Tag Archives: user experience

Big Data Graphs Revisited

Some time ago I’ve outlined Six Graphs of Big Data as a pathway to the individual user experience. Then I’ve did the same for Five Sources of Big Data. But what’s between them remained untold. Today I am going to give my vision how different data sources allow to build different data graphs. To make it less dependent on those older posts, let’s start from the real-life situation, business needs, then bind to data streams and data graphs.


Context is a King

Same data in different contexts has different value. When you are late to the flight, and you got message your flight was delayed, then it is valuable. In comparison to receiving same message two days ahead, when you are not late at all. Such message might be useless if you are not traveling, but airline company has your contacts and sends such message on the flight you don’t care about. There was only one dimension – time to flight. That was friendly description of the context, to warm you up.

Some professional contexts are difficult to grasp by the unprepared. Let’s take situation from the office of some corporation. Some department manager intensified his email communication with CFO, started to use a phone more frequently (also calling CFO, and other department managers), went to CFO office multiple times, skipped few lunches during a day, remained at work till 10PM several days. Here we got multiple dimensions (five), which could be analyzed together to define the context. Most probably that department manager and CFO were doing some budgeting: planning or analysis/reporting. Knowing that, it is possible to build and deliver individual prescriptive analytics to the department manager, focused and helping to handle budget. Even if that department has other escalated issues, such as release schedule or so. But severity of the budgeting is much higher right away, hence the context belongs to the budgeting for now.

By having data streams for each dimension we are capable to build run-time individual/personal context. Data streams for that department manager were kind of time series, events with attributes. Email is a dimension we are tracking; peers, timestamps, type of the letter, size of the letter, types and number of attachments are attributes. Phone is a dimension; names, times, durations, number of people etc. are attributes. Location is a dimension; own office, CFO’s office, lunch place, timestamps, durations, sequence are attributes. And so on. We defined potentially useful data streams. It is possible to build an exclusive context out of them, from their dynamics and patterns. That was more complicated description of the context.


Interpreting Context

Well, well, but how to interpret those data streams, how to interpret the context? What we have: multiple data streams. What we need: identify the run-time context. So, the pipeline is straightforward.

First, we have to log the Data, from each interested dimension. It could be done via software or hardware sensors. Software sensors are usually plugins, but could be more sophisticated, such as object recognition from surveillance cameras. Hardware sensors are GPS, Wi-Fi, turnstiles. There could be combinations, like check-in somewhere. So, think that it could be done a lot with software sensors. For the department manager case, it’s plugin to Exchange Server or Outlook to listen to emails, plugin to ATS to listen to the phone calls and so on.

Second, it’s time for low-level analysis of the data. It’s Statistics, then Data Science. Brute force to ensure what is credible or not, then looking for the emerging patterns. Bottleneck with Data Science is a human factor. Somebody has to look at the patterns to decrease false positives or false negatives. This step is more about discovery, probing and trying to prepare foundation to more intelligent next step. More or less everything clear with this step. Businesses already started to bring up their data science teams, but they still don’t have enough data for the science:)

Third, it’s Data Intelligence. As MS said some time ago “Data Intelligence is creating the path from data to information to knowledge”. This should be described in more details, to avoid ambiguity. From Technopedia: “Data intelligence is the analysis of various forms of data in such a way that it can be used by companies to expand their services or investments. Data intelligence can also refer to companies’ use of internal data to analyze their own operations or workforce to make better decisions in the future. Business performance, data mining, online analytics, and event processing are all types of data that companies gather and use for data intelligence purposes.” Some data models need to be designed, calibrated and used at this level. Those models should work almost in real-time.

Fourth, is Business Intelligence. Probably the first step familiar to the reader:) But we look further here: past data and real-time data meet together. Past data is individual for business entity. Real-time data is individual for the person. Of course there could be something in the middle. Go find comparison between stats, data science, business intelligence.

Fifth, finally it is Analytics. Here we are within individual context for the person. There worth to be a snapshot of ‘AS-IS’ and recommendations of ‘TODO’, if the individual wants, there should be reasoning ‘WHY’ and ‘HOW’. I have described it in details in previous posts. Final destination is the individual context. I’ve described it in the series of Advanced Analytics posts, link for Part I.

