Re: Relational algebra and signal processing
I would say a similar theory applies. Some things are different when you're
dealing with streams. Mainly joins and aggregations. Semantics are
necessarily different whenever you have operations involving more than one
row at a time from the input stream. When dealing with a relation an
aggregation is straightforward since you just consume the entire input, and
output the result of the aggregation. Since streams don't end, you need to
decide how this is handled which usually amounts to a choice of windowing
algorithm. There are a few other things to think about. The presentation
linked below from Julian Hyde has a nice overview
Le mar. 18 déc. 2018 à 02:28, Julian Feinauer <j.feinauer@xxxxxxxxxxxxxxxxx>
a écrit :
> Hi Michael,
> yes, our workloads are usually in the context of streaming (but for replay
> or so we also use batch).
> But, if I understand it correctly, the same theory applies to both, tables
> ("relations") and streaming tables, or?
> I hope to find time soon to write a PLC4X - Calicte source which creates
> one or many streams based on readings from a plc.
> Am 18.12.18, 03:19 schrieb "Michael Mior" <mmior@xxxxxxxxxx>:
> Perhaps you've thought of this already, but it sounds like streaming
> relational algebra could be a good fit here.
> Michael Mior
> Le dim. 16 déc. 2018 à 18:39, Julian Feinauer <
> a écrit :
> > Hi Calcite-devs,
> > I just had a very interesting mail exchange with Julian (Hyde) on the
> > incubator list . It was about our project CRUNCH (which is mostly
> > time series analyses and signal processing) and its relation to
> > algebra and I wanted to bring the discussion to this list to
> continue here.
> > We already had some discussion about how time series would work in
> >  and it’s closely related to MATCH_RECOGNIZE.
> > But, I have a more general question in mind, to ask the experts here
> > the list.
> > I ask myself if we can see the signal processing and analysis tasks
> > proper application of relational algebra.
> > Disclaimer, I’m mathematician, so I know the formals of (relational)
> > algebra pretty well but I’m lacking a lot of experience and
> knowledge in
> > the database theory. Most of my knowledge there comes from Calcites
> > code and the book from Garcia-Molina and Ullman).
> > So if we take, for example, a stream of signals from a sensor, then
> we can
> > of course do filtering or smoothing on it and this can be seen as a
> > between the input relation and the output relation. But as we
> usually need
> > more than just one tuple at a time we lose many of the advantages of
> > relational theory. And then, if we analyze the signal, we can again
> > it as a mapping between relations, but the input relation is a “time
> > series” and the output relation consists of “events”, so these are
> in some
> > way different dimensions. In this situation it becomes mostly
> obvious where
> > the main differences between time series and relational algebra are.
> > of something simple, an event should be registered, whenever the
> > switches from FALSE to TRUE (so not for every TRUE). This could also
> > modelled with MATCH_RECOGNIZE pretty easily. But, for me it feels
> > “unnatural” because we cannot use any indices (we don’t care about
> > ratio of TRUE and FALSE in the DB, except for probably some very
> > outer bounds). And we are lacking the “right” information for the
> > like estimations on the number of analysis results.
> > It gets even more complicated when moving to continuous valued
> > (INT, DOUBLE, …), e.g., temperature readings or something.
> > If we want to analyze the number of times where we have a temperature
> > change of more than 5 degrees in under 4 hours, this should also be
> > with MATCH_RECOGNIZE but again, there is no index to help us and we
> have no
> > information for the optimizer, so it feels very “black box” for the
> > relational algebra.
> > I’m not sure if you get my point, but for me, the elegance of
> > algebra was always this optimization stuff, which comes from
> > and ends in an “optimal” physical plan. And I do not see how we can
> > much of this for the examples given above.
> > Perhaps, one solution would be to do the same as for spatial queries
> > the JSON / JSONB support in postgres, ) to add specialized
> > statistics and optimizer rules. Then, this would make it more
> > algebra”-esque in the sense that there really is a possibility to
> > transformations to a given query.
> > What do you think? Do I see things to complicated or am I missing
> > something?
> > Julian
> > 
> > 
> >  https://www.postgresql.org/docs/9.4/datatype-json.html