Data science has quickly emerged as a global paradigm to extract value out of data virtually in all aspects of our lives. Queriosity is a new system that addresses two of the fundamental problems in this area, making data science more intuitive and more interactive.
ueriosity makes data science more interactive and intuitive
The first problem is response time; given the complexity of the computations involved and the growing amounts of data in typical data science pipelines, performance quickly becomes a major bottleneck. Queriosity accelerates data science pipelines by smartly synthesizing results out of basic primitives as opposed to recomputing from scratch every time over raw data. This accelerates both current data science and ML algorithms in addition to future algorithms that although will be different are expected to rely on the very same primitives.
The second key aspect in Queriosity is that it accelerates data science pipelines by providing hints on interesting data areas and patterns to turn the attention of data scientists to promising data areas. This accelerates the process of discovery as typically human understanding and decisions are the major bottlenecks.
Overall, Queriosity accelerates data science, making it more interactive and more intuitive. Queriosity is currently being built in C++ and it also includes a virtual reality front-end. The first critical component is described in our SIGMOD 2017 paper called Data Canopy; it allows to synthesize statistics out of basic primitives as opposed to recomputing from scratch with every request, bringing a speed up of several orders of magnitude to any task that involves statistical computations. Stay tuned for more!
ueriosity guides you to what is interesting in your data set