Data Streams

Data streams come from data sources. Same source could produce multiple streams. Some ideas below, the list is unordered. Remember that special Data Intelligence must be put on top of the data from those streams.

In-door positioning via Wi-Fi hotspots contributing to mobile/mobility/motion data stream. Where the person spent most time (at working place, in meeting rooms, on the kitchen, in the smoking room), when the person changed location frequently, directions, durations and sequence etc.

Corporate communication via email, phone, chat, meeting rooms, peer to peer, source control, process tools, productivity tools. It all makes sense for analysis, e.g. because at the time of release there should be no creation of new user stories. Or the volumes and frequency of check-ins to source control…

Biometric wearable gadgets like BodyMedia to log intensity of mental (or physical) work. If there is low calories burn during long bad meetings, then that could be revealed. If there is not enough physical workload, then for the sake of better emotional productivity, it could be suggested to take a walk.


Data Graphs from Data Streams

Ok, but how to build something tangible from all those data streams? The relation between Data Graphs and Data Streams is many to many. Look, it is possible to build Mobile Graph from the very different data sources, such as face recognition from the camera, authentication at the access point, IP address, GPS, Wi-Fi, Bluetooth, check-in, post etc. Hence when designing the data streams for some graph, you should think about one to many relations. One graph can use multiple data streams from corresponding data sources.

To bring more clarity into relations between graphs and streams, here is another example: Intention Graph. How could we build Intention Graph? The intentions of somebody could be totally different in different contexts. Is it week day or weekend? Is person static in the office or driving the car? Who are those peers that the person communicates a lot recently? What is a type of communication? What is a time of the day? What are person’s interests? What were previous intentions? As you see there could be data logged from machines, devices, comms, people, profiles etc. As a result we will build the Intention Graph and will be able to predict or prescribe what to do next.


Context from Data Graphs

Finally, having multiple data graphs we could work on the individual context, personal UX. Technically, it is hardly possible to deal with all those graphs easily. It’s not possible to overlay two graphs. It is called modality (as one PhD taught me). Hence you must split and work with single modality. Select which graph is most important for your needs, use it as skeleton. Convert relations from other graphs into other things, which you could apply to the primary graph. Build intelligence model for single modality graph with plenty of attributes from other graphs. Obtain personal/individual UX at the end.

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Mobile Home Screens

Mobile Home Screens

Better Home Screen?

Definitely without iPhone’ish glossy icons,
wasting potentially useful space around.
Want it or not, but glossy must stay in the past.
Near future is flat.

With aligned multi-sized widgets like Winphone.
With variety of widgets like Android.
But much more aesthetic than they are today!

With more information embedded into the icon-sized area,
like Facebook Home, three faces into small context area.
Ideally the home screen can delivery useful information in five-six different contexts even without any clicks on them.

Smaller widgets will remain and prevail,
because they are sized to our finger…

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Mobile UX: home screens compared


Some time in 2010 I’ve published my insight on the mobile home screens for four platforms: iOS, Android, Winphone and Symbian. Today I’ve noticed it got more than 35K views:)

What now?

What changed since that time? IMHO Winphone home page is the best. Because it allows to deliver multiple locuses of attention, with contextual information within. But as soon as you go off the home screen, everything else is poor there. iOS and Android remained lists of dumb icons. No context, no info at all. The maximum possible is small marker about the number of calls of text messages. And Symbian had died. RIP Symbian.

So what?

Vendors must improve the UX. Take informativeness of Winphone home screen, add aesthetics of iOS graphics, add openness & flexibility of Android (read Android First) and finally produce useful hand-sized gadget.

Winphone’s home screen provides multiple locuses of attention, as small containers of information. They are mainly of three sizes. The smallest box has enough room to deliver much more context information than number of unread text messages. By rendering the image within the box we can achieve the kind of Flipboard interaction. You decide from the image whether you interested in that or not. It is second question how efficiently the box room is used. My conclusion that it is used inefficiently. There are still number of missed calls or texts with much room left unused:( I don’t know why the concept of the small contexts has been left underutilized, but I hope it will improve in the future. Furthermore, it could improve on Android for example. Android ecosystem has great potential for creativity.

