IN DEGREE PROJECT COMPUTER SCIENCE AND ENGINEERING,SECOND CYCLE, 30 CREDITS

, STOCKHOLM SWEDEN 2019

Real-time decision support system using visualization of a global decentralized financial system

PER JULIAN HEDÉN

KTH ROYAL INSTITUTE OF TECHNOLOGYSCHOOL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE

Sammanfattning Decentraliserade digitala valutor växer fram i dagens samhälle. Bitcoin var den första

som skapades i slutet av 2009 och idag finns det tusentals digitala valutor med olika

egenskaper. Den stora mängd transaktionsdata som genereras från decentraliserade

system kan vara svår att förstå. Informationsvisualisering kan användas för att

förenkla denna förståelsen. Det är ett verktyg som gör det möjligt för oss att effektivt

förstå komplex data genom att projicera det till ett visuellt medium.

Detta är en undersökning på multipla användare för ett webbaserat realtid

beslutstödssystem för att visualisera transaktionsdata i ett globalt decentraliserat

finansiellt system med mål att utarbeta användarnas krav för att kunna ge dom stöd.

För att uppnå detta genomfördes en förberedande intervju för att få kunskap om

kraven för varje typ av användare. Därefter gjordes en prototyp av

beslutsstödsystemet, byggt på kraven och grundläggande principer för

informationsvisualisering.

Prototypen är web kompatibel med utrymme för konfigurationer för att personifiera.

Den passar bäst för att ge en översikt. Kompletterande forskning kan vara ytterligare

studier på intressenterna för att förbättra användbarheten eller utveckla ytterligare

stödfunktioner för att göra det till ett smart system.

Stockholm, July 2019

Per Julian Hedén

Real-time decision support system using visualization of aglobal decentralized financial system

Per Julian Hedénpjheden@kth.se

Royal Institute of TechnologyStockholm, Sweden

Figure 1: The force graph and world map of the Decision Support System prototype.

ABSTRACTDecentralized digital currencies are emerging in today’s society.Bitcoin was the first to surface in late 2009, and today there arethousands of digital currencies with various properties. The vastamount of transaction data being generated from decentralizedsystems can be difficult to comprehend. Information visualizationcan be used to simplify this apprehension difficulty. It is a tool thatallows us to effectively understand complex data by projecting itto a visual medium.

This is a research study on multiple stakeholders for a web-basedreal-time decision support system for visualizing transaction datain a global decentralized financial system and set out to explore thestakeholders requirements in order to provide them support.

To achieve this, an exploratory interview was conducted in orderto gain knowledge of the requirements for each type of stakehold-ers. After which, a prototype of the decision support system wasmade, built upon the requirements and fundamental principles ininformation visualization.

The prototype is web compliant with room for configurationsto personalize. It is best suited for an overview. Complementaryresearch could be additional studies on the stakeholders to improvethe usability or develop additional supportive features to make it asmart system.

KEYWORDSInformation Visualization, real-time, decentralized financial sys-tems, decision support system

1 INTRODUCTIONIn the early days of our society, we did not have paper money,credit cards, or other modern transaction services. The money ofthat time was goods or services, and one usually traded what they

had in surplus for something they were lacking. Goods was barteredbetween people alike. Eventually, an official currency was adoptedinto various societies. The currency was coins, made out of metal,and had two revolutionary concepts. The first concept was that acoin had an intrinsic value that was agreed upon by the market.For example, a chicken could be worth two coins or a cow couldbe worth four coins. The second concept was that the coin hadglobal value itself, as the metal was precious and allowed for tradesacross the world [11]. Eventually paper money arrived and workedin addition to coins. Next, we had the first credit card, a tool forhandling transactions, and ever since then it has become more andmore popular. Even other applications like mobile software andcontact-less equipment allowed us to purchase goods. The latestand currently ongoing invention of money innovation is digitalmoney.

Bitcoin is considered to be the first decentralized digital currencyand was published in a paper authored by a presumed pseudonym,Satoshi Nakamoti, in late 2008. A few months later, 3 January 2009,the system started operating. It runs on a decentralized systemwhich makes use of cryptography and a public ledger or blockchainto allow for secure transactions [23]. In later years such globaldecentralized financial systems have increased in popularity. Withthe second digital currency, Namecoin [17], emerging in April 2011,until today where there are thousands of digital currencies withdifferent properties [15]. Where each new currency tries to improveon the shortcomings of the previous, or has a completely newmethod of attack.

The blockchain technology allows for a trust-less system, whereits inner workings, such as the transaction history or security meth-ods, are public information. The verification and security of pay-ments can then be done in the absence of a centralized custodianand eliminates the need to trust any organization. Even in digi-tal currencies, we have trading of assets to make a profit, namely

foreign exchange trading. Together with the rise of popularity, themarket has evolved dramatically. As of February 2018, trading andarbitrage in cryptocurrency is done on more than 100 exchangesworldwide, reaching $5 billion average daily trading volume [22].Where the average daily trading volume are the amount of sharestraded each day.

Each trade generates data and adds up to a large amount. Theadvancements in hardware technology have allowed computersystems to store large quantities of data. In today’s society, we live ina flow of information where everything from financial transactionsto calorie intake are recorded by computers. The amount of databeing generated per day is at an all-time high [18].

Data collected by industries and businesses are often so com-plex that it can be difficult to comprehend these large data sets.Therefore, it is important to simplify the way we look at the datasets to make it understandable for humans. One method is to use adifferent medium, and instead of raw numbers we can use a visualrepresentation which increases the efficiency in understanding thedata. This is referred to as information visualization, and it am-plifies cognition by reducing search for information, enhancingpattern detection, enabling perception interference operations andincreases the memory and processing resources for the user [9].

Visualizing information benefits users making decisions basedon data and using this as a helpful tool to do so. A decision supportsystem has many suggested definitions and it is observed that thesedefinitions agree that the system must aid a decision maker insolving problems [7]. A well-constructed system should follow themantra of Ben Shneiderman: Overview first, zoom and filter thendetails on demand [30], to prevent the user from being overwhelmedby the abundance of data.

1.1 Case DescriptionThe principal, Healwiz AB [10], is developing a product calledCentiglobe, a global payment system. Their vision is to allow fortrades with high security and without any barriers. It is donethrough the development of a digital currency, which operateson a blockchain and makes use of cutting edge distributing ledgertechnology.

