In order to have all my travel arrangements in one place I use a service called TripIt. When I receive eMail confirmations from airlines, hotels, train planners and so, I simply forward those to plans@tripit.com, and within seconds they build or amend to an itinerary for me that is available in an app.
Today I noticed a slight flaw though. I was going by train from London, UK up to the Midlands via a large town in the UK called Reading.
The strange thing in the itinerary was that the interchanges in Reading was placed in chronology after arriving at and leaving the final destination.
A closer look at the data revealed two strange issues:
- Reading was spelled Reading, PA
- The time zone for the interchange was set to EST
Hmmm… There must be a town called Reading in Pennsylvania across the pond. Tripit must, when automatically reading the eMail, have chosen the US Reading for this ambiguous town name and thereby attached the Eastern American time zone to the interchange.
Picking the right Reading for me in the plan made the itinerary look much more sensible.
Liliendahl on Data Quality 
Really nice example, Henrik. A good example of the requirement for location identifiers (e.g. place names and addresses) to be unambiguous within a commonly agreed scope – otherwise strange and unwanted events can occur. Imagine how the travel directions would have been – or the recommended minimal speed to get there in time 🙂
Thanks Morten for commenting. Yes, this issue do exist. Sometimes you read a ”funny” story in the news about people ending up the wrong place very far away from the intended one, but also we have lots of daily challenges.
And this is a great example of why the human brain will always beat a computer. You will parse the information as you are reading Reading and know which one you mean. 😉
Richard, thanks for joining in. The question about where a computer can beat the human is indeed very exciting.
In this case the computer usually does the job much faster than a human could do and it works 24/7/365.
It made a mistake. Humans do too.
Without knowing anything about the algorithms at TripIt I guess it is a question about using external reference data better by using facts as it is a train journey and the location of the origin and destination of the journey. This is what our brain do too.
This also brings me to my favorite subject, which is using external reference data in master data management and data quality improvement.
Yes, Reading, PA is near Philadelphia. I grew up in the region. Perhaps this is another type of American Exceptionalizm? 🙂
Could be Gino. The algorithm may favorite US towns before considering “foreign” towns.
Well, sorry! That algorithm is in no way ‘smart’ by favoring US locations; it is just – for the time being – dumb. How hard would be to check, when more than one Reading could be picked, for reasonableness in respect of neighborhood / distance in combination with the mode of transport? Even a first year CS student can code a sorting algorithm that would work.