have a mountain of non-personal, previously unstructured data categorizations, available for their own proprietary aggregation and analysis.

Data Mapping and Sequence Alignment

Bioinformatics is an interdisciplinary field that develops and improves on methods for storing, retrieving, organizing and analyzing biological data (http://en.wikipedia.org/wiki/Bioinformatics). And projects like 23andme.com and the Personal Genome Project (http://www.personalgenomes.org) have made not only one's own genome or DNA sequence available, but one can find that millions of other organisms' DNA, RNA, and protein sequences are readily available online via many other websites and services. Sequence alignment is a way of arranging the sequences of DNA, RNA, or protein to identify regions of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequences (http://en.wikipedia.org/wiki/Sequence_alignment).

It is possible to use the MADEsparq Project and map bioinformatic data to a block chain. For example, by dividing the hexadecimal characters into four intervals and mapping each interval to a nucleotide base (A, C, G, and T) as follows:

[0:3] → A [4:7] → C [8:B] → G [C:F] → T



http://en.wikipedia.org/wiki/Sequence_alignment

http://en.wikipedia.org/wiki/Structural_biology

http://en.wikipedia.org/wiki/RNA

http://en.wikipedia.org/wiki/DNA

http://www.personalgenomes.org/

http://en.wikipedia.org/wiki/Bioinformatics

http://www.coindesk.com/mastercoin-to-create-new-altcoins-in-bitcoins-block-chain/

http://invictus-innovations.com/bitshares2

http://www.netcoin.io/

For instance, this DNA segment of length 25, from the DNA sequencing page on wikipedia.org:

dna = 'atgcttcggcaagactcaaaaaata'

has highest match accuracy within the hash string of block one (omitting leading zeroes):

block 1 = 'c98ca5027576dcdf662cedd271db881f5fe6e781a220bdd08e11da1a094'

starting at position 22 with 52% accuracy.

We can map segments, if not whole, DNA sequences onto the block chain and allow for relationships with other knowledge-domains to emerge and be discovered over time as more data is mapped onto the block chain. And these relationships that are discovered over time may give a hint to anyone who has an interest in a particular sequence or section of the block chain and the consequent data mapped to it (more of this below when increased intrinsic value of the CB block chain is discussed).

Mapping DNA sequences onto the block chain helps to illustrate how this idea can similarly be applicable to any sequence of characters. Moreover, a sequence of hash strings can represent a matrix (http://en.wikipedia.org/wiki/Matrix_(mathematics)). Not only can matrices be mapped to a block chain, a character sequence can be mapped to a row of a matrix which is mapped to a block chain. Thus one would have a mapping within a mapping (ie. a composite function, http://en.wikipedia.org/wiki/Function_composition). And many of these mappings possess mathematical characteristics, like, for example, group symmetry (http://en.wikipedia.org/wiki/Symmetry_group), or group affinity (http://en.wikipedia.org/wiki/Affine_group). So, data from a variety of fields can be aligned and mapped and related to each other because of their mappings onto the same base structure, that is, the Cryptogenic Bullion block chain.

Categorizing the Block Chain using Fractal Dimension and Other Measures of Self-similarity and Complexity

Another mathematical characteristic of data that can help give insight into the relationships between the data and their mappings onto the block chain is fractal dimension. A fractal dimension is an index for characterizing fractal patterns or sets quantifying their complexity as a ratio of the change in detail to the change in scale (http://en.wikipedia.org/wiki/Fractal_dimension). Since the block chain is public, and DJs have the option of making their categorizations public, data and their mappings can be readily measured and categorized by a facet of mathematical tools like fractal dimension (Kumaraswamy, 2002). In other words, a DJ can know before attempting to map data to the block chain what degree of complexity or self-similarity the block chain's hash strings need to be. Thus, a DJ, after measuring, or observing another DJ's measurement of the fractal dimension of a section of the block chain, and meeting her minimum or maximum requirement,



http://en.wikipedia.org/wiki/Fractal_dimension

http://en.wikipedia.org/wiki/Affine_group

http://en.wikipedia.org/wiki/Symmetry_group

http://en.wikipedia.org/wiki/Function_composition

http://en.wikipedia.org/wiki/Matrix_(mathematics)

can then concentrate her efforts to find a mapping that will align a set of data to a very particular section of the block chain, in perhaps, a very particular way. The power of this project is in the abstraction of the mappings. But more details of using fractal dimension to categorize mappings onto the block chain will be discussed in a future paper.

