Lukas Vlcek wrote:
Hi Enis,
Hi again,
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).
Well i think you can at least store the size of the index in another
file, and log to the changes in the index size from there. The
motivation for this comes from storage efficiency. You may not want to
store the same index size over and over again in n queries before the
index size changes, but store it once, with the time, per change.
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.
You do not have to implement this from scratch. You just have to specify
your data mining tasks, then write scripts(in pig latin) or write
map-reduce programs (in hadoop). Either of these are not that hard. I do
not think that there is any tool which may satisfy all you information
needs. So at the risk of repeating myself i suggest you to look at pig
at write some scripts to mine the data.
Coming to literature, i can hardly suggest any specific paper, since i
am not very into the subject either. But i suggest you to skip this
step, first build you data structures (log format), then start
extracting some naive statistical information from the data. For
example, initially you may want to know
1. avarage query execution time
2. avarage query execution time per query type(boolean, fuzzy, etc.)
3. histogram of query types (how many boolean queries, etc.)
4. avarage #of queries per user session.
5. etc.
The list can go on and on depending on the data you have and information
you want. These kind of simple statistical analysis can be very easy to
extract and relatively easy to interpret.
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
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Anyway, thanks for your reply!
BR
Lukas