The structure of the blockchain is of a centralized nature witha central node in the middle and multiple outer nodes. An outernode is called a gateway and is only connected to the central node.It holds a set of assets and can make trades. The central node, alsoreferred to as the basket, is owned by the system and is responsiblefor assisting in trades to keep a stable value. It has a portfolio ofthe assets in the system where the distribution between the assetsare decided algorithmic.

Understanding the state of a blockchain system can be a dauntingtask. It has no visual components by nature, and the only wayfor it to be explored is by manually going through the public dataledger. During high usage, multiple transactions occur every secondmaking the manual route unfeasible. The aim of this thesis is tocreate a real-time decision support tool using visualization. Therequirements of the users will be explored and used as a basis for thetool together with functional requirements stated by the principle.The functional requirements are both real-time data, so the user cangrasp the current state and historical data to help give an overview

and aid in analysis and prediction, and the use of visualizationconventions to have an intuitive and useful system and be effectivefor different types of users, whether it is for exploring, trading oracquiring an understanding.

As briefly mentioned there are multiple types of users who willhereby be referred to as stakeholders. The stakeholders have differ-ent motivations and goals and interact in different ways.

1.1.1 StakeholdersA stakeholder is a person or organization who is a user and hasan interest in the system. They have influence and can cause ef-fect through interactions, such as trading, deposits, withdraws orvoting. The stakeholder can be affected by the system throughchanges or events, generally caused by other users. The varioustypes are divided into four subgroups each having their own set oftasks and goals. To achieve their goals, there are requirements ofwhat information is necessary. The requirements are individual perstakeholder type.

The first one is the asset holder, who has an interest in securingtheir assets by looking at current and future liquidity risks.

There is also the liquidity Trader. They trade assets back andforth in order to make a profit and their goal is to make as efficientand profitable trades as possible.

The system liquidity manager wants to improve the market effi-ciency in the whole system. Improve the spread and globalization,and also ensure that the supply of assets meets the demand. Man-aging the supply is either done by adding additional gateways withthe same supply of asset or by increasing a gateways asset throughcollaboration.

Technicians monitors the system. They aim to detect and lookfor system abuse, both intentional and unintentional and preventthem from occurring.

1.2 Research QuestionThe thesis is a research study focusing on design that is basedon user requirements. The goal of the study is to explore and de-fine multiple stakeholders in a global decentralized system, and todevelop a prototype fulfilling their needs.

The research question to be answered is formulated:

"What are the necessary requirements for the design of a web-basedmulti-stakeholder real-time decision support system for visualizingtransaction data in a global decentralized financial system?".

Important sub-questions to help answer this question are alsostated as such.

• "What are the different stakeholders?"• "What information do each stakeholder require?"• "How do we best relay that information?"

1.3 LimitationsAt the time of this thesis, the principle’s system is not in productionand therefore is not creating transactions. The data used in theDSS comes from a mixture of two sources. One being data that israndomly generated within reasonable boundaries, and the secondone is from a similar system, namely BitShares[28, 29].

2

The study will explore the requirements for all stakeholders,but the system implementation will focus on two prioritized roles,namely asset holder and liquidity trader. The prioritization comesfrom the principle and is due to time limitations.

2 BACKGROUND & THEORYThis chapter will establish the the relevant work that has been doneand specific domain knowledge.

2.1 Related WorkPresenting a copious amount of complex data can be overwhelmingto a user, which limits the possibility to comprehend the informa-tion. If the data is too extensive, it renders to be incoherent for thehuman cognition to parse. The field of visual analytic handles thisproblem by combining analysis techniques with interactive visu-alizations for an effective understanding, reasoning, and decisionmaking [19]. There are also techniques and guidelines for drawingspecial attention to patterns and certain information. One of them isa set of principles in psychology called The Gestalt Principles. Theireffect is due to the mind’s innate ability to perceive patterns basedon certain rules. The principles are divided into five categories:Proximity, Similarity, Continuity, Closure and Connectedness [27].Following the principles the information of object similarities canbe effectively conveyed by positioning, shapes, colors and more.Colors can be used to highlight, like using strong colors that drawsthe attention. Additionally, they can give a sense of emotion and de-liver a deliberate feeling regarding the information. It is importantto be wary about the selection of color. Studies show that selectingthe right color for the right emotion is not a trivial task as differentcultures might respond differently [1].

When designing a dashboard system, visualizing data for ex-ploration, it is wise to consider the mantra of Ben Shneiderman:Overview first, zoom and filter then details on demand [30]. Sug-gesting that the dashboard should initially present the user withan overview and allow for zooming in on specific parts with thepossibility to filter out unwanted data from the visualization. De-tailed information should be readily available upon request by aninteraction. Following this mantra might result in a solid user-friendly system, that is not overwhelming and supports the user inexploration.

In the field of decision support systems, having access to real-time data can have an effect on the decision making process. Oftena mix of real-time and historical information is critical to the user.In 2015 Carl Ahrsjö provides evidence for the benefits of usingreal-time by the construction of a dashboard for real-time eventbased visualization [2]. It was built by using agile methods andcontinuously evaluating the prototype. Through interviews withboth novice and expert users, the final design was iterated and thenwas implemented. The last prototype was evaluated, where theylooked at its intuitiveness and usability. It was done by interviewingthe users and measuring qualitative data from their task solvingexercises. The research show that visualizing data in real-timeprovides interest and engagement with the user, and this can beused for detecting patterns early.

Much research has been conducted in the area of visual represen-tation of system monitoring which is in close relation to decision

support systems. A substantial overview of research in regards toDSS can be found in [7]. The systems can specialize and focus on asingular problem area or be general and functioning on a broaderscale. Where the preferred type is depending on the task, area andtarget user. The more popular types are personal-, intelligent- andmanagement-based -decision support systems. Today, DSS is an in-tegral part of most managers work, especially Business Intelligenceand personal systems.

