Some thoughts and web articles about tech'revolution
samedi 22 février 2014
Microsoft reportedly dropping licensing fees for Windows 8.1 amid competition from Apple and Google
Google and Apple definitely shaked the PC market.
mercredi 19 février 2014
Data quality @home
I recently began to go through my own photo library. To give you some ideas, it contains 16,500 photos from year 2000 to today. I had several ways to sort it, but one that made sense to me was to:
In other words, for each photo being considered as a "data", I defined several properties (or "meta-data") to index and retrieve it afterward. And then arise some echoes with my current work around data quality.
I propose to have a look on each of these properties through a data quality categorization.
Completeness
I will consider a picture as totally indexed if:
A first level of completion is obtained with the three first assertions. Let’s call it C1. If I add the fourth assertion, I obtain a full completion that I call C2.
Accuracy
Dating is provided as an automatic feature when you shoot with a digital camera. Unfortunately, it appears that sometimes camera settings are not properly set (in particular when you change batteries or considering jet lag). You may then enter into a real nightmare... Believe me. So the typical question is: what makes sense to me in matter of dating accuracy? In order to retrieve photos back, I would say that an accuracy of days or so should be sufficient. Hours may have a sense also, but more to have a sorted display of photos on a given day.
Keywords have to be accurate enough to help me to differentiate photos. If I put something like "family", the risk is to have too many photos responding to this criterion and not being able to find a specific one. At the other extremity of the spectrum, providing too precise criteria would be useless, as I would not think about it when searching for particular photos. Providing that, I opt for a finite set of keywords on which I provide my own semantic. As an example: "vacations" will be used for any holiday's period of more than 2-3 days or so. "Travel" will be added when I consider a travel abroad from France. This is clearly my own semantic and provides only sense to myself.
Geo-tagging on its side is a huge matter. I recently had a trip to Japan. I may geo-tag all my photos to Japan, but would it be sufficient? In particular, it won't help me to retrieve pictures from (and only from) Tokyo. So I would choose an accuracy of city. The only problem with this choice is that in the particular case of Paris, city where I live, it would clearly be not enough as I have so many pictures from Paris. I did not find the perfect level of accuracy for geo-tagging so far, or more exactly, I do not know how to rationalize it. Considering that I use geo-tagging to retrieve pictures afterwards, I just use my own memory to "geo-tag" it and it should be enough. The only limit on that point is that my wife as a pretty better memory than I have, and by this way is far more accurate in its pictures searches than I am... The good point for the future is that more and more camera provides now an automatic geo-tagging feature. This is already the case for most of smartphones and they are becoming a common source of pictures. So the technology will certainly help in the future my poor human memory.
People represented on photo also needs some decisions regarding accuracy criteria. Do I want to search any people I encountered once or twice that appear in only a couple of pictures (yeah... I am sure you have such a case, just have a look on the pictures of the last weeding you attend)? I certainly wouldn’t. So I decided to restrain my tags to people of my first circle of family and friends. And it represents already a big piece of work.
Uniqueness
It could seem a bit weird to address such a topic in this context, but if you consider all the operations (copy-paste, editions and so on…) that you may do on a daily basis on your pictures, you inevitably will encounter such issues. Having used an automatic tool to detect these multiples, I had found about 150 of them and even after removing it; I still found some rare evidence of pictures being present several times (not considering pictures being present in colours as well as B&W or with and without frames around it).
Rating
After a first manual data cleansing operation I obtain estimations of the data quality rate of my photo library.
Considering completeness, I have so far a rating of 85% of pictures considered as C1-complete. Adding the fourth one would of course drastically decrease this rate, considering that 11,300 people are detected in my pictures library without having been properly identified (9,500 for the C2-completed pictures).
For accuracy, I am still searching for a proper way to measure it, either on keywords or geo-tag. Next step will be to execute some data profiling to help defining some rules to measure.
Last, but not least, regarding uniqueness I now consider that the cleansing has been done, reaching a rate of 99,99% or so.
Considering the whole picture the Pareto's law, it seems that a long way is still in front of me. But this is quite typical for a data quality topic, isn’t?
- Ensure proper dating of photos
- Associate keywords to them
- Localize (or “geo-tag”) them
- Identify people represented in it
In other words, for each photo being considered as a "data", I defined several properties (or "meta-data") to index and retrieve it afterward. And then arise some echoes with my current work around data quality.
I propose to have a look on each of these properties through a data quality categorization.
Completeness
I will consider a picture as totally indexed if:
- It has a proper dating (so potentially redressed if needed)
- It has at least one keyword associated
- It is geo-tagged
- All the people represented in it and belonging to my first family and friend circle are identified
A first level of completion is obtained with the three first assertions. Let’s call it C1. If I add the fourth assertion, I obtain a full completion that I call C2.
