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Copy file name to clipboardExpand all lines: README.md
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## 12. Publications
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-[Scalable Online Interval Join on Modern Multicore Processors in OpenMLDB](docs/paper/scale_oij_icde2023.pdf). Hao Zhang, Xianzhi Zeng, Shuhao Zhang, Xinyi Liu, Mian Lu, and Zhao Zheng. In 2023 IEEE 39rd International Conference on Data Engineering (ICDE) 2023. [[code]](https://github.com/4paradigm/OpenMLDB/tree/stream)
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-[FEBench: A Benchmark for Real-Time Relational Data Feature Extraction](https://github.com/decis-bench/febench/blob/main/report/febench.pdf). Xuanhe Zhou, Cheng Chen, Kunyi Li, Bingsheng He, Mian Lu, Qiaosheng Liu, Wei Huang, Guoliang Li, Zhao Zheng, Yuqiang Chen. In submission. [[code]](https://github.com/decis-bench/febench).
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-[FEBench: A Benchmark for Real-Time Relational Data Feature Extraction](https://github.com/decis-bench/febench/blob/main/report/febench.pdf). Xuanhe Zhou, Cheng Chen, Kunyi Li, Bingsheng He, Mian Lu, Qiaosheng Liu, Wei Huang, Guoliang Li, Zhao Zheng, Yuqiang Chen. International Conference on Very Large Data Bases (VLDB) 2023. [[code]](https://github.com/decis-bench/febench).
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-[A System for Time Series Feature Extraction in Federated Learning](https://dl.acm.org/doi/pdf/10.1145/3511808.3557176). Siqi Wang, Jiashu Li, Mian Lu, Zhao Zheng, Yuqiang Chen, and Bingsheng He. 2022. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (CIKM) 2022. [[code]](https://github.com/4paradigm/tsfe).
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-[Optimizing in-memory database engine for AI-powered on-line decision augmentation using persistent memory](http://vldb.org/pvldb/vol14/p799-chen.pdf). Cheng Chen, Jun Yang, Mian Lu, Taize Wang, Zhao Zheng, Yuqiang Chen, Wenyuan Dai, Bingsheng He, Weng-Fai Wong, Guoan Wu, Yuping Zhao, and Andy Rudoff. International Conference on Very Large Data Bases (VLDB) 2021.
-[Scalable Online Interval Join on Modern Multicore Processors in OpenMLDB](docs/paper/scale_oij_icde2023.pdf). Hao Zhang, Xianzhi Zeng, Shuhao Zhang, Xinyi Liu, Mian Lu, and Zhao Zheng. In 2023 IEEE 39rd International Conference on Data Engineering (ICDE) 2023. [[code]](https://github.com/4paradigm/OpenMLDB/tree/stream)
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-[FEBench: A Benchmark for Real-Time Relational Data Feature Extraction](https://github.com/decis-bench/febench/blob/main/report/febench.pdf). Xuanhe Zhou, Cheng Chen, Kunyi Li, Bingsheng He, Mian Lu, Qiaosheng Liu, Wei Huang, Guoliang Li, Zhao Zheng, Yuqiang Chen. In submission. [[code]](https://github.com/decis-bench/febench).
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-[FEBench: A Benchmark for Real-Time Relational Data Feature Extraction](https://github.com/decis-bench/febench/blob/main/report/febench.pdf). Xuanhe Zhou, Cheng Chen, Kunyi Li, Bingsheng He, Mian Lu, Qiaosheng Liu, Wei Huang, Guoliang Li, Zhao Zheng, Yuqiang Chen. International Conference on Very Large Data Bases (VLDB) 2023. [[code]](https://github.com/decis-bench/febench).
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-[A System for Time Series Feature Extraction in Federated Learning](https://dl.acm.org/doi/pdf/10.1145/3511808.3557176). Siqi Wang, Jiashu Li, Mian Lu, Zhao Zheng, Yuqiang Chen, and Bingsheng He. 2022. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (CIKM) 2022. [[code]](https://github.com/4paradigm/tsfe).
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-[Optimizing in-memory database engine for AI-powered on-line decision augmentation using persistent memory](http://vldb.org/pvldb/vol14/p799-chen.pdf). Cheng Chen, Jun Yang, Mian Lu, Taize Wang, Zhao Zheng, Yuqiang Chen, Wenyuan Dai, Bingsheng He, Weng-Fai Wong, Guoan Wu, Yuping Zhao, and Andy Rudoff. International Conference on Very Large Data Bases (VLDB) 2021.
Copy file name to clipboardExpand all lines: docs/en/deploy/compile.md
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This section describes the steps to compile and use OpenMLDB inside its official docker image [hybridsql](https://hub.docker.com/r/4pdosc/hybridsql).
