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Nearly years ago, Hellerstein, Haas and Wang proposed online aggregation (OLA), a technique that allows users to ( ) observe the progress of a query by showing iteratively re ned approximate answers, and ( ) stop the query execution once its result achieves the desired accuracy. In this demonstration, we present G-OLA, a novel mini-batch execution model that generalizes OLA to support general OLAP queries with arbitrarily nested aggregates using e cient delta maintenance techniques. We havedoi:10.1145/2723372.2735381 dblp:conf/sigmod/ZengADAS15 fatcat:a66fy635njbhzopnu6umkodlwu