Accuracy
Dating is provided as an automatic feature when you shoot with a digital camera. Unfortunately, it appears that sometimes camera settings are not properly set (in particular when you change batteries or considering jet lag). You may then enter into a real nightmare... Believe me. So the typical question is: what makes sense to me in matter of dating accuracy? In order to retrieve photos back, I would say that an accuracy of days or so should be sufficient. Hours may have a sense also, but more to have a sorted display of photos on a given day.
Keywords have to be accurate enough to help me to differentiate photos. If I put something like "family", the risk is to have too many photos responding to this criterion and not being able to find a specific one. At the other extremity of the spectrum, providing too precise criteria would be useless, as I would not think about it when searching for particular photos. Providing that, I opt for a finite set of keywords on which I provide my own semantic. As an example: "vacations" will be used for any holiday's period of more than 2-3 days or so. "Travel" will be added when I consider a travel abroad from France. This is clearly my own semantic and provides only sense to myself.
Geo-tagging on its side is a huge matter. I recently had a trip to Japan. I may geo-tag all my photos to Japan, but would it be sufficient? In particular, it won't help me to retrieve pictures from (and only from) Tokyo. So I would choose an accuracy of city. The only problem with this choice is that in the particular case of Paris, city where I live, it would clearly be not enough as I have so many pictures from Paris. I did not find the perfect level of accuracy for geo-tagging so far, or more exactly, I do not know how to rationalize it. Considering that I use geo-tagging to retrieve pictures afterwards, I just use my own memory to "geo-tag" it and it should be enough. The only limit on that point is that my wife as a pretty better memory than I have, and by this way is far more accurate in its pictures searches than I am... The good point for the future is that more and more camera provides now an automatic geo-tagging feature. This is already the case for most of smartphones and they are becoming a common source of pictures. So the technology will certainly help in the future my poor human memory.
People represented on photo also needs some decisions regarding accuracy criteria. Do I want to search any people I encountered once or twice that appear in only a couple of pictures (yeah... I am sure you have such a case, just have a look on the pictures of the last weeding you attend)? I certainly wouldn’t. So I decided to restrain my tags to people of my first circle of family and friends. And it represents already a big piece of work.
Uniqueness
It could seem a bit weird to address such a topic in this context, but if you consider all the operations (copy-paste, editions and so on…) that you may do on a daily basis on your pictures, you inevitably will encounter such issues. Having used an automatic tool to detect these multiples, I had found about 150 of them and even after removing it; I still found some rare evidence of pictures being present several times (not considering pictures being present in colours as well as B&W or with and without frames around it).
Rating
After a first manual data cleansing operation I obtain estimations of the data quality rate of my photo library.
Considering completeness, I have so far a rating of 85% of pictures considered as C1-complete. Adding the fourth one would of course drastically decrease this rate, considering that 11,300 people are detected in my pictures library without having been properly identified (9,500 for the C2-completed pictures).
For accuracy, I am still searching for a proper way to measure it, either on keywords or geo-tag. Next step will be to execute some data profiling to help defining some rules to measure.
Last, but not least, regarding uniqueness I now consider that the cleansing has been done, reaching a rate of 99,99% or so.
Considering the whole picture the Pareto's law, it seems that a long way is still in front of me. But this is quite typical for a data quality topic, isn’t?
lundi 17 février 2014
samedi 25 janvier 2014
Put the price apart, when it comes to quality, mac becomes the number 1 choice
I recently had a discussion with a guy from an IT company who has a BYOD policy. In few words, they give their employees 1200 € (about 1600 bucks) to buy a personal computer with a 3-year 24/24 warranty and use it at work. After one year, the PC is their own.
What appears is that 80% of people buy a Mac. To be honest, I could not imagine this. But this is the fact. If you have enough money and only care about quality, Mac is really a bargain.
Apple should think about it. They have the quality but their prices is clearly high. This is a market position that I may understand, but they have a clear advantage in terms of image to people (even IT ones, which - as an IT guy - I didn't believe). What about a low-end "MacBook c" for young people, students and so on (and why not, with delightful candy colors that had success in the past ?
What appears is that 80% of people buy a Mac. To be honest, I could not imagine this. But this is the fact. If you have enough money and only care about quality, Mac is really a bargain.
Apple should think about it. They have the quality but their prices is clearly high. This is a market position that I may understand, but they have a clear advantage in terms of image to people (even IT ones, which - as an IT guy - I didn't believe). What about a low-end "MacBook c" for young people, students and so on (and why not, with delightful candy colors that had success in the past ?
dimanche 5 janvier 2014
mercredi 1 janvier 2014
"Le futur de la photo : Canon, Nikon et Sony ?"
Les mutations numériques touchent de plein fouet de nombreux secteur. La photographie, la vidéo, le livre, la musique.
Tout est désormais disponible dans une seule main sous la forme d'un smartphone.
Tout est désormais disponible dans une seule main sous la forme d'un smartphone.
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