Hi Enis, On 8/10/07, Enis Soztutar <[EMAIL PROTECTED]> wrote: > > Hi, > > Lukas Vlcek wrote: > > Hi, > > > > I would like to keep user search history data and I am looking for some > > ideas/advices/recommendations. In general I would like to talk about > methods > > of storing such data, its structure and how to turn it into valuable > > information. > > > > As for the structure: > > ============== > > For now I don't have exact idea about what kind of information I should > > keep. I know that this is application specific but I believe there can > be > > some common general patterns. as of now I think can be useful to keep is > the > > following: > > > > 1) system time (time of issuing the query) and userid > > 2) original user query in raw form (untokenized) > > 3) expanded user query (both tokenized and untokenized can be useful) > > 4) query execution time > > 5) # of objects retrieved from index > > 6) # of total object count in index (this can change during time) > > 7) and possibly if user clicked some result and if so then which one > (the > > hit number) and system time > > > > > Remember that you may not want to store all the information available at > runtime of the query, since it may result in great performance burden. > For example you may want to store the raw form of the query, but not > parsed form since you can later parse the query anayway (unless you have > some architecture change). Similarly 6 seemed not a good choice for > me(again you can store the info externally). You can look at the common > and extended log formats which are stored by the web server.
The problem is that all the information do chance in time. The index is updated continuously which means that expanded queries and total number of documents in index do change as well. But you are right that getting some of this info can cause extra performance expenses (then it would be question of later optimization of architecture design). > > As for the information I can get from this: > > ============================= > > Such minimal data collection could show if the search engine serves > users > > well or not (generally said). I should note that for the users in this > case > > the only other option is to not use the search engine at all (so the > data > > should not be biased by the fact that users are using alternative search > > method). I should be able to learn if: > > > > 1) there are bottleneck queries (Prefix,Fuzzy,Proximity queries...) > > 2) users are finding what they want (they can find it fast and results > are > > ordered by properly defined relevance [my model is well tuned in terms > of > > term weights] so the result they click is among first hits) > > 3) user can formulate queries well (do they issue queries which return > all > > index documents or they can issue queries which return just a couple of > > documents) > > 4) ...?... etc... > > > > > Web server log analysis is a very popular topic nowadays, and you can > check for the literature, especially clickthrough data anaysis. All the > major search engines has to interpret the data to improve their > algorithms, and to learn from the latent "collective knowlege" hidden in > web server logs. It seems I have to do my homework and check CiteSeer for some papers :-) Is there any paper you can recommend me? Some good one to start with? What I want to achieve is far beyond the scope of the project I am working on right now thus I cannot spend all my time on research (in spite of the fact I would love to) so I can either a) use some tool which is already available (open sourced) and directly fits my needs (I don't think there is any tool which I could use out-of-box) or b) implement something new from scratch but with just very limited functionality. > As for the storage method: > > =================== > > I was planning to keep such data in database but now it seems to me that > it > > will be better to keep it directly in index (Lucene index). It seems to > me > > that this approach would allow me for better fuzzy searches across > history > > and extracting relevant objects and their count more efficiently (with > > benefit of the relevance based search on top of history search corpus). > > > > I think that more scalable solution would be to keep such data in pure > flat > > file and then periodically recreate search history index (or more > indices) > > from it (for example by Map-Reduce like task). Event better the flat > file > > could be stored in distributer file system. However, for now I would > like to > > start with something simple. > > > I would rather suggest you to keep the logs in rolling flat files. An > access to the database for each search will take lots of time. Then you > may want to flush those logs to the db once a day if you indeed want to > store the data in a relational way. > > I infer that you want to mine the data, but you do not know what to > mine, right? I suggest you to look at hadoop and pig. Pig is a is > designed especially for this purpose. You've hit the nail on the head! I am very curious about how one can use such data to improve user experience with search engine (given my project schedule time constraints). > I know this is a complex topic... > > > > Regards, > > Lukas > > > > > > --------------------------------------------------------------------- > To unsubscribe, e-mail: [EMAIL PROTECTED] > For additional commands, e-mail: [EMAIL PROTECTED] > > Anyway, thanks for your reply! BR Lukas
