Not the OP but personally I would recommend looking into constraint programming which is related but different to logic programming (Prolog has extensions for Constraint Logic Programming which combines the two).
You mentioned you're looking for something new that doesn't have to be related to data analytics, well constraint programming (and similar) is basically the mirror problem. Instead of data you have the rules of the "game" and the solver's job can be to: find a solution, find the optimal solution, find all solutions, or prove there is no solution.
Things like scheduling problems, resource allocation problems, etc. A real-world example would be finding the most efficient cell placement when developing a microchip, this is basically an advanced rectangle packing puzzle.
Much like prolog you define the rules (constraints) and the solver takes over from there. Part of the fun is figuring out the most effective way to model a real-world problem in this manner.
The closest thing to Prolog in this domain would be ASP, with Clingo/Clasp being the best solver available. But you also have regular constraint programming (look into MiniZinc or Google's OR-Tools), mixed-integer programming which is mainly for hardcore optimization problems (commercial solvers sell for tens of thousands of dollars), satisfiability modulo theories (often used for software verification and mathematical proofs), etc.
The mind-blowing bit is that this sort of problem is NP-complete but these solvers can find solutions to utterly massive problems (millions of variables and constraints) in milliseconds sometimes.
You mentioned you're looking for something new that doesn't have to be related to data analytics, well constraint programming (and similar) is basically the mirror problem. Instead of data you have the rules of the "game" and the solver's job can be to: find a solution, find the optimal solution, find all solutions, or prove there is no solution.
Things like scheduling problems, resource allocation problems, etc. A real-world example would be finding the most efficient cell placement when developing a microchip, this is basically an advanced rectangle packing puzzle.
Much like prolog you define the rules (constraints) and the solver takes over from there. Part of the fun is figuring out the most effective way to model a real-world problem in this manner.
The closest thing to Prolog in this domain would be ASP, with Clingo/Clasp being the best solver available. But you also have regular constraint programming (look into MiniZinc or Google's OR-Tools), mixed-integer programming which is mainly for hardcore optimization problems (commercial solvers sell for tens of thousands of dollars), satisfiability modulo theories (often used for software verification and mathematical proofs), etc.
The mind-blowing bit is that this sort of problem is NP-complete but these solvers can find solutions to utterly massive problems (millions of variables and constraints) in milliseconds sometimes.