Big data processing can be expensive and slow, a problem made worse when your data set keeps changing, forcing you to reanalyze it repeatedly. Incremental computation can speed things up by minimizing the work that must be done to update output in response to changing input, but many previous efforts at incremental computation have been limited in the algorithms they can express or require a lot of effort on the part of the application programmer to implement. ThomasDB seeks to solve these problems through self-adjusting computation, an incremental technique capable of expressing a wide range of computations efficiently, and by providing a set of higher order list operations that make expressing incremental algorithms simple.