Moreover, it's possible to define rules to parse inherent graph and lattice structures within the Cryptogenic Bullion block chain. Thus helping to further categorize and measure predetermined levels of structural or geometric complexity within the block chain (ie. by applying a parsing algorithm to the hexadecimal characters of the block chain). More on defining parse rules to discover graph structures, in particular, sparse trees, in a future paper.

Modeling Cellular Automata by Regex Events Along the Block Chain

It will be possible to assign movement rules to cellular automata for simulation. A trivial example would be if there is a 8x8 square grid composed of two classes of cellular automata, four of each class randomly positioned on the grid at some starting time. And for each automata we increment along each character of the block chain with the following interval assignment:

class A: [0,4] → move up, [5,9] → move right, [a,f] → move downclass B: [0,3] → move right, [4,7] → move left, [8,f] → move up

Note, all automata move in their assigned direction if possible. That is, if an automata isn't at the boundary of the grid and a new move moves it off of the grid, or an automata of the same class is not in a destination position.

Consequently, an If/Then rule can be defined for when two automata of different classes occupy the same position. For instance, in a predator-prey simulation (http://new.math.uiuc.edu/math198/MA198-2010/suharsh_sivakumar/predpreyfinal.pdf), it can be defined that class A eats class B and thus would be the sole occupant of a position when two automata of different classes land on the same position. More info on standards for celluar automata modeling will be released at a future date.

Note, concurrency is an issue and the process will be modeled sequentially at first. But there are development plans to integrate more concurrent programming for such applications of the MADEsparq Project like modeling cellular automata. And the hope is that as more researchers become engaged with the MADEsparq Project, then the sooner concurrency can be integrated into the framework.

Quantitative and Hybrid Quantitative/Descriptive Examples for Methods of Data Mapping

As mentioned, the power in this data paradigm is in the abstraction—there are a plethora of ways to categorize data based on one's own interests. The data can be quantitative or



http://new.math.uiuc.edu/math198/MA198-2010/suharsh_sivakumar/predpreyfinal.pdf

qualitative. And if there is not a suitable method for mapping a data set, then a participant can use a SPARQtag (see below) to broadcast and share the problem with mapping the data. Thus, the open-collaborative setting of MADEsparq Project can help even when a participant is having difficulty fitting and mapping data onto the block chain.

As should be obvious by now, the MADEsparq Project searching of the block chain is conducted using regular expressions (abbreviated regex) and well-known string parsing algorithms. There is nothing fancy going on with regards to search computation and complexity. And over time, optimizing search with respect to time and space complexity will occur as more researchers get involved with the MADEsparq Project.

Below are some examples of methods that can be used to map quantitative or qualitative (or hybrid) data onto the Cryptogenic Bullion block chain and a basic mathematical explanation as to why certain methods are effective or even possible. These examples assume some understanding of basic statistical terms like mean, variance, and fit.

