Details
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New Feature
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Status: Closed
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Major
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Resolution: Fixed
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None
Description
Story
As a data scientist, I want to perform session reconstruction on my data set, so that I can prepare for input into other algorithms like path functions, or predictive analytics algorithms.
Details
1) The PDL Tools module sessionization module [1] is one example implementation. Source code is located at [2]. Also see [7].
2) How to sessionize. PDL Tools uses a time based session reconstruction that defines a session as a sequence of events by a particular user where no more than n seconds has elapsed between successive events. That is, if we don’t see an event from a user for n seconds, start a new session. The requirement for MADlib is similar but with the following addition:
- generalize partition expression
3) Proposed interface:
sessionize ( source_table, output_table, partition_expr, time_stamp, max_time)
where
output_table
add 2 new columns to the source_table: session_id and new_session:
- session_id=1,2, ...n where n is the number of sessions in the partition
partition_expr
VARCHAR. The 'partition_expr' can be a single column or a list of comma-separated columns/expressions to divide all rows into groups, or partitions. Matching is applied across the rows that fall into t he same partition. This can be NULL or '' to indicate the matching is to be applied to the whole table.
time_stamp
Column name with time used for sessionize calculation. Cannot be a PostgreSQL ORDER BY expression. This is simply a column name.
max_time
Delta time between subsequent events to define a sessions, i.e., session timeout.
Questions
1) Q: Do we need separate 'order_expr' and 'time_stamp' columns? Aster does it this way.
A: No, we can't come up with a reason why a user would need this. If we want to add later, we can add as an optional parameter.
2) Q: What to do if negative delta_t between events?
A: Do not include in session and output a warning message.
References
[1] PDL Tools sessionization module
http://pivotalsoftware.github.io/PDLTools/group__grp__sessionization.html
[2] PDL tools source code
https://github.com/pivotalsoftware/PDLTools
[3] Blog on bot signatures from Akamai
https://blogs.akamai.com/2013/06/identifying-and-mitigating-unwanted-bot-traffic.html
[4] Aster Analytics users guide, see "sessionize" function
http://www.info.teradata.com/edownload.cfm?itemid=143450001
http://www.info.teradata.com/templates/eSrchResults.cfm?txtpid=&txtrelno=&prodline=all&frmdt=&txtsrchstring=aster%20analytics&srtord=Desc&todt=&rdSort=Date
https://www.youtube.com/watch?v=C760M9ttK9Q
[5] General information on sessionization
https://en.wikipedia.org/wiki/Session_(web_analytics)
[6] See path function for partition and order by params
http://madlib.incubator.apache.org/docs/latest/group__grp__path.html
[7] SQL sessionization example from blog
https://blog.pivotal.io/pivotal/products/time-series-analysis-1-introduction-to-window-functions
[8] Postgres example of SQL based sessionization
http://randyzwitch.com/sessionizing-log-data-sql/
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