An advanced open source software that focuses on the field ofnetwork analysis is Gephi [3]. It uses a 3D render engine that allowsfor rendering of graphs in real-time with more than 20,000 nodes,by using a multi-task model which takes advantage of multi-coreprocessors on the computer graphic card. Similar to this thesis itaims to provide interactive exploration and clarity of networks inreal-time. Where the real-time aspect is assessed as a necessity forthe users exploration process. Gephi also identify vital requirementsfor an exploration tool: high quality layout algorithms, data filtering,clustering, statistics and annotation. One of their quality layoutalgorithms is Force Atlas, a special force-directed algorithm [16].Not only is it fast and scalable, but it brings an important feature toa network visualization, namely spatialization. Where it simulatesa physics system. Each node repulse each other like magnets andthe edges acts like springs, attracting their respective nodes.

2.2 Domain Theory2.2.1 BlockchainA blockchain is a public distributed ledger with immutable history[25]. The ledger stores all transactions and is shared among nodes,where each node has an identical copy. A blockchain system hasprotocols and rules, to operate and react based on occurrences.The protocols run through scripting languages and are often pre-determined, they handle assets creation, monetization incentivesfor running a node, ledger configurations and so on. The nodes area network of computers that aid in securing, storing and verifyingall transactions, also referred to as mining. Each node works inde-pendently and is encouraged economically for its work. In orderfor a transaction to be verified the majority of nodes has to vali-date it. Preventing any single point of failure, as you typically havewith centralized systems but may cause additional computationalintensive operations.

The transaction in between nodes makes use of public-key cryp-tography. Each agent is assigned a key pair, one key being privateand kept as a secret and the other is a public key meant to be sharedwith other agents. Together with signatures and hashing functions,these keys are then used to verify that both parties agree upon atransaction, thus transferring the ownership of a coin [26].

2.2.2 Trading AssetsAsset trading is when the ownership of an asset is transferred and inreturn the previous owner gets another asset as payment. The exactdetails of the trade is specified by issuing an order. The order hasthe amount of each asset and their direction in the trade. Usuallyone can advertise their order in hopes that someone agrees withthe terms and accepts it, thus creating a limit order. A limit order isan order to either buy or sell an asset at a specific price. Any offers

3

that is beneficial for the owner is also accepted. Meaning a higherprice for a sell order and a lower price for a buy order.

The limit orders are often visualized in an aggregated manner.In figure 2 you can see a typical graphical view, with price on thehorizontal axis and volume on the vertical axis. The left-most area(3) is the buy orders and is typically colored green, the right-mostarea (4) is the sell orders and is filled with the color red. The gapbetween the areas is the price gap (1), and the line (2) is the priceof the latest transaction which is not necessarily in the middle.

Figure 2: A typical order book. With (1) being the price gap,(2) is the price, (3) are the green bids (or buy orders) and (4)are the asks (or sell orders).

3 METHOD AND IMPLEMENTATIONThe practical work of this thesis started with an exploration andcategorization of the users within the system. Afterwards, the userswere interviewed to understand their use and define their require-ments for the system. This was done by holding interviews with thestakeholders and analyzing the results. Following the initial studyphase was the development of the web-based visualizations. Firstthrough creating and discussing low fidelity prototypes and laterby implementing them fully. Finally, the high fidelity prototype isexplored, discussed and reviewed.

3.1 Stakeholder interviews3.1.1 Qualitative semi-structured interviewsInterview variants range through a continuum, from structuredto unstructured interviews, with semi-structured interviews beingsomewhere in between [8]. Structured interviews are of the quanti-tative matter and strictly follow a list of questions. The remainingtwo interview methods are used in qualitative research and areone of the most widely used methods [14]. Despite the large varia-tion semi-structured interviews has some core features that shouldalways be present:

• Exchange of dialogue• A set of topics to be explored• A perspective regarding knowledge

A semi-structured interview is open and allows for areas and newideas to be discovered during the process to explore a set of topics as

a framework. The framework should be created with some insightand knowledge of the context. To ensure that relevant informationis brought to focus and the necessary knowledge is produced. Thegeneral goal of the semi-structured interview is to gather informa-tion regarding a set of topics, while it also allows for new topics toappear during the process. This is mostly used when some knowl-edge is already obtained but extended details are necessary [31].There are also some demands to be a successful interviewer. Kvale[21], lists 10 criteria to be a successful interviewer:

• Knowledgeable - familiar with the domain and research ques-tions

• Clear - Use of language appropriate for interviewee• Gentle - Gives interviewee sufficient time to respond• Structuring - Explains the goal and answers any questionsregarding the process

• Sensitive - Good listener• Open - Flexible about topics• Steering - Steers the interviewee towards the main topic• Critical - Challenges inconsistencies gently• Remembering - Recall previous interviewee statements• Interpreting - Clarify and extend statements truthfully

3.1.2 The InterviewsBefore starting the interviews, a knowledge base had to be estab-lished and through discussions with the principle the topics wasdeveloped. It was important to declare what types of stakeholdersexisted, their goals and motivation, and finally try to figure outhow this thesis could aid them in achieving that. The initial set ofstakeholders and their goals are defined in section 1.1.1, excludingsystem liquidity manager which was added during the process.

With the topics defined, the semi-structured interviews couldbe conducted. The interview was designed to be a flow of dialoguein order to explore any new topics, but had five questions as anunderlying guide. The questions are as following:

(1) What role do you identify with? If none feel free to defineyour own!

(2) What are the goal(s) or task(s) in your role?(3) How do you usually solve said goal(s) or task(s)?(4) What information do you need to solve the goal(s) or task(s)?(5) What is the most difficult part about your goal(s) or task(s)?

Where the first question was accompanied by definitions.Upon interviewing a subject a new user type was defined and

added to the set of stakeholders, namely system liquidity manager.In total, 9 interviews were conducted over 5 subjects with some

having multiple roles which necessitated an interview with eachrole as their persona. The stakeholders interviewed were 2 tech-nicians, 3 liquidity traders, 3 asset holders and 1 system liquiditymanager, seen more in detail in table 1.

The interviews were mainly performed through a physical meet-ing, but in some cases a digital meeting and an online form wasarranged due to the subjects availability. In the case of the latter,additional questions was asked through email as a substitute forthe missing exchange of dialogue. The interviews were recordedand transcribed in order to keep notes and to allow the interviewerto stay present and active during the session.