I) Assume, for simplicity, you have the following set of numbers: [7, 12, 55, 23, 36, 21, 57, 88, 31, 24, 23], and you want to map it to a section of the CB block chain with the best 'fit'. There are many statistical methods to fit data (http://orion.math.iastate.edu/danwell/ma378/chapter9.pdf). We can easily calculate that this data set has mean, m = 34.27 and variance = 511.11.We can treat the hexadecimal characters of a hash string as numbers in a variety of ways: we can convert each character into its decimal value and simply treat the concatenation of any number of converted characters as a decimal value. For example, D37 has the value 1337 in decimal notation since the hexadecimal character D has the value 13 in decimal notation, while 3 and 7 have the same notation as decimal values, and the concatenation of 13, 3, and 7 is 1337. There are seemingly no limits to how a participant can treat the characters of a hash string as decimal values. This method to convert hexadecimal characters to decimal notation is defined by the MADEsparq keyword HEX_TO_DEC_1_CON (more about MADEsparq keywords below), which would be placed as one of the 'id' values in the METHODS block in the data template using HTML, for instance. Thus, when the mapping is fetched, then the MADEsparq system or participant knows how to interpret the hexadecimal characters of the block chain. If a DJ wanted to convert every hexadecimal substring of length 4 into its literal decimal value, then the MADEsparq keyword would be HEX_TO_DEC_4_LIT. Once the method to convert the block chain into decimal values has been decided, then the participant has many statistical methods at her disposal for mapping the data. Again, there are no restrictions. The participant can map one number to one decimal value of a hash string or all numbers can be mapped to a single hash string. A participant can even concentrate on a single hash string and exhaustively search its power set (http://en.wikipedia.org/wiki/Power_set) for a mapping. Note that if it is known the distribution of data is normal (http://en.wikipedia.org/wiki/Normal_distribution), it's possible to define a method of mapping with a normal distribution and equal mean, given a suitable (and repeatable) algorithm for sampling hexadecimal characters from the block



http://en.wikipedia.org/wiki/Normal_distribution

http://en.wikipedia.org/wiki/Power_set

http://orion.math.iastate.edu/danwell/ma378/chapter9.pdf

chain and converting their values into decimal notation, by the central limit theorem (http://en.wikipedia.org/wiki/Central_limit_theorem), if the sampling method can convert a large number of hexadecimal characters into decimal notation.

II) Perhaps the participant has defined a tree parsing rule (http://en.wikipedia.org/wiki/Parse_tree) to her data set and would like to fit the data both by quantitative as well as a syntactical description. Then both the quantitative and the syntactical rules would be entered into MADEsparq's search interface. Or, similarly, the METHOD and METHOD_DEF attributes as well as their contents of her MADEsparq subdomain would be filled and perhaps she can collaborate with other participants for possible mappings.

III) Similarly to the tree parsing rule in II) above, perhaps the data is strictly qualitative. As long as there is an algorithm defining the qualitative nature of the data, and the algorithm can be translated into a regex algorithm, then a mapping onto the block chain can be searched.

So, again, when it is said there exists a 'mapping' for the data set onto the CB block chain, then that means there exists a regex algorithm which has been applied to the hexadecimal characters of the hash strings of the CB block chain and a match has been found. But it is not necessary for a match to be found (more on this below).

Just to emphasize, there are no restrictions for data mappings. Mappings can be limited to a single hash string, or only even numbered hash strings, or the whole block chain, or there can be two separate methods with one assigned to only odd numbered hash strings of the block chain and the other only even numbered and their results can be compared (see Modeling Probabilities by Interval Sampling, and examples and applications below). The power is in the abstract nature of mappings.

To further explain this abstraction, and to further describe a strictly qualitative mapping, lets continue with our example from above. Recall, Jack was interested in searching for a movie similar to Pi, by Darren Aronofsky. He could have assigned attributes to Pi which would make it similar but more suitable to his tastes. People do this all the time, as shallow as it is. For instance, when a woman thinks about one of her pursuers, “I like this and this about him but I don't like that...if only that part was taken out and replaced with this.” Likewise, Jack can do the same thing about the movie Pi:

Pi_Style = {“visual”:0}Pi_Theme = {“more_art” : 7, “less_math” : 9}

This is just one example for categorization, but it makes use of the Python dictionary syntax (http://anh.cs.luc.edu/python/hands-on/3.1/handsonHtml/dictionaries.html). Note that the 0 value for visual means that Jack likes the current visual style in Pi, he doesn't want to change it. Also note, negative numbers cannot be represented on the block chain, so instead of {“math” :



http://anh.cs.luc.edu/python/hands-on/3.1/handsonHtml/dictionaries.html

http://en.wikipedia.org/wiki/Parse_tree

http://en.wikipedia.org/wiki/Central_limit_theorem

-9} we use {“less_math” : 9}. The values are from the interval [1 , 10], but they can be defined to be anything in the METHODS block, using MADEsparq keyword syntax as follows: INTERVAL_1_10_Pi_Style.