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The docker image has packed required tools and dependencies, so there is no need to set them up separately. To compile without the official docker image, refer to the section [Detailed Instructions for Build](#detailed-instructions-for-build) below.
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Keep in mind that you should always use the same version of both compile image and [OpenMLDB version](https://github.com/4paradigm/OpenMLDB/releases). This section demonstrates compiling for [OpenMLDB v0.8.0](https://github.com/4paradigm/OpenMLDB/releases/tag/v0.8.0) under `hybridsql:0.8.0` ,If you prefer to compile on the latest code in `main` branch, pull `hybridsql:latest` image instead.
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Keep in mind that you should always use the same version of both compile image and [OpenMLDB version](https://github.com/4paradigm/OpenMLDB/releases). This section demonstrates compiling for [OpenMLDB v0.8.1](https://github.com/4paradigm/OpenMLDB/releases/tag/v0.8.1) under `hybridsql:0.8.1` ,If you prefer to compile on the latest code in `main` branch, pull `hybridsql:latest` image instead.
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1. Pull the docker image
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docker run -it 4pdosc/hybridsql:0.8 bash
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```
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3. Download the OpenMLDB source code inside the docker container, and setting the branch into v0.8.0
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3. Download the OpenMLDB source code inside the docker container, and setting the branch into v0.8.1
Copy file name to clipboardExpand all lines: docs/en/deploy/install_deploy.md
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* The number of cores is recommended to be no less than 4 cores. If the CPU does not support the AVX2 instruction set in the Linux environment, the deployment package needs to be recompiled from the source code.
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## Deployment Package
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The precompiled OpenMLDB deployment package is used by default in this documentation ([Linux](https://github.com/4paradigm/OpenMLDB/releases/download/v0.8.0/openmldb-0.8.0-linux.tar.gz) , [macOS](https://github.com/4paradigm/OpenMLDB/releases/download/v0.8.0/openmldb-0.8.0-darwin.tar.gz)), the supported operating system requirements are: CentOS 7, Ubuntu 20.04, macOS >= 10.15. If the user wishes to compile by himself (for example, for OpenMLDB source code development, the operating system or CPU architecture is not in the support list of the precompiled deployment package, etc.), the user can choose to compile and use in the docker container or compile from the source code. For details, please refer to our [compile documentation](compile.md).
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The precompiled OpenMLDB deployment package is used by default in this documentation ([Linux](https://github.com/4paradigm/OpenMLDB/releases/download/v0.8.1/openmldb-0.8.1-linux.tar.gz) , [macOS](https://github.com/4paradigm/OpenMLDB/releases/download/v0.8.1/openmldb-0.8.1-darwin.tar.gz)), the supported operating system requirements are: CentOS 7, Ubuntu 20.04, macOS >= 10.15. If the user wishes to compile by himself (for example, for OpenMLDB source code development, the operating system or CPU architecture is not in the support list of the precompiled deployment package, etc.), the user can choose to compile and use in the docker container or compile from the source code. For details, please refer to our [compile documentation](compile.md).
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## Configure Environment (Linux)
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Copy file name to clipboardExpand all lines: docs/en/quickstart/java_sdk.md
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<dependency>
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<groupId>com.4paradigm.openmldb</groupId>
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<artifactId>openmldb-jdbc</artifactId>
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<version>0.8.0</version>
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<version>0.8.1</version>
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</dependency>
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<dependency>
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<groupId>com.4paradigm.openmldb</groupId>
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<artifactId>openmldb-native</artifactId>
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<version>0.8.0</version>
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<version>0.8.1</version>
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</dependency>
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```
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### Package Installation on Mac
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<dependency>
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<groupId>com.4paradigm.openmldb</groupId>
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<artifactId>openmldb-jdbc</artifactId>
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<version>0.8.0</version>
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<version>0.8.1</version>
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</dependency>
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<dependency>
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<groupId>com.4paradigm.openmldb</groupId>
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<artifactId>openmldb-native</artifactId>
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<version>0.8.0-macos</version>
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<version>0.8.1-macos</version>
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</dependency>
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```
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Note that since `openmldb-native` contains the C++ static library compiled by OpenMLDB, by default it is a Linux's static library. On macOS, the version of the above openmldb-native needs to be changed to `0.8.0-macos`, and the version of openmldb-jdbc remains unchanged.
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Note that since `openmldb-native` contains the C++ static library compiled by OpenMLDB, by default it is a Linux's static library. On macOS, the version of the above openmldb-native needs to be changed to `0.8.1-macos`, and the version of openmldb-jdbc remains unchanged.
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The macOS native relase only supports macos-12. If you want use in macos-11 or macos 10.15, you should build openmldb-native from source in macos-11/macos-10.15, see [Build Java SDK](../deploy/compile.md#build-java-sdk-with-multi-processes) for details.
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