Consumer Tech

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Twitter traffic jams in Washington, created by… John Oliver

Summary: In the first week of June, 20% of the Tweets about traffic, delays and congestion by people around the Washington Beltway were caused by John Oliver’s “Last Week Tonight” segment about Net Neutrality.

At work, we are always exploring a wide range of sensors to obtain useful insights that can used to make work and routine activities faster, more efficient and less risky. One of our Alpha Tests is examining use of “arrays” of high-targeted Twitter sensors to detect early indications of traffic congestion, accidents and other sources of delays. Specifically we are training our system how to use Twitter is a good traffic sensor (by good, in “data science speak” we are determining whether we can train a model for traffic detection that has a good balance of precision and recall, and hence a good F1 Score). To do this, I setup a test bed around the nation’s second-worst commuter corridor: the Washington DC Beltway (our my backyard).

Earlier this month our array of geographic Twitter sensors picked up an interesting surge in highly localized tweets about traffic-related congestion and delays. This was not an expected “bad commuter-day”-like surge. The number of topic- and geographically-related tweets seen on June 4th was more than double the expected number for a Tuesday in June around the Beltway; the number seen during lunchtime was almost 5x normal.

So what was the cause? Before answering, it is worth taking a step back.

The folks at Twitter have done a wonderful job at not only allowing you to fetch tweets based on topics, hash tags and geographies. They have also added some great machine learning-driven processing to screen out likely spammers and suspect accounts. Nevertheless Twitter data, like all sensor data, is messy. It is common to see Tweets with words spelled wrong, words used out of context, or simply nonsensical Tweets. In addition, people frequently repeat the same tweets throughout the day (a tactic to raise social media exposure) and do lots of other things that you must train the machine to account for.

That’s why we use a Lambda Architecture to process our streaming sensor data (I’ll write about why everyone–from marketers to DevOps staff should be excited about Lambda architectures in a future post). As such, not only do use Complex Event Processing (via Apache Storm) to detect patterns as they happen; we also keep a permanent copy of all raw data that we can explore to discover new patterns and improve our machine learning models).

That is exactly what we did as soon as we detected the surge. Here is what we found: the cause of the traffic- and congestion-related Twitter surge around the Beltway was… John Oliver:

  1. In the back half of June 1st’s episode of “Last Week Tonight” (HBO, 11pm ET), John Oliver had an interesting 13-minute segment on Net Neutrality. In this segment he encouraged people to visit the FCC website and comment on this topic.
  2. Seventeen hours later, the FCC tweeted that “[they were] experiencing technical difficulties with [their] comment system due to heavy traffic.” They tweeted a similar message 74-minutes later.
  3. This triggered a wave of re-tweets and comments about the outage in many places. Interestingly this wave was delayed in the Beltway. It surged the next day, just before lunchtime in DC, continuing throughout the afternoon. The two spikes were at lunchtime and just after work . Evidently, people are not re-tweeting while working. The timing of the spikes also reveals some interesting behavior patterns on Twitter use in DC.
  4. By 4am on Wednesday the surge was over. People around the Beltway were back to their normal tweeting about traffic, construction, delays, lights, outages and other items confounding their commute.

Of course, as soon as we saw the new pattern, we adjusted our model to account for this pattern. However, we thought it would be interesting to show in a simple graph how much “traffic on traffic, delays and congestion” Mr. Oliver induced in the geography around the Beltway for a 36-hour period. Over the first week of June, one out of every five Tweets about traffic, delays and congestion by people around the Beltway were not about commuter traffic, but instead around FCC website traffic caused by John Oliver:

Tweets from people geographically Tweeting around the Washington Beltway on traffic, congestion, delays and related frustration for first week of June. (Click to enlarge.)
Tweets from people geographically Tweeting around the Washington Beltway on traffic, congestion, delays and related frustration for first week of June. (Click to enlarge.)

Obviously, a simple count of tweets is a gross measure. To really use Twitter as a sensor, one needs to factor in many other variables: use text vs. hash-tags, tweets vs. mentions and re-tweets, the software client used to send the tweet (e.g., HootSuite is less likely to be a good source for accurate commuter traffic data); the number of followers the tweeter has (not a simple linear weighting) and much more. However, the simple count is simple first-order visualization. It also makes interesting “water-cooler conversation.”

The Expanding (Digital) Universe: Visualizing How BIG a Zettabyte Really Is

Note: This post was originally published at Oulixeus Consulting

A lot of news articles recently (Google News currently shows 1,060 articles) are citing the annual EMC-IDC Digital Universe studies of the massive growth of the digital universe through 2020. If you have not read the study, it indicates that the digital universe is now doubling every two years and will grow 44-fold 50-fold now 55-fold from 0.8 Zettabytes (ZB) of data in 2009 to 35 40 now 44 Zettabytes in 2020. (Every year IDC has revised the growth curve upward by several Zettabytes.)

Usually these articles show a diagram such as this:

DigitalDecade

This type of diagram is great at showing how much 44-fold growth is. However it really does not convey how big a Zettabyte really is—and how much data we will be swimming (or drowning in) by 2020.

A Zettabyte (ZB) is really, really big – in terms of today’s information systems. It is not a capacity that people encounter every day. It’s not even in Microsoft Office’s spell-checker, Word “recommended” that I meant to type “Petabyte” instead 😉

The Raw Definition: How big is a Zettabyte?

A Computer Scientist will tell you that 1 Zettabyte is 270 bytes. That does not sound very big to a person who does not usually visualize think in exponential or scientific notation—especially given that a one-Terabyte (1 TB) solid state drive has a capacity to store 240 bytes.

Wikipedia describes a ZB (in decimal math) as one-sextillion bytes. While this sounds large, it is a hard to visualize. It is easier to visualize 1 ZB (and 44 ZBs) in relation to things we use everyday.

Visualizing Zettabytes in Units of Smartphones

The most popular new smartphones today have 32 Gigabytes (GB) or 32 x 230 bytes of capacity. To get 1 ZB you would have to fill 34,359,738,368 (34.4 billion) smartphones to capacity. If you put 34.4 billion Samsung S5’s end-to-end (length-wise) you would circle the Earth 121.8 times:

1ZB-Earth-Distance
Click to see a higher resolution image and the dot that represents Earth to-scale vs. the line

You can actually circumnavigate Jupiter almost 11 times—but that is not obvious to visualize.

The number of bytes in 44 Zettabytes is a number too large for Microsoft Excel to compute correctly. (The number you will get is so large that Excel will cut off seven digits of accuracy–read that as a potential rounding error up to one million bytes). Assuming that Moore’s Law will allow us to double the capacity of smartphones three times between now and 2020, it would take 188,978,561,024 (188+ trillion) smartphones to store 44 ZB of data. Placing these end-to-end- would circumnavigate the world over nearly 670 times.

This is too hard to visualize, so lets look at it another way. You could tile the entire City of New York two times over (and the Bronx and Manhattan three times over) with smartphones filled to capacity with data to store 44 ZBs. That’s a big Data Center!

Clik
Amount of Smartphones (with 2020 tech) you would need to store 44 ZB (click for higher resolution)

This number also represents 25 smartphones per person for the entire population of the planet. Imagine the challenge of managing data spread out across that many smartphones.

Next Page: Visualizing Zettabytes in Units of Facebook