To emphasize yet again, the MADEsparq Project will make available a variety of programming languages and standards to decrease barriers to entry for participation. A catalog of keywords and standards will be freely available. And yet, conventional programming standards will also allow the experienced programmers to not have to change their habits in order to participate in the MADEsparq Project.

There will be more information and developments regarding keyword syntax and standards and a consequent paper will be released soon.

Graph Theory—Homeomorphisms and Isomorphisms Between Data Mappings

More detail of the graph theoretical structures that may emerge between data sets and consequent relations between them will be given in a future paper. But graph theoretical homeomorphisms and isomorphisms between mappings of data sets are very crucial to the possible insights to be garnered from the MADEsparq Project, and so we include this brief statement in this section.

Visualization

Having all this data mapped to more raw data is great, but the task of visualizing certain aspects of these mappings will be just as crucial as the mappings themselves. Thus, a major opportunity arises for visual artists and scientific visualization (http://en.wikipedia.org/wiki/Scientific_visualization). MADEsparq will create a medium where DJs can offer jobs, either for pay or pro bono, to visualize data sets or their mappings or both. Having crypto-currencies intricately involved in the project makes the matter of pay an easy matter to deal with.

Analytical Web Services

Similarly to DJs offering visualization jobs in the previous section, there can be job offers for analytical work using the MADEsparq Project. For example, a small business video game store would like to offer an exhaustive data modeling package for a game recently released, so the store owner would broadcast her job opening on MADEsparq.org (see Video Game Analysis and Practice below).



http://en.wikipedia.org/wiki/Scientific_visualization

3 Some Industry Specific Applications and Examples

Video Game Analysis and Practice

World of Warcraft (WoW) is a massive multi-player online role playing game. Players take control of a character and increase its level to a maximum of 90. This is done in many ways, whether by exploring the vast virtual world or winning battles against enemies. And as the character's level increases then so does its abilities. Thus, the difficulty in effectively playing the character increases. For example, at maximum level, a warlock can have well over 20 base abilities and consequently there would be dozens of combinations of abilities to use, depending on numerous situations. Below are some examples on how a player can use the client software and map certain aspects of the game control to improve upon or analyze. Note that all these examples can be applied to any video game genre, whether it is a real-time strategy game like Starcraft or a console fighting game like Mortal Kombat.

● A gamer can use the MADEsparq Project to map a character's abilities to hexadecimal characters or short strings (ie. strings of length at most 4) and use the client software to help memorize the keyboard bindings corresponding to the abilities and combination of abilities depending on certain situations (which have also been mapped to hexadecimal characters or short strings) via an event driven program (This functionality will be included with the alpha release and the initial open source files of the MADEsparq client software).

● A gamer can map abilities' effectiveness (possibly defined by the gamer herself, or by another DJ) to the frequency of regex matches of short strings on the block chain to mimic probabilities (ie. interval sampling, mentioned above) and use the randomness of the hash strings of the block chain to test the character abilities' effectiveness in certain situations using If/Then decision models, which have also been mapped to the block chain to model the decision making process in a game environment via an event driven program.

● A gamer can map different characters' abilities and compare simulation results, perhaps using another DJ's data mapping for one or more of the characters.

Note that the mappings and the public nature of the MADEsparq Project answer the question to 'Why do this based on the randomness of the block chain?' It's because the public and inherently analytical nature of the MADEsparq Project, mediated by the block chain as a base structure, begets sharing and collaboration and analysis between gamers—expert or novice—in a way that does not currently exist.

Fantasy Sports Analysis

From the fantasy sport wiki:



“A fantasy sport (also known as rotisserie, roto, or owner simulation) is a game where participants act as owners to build a team that competes against other fantasy owners based on the statistics generated by the real individual players or teams of a professional sport. Probably the most common variant converts statistical performance into points that are compiled and totaled according to a roster selected by a manager that makes up a fantasy team...Fantasy Sports is estimated to have a $3–$4 Billion annual economic impact across the sports industry.”

