Data filtering in spreadsheets is a common problem faced by millions of end-users. The task of data filtering requires a computational model that can separate intended positive and negative string instances. We present a system, FIDEX, that can efficiently learn desired data filtering expressions from a small set of positive and negative string examples.
There are two key ideas of our approach. First, we design an expressive DSL to represent disjunctive filter expressions needed for several real-world data filtering tasks. Second, we develop an efficient synthesis algorithm for incrementally learning consistent filter expressions in the DSL from very few positive and negative examples. A DAG-based data structure is used to succinctly represent a large number of filter expressions, and two corresponding operators are defined for algorithmically handling positive and negative examples, namely, the intersection and subtraction operators. FIDEX is able to learn data filters for 452 out of 460 real-world data filtering tasks in real time (0.22s), using only 2.2 positive string instances and 2.7 negative string instances on average.
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13:30 - 13:55 Talk | Deriving Divide-and-Conquer Dynamic Programming Algorithms using Solver-Aided Transformations OOPSLA Shachar ItzhakyMIT CSAIL, Rohit SinghMIT, Rezaul ChowdhuryStony Brook University, Kuat YessenovMIT, Yongquan LuMIT, Charles E. LeisersonMIT, Armando Solar-LezamaMIT CSAIL DOI Pre-print Media Attached | ||
13:55 - 14:20 Talk | Speeding Up Machine-Code Synthesis OOPSLA Venkatesh SrinivasanUniversity of Wisconsin - Madison, Tushar SharmaUniversity of Wisconsin - Madison, USA, Thomas RepsUniversity of Wisconsin - Madison and Grammatech Inc. DOI Pre-print Media Attached | ||
14:20 - 14:45 Talk | Automated Reasoning for Web Page Layout OOPSLA DOI Media Attached | ||
14:45 - 15:10 Talk | FIDEX: Filtering Spreadsheet Data using Examples OOPSLA DOI Media Attached |