4

Table 1: Interview role distributions

Subject # Roles

Subject 1 Technician, asset holder

Subject 2 Technician, liquidity trader,asset holder, system liquidity manager

Subject 3 Liquidity traderSubject 4 Liquidity traderSubject 5 Asset holder

3.1.3 Interview resultsExtensive information regarding the stakeholders was discovered,and, after combining the qualitative responses, the discoveries aredefined as following.

Asset holder, An asset holder aims to manage liquidity risks.Preferably it should be done proactively, but sometimes a quick andagile solution is necessary. This is done to secure the stakeholderscurrent assets and make sure they do not lose value. It is necessaryto be able to find out "...how the system is used, understanding ofsystem users and knowledge of the traditional market in relationto this system" [Subject 5]. Their own and the baskets portfolioshould be accessible and simple to parse visually. Order books, turnover rate and monetary flow are necessary to determine the stateand direction of assets.

Liquidity trader, Subject 2-4’s goal as a liquidity trader is to makeprofitable trades. Subject 2 specifies the type of trades as arbitragetrades and that it is not a speculative market. Subject 3-4 defines itas a foreign exchange market and refers to it as foreign exchangetrading. The information needed to make decision regarding suchtrades are volume turnover, spread and order books.

System liquidity manager, System liquidity manager is a role thatcame about during the interview process, and was not part of theinitial three roles. Only one subject identified with the role. The goalof the role was to "improve market efficiency whole system"[Subject2], in other words to balance the monetary distribution. It is impor-tant to identify early a need for monetary liquidity in certain areasof the network, to be able to proactively influence the networkto provide stability in this area. To gain such insight informationregarding currency creation and destruction is vital.

Technician, Both users said that a goal as a technician is to upkeepthe stability of the system, and the need to both determine thecurrent state of stability and the direction it is heading. Subject 2also mentions that a technician should make sure the system isonline "... not okay if trades are not going through"[Subject 2]. Tofulfill these goals the technician shall monitor system parameters,this will allow him/her to determine the stability and make ananalysis of the direction of the system. An additional parameter isneeded for checking the online status of the system. "...Last responsetime or some complex computation that prove they are online andfunctional is sufficient"[Subject 2].

3.2 RequirementsThe interview process made a set of requirements per stakeholderclear. Below is the summation of each stakeholder requirementslisted.

3.2.1 Asset holderTo maintain and secure their assets, the asset holder’s task is toidentify, control and handle liquidity risks. Preferably in a proactivemanner, as the consequences can be major for the stakeholder. Therequirements are:

• See their personal data• Understanding of the system users and their behaviour• Understanding of assets state and current change• View the basket and its properties

3.2.2 Liquidity traderThe liquidity trader aims to make a profit through trading of assets.Either by foreign exchange trading or arbitrage trading. To makethis a possibility the requirements are as following:

• Gain overview of historical exchange rates• Gain overview of current exchange rates• See the network activity• View the basket asset distribution

3.2.3 System liquidity managerTo manage the goals of balancing the monetary supply and detect-ing shortcomings in assets the system liquidity manager requires:

• Viewing assets exchange rates• Viewing assets net currency creation

3.2.4 TechnicianIn order make sure the system is working as intended, and prevent-ing system abuse, the technician must:

• See the current state or status of the system• Determine the system stability• Determine system availability• Detect anomaly behaviour

3.3 Design iterationsThe graphical user interface requires structure for ease of use andintuitiveness, and the data should be displayed in a way that en-hances exploration. During the design process the prototypes wereiterated and developed in a low fidelity manner.

In the first iteration, the prototype had dynamic features cov-ering the requirements for each separate stakeholder. It made useof configuration files that determined what views to show, theirlocation, features, and dimensions. One file was made for eachstakeholder tailored for their requirements. Each file was distinct,but had one common denomination, the screen display controls. Inthe top left of each screen are controls to allow the user to selectbetween the presets. Each configuration has a main view, that liesin the background across the entire screen and multiple sub viewsthat overlaps the screen.

Figure 3 shows the decision tool configured for the liquiditytrader. In the main view lies a network-graph representing theblockchain structure with the dark blue basket as a central nodeand lighter blue connected nodes around it. In the bottom left thereis a sorted bar chart displaying the amount of volume transactedfor the last 24 hour per node sorted from high to low. To its right isan order book for a node selected by clicking the network-graph.In the far right, there are two graphs displaying parameter values.

5

Initially only the main view is shown extended across the entirescreen allowing for exploration with pan and zoom. Not until anode is selected, the extensive details will appear. Following theideas of Ben Shneidermans mantra [30].

The prototype will update the graphs and network in real-timeto provide interest and engagement with the user and detect pat-terns early during the users exploration process. A necessity inaccordance to research [2, 3].

Figure 3: First low-fi prototype with configurations for liq-uidity trader.

In the next iteration, the number of configurations was reduced.The settings for liquidity trader, asset holder, and technician overlapand could merge into one extensive configuration and cater to alltheir requirements. The system liquidity trader remains on its own,as it has some unique differences. In figure 4, we see the configu-ration for system liquidity manager. For its main view the worldmap is displayed, showing the outer nodes as light blue placed intheir geographical position, and the dark blue basket placed in theAtlantic ocean. In the bottom it displays the order book of any se-lected node and in the top right is the portfolio view. Declaring whatassets the selected node have, and their distribution. On the middleright there is a view with stats showing different measurementsand numbers and in the bottom right is the Centiglobe logo.

The design iteration was accepted and was the ground on whichthe system was developed.

Figure 4: Second iteration low-fi prototype with configura-tions for system liquidity manager.

3.4 Decision Support Tool3.4.1 Software and librariesThe prototype was created using web tools making it accessibleand compatible with the web. D3.js is used for the visualizationswhich is a popular JavaScript library for data driven visualizations.Many visualization tools that has been recently developed are typi-cally created with D3.js. It is a JavaScript library which focuses onDocument Object Model manipulation based on data.

To connect to the blockchain and retrieve data, a JavaScriptwebsocket interface Bitshares.js WS [6] is used. It allows for an easyconnection and can access all local nodes and the public BitsharesAPI.

The remaining functionality, such as buttons and layouts, is builtwith HTML5, CSS3 and JavaScript.

3.4.2 Development ProcessThe development process was done with close communicationwith the principle in order to get continuous feedback and followthe typical principles of Agile Development that is proven to beeffective. Those being "Individuals and interactions over processes andtools, Working software over comprehensive documentation, Customercollaboration over contract negotiation and Responding to change overfollowing a plan" [4].