Some demographic stats of the Fantasy Sports industry can be found here: http://www.fsta.org/industry_demographics.

An example of how the MADEsparq Project can be used by a fantasy player is that interval mappings of short strings and their frequency of regex matches on the block chain can be used to model probabilities of professional sports player stats (eg. Lebron James's field goal percentage, or Tom Brady's pass completion percentage) and simulate the points gained from certain aspects of a game (which has also been mapped to the block chain; for example, if the two character string '00' is found along the block chain then that is interpreted as a blitz by the defense and thus a reassignment to Tom Brady's probability for throwing an interception is increased for that particular play). And there should be much to debate and collaborate between DJs and DLs about not only the results but of the mappings themselves.

Spatio-Temporal Modeling

Both applications above could be done using a defined spatio-temporal (Geerts, 2002) model or data categorization. More about spatio-temporal modeling in a future paper.

Using the MADEsparq Client Software as a Gaming Framework

The use of defined pattern recognition algorithms using regex along the Cryptogenic Bullion block chain might lead to interesting game development. Future information on this subject will be released. The Cryptogenic Bullion development team will also be releasing a game utilizing the MADEsparq Project's framework in the summer of 2014.

4 The MADEsparq Project Creates and Increases Intrinsic Value for CB

There are no limits to the variety of data sets mapped to the Cryptogenic Bullion (CB) block chain and hence the variety of knowledge-domains participating in the MADEsparq Project. As more data sets are mapped to the CB block chain, the potential for discovering insightful relationships increase. History has shown how gifted individuals with vast cross-domain knowledge (ie. polymaths), like Leonardo da Vinci, Nicolaus Copernicus, or Thomas Edison, have been the founders to amazing discoveries leading to amazing new insights and industries.



http://www.fsta.org/industry_demographics

http://en.wikipedia.org/wiki/Sport

http://en.wikipedia.org/wiki/Statistics

http://en.wikipedia.org/wiki/Game

Moreover, history has shown how chance occurrences and discoveries play a crucial role into the advancement of our civilization—for instance, the discovery of antibiotics was an accident (http://www.historylearningsite.co.uk/antibiotics.htm). But even in mathematics and statistics, physics, biology and chemistry, there are well-known and profound algorithms and processes for achieving a specific task that are superior exactly because of the feedback mechanisms integrated or chance events which are intricately involved with introducing randomness into their results. Some examples of this are: Kalman Filters, Stochastic Resonance, molecular mutation, genetics programming, Dr. Hoftstader's CAT analogy machine (http://en.wikipedia.org/wiki/Douglas_Hofstadter).

The MADEsparq Project's goal is to accelerate cross-domain sharing and collaboration, as well as increasing the potential for chance occurrences to lead to new and amazing discoveries. Moreover, with the power of abstraction and consequent use of analogy, the MADEsparq Project can spark new ways of communicating with one another.

And since the virtual commodity, Cryptogenic Bullion, is intricately connected with the MADEsparq Project, its intrinsic value should be directly correlated with the number of data categorizations and alignments mapped onto its block chain. In other words, as more users contribute to the MADEsparq Project then the potential for new relationships between data sets and/or their mappings onto the block chain increases, and thus so should the value of Cryptogenic Bullion.

There are currently over a dozen services and online stores accepting Cryptogenic Bullion (CB) as a payment method and it is steadily growing. And as the CB web-ecosystem grows, its intrinsic value correlated with the MADEsparq Project should aid in stabilizing and maintaining a healthy economic and monetary growth outlook.

