ARGH!!!! This is what we would like you to *not* have to do. Sorry...
Our aim is to be the Big Data antidote....
@Yingyi: Maybe you could take a quick peek at the query issue and
see if there is any low-hanging hope there?
@Yiran: How big are your windows, typically? (Number of data points.)
Cheers,
Mike
On 2/21/16 2:42 PM, Yiran Wang wrote:
Thank you Mike for your update and suggestions! And thank you Ian
again for working with me.
A little update from my end:
I have been working on query (1) over the weekend for a work-around. I
tried to simultaneously calculate the stdev on the new copy of the
list of values with outliers removed, which in nature is the same as
the query (2). So I ran into the same problem that the query did not
compile.
What I did was to export the entire dataset with the outliers removed
into Excel and calculate the stdev in Excel. However, the entire
dataset is now 363,466 x 200+ in dimension. Though they do not exceed
the row x col limit in Excel, the memory of my computer is not big
enough to do anything useful without crashing. So I've been breaking
the dataset into smaller parts and working on each separately.
Yiran
On Sun, Feb 21, 2016 at 2:10 PM, Mike Carey <[email protected]
<mailto:[email protected]>> wrote:
Ian,
Thanks working with Yiran on this! I think there is "good" and
bad news w.r.t these queries:
- The bad news is that they go beyond what we are likely to
optimize at all well at present,
as they go beyond what typical DB aggregate functions like
min/max/avg/count/sum do.
(I would try forming the groups and then doing these things on
the groups, but saying
them in AQL will be tricky, and may lead to queries that hit
edge cases in the optimizer.
For some of these my thought was to try using a positional
variable within a group...?)
- The "good" news (only for AsterixDB) is that this is exactly
the sort of inspiration that we
are looking for in terms of understanding how to better for
query-based analytics in real
use cases (and this is a very real one!).
To quote a short paper I reviewed just this AM on SQL queries kind
of like these: "Percentage
queries are more complex than their conventional counterparts and
introduce new challenges
for optimization." (The paper didn't have an applicable solution
for us, sadly.)
A more general facility that I wish we could offer was to do
grouping in AsterixDB but then
have the ability to pass a group to (e.g.) R and then get results
back for the group. When
groups are small-ish (like Yiran's windows) that would be pretty
cool - then one could do
all sorts of advanced things per group.
Cheers,
Mike
On 2/21/16 12:35 AM, Ian Maxon wrote:
Yiran and I came up with possible answers for these...
For 1) , a function could be used that looks something like this:
declare function minmax($x){
let $stdv := (avg(for $z in $x return $z*$z) - avg($x) * avg($x))^(0.5)
for $y in $x
where $y < (2*$stdv) + avg($x)
and $y > avg($x) - (2*$stdv)
return $y
}
And then applied to return a new copy of the list of values, removing ones
that are outside of 2 stdev.
For 2), we also did come up with a potential solution ,but the query fails
to compile (Filed ashttps://issues.apache.org/jira/browse/ASTERIXDB-1308 )
Any thoughts on these queries would be welcome :) 1) especially seems
inefficient to do as a function.
- Ian
On Fri, Feb 19, 2016 at 3:37 PM, Yiran Wang<[email protected]>
<mailto:[email protected]> wrote:
Hi Asterix team,
I have two queries I'm struggling with. I'm hoping you could provide a
direction for me. Thanks in advance!
Here is what the data structure looks like:
create type HRMType as closed {
row_id: int32,
sid: int32,
date: date,
day: int32,
time: time,
bpm: int32,
RR: float
};
create dataset HRM (HRMType)
primary key row_id;
Previously I have used the time bin function to calculate the standard
deviation of bpm for each time bin:
for $i in dataset HRM
group by $sid := $i.sid, $gdate := $i.date, $gday := $i.day, $timebin :=
interval-bin($i.time, time("00:00:00"), day-time-duration("PT1M")) with $i
return {
"sid": $sid,
"gdate": $gdate,
"gday": $gday,
"timebin": $timebin,
"stdv": (avg(for $ii in $i return $ii.RR * $ii.RR) - avg(for $ii in $i
return $ii.RR) * avg(for $ii in $i return $ii.RR))^(0.5)};
Now I have two things I am hoping to do but need help with:
1. For each 1-min time bin, remove the bpm values that are above the top
5% or below the bottom 5%. I thought about using the min/max function for a
few times to achieve this, but realized that it was not a good idea because
in each time bin, the number of instances are not always the same. So for
each 1-min time bin, we do need to calculate the 5% and 95% threshold, and
remove instances accordingly, which I don't know how to do.
2. After removing the outliers of bpm for each 1-min time bin, calculate a
median absolute deviation (MAD) for each 1-min time bin (as another measure
of variation besides the standard deviation). MAD =
median(abs(x-median(x)). I'm not sure how to write a query to do the median
function in Asterix.
Thank you so much in advance. Let me know if my questions are clear.
Yiran
--
Best,
Yiran
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