May be I visualize this when get some spare time… Keep in touch here or Slideshare.

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Mobile EMR, Part V

Some time ago I’ve described ideation about mobile EMR/EHR for the medical professionals. We’ve come up with tablet concept first. EMR/EHR is rendered on iPad and Android tablets. Look & feel is identical. iPad feels better than Samsung Galaxy. Read about tablet EMR from four previous posts. BTW one of them contains feedback from Edward Tufte:) Mobile EMR Part I, Part II, Part III, Part IV.

We’ve moved further and designed a concept of hand-sized version of the EMR/EHR. It is rendered on iPhone and Android phones. This post is dedicated to the phone version. As you will see, the overall UI organization is significantly different from tablet, while reuse of smaller components is feasible between tablets and phones. Phone version is totally SoftServe’s design, hence we carry responsibility for design decisions made there. For sure we tried to keep both tablet and phone concepts consistent in style and feel. You could judge how good we accomplished it by comparing yourself:)


The lack of screen space forces to introduce a list of patients. The list is vertically scrolled. The tap on the patient takes you to the patient details screen. It is possible to add very basic info for each patient at the patient list screen, but not much. Cases with long patient names simply leave no space for more info. I think that admission date, age and sex labels must be present on the patient list in any case. We will add them in next version. Red circular notification signals about availability of new information for the patient. E.g. new labs ready or important significant event has been reported. The concept of interaction design supposes that medical professional will click on the patient marked with notifications. On the other hand, the list of patients is ordered per user. MD can reorder the list via drag’n’drop.

Patient list

Patient list

MD can scan the wristband to identify the patient.

Wristband scanning

Wristband scanning

Patient details

MD goes to the patient details by tapping the patient from the list. That screen is called Patient Profile. It is long screen. There is a stack of Vital Signs right on top of the screen. Vital Signs widget is totally reused from tablets on the phones. It fits into the phone screen width perfectly. Then there is Meds section. The last section is Clinical Visits & Hospitalization chart. It is interactive (zoomable) like on iPad. Within single patient MD gets multiple options. First options is to scroll the screen down to see all information and entry points for more info available there. Notice a menu bar at the bottom of the screen. MD can prefer going directly to Labs, Charts, Imagery or Events. The interaction is organized as via tabs. Default tab is patient Profile.

Patient profile

Patient profile

Patient profile, continued

Patient profile, continued

Patient profile, continued

Patient profile, continued


There is not much space for the tables. Furthermore, labs results are clickable, hence the size of the rows should be relative to the size of the the finger tap. Most recent labs numbers are highlighted with bold. Deviation from the normal range is highlighted with red color. It is possible to have the most recent labs numbers of the left and on the right of the table. It’s configurable. The red circular notification on the Labs menu/tab informs with the number how many new results available since last view on this patient.




Here we reuse ‘All Data’ charts smoothly. They perfectly fit into the phone screen. The layout is two-column with scrolling down. The charts with notifications about new data are highlighted. MD can reorder charts as she prefers. MD can manage the list of charts too by switching them on and off from the app settings. There could be grouping of charts based on the diagnosis. We consider this for next versions. Reminder about the chart structure. Rightmost biggest part of the chart renders most recent data, since admission, with dynamics. Min/max depicted with blue dots, latest value depicted with red dot. Chart title also has the numeric value in red to be logically linked with the dot on the chart. Left thin part of the chart consist of two sections: previous year data, and old data prior last year (if such data available). Only deviations and anomalies are meaningful from those periods. Extreme measurements are comparable thru the entire timeline, while precise dynamics is shown for the current period only. More information about the ‘All Data’ concept could be found in Mobile EMR, Part I.

Measurements in 'All Data' charts

Measurements in ‘All Data’ charts

Tapping on the chart brings detailed chart.

