Abstract: Democratizing data does not mean dropping a huge spreadsheet on everyone's desk and saying, "good luck". It means making data mining, machine learning and AI methods useable in such a way that people can easily instruct machines to have a "look" at the data and help them to understand and act upon it. A promising approach is the declarative "Model + Solver" paradigm that was and is behind many revolutions in computing in general: instead of outlining how a solution should be computed, we specify what the problem is using some modeling language and solve it using highly optimized solvers. Analyzing data, however, involves more than just the optimization of an objective function subject to constraints. Before optimization can take place, a large effort is needed to not only formulate the model but also to put it in the right form. We must often build models before we know what individuals are in the domain and, therefore, before we know what variables and constraints exist. Hence modeling should facilitate the formulation of abstract, general knowledge. This not only concerns the syntactic form of the model but also needs to take into account the abilities of the solvers; the efficiency with which the problem can be solved is to a large extent determined by the way the model is formalized. In this talk, I shall review our recent efforts on relational linear programming. It can reveal the rich logical structure underlying many AI and data mining problems both at the formulation as well as the optimization level. Ultimately, it will make optimization several times easier and more powerful than current approaches and is a step towards achieving the grand challenge of automated programming as sketched by Jim Gray in his Turing Award Lecture.
Joint work with Martin Mladenov and Pavel Tokmakov and based on previous joint works together with Babak Ahmadi, Amir Globerson, Martin Grohe, Fabian Hadiji, Marion Neumann, Aziz Erkal Selman, and many more.