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.
— Seung-Hwan > On May 4, 2018, at 6:40 AM, Riccardo Tommasini <[email protected]> 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) > streamreasoning.org > > Submitted from an iPhone, I apologise for typos. > > On 4 May 2018, 00:58 +0200, Edmon Begoli <[email protected]>, 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 >> analysis. >> >> Thank you, >> Edmon >> >> On Thu, May 3, 2018 at 6:54 PM, Michael Mior <[email protected]> wrote: >> >>> Hi all, >>> >>> I recently had a chat with the VerdictDB (http://verdictdb.org/) team >>> about >>> 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 >>> know! >>> >>> -- >>> Michael Mior >>> [email protected] >>>
