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+---
+license: |
+   Licensed to the Apache Software Foundation (ASF) under one or more
+   contributor license agreements.  See the NOTICE file distributed with
+   this work for additional information regarding copyright ownership.
+   The ASF licenses this file to You under the Apache License, Version 2.0
+   (the "License"); you may not use this file except in compliance with
+   the License.  You may obtain a copy of the License at
+   
+           http://www.apache.org/licenses/LICENSE-2.0
+   
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+layout: publication
+title: Publication
+subtitle: >
+   RHEEMix in the Data Jungle: A Cost-based Optimizer for Cross-Platform Systems
+link-name: VLDB Journal 29(6), 2020
+img-thumb: assets/img/screenshot/rheem.png
+authors: Sebastian Kruse, Zoi Kaoudi, Sanjay Chawla, Felix Naumann, Bertty Contreras-Rojas and Jorge-Arnulfo Quiané-Ruiz
+year: 2020
+month: 11
+day: 01
+link-paper: assets/pdf/paper/journal_vldb.pdf
+link-external: false
+---
+
+Data analytics are moving beyond the limits of a single platform. In this paper, we present the cost-based optimizer of Rheem, an open-source cross-platform system that copes with these new requirements. The optimizer allocates the subtasks of data analytic tasks to the most suitable platforms. Our main contributions are: (i) a mechanism based on graph transformations to explore alternative execution strategies; (ii) a novel graph-based approach to determine efficient data movement plans among subtasks and platforms; and (iii) an efficient plan enumeration algorithm, based on a novel enumeration algebra. We extensively evaluate our optimizer under diverse real tasks. We show that our optimizer can perform tasks more than one order of magnitude faster when using multiple platforms than when using a single platform
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