4 RESULTSThis chapter will present the resulting views from the final proto-type. Each view will be introduced, justified, and explained. Finallythey will be shown in combination that constructs the DSS andshowcase the requirements it fulfill.

4.1 Views4.1.1 PortfolioThe portfolio shows the assets owned by a gateway and its relativedistribution. The display is based on the gateway selected by theuser and in figure 5, the account 1.2.1002953 is shown.

Figure 5: Portfolio View of account 1.2.1002953 from Bit-Shares.

A table is used to give a fully detailed view of all assets ownedby that node. The table’s columns are Symbol, Quantity and CoreAsset Value and each row is an asset entry. Exact values are shown

6

up to a decimal of 8, and, after that, they are formatted in order tokeep the compact view. Initially, the most important assets shouldbe shown first. Therefore, the list is ordered by core asset valuefrom high to low. The list also allows for filtering, at the top you canenter free text to allow quick search for specific asset(s), any symbolthat includes the search-word will be shown and again rearrangedby core asset value.

A pie chart displays proportions of multiple classes relative toeach other. It is visually simple and easily understood, mainly dueto widespread use in business. Here the pie chart is used in theportfolio view. Each pie represent an asset, where the radial sizeis the assets basket value in comparison to the others. The piesalways add up to a full circle and is simple to see the relative sizesin assets. The color for each pie is consistent and set to that assetgiving a familiarity with the connection between a color and anasset.

The basket node has an asset distribution that is decided algorith-mic and has a target distribution. Therefore, it is important to showboth the current and the target or the difference between the two.This is displayed in a bar chart. The color of the bar representingthe direction of the change, while it’s height shows the magnitude.The bars are also sorted in order of magnitude to show the mostradical changes first and foremost. It is an optional feature and canbe seen in figure 12.

4.1.2 Price HistoryThe price is displayed in a line chart, showing time in the horizontal-and price in the vertical-axis. It allows us to show the historicalin conjunction with the actual price, giving opportunity to thestakeholder to see how the price have fluctuated over time andcompare it to the current, as seen in figure 6.

Figure 6: Price History of OPEN.BTC from BitShares.

The current price of an asset is defined as the price it was lastsold for, also referred to as last price. For example, in historicaldata the price for a specific day or year, and it is defined as theclosing price for that time. Meaning the last price within that timeframe, and as the digital market is constantly open the market isconsidered to close at the end of the day.

The price is shown for the last seven days, with a days interval,to give a sense of the latest trend.

4.1.3 Volume HistoryVolume, also referred to as turnover rate, is defined as the sum ofthe amount of assets that have been in a transaction for a given

time frame. The more assets being transacted back and forth, thehigher the volume.

How much an asset is traded varies significantly, and in manycases with less active assets, the turnover rate can reach zero. There-fore a bar chart is used to give a sense for the trade activity, bydisplaying the historical and current volume. In figure 7 the volumefor BRIDGE.BTC is displayed over the last 24-hours. The graphshows every hour from now on the horizontal axis, and the volumeof the asset in units on the vertical axis.

Figure 7: Volume History BRIDGE.BTC from BitShares.

The volume is shown in a smaller time frame, namely the lastday, with an hour interval.

4.1.4 Order bookTo show the relation between an asset and the core asset, the orderbook is used and displayed in figure 8. It gives information suchas the latest exchange rate, spread, and shows the orders. Thedistribution of the orders is used to get a sense of the direction themarket is heading.

Figure 8: Order book between BRIDGE.BTC and BTS fromBitShares.

The order book follows the classic color scheme in stock ex-change, also used by major cryptocurrency trading sites, such asBinance [5], and Kraken [20]. Namely, green for bids and red forasks to allow the stakeholder to use their previously learned intu-ition.

The orders are filtered to the most relevant ones, i.e. the onesthat are closest to the spread. That means for the bids it is the ordersat the highest price, and for asks it is the orders with the lowestprice. For each side the limit is at 50 orders.

As an addition to the classical order book, a dashed line is added.The dashed line is a calculation from the order book and showsthe average price per coin. Answering a typical question liquiditytraders might have: "What is the price per asset if I want to buy

7

or sell an exact amount?". It is an optional feature which can betoggled off and on.

4.1.5 GatewaysGateways are represented as circles and has many visual propertiessuch as radius, color and positioning.

The radius of the node describes the turnover rate for the last24 hours. Ensuring that the most active nodes have a larger radius,and the less active are smaller. Effectively making the more activegateways easier to find. Since the turnover rates can vary with bigmargins, a logarithmic scale is used to even out the distribution ofthe sizes.

The color is another aspect of the circle. Here it corresponds towhen the last transactionwas completed. It is displayed in a gradientmanner with the end-points being red and green. The gradientscale is divided into two parts. The first scale is between green andyellow, where green represent a transaction within the minute andyellow is within an one hour. The second scale is between yellowand red, yellow still represents an hour and red is for 7 days ormore since the last transaction. The color gradient scale is shownvisually in figure 9. The two part scaling gives two benefits. Firstly,it shows the real time network activity by constantly changingcolors of gateways that are active within the last hour. Secondly,it gives a quick overview of what nodes are active in a broadertime scale. Giving an understanding of who are active within afew hours, and which ones who are presumably offline or unused.A study by Naz Kaya and Helena H. Epps show that there is arelationship between color and emotion. Red is associated withnegative emotions, such as anger and sadness. Green, on the otherhand, is connected to positive emotions, such as calm, happinessand comfort [24]. Therefore it is fitting to choose the color gradient,as described above.

Figure 9: Color gradient scale for gateways activity.

The position of the circle has various meanings depending onwhere it is used. If the gateways are added to the World Map, thentheir position represent a geographical location. In the Networkview, the gateways are placed around the basket in a radial manner,and then the position comes down to radius and angle. Wherethe radius shows their turnover volume related to each other, andthe angle is a consistent position to give familiarity. The last view,where the gateways are used, is the force graph. In this view, thehorizontal coordinate represents turnover rate, and the verticalcoordinate is the net currency creation.

