Re: Approximate query processing in Calcite
Looking at query examples in VerdictDB, most of TPC-DS queries can earn a big benefit. They are mostly top-k queries over aggregated results. And the availability of various sampling techniques make the VerdictDB attractive to machine learning/graph analysis cases.
> On May 4, 2018, at 6:40 AM, Riccardo Tommasini <tomma156@xxxxxxxxx> wrote:
> I also find it quite interesting! Iot and social media can be relevant domains of application
> Riccardo Tommasini
> Master Degree Computer Science
> PhD Student at Politecnico di Milano (Italy)
> Submitted from an iPhone, I apologise for typos.
> On 4 May 2018, 00:58 +0200, Edmon Begoli <ebegoli@xxxxxxxxx>, wrote:
>> I am excited that you are considering taking Calcite in this direction.
>> Approximate querying and probabilistic databases are of great interest to
>> me, and I might be able to provide some applied research scenarios.
>> One domain that comes to mind where we had some use cases is a sensor data
>> Thank you,
>> On Thu, May 3, 2018 at 6:54 PM, Michael Mior <mmior@xxxxxxxxxxxx> wrote:
>>> Hi all,
>>> I recently had a chat with the VerdictDB (http://verdictdb.org/) team
>>> possible integration with Calcite. VerdictDB sits between an application
>>> and a database to enable the approximation of query results which are
>>> expected to be highly accurate while consuming significantly fewer
>>> resources on the backend.
>>> I'm curious to talk to anyone who might have a use case for this.
>>> Particularly those using Calcite to power analytics systems that can
>>> tolerate approximate results. We'll likely be looking at putting together a
>>> proof of concept in the next few weeks if there's any interest. Let me
>>> Michael Mior