Approaching Big Data! :-) Interesting....!
Q: Is each post-binning row a bin, meaning there are only ~86 rows per bin?
(Am I interpreting that correctly?)
If so, that's good news; materializing any one given bin shouldn't be a
problem for our runtime, so maybe we can indeed get this to work in the
short term.
Sorry for the hassles w/this.....!
Cheers,
Mike
On 2/22/16 7:09 AM, Yiran Wang wrote:
Mike,
The original dataset has 31132597 rows of records. After binning it
into 1-min time bin dataset, it has 363466 rows of records.
Thanks,
Yiran
On Sun, Feb 21, 2016 at 2:55 PM, Mike Carey <[email protected]
<mailto:[email protected]>> wrote:
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
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Best,
Yiran
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