The mentioned properties assist the humans cognitive ability todetect patterns by following the previously mentioned Gestalt Prin-ciples [27]. The radius and color represents the similarity patternwhile the positioning follows the principle of proximity. Effectivelyallowing the user to find abnormal gateways.

4.1.6 Order book combinations and variantsCombination of views are helpful in order to make them easier tobe directly compared. This is possible if the views share one or

multiple domains. The visual values are directly comparable, and itis easy to see any specific differences in magnitudes or trends.

In the case of the volume history 4.1.3 and order book 4.1.4 view,they both share the same variable for the y axis, namely quantity.By ensuring the axis has the same domain we can see the volumehistorically traded compared to the volume size of the order book.Giving an indication of how volatile the order book is. An exampleof this is shown in figure 10.

Figure 10: Volume History and Order book combined forBRIDGE.BTC.

The same has been done with the price history 4.1.2 view withthe exception of the order book has been rotated clockwise 90degrees to match the axis. It gives an clear indication of where theorders are concentrated in relation to the historical price.

Another combination of views are the with the nodes and theorder book, shown in figure 11. As an analytic technique the orderbook is projected to a circular domain and placed around the node,in order to simplify the understanding as described in [19]. It doesnot help us view detailed information, but gives us knowledge aboutthe price spread and any unbalances in orders for multiple gatewaysat once. The green and red lines are the lowest and highest pricefor the last 24-hours respectively.

Figure 11: BRIDGE.BTC Gateway with circular order book.

The fully detailed order book is still available by selecting a node.

4.1.7 World MapA world map is used to give a geographical connection to gateways.The exact position is not of interest and instead is important toknow in what region a gateway lies like the country or continent.Therefore, the map is set as a background and uses neutral colors,soft edges, and a low detailed value. Zooming is enabled to give

8

the user high precision location on demand and helps in the caseof any graphical overlapping.

There are two options for a gateways geographical position. Wehave the actual position of the computer running the node obtainedfrom the advertised IP address. It could serve as a useful tool for thetechnician to detect any area that fails to maintain online. What isused for the DSS is to allow the gateway’s owner decide their ownlocation typically in connection to where their business main focuslies or in connection to what traditional currency they use.

Earth resembles a spherical shape and to draw it on a map aprojection is required. A map projection is a systematic approachto transforming the sphere surface to a plane and necessarily dis-tortions of the original image occurs. In the DSS the Mercatorprojection is used. Objects size are distorted based on their distanceto Equator. Increasing towards infinity by the poles. It effectivelymakes Europe and Sweden appear larger than they are, which is abeneficial trait due to the blockchain’s popularity in this region.

4.1.8 Force GraphAnother way to organize and place the gateways is with a simplegraph of two variables. On the horizontal axis they are ordered afterthe net currency creation which is defined as the absolute sum ofthe created and destroyed asset units. The variable for the verticalaxis is the 24h volume turnover. Initially this view was in a radialmanner as discussed in section 3.3 but was changed to somethingmore concise.

If a gateway has, or close to, identical values, then overlappingoccurs. This is undesirable in the initial view as it causes difficultyin readability and obfuscates the overview. Therefore, a physicssystem, designed by Tim Dwyer [13], is implemented. Similar to theForce Atlas algorithm [16], it improves the feature of spatializationbut functions by using constrained graph layout methods. Theplacement and style constraints used are: horizontal and verticalalignment, non-overlapping boundaries, and containment withinpage boundary. This ensures that the gateways are as close aspossible to their target position to prevent overlapping borders.Upon zooming in the view, the gateways keep a constant size,while the axis does not. This makes it so that their position in thephysics system is updated, moving closer to their target and theirexact position, in regards to the variables, is revealed.

A dashed line is added as an indication to show that the gatewayis not in its exact position. This line is set from the center of thegateway to their target positions.

This view gives a simple and strong overview with little clutteror overlapping which gives a clear possibility to spot any clusteringpatterns or outliers.

4.2 Decision Support SystemThe decision support system is a tool to configure the differentcombination of views. The views communicate with each otherthrough user actions like the selection of a gateway to update therelevant views. There are also tools that interact with the views. Forexample, the search bars for filtering. With a suiting configurationthe DSS supports the stakeholder and fulfill their requirements.

The liquidity trader has the force graph as their main view, priceand order book combination in the bottom left, order book in bottomright and the full portfolio in the top right. The price and order

book view gives the stakeholder a good overview of the historicalexchange rates for the selected gateway and allows for simplecomparison for the order book, representing the current exchangerate. It is also necessary for a detailed representation of the orderbook given by the larger order book. The full portfolio in the topright is always showing the basket and its properties. The finalrequirement is to see the network activity which is fulfilled by theforce graph. The configuration can be seen in figure 12.

The same configuration is set for the system liquidity manager.Order books are a necessity to explore assets exchange rates bothon a top level and on a granular scale by demand. The circular orderbooks around the gateway, and the full scale order book offers theseservices respectively. As for viewing assets net currency creationbeing one of the requirements, the force graph serves a just purpose,and again allowing for an overview, detecting patterns and anyanomaly, with details upon zoom.

The configuration for stakeholder asset holder is seen in figure13. As the main view, the world map is used in order to give asense of understanding of the system users and their behaviourin connection to geographical position making possibilities of de-tecting regional opportunities or threats. Their bottom left corneris populated with price and order book accompanied by volumeand order book in the right corner. Both views aim to support therequirement of understanding an assets current state and in whatdirection it is changing. The simplified portfolio is placed in the topright covering parts of the screen. It shows detailed information ofany selected gateway to give the stakeholder a possibility to seetheir collection and distribution of assets and also to explore thesame information from other gateways.

Technician can fill their requirements with the same configu-ration as liquidity trader and system liquidity manager. The forcegraph in combination with gateways fulfills the stakeholders re-quirements. The node colors give an good indication of the systemavailability, and its current status. The overview of orders given bythe circular order books, and the gateways positioning allows fordetection of any abnormal behaviour. Finding maleficent behaviouror system instabilities that needs attention.

Figure 12: Force Graph view with the gateway OPEN.BTCselected.

9

Figure 13: World map view with the gateway OPEN.BTC se-lected.

5 DISCUSSIONThis chapter will discuss the effect of the preconceived proficiencyof the users joined by the resulting views and the possibilities ofthe decision support system.

Themajority of the users are experts in the fields of digital money,stock exchange trading, and the Centiglobe system. A consciousdecision that is reflected in the lack of descriptions and helpful textsmaking the target group of the DSS to be experts. More specifically;legends, axis labels or graph descriptors are not included. The expertuser is assumed to know how to read an aggregated order book,pie chart portfolio and line charts. The unfortunate downside is thelack of intuitiveness for an user without the domain knowledge,which might discourage newcomers. In return, the system holds acompact structure and there is no redundant information for thealready familiar user allowing for visualizations that might be moreeffective and valuable.

Throughout the process of development it was difficult keepingthe views compact while maintaining the necessary detail of in-formation. An extensive challenge was the tick labels as assets onthe BitShares market operate in various domains and can be spaceconsuming with up to 18 decimals [29]. The implemented methodsto attack the issue of dynamically limiting number of ticks andformatting numbers are both interchangeable. The tick limitationsare useful for small numbers and show certain tick labels in full.These labels are placed equidistant throughout the axis allowing foroverall readability but can miss key point areas with high volatility.The secondary method is to format numbers into a denser repre-sentation using symbol suffixes. However, replacing digits with asuffix can cause loss of information and readability issues as not allsymbols are commonly known.

The idea of keeping the views closely packed is a theme through-out the system as some graphs are too small to be read comfortably.It suggests that the main use case of the DSS is most suited as anoverview with the possibility of an additional system.

The circular order book main focus lies in overview. The pro-jection creates a stretched representation and might be difficultto compare to its original. Yet it holds some innovative strengthin its ability to display a simplified and compact version allowingfor multiple order books to be viewed side by side. As it does notshows numbers of the price or volume, the representation givesa relative value where the bid orders are comparable to the ask

orders. Increasing possibility to detect imbalances as an indicationto investigate further. There is also the possibility that a node hasno active orders. This becomes apparent and an example is visiblein figure 1.

The other combinations, like the price and volume next to theorder book, are features welcomed by the principle. The abilityto compare the fluctuation of price directly to the order book isa helpful feature to gain insight. The latter combination offers asimilar possibility of comparison that only the order book can keepits orientation and maintain the readability through familiarity. Tohave both of these views, or in combination with the full scale orderbook, in the same display is not space efficient. There is duplicateinformation in having the order book displayed multiple times indifferent orientations and transformations.

The system uses colors with different intentions. In the caseof the order book, it follows the color scheme of traditional stockexchanges to keep familiarity while the portfolio uses colors toteach familiarity. The gateway implements a gradient between redand green and is displayed to give information in connection toan emotion. The color represents the activity which is a desirablefeature in the eye of the stakeholder. However, the selected colormap can be challenged through different perspectives. Due to itspositive associations green is used to represent high activeness. Onecan alternatively imagine an active gateway to be hot and vibrantwhen it is involved in many transactions. Making red, a salient colorassociated with activity a viable option. For the inactive gateway,blue could be used as a variant. It is a calm and cool color thataccurately describes an inoperative gateway, but it is also associatedwith good across cultures causing conflicting associations fromdifferent perspectives [1].

The system aids in comprehension, allows for exploration, andsupports in decision making. In this way, the complex world ofblockchain can be explained in a simpler manner by using a vi-sual medium. The simplification of digital assets can increase theirusability, as the lack of understanding can be the limiting factorfor world adoption. Now, it is possible that we might close thegap between the two domains and increase the understanding andusage of cryptocurrencies by using visualizations familiar to thestock exchange trader.

5.1 Method CriticismThe answers from the semi-structured interviews was varied acrossthe stakeholders. Requirements that were overlapping were deemedimportant and the requirements only mentioned once were up forconsideration. Due to the low amount of stakeholders interviewed,it is not impossible to imagine that important requirements wereoverlooked and not implemented in the final product. A welcomedimprovement would be to re-evaluate the requirements study whenmore stakeholders are present.

The system has a global audience and not all conventions mighttranslate across cultures. Particularly with color associations. In thesystem green and red has been used on multiple occasions. Wheregreen has represented a positive indication, and red has been usedfor the opposite, negative indications. This convention works wellin Scandinavia, but the color association and intuition might nothold just in an universal context. Mario De Bortoli and Jesús Maroto

10

explore the meaning color can have in different cultures. An exam-ple being orange which is considered a positive and life-affirmingcolor in Asia, while the US regards it as fast-food restaurants, trafficdelays and road hazards [12].

5.2 Future WorkThis chapter will propose further advancements for the design ofthe system and the research around it.

5.2.1 ResearchThe study of requirements would benefit from an increase in theamount of users partaking the interviews. Creating a more solidbasis for our requirements and possibly discovering new ones. Thetypes of stakeholders might also increase as Centiglobe goes onlineand expands it’s user base.

A study of the usability and perception can have value in im-proving the system. When it comes to the perception, assumptionsregarding the users proficiency has been made, disclosing the needfor helpful texts, guides and tips. It could show that this is not thecase, and that helpful information regarding symbolic meanings,graph explanations and so on, are necessary. In the case of theusability, a study regarding the use can help find shortcomings.Anything from the flow of the exploration, unnecessary interac-tions to missing information or misconceptions.

These studies might bring forth additional views to be imple-mented to compliment the system.

5.2.2 DevelopmentImplementing detection algorithms can further enhance the DSS.They can alert the stakeholder by following certain pre-definedrules. Anything from having thresholds that indicate warnings tomore advance techniques, such as anomaly detection algorithms.These methods would act suggestively and help the stakeholder,mainly Technician, to adjust their focus in the right place.

Predictive analysis is a great feature to implement. It can providemultiple levels of future predictions depending on each view, mak-ing use of historical data or typical patterns. The extension can besimple by using the current trajectory or make use of more advancetechniques. Such as curve fitting, Markov chains or deep neuralnetworks. In the price and volume view, additional data entries canbe shown with an distinct color to indicate their uniqueness. Inany of the views displaying the gateways, a time-line can be pre-sented with controls to alter the time. Showing both the gatewayshistorical, current and the predicted future position.

