Michael Armbrust, Doug Bateman, Reynold Xin, M. Zaharia
Originally started as an academic research project at UC Berkeley, Apache Spark is one of the most popular open source projects for big data analytics. Over 1000 volunteers have contributed code to the project; it is supported by virtually every commercial vendor; many universities are now