When we need to solve an optimization problem we usually use the best available algorithm/software or try to improve it. In recent years we have started exploring a different approach: instead of improving the algorithm, reduce the input data and run the existing algorithm on the reduced data to obtain the desired output much faster on a streaming input, using a manageable amount of memory, and in parallel (say, using Hadoop, cloud service, or GPUs).
A core-set for a given problem is a semantic compression of its input, in the sense that a solution for the problem with the (small) core-set as input yields an approximate solution to the problem with the original (Big) data. In this talk I will describe the core-set approach and recent algorithmic achievements for computing core-sets with performance guarantees. I will also describe applications of this magical new paradigm in Machine Learning, Robotics, Computer Vision, and Privacy. Finally, I will describe in detail iDiary: a system that turns large sensor signals collected from smart-phones into textual descriptions of the trajectories. The system features a user interface similar to Google Search that allows users to type text queries on their activities (e.g., Where did I buy books?) and receive textual answers based on their GPS signals.