Virtual Commodity Versus Virtual Currency

A quick note about the distinction between virtual commodity versus virtual currency. In the United States, capital gains by currency trading is taxed at the short-term capital gains rate of 35%. Whereas commodities are taxed as 60/40: 60% of gains will be taxed at 15%, which is the long-term capital gains rate; 40% of gains will be taxed at the short-term capital gains rate of 35%. The mix of the long-term and short-term rates equals to an overall 23% tax rate for commodities (http://commodities.about.com/od/taxissues/a/tax-benefits.htm ). Now, obviously, the US government will have the final say on how capital gains on Cryptogenic Bullion will be taxed. But the Cryptogenic Bullion community has the right to voice their opinion on the matter, and if possible, with the release of the MADEsparq Project to strengthen our stance, argue that CB be recognized and taxed as a commodity and not as a currency. There will be more about taxation in a future paper.



http://commodities.about.com/od/taxissues/a/tax-benefits.htm

http://en.wikipedia.org/wiki/Douglas_Hofstadter

http://www.historylearningsite.co.uk/antibiotics.htm

Education and Pedagogical Medium

Almost anyone can remember at least one point in their educational career when the excitement of learning something new was dimmed by the fact that the problems one solved were either trivial or solved by hundreds if not thousands of students previously. If a teacher or university professor chose to incorporate the MADEsparq Project into the curriculum of, say, a Graph Theory or discrete math course or a Basic Programming course where regular expressions are introduced, or an organic chemistry or genetics course, then mappings and categorizations of the solutions of these trivial problems are no longer trivial because of the contribution to increasing chance occurrences for new insights to be discovered from other data mappings from other knowledge-domains onto the Cryptogenic Bullion block chain. And likewise the MADEsparq Project would benefit from the steady data mappings from these courses, increasing the chance for all participants to discover new relationships between data mappings from separate knowledge-domains.

5 Synergy Between MADEsparq Development and CB's Block Chain

As the Cryptogenic Bullion (CB) block chain is steadily categorized and insights gained by the variety of new researchers and developers participating in the MADEsparq Project, there will be a constant assessment towards upgrading the Cryptogenic Bullion client wallet. Once the MADEsparq Project gains momentum, participants will have a vested interest in assuring the Cryptogenic Bullion block chain and client wallet remains stable and, if possible, to upgrade and increase performance. Herein lies the strength of being and open-source, open-collaboration, project.

One possible end-result from this synergy between the MADEsparq Project's increased acceptance and the Cryptogenic Bullion block chain is as follows. There are preliminary ideas currently being discussed by the Cryptogenic Bullion Team in regards to block chain bloat, which is the increased size and overhead of the block chain and the difficulties that arise for scalability due to increased usage. Moreover, the time needed to download the block chain is steadily increasing. At the time of writing this paper, it takes about two hours to download the block chain with a 1 mb/s internet speed. Using some of the ideas from Markus Jakobsson's paper on fractal hash chains (Jakobsson, 2002), and possibly gaining more insight from the data categorizations onto and of the block chain, and from the researchers participating in the MADEsparq Project, the Cryptogenic Bullion Team will be researching possible hash chain folding/unfolding methods and algorithms to help remedy the potential block chain bloat problem. It is very likely that block chain bloat will not be much of an issue anytime soon because of the rate at which hardware storage is advancing while also becoming cheaper (ie. because of Moore's Law, http://en.wikipedia.org/wiki/Moore's_law), and because of the rate at which the global average internet speeds is increasing (http://www.akamai.com/stateoftheinternet/). But there is research on Moore's Law ending sooner than many people think (http://www.lockss.org/locksswp/wp-content/uploads/2012/09/unesco2012.pdf), and so block bloat is something that should not be



http://www.lockss.org/locksswp/wp-content/uploads/2012/09/unesco2012.pdf


http://www.akamai.com/stateoftheinternet/

http://en.wikipedia.org/wiki/Moore's_law

ignored.

6 The Vision for the Cryptogenic Bullion and MADEsparq Global Brand Name and Marketing Initiative

A massive marketing campaign will initiate almost immediately. Moreover, there will be registrations to talk at conferences, academic or commercial, about the details of the MADEsparq Project. The goal would be to help spread the word of this new paradigm for blurring the defining lines between money and data and technology. Internet and computing technology has brought the business world in the midst of a 'Big Data' revolution (http://www.forbes.com/sites/boozandcompany/2013/09/26/big-data-lessons-from-earlier-revolutions/, http://www.businessinsider.com/big-data-is-the-next-business-paradigm-2013-11). The current state of virtual currencies does not properly leverage its inherent structure to accelerate global, mass adoption. The MADEsparq Project aims to open new possibilities for people being more informed and independent, both financially and socially. And thus, as the MADEsparq Project gains in acceptance, then the hope is that so will Cryptogenic Bullion.

As for brand name building, someone using Cryptogenic Bullion will be someone who is a searcher, well-rounded, or just respective of knowledge. All the things that the MADEsparq Project represents will also be applicable to Cryptogenic Bullion with regards to brand name building.

It is also the (ambitious) goal of the MADEsparq Project that the abstract nature of categorizations and the sharing and open-collaboration of said categorizations will enable people to personalize their experience online, regardless of their interests. True personalization is something that is lacking on the internet today, and the MADEsparq Project aims to mediate one's ability to categorize and organize and share interests without a preset limit on functionality (for example, clicking 'likes' on Reddit, Facebook or Youtube, which is a basic binary categorization). The MADEsparq Project aims to enable each patron of the internet to have their own guide, aggregating all their interests and frequent destinations online (eg. Facebook, Youtube, Netflix, Blogs, etc), akin to how the TVguide was 'the guide' for television. But the MADEsparq Project is to span all knowledge-domains and interests, the highly complex and analytical as well as the purely social.

Although the current plan is to make it as easy as possible to be a DJ, there will be some skill involved in being a 'good' DJ and developing a strong following on social-networks or microblog networks, as some of the previous examples showed. Moreover, web storefronts can use a MADEsparq subdomain or have a MADEsparq compatible API available as another avenue to market certain products or services. For instance, coingas.com is a video game retailer selling Steam game codes that is currently one of the e-commerce websites accepting Cryptogenic Bullion as a method of payment. The site owner can create a MADEsparq subdomain, or make a MADEsparq API available, and post interesting game testing and practice models, or to offer



http://www.businessinsider.com/big-data-is-the-next-business-paradigm-2013-11

http://www.forbes.com/sites/boozandcompany/2013/09/26/big-data-lessons-from-earlier-revolutions/

http://www.forbes.com/sites/boozandcompany/2013/09/26/big-data-lessons-from-earlier-revolutions/

preset keybindings, so to moderate the learning-curve of certain games. Thus, utilizing the MADEsparq Project's API standards would be another medium for marketing services, and to further market certain games sold on coingas.com. Perhaps registered users of coingas.com can also have the option of posting their own MADEsparq data models and keybindings, so that coingas.com is no longer just a web store for video games, but a place online for collaboration and analysis between people with similar interests.

As a result of situations like the above example, it is expected that the MADEsparq Project will be a major driver for merchant adoption of Cryptogenic Bullion as a payment method.

And the greatest virtue of this new paradigm is that there is no need to wait on MADEsparq.org or the client-software to be developed. Anyone can start a MADEsparq subdomain and start sharing their categorizations or their mapping results onto the Cryptogenic Bullion block chain using existing technologies (ie. JSON, XML, RSS, etc)—the standards will be decided upon based on exactly this kind of activity and organic growth online. The standards will be finalized based on how participants choose to share their own categorizations and collaborate.

7 Concluding Remarks

When Cryptogenic Bullion was released to the public, it was said the name 'Cryptogenic' was chosen since it sounded like a combination of 'cryptography' and 'cryogenic', because it was being marketed as a long-term store of value...something to lock away in 'cold storage'...something to 'multiply the value of your bitcoins' over time. Although still true, this was also misdirection. Now with the MADEsparq Project public, the 'Cryptogenic' name is more fitting, given the heavy application of sequence alignments and the possibility of discovering something new with unknown origins amid all the data mappings within the block chain.

As noted above, there will be similarities with wikipedia.org. Market capitalization valuations of wikipedia.org range from as little to $100 million to as high as $2.2 billion (http://www.centives.net/S/2012/how-much-is-wikipedia-worth/).

And Cryptogenic Bullion's market capitalization in the context of other virtual currencies at the time of writing this paper is as follows:

Crypto-currency Market Capitalization($) Coins Outstanding Price($)Bitcoin 4.26 Billion 11.98 Million 355.47Litecoin 91.6 Million 22.94 Million 3.99Ppcoin 13.3 Million 20.73 Million 0.64Primecoin 2.4 Million 3.02 Million 0.79Cryptogenic Bullion 426 Thousand 897 Thousand 0.47

The MADEsparq Project is initially only supporting Cryptogenic Bullion's block chain. It is quite



http://www.centives.net/S/2012/how-much-is-wikipedia-worth/

possible that other virtual currencies' block chains are supported in the future. But this depends in large part by which major exchanges support Cryptogenic Bullion trading in the future. Virtual currency exchanges have gained much power in recent months, and they threaten to dull the decentralized virtue of virtual currencies. It is the hope of the MADEsparq Project that Cryptogenic Bullion will have a say in the future of which virtual commodities and virtual currencies will be the driving forces of the industry.

But, then again, with peer-to-peer exchanges also on the horizon (http://emunie.com/), the idea of an 'exchange' as we know it today might be completely different in the very near future.

Cryptogenic Bullion did not try to reinvent the wheel with respect to the structure and mechanisms of its predecessors like Bitcoin, ppcoin, or Novacoin. The intention was to take the existing system and build something amazing on top of it.

With the MADEsparq Project as Cryptogenic Bullion's killer app, so to speak, it can be assured that Cryptogenic Bullion will remain relevant, regardless of what evolutionary changes are to take place in the virtual currency world.



http://emunie.com/

References

Back A. (2002): Hashcash – A Denial of Service Counter-Measure.

Nakamoto S. (2008): Bitcoin: A peer-to-peer electronic cash system. (http://bitcoin.org/bitcoin.pdf )

King S., Nadal S., (2012): PPCoin: Peer-to-Peer Crypto-Currency with Proof-of-Stake. (http://peercoin.net/peercoin-paper.pdf)

Cowgill B., Wolfers J., Zitzewitz E., (2009): Using Prediction Markets to Track Information Flows: Evidence from Google. (http://www.eecs.harvard.edu/cs286r/courses/fall10/papers/GooglePredictionMarke tPaper.pdf )

Arrow K., et al, (2008): The Promise of Prediction Markets. (http://hanson.gmu.edu/promisepredmkt.pdf)

Conitzer V., (2009): Prediction Markets, Mechanism Design, and Cooperative Game Theory. (http://www.cs.duke.edu/~conitzer/predictionUAI09.pdf)

Coindesk.com: MasterCoin to create new altcoins in Bitcoin's block chain, (August, 2013). (http://www.coindesk.com/mastercoin-to-create-new-altcoins-in-bitcoins-block-chain/)

Jakobsson M., (2002): Fractal Hash Sequence Representation and Traversal. (http://eprint.iacr.org/2002/001.pdf)

Rosenthal D., et al (2012): The Economics of Long-Term Digital Storage. (http://www.lockss.org/locksswp/wp-content/uploads/2012/09/unesco2012.pdf)

Kumaraswamy K., (2002): Fractal Dimension for Data Mining.

Geerts F., et al, (2002): A Theory of Spatio-Temporal Database Queries.




http://eprint.iacr.org/2002/001.pdf

http://www.coindesk.com/mastercoin-to-create-new-altcoins-in-bitcoins-block-chain/

http://www.cs.duke.edu/~conitzer/predictionUAI09.pdf

http://hanson.gmu.edu/promisepredmkt.pdf

http://www.eecs.harvard.edu/cs286r/courses/fall10/papers/GooglePredictionMarketPaper.pdf

http://www.eecs.harvard.edu/cs286r/courses/fall10/papers/GooglePredictionMarketPaper.pdf

http://peercoin.net/peercoin-paper.pdf

http://bitcoin.org/bitcoin.pdf

madesparq intro paper v0.1

Documents