21. 09. 2018 Susanne Greiner EriZone & OTRS, NetEye

HackTheAlps Challenge with Würth Phoenix

We are proud to participate as HackTheAlps PlatinSponsor and really curious about what you are going to do to our data!

Visual Synthetic Monitoring with Alyvix – User Experience – Enjoy Your Holiday!

Most people hate being away from the Internet even when they are on holiday. Many of them might choose a location where they can get a fast and reliable connection while abroad. An emerging Internet provider has decided to propose an innovative solution to hotels and communities all over South Tyrol by offering tourists and inhabitants free Internet at never-before-seen speed. Würth Phoenix was asked to help answer the question of whether this service can deliver what it promises.
Würth Phoenix used the Alyvix visual synthetic monitoring approach to address the question: Does a synthetic real user interact with a browser just as a human would do?  We measured and reported the performance of application transactions,  giving an overview of service availability and responsiveness. We placed Alyvix devices at 84 strategic locations, where the expected coverage by the Internet provider is well known.
This data concerning Internet availability at the 84 strategic locations opens an avenue to quite a few investigations.  Just to mention a few: Where should I go on holiday if Internet connectivity matters a lot to me? Is the provider right in claiming always-available free Internet at speeds never seen before? Where should the provider strengthen its presence to reach maximum impact in terms of satisfied users?
Just a tiny little hint: E.g. on http://qlikview.services.siag.it/QvAJAXZfc/opendoc.htm?document=Daticomunali.qvw&host=QVS%40titan-a&anonymous=true, you can find facts and numbers about inhabitants and tourists. But you are in no way bound to use (only) this source.

Alyvix is an Open Source Visual Synthetic Monitoring Tool that can help study service quality and/or availability. User Experience is measured actively as an agent tries to perform certain predefined transactions, one after the other, just as a human user would do.  “Test performance” is then measured via the duration of each step. To make this clearer, imagine a small robot executing a test case that is trying for example to:

  • access Google.com
  • search for the key word “dolomites”
  • try to access Instagram
  • search for a certain profile
  • try to access Facebook
  • post a dummy post with its profile
  • cancel the dummy post

the duration and the state of each transaction/step is then written to a data base.
Possible test states are:

  • OK: the transaction was performed successfully
  • warning: the transaction was performed successfully, but it took longer than a certain warning threshold
  • critical: the transaction was performed successfully, but it took longer than a certain critical threshold
  • timed out: the transaction started, but it took longer than expected and went into time out
  • not executed: the transaction was not executed (e.g., due to an error occurring earlier in the test case)

The challenge dataset:

Data from two days at 84 stations located all over South Tyrol

Data Structure:

  • timestamp: a timestamp in YYYY-MM-DD hh:mm:ss.######+00:00 format indicating the start time of the test case
  • name: the name of the station
  • radius: the radius in kilometers around the position specified by the geohash code that can be expected to experience what the synthetic user is experiencing. E.g. 4.5 means that people (tourists, inhabitants) within a circle with a radius of 4.5 km around the location indicated by the geohash code can be expected to have a satisfying user experience when the test case finishes in ‘ok’ state.
  • duration: the cumulative duration of the test case (you will not be able to see which step took longer or which step made the test case break)
  • state: the state of the last transaction of the test case execution
  • geohash: the location of the station

Example:

timestamp,name,radius,duration,state,geohash
2018-01-27 01:25:13.972999+00:00,Gsies Pfinnalm,4.08674918307,90656,not_executed,u22dmz6b8mc8
2018-01-27 00:13:12.916000+00:00,Gsies Pfinnalm,4.11743484082,60999,ok,u22dmz6b8mc8

Complete Dataset:

csv file – delimiter: ” – separator: ,

hacktheapls_data

Price:

A skiing weekend (2 nights with breakfast) in the heart of the Dolomites and dinner with our R&D Team.

Susanne Greiner

Susanne Greiner

Hi there! My name is Susanne and I joined Würth-Phoenix early in 2015. Ever since I can remember computers and the perfection that can be reached by them have been very fascinating for me. I built my first personal PC using components from about 20 broken ones at the age of 11 and fell in love with open source, visualization and data analysis shortly afterwards. I hold a master in experimental physics (University of Erlangen, Germany) and a PhD in computer science (Universtiy of Trento, Italy) my main interests are machine learning, visualization techniques, statistics and optimization. As long as an algorithm of mine runs at night and I get new interesting results the morning after I am able to sleep well. Beside computers I also like music, inline skating, and skiing.

Author

Susanne Greiner

Hi there! My name is Susanne and I joined Würth-Phoenix early in 2015. Ever since I can remember computers and the perfection that can be reached by them have been very fascinating for me. I built my first personal PC using components from about 20 broken ones at the age of 11 and fell in love with open source, visualization and data analysis shortly afterwards. I hold a master in experimental physics (University of Erlangen, Germany) and a PhD in computer science (Universtiy of Trento, Italy) my main interests are machine learning, visualization techniques, statistics and optimization. As long as an algorithm of mine runs at night and I get new interesting results the morning after I am able to sleep well. Beside computers I also like music, inline skating, and skiing.

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