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 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... 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 know this is a complex topic... Regards, Lukas