Measurement details

Measurement details


There was no a big deal to design entry point into the imagery. Just two-column with scroll down layout, like for the Measurements. Tap on the image brings separate screen, completely dedicated to that image preview. For the huge scans (4GB or so) we reused our BigImage solution, to achieve smooth image zoom in and zoom out, like Google Maps, but for medical imagery.



Tissue scan, zoom in

Tissue scan, zoom in

Significant events & notes

Just separate screen for them…

Significant events

Significant events

Conclusion: it’s BI framework

Entire back-end logic is reused between tablet and phone versions on EMR. Vital Signs and ‘All Data’ charts are reusable as is. Clinical Visits & Hospitalization chart is cut to shorter width, but reused easily too. Security components for data encryption, compression are reused. Caching reused. Push notification reused. Wristband scanning reused. Labs partially reused. Measurements reused. BigImage reused.

Reusability is physical and logical. For the medical professional, all this stuff is technology agnostic. MD see Vital Signs on iPad, Android tablet, iPhone and Android phone as a same component. For geeks, it is obvious that reusability happens within the platform, iOS and Android. All widgets are reusable between iPad and iPhone, and between Samsung Galaxy tab and Samsung Galaxy phone. Cloud/SaaS stuff, such as BigImage is reusable on all platforms, because it Web-based and rendered in Web containers, which are already present on each technology platform.

Most important conclusion is a fact that mEMR is a proof of BI Framework, suitable for any other industry. Any professional can consume almost real-time analytics from her smartphone. Our concept demonstrated how to deliver highly condensed related data series with dynamics and synergy for proper analysis and decision making by professional; solution for huge imagery delivery on any front-end. Text delivery is simple:) We will continue with concept research at the waves of technology: BI, Mobility, UX, Cloud; and within digitizing industries: Health Care, Biotech, Pharma, Education, Manufacturing. Stay tuned to hear about Electronic Batch Record (EBR).

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Mobile EMR, Part III

This is continuation of previous posts Mobile EMR, Part I and Mobile EMR, Part II

We’ve met with Mr.Tufte and demo’ed this EMR concept. He played with it for a while and suggested list of improvements, from his point of view.

‘All Data’ charts

Edward Tufte insists that sparklines work better than dots. It is OK that sparklines will be of different sizes. It is natural that each measurement has its own normal range. Initially we tried to switch the charts to the lines, but then we rolled back. Seems that we should make this feature configurable, and use sparklines by default. But if some MD wants dots, she can manually switch it in app settings.

Partially our EMR concept has been switched to sparklines – for display of Vital Signs. Below is a snapshot:

Vital Signs

One more thing related to the Vital Signs, we did great by separating on the widget on top, and grouping them together. It adds much value, because they are related to each other. It is important to see what happened to them at each moment. Our approach, based on user testing, appeared to be a winning one!


Current use of the space could be improved even more. First reason is that biggest value of that research was keeping ‘All Information’ on single screen. Human eye recognizes perfectly which type of information is needed. All space is tessellated into multiple locuses of attention. Then human eye locks the desired locus and then focuses within that locus. Second reason is iPad resolution. We can squeeze more from retina resolution without degradation of usability (like size of labels and numbers). It is possible to scale to the newspaper typography on iPad, hence fit more information into the screen estate.


This confirms the modern trend to genetics and genetic engineering. Genogram is a special type of diagram, visualizing patient’s family relationships and medical history. In medicine, medical genograms provide a quick and useful context in which to evaluate an individual’s health risks. Many new treatments are tailored by genotype of the patients. E.g. Steve Jobs’s cancer was periodically sequenced and brand new proteins where applied, to prevent disease spread. All cells are built from the proteins, reading other proteins as instructions. This is true for the cancer cells. Thus if they read instructions from fake proteins, then they can not build themselves properly. We like this idea immediately, because its value is instant and big, its importance is as high as allergy. Below is sample genogram, using special markers for genetically influenced diseases.

Sample Genogram

There are other cosmetic observations which will be improved shortly. We continue usability testing with medical doctors. More to come. It could be Mobile EMR on iPhone. Stay tuned.

UPDATE: Continued on Mobile EMR, Part IV.

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