6 CONCLUSIONThis thesis has discovered the necessary requirements for a web-based multi-stakeholder real-time decision support system usingvisualizing of a global financial system. To answer the researchquestion, semi-structured interviews were conducted with the goalof discovering the stakeholders requirements. In addition to infor-mation visualization, the set of requirements is used for the decisionsupport system in order to create a valuable tool. During the de-velopment there was an open channel of communication with theprinciple, giving continuous feedback on the design. The resultingprototype is an operational web-based system, giving an overview

and detailed information of the principles blockchain network inreal-time.

Visualizing blockchain data in a meaningful way, supports thestakeholder in decision making by allowing for understanding ofthe systems state through exploration. The stakeholders have re-quirements of what information should be accessible to performtheir tasks. Data-driven views are developed targeting these re-quirements and are combined into a collection, a decision supportsystem, for the stakeholder.

The DSS is a tool for achieving an overall view and allows formonitoring through real-time updates. Detailed information is read-ily available, but it should be used in combination with anothersystem for exact and critical operations.

The thesis is an experimental research study. The design servesas a good basis for a DSS with many advancements to make, suchas additional layout work, predictive algorithms, anomaly detectionand filtering methods.

ACKNOWLEDGMENTSI would like to thank all the stakeholders who participated in theinterviews and helped with continuous feedback regarding theprototypes. I also extend my gratitude to the principle, HenrikGradin, for being as available as he was and making sure I had allmy questions answered. Last, but certainly not least, I thank mysupervisor at KTH, Björn Thuresson, for being patient and helpfulthroughout the process.

REFERENCES[1] Francis M Adams and Charles E Osgood. 1973. A cross-cultural study of the

affective meanings of color. Journal of cross-cultural psychology 4, 2 (1973),135–156.

[2] Carl Ahrsjö. 2015. Real-time event based visualization of multivariate abstractdatasets: Implementing and evaluating a dashboard visualization prototype.

[3] Mathieu Bastian, Sebastien Heymann, and Mathieu Jacomy. 2009. Gephi: an opensource software for exploring and manipulating networks. In Third internationalAAAI conference on weblogs and social media.

[4] Kent Beck, Mike Beedle, Arie Van Bennekum, Alistair Cockburn, Ward Cunning-ham, Martin Fowler, James Grenning, Jim Highsmith, Andrew Hunt, Ron Jeffries,et al. 2001. Manifesto for agile software development. (2001).

[5] Binance [n. d.]. Binance Cryptocurrency exchange. https://www.binance.com.Accessed: 2019-05-02.

[6] BitShares.js WS [n. d.]. BitShares.js WS Bitshares websocket interface. https://github.com/bitshares/bitsharesjs-ws. Accessed: 2019-03-28.

[7] Robert H Bonczek, C. W Holsapple, and Andrew B Whinston. 2014. Foundationsof Decision Support Systems. Academic Press.

[8] Alan Bryman. 2012. Social research methods (4. ed.. ed.). Oxford University Press,Oxford.

[9] Stuart Card, JD Mackinlay, and B Shneiderman. 2009. Information visualization.Human-computer interaction: Design issues, solutions, and applications 181 (2009).

[10] Centiglobe [n. d.]. Centiglobe Principle company. https://www.centiglobe.com/.Accessed: 2019-02-12.

[11] Glyn Davies. 2010. History of money. University of Wales Press.[12] Mario De Bortoli and Jesus Maroto. 2001. Colours across cultures: Translating

colours in interactive marketing communications. Paisley University LanguagePress UK, 3–4.

[13] Tim Dwyer. 2009. Scalable, versatile and simple constrained graph layout. InComputer Graphics Forum, Vol. 28. Wiley Online Library, 991–998.

[14] Rosalind Edwards. 2013. What is qualitative interviewing?[15] Garrick Hileman and Michel Rauchs. 2017. Global cryptocurrency benchmarking

study. Cambridge Centre for Alternative Finance 33 (2017).[16] Mathieu Jacomy, Tommaso Venturini, Sebastien Heymann, and Mathieu Bastian.

2014. ForceAtlas2, a continuous graph layout algorithm for handy networkvisualization designed for the Gephi software. (2014).

[17] Harry A Kalodner, Miles Carlsten, Paul Ellenbogen, Joseph Bonneau, and ArvindNarayanan. 2015. An Empirical Study of Namecoin and Lessons for DecentralizedNamespace Design.. In WEIS.

11

[18] D. A. Keim. 2002. Information Visualization and Visual Data Mining. IEEETransactions on Visualization & Computer Graphics 8 (2002), 1–8.

[19] Jörn Kohlhammer, Daniel Keim, Margit Pohl, Giuseppe Santucci, and GennadyAndrienko. 2011. Solving problems with visual analytics. Procedia ComputerScience 7 (2011), 117–120.

[20] Kraken [n. d.]. Kraken Cryptocurrency exchange. https://www.kraken.com.Accessed: 2019-05-02.

[21] Steinar Kvale. 2008. Doing interviews. Sage.[22] Igor Makarov and Antoinette Schoar. 2018. Trading and arbitrage in cryptocur-

rency markets. (2018).[23] Satoshi Nakamoto. 2008. Bitcoin: A peer-to-peer electronic cash system. (2008).[24] KAYA NAz and Helena Epps. 2004. Relationship between color and emotion: A

study of college students. College Student J 38, 3 (2004), 396.

[25] Michael Nofer, Peter Gomber, Oliver Hinz, and Dirk Schiereck. 2017. Blockchain.Business & Information Systems Engineering 59, 3 (2017), 183–187.

[26] Marc Pilkington. 2016. 11 Blockchain technology: principles and applications.Research handbook on digital transformations 225 (2016).

[27] Philip T Quinlan and Richard N Wilton. 1998. Grouping by proximity or similar-ity? Competition between the Gestalt principles in vision. Perception 27, 4 (1998),417–430.

[28] Fabian Schuh and Daniel Larimer. 2015. BitShares 2.0: Financial Smart ContractPlatform. (2015).

[29] Fabian Schuh and Daniel Larimer. 2017. Bitshares 2.0: General overview. (2017).[30] Ben Shneiderman. 1996. The eyes have it: A task by data type taxonomy for

information visualizations. In Visual Languages. 336–343.[31] Chauncey Wilson. 2013. Interview techniques for UX practitioners: A user-centered

design method. Newnes.

12

www.kth.se

TRITA -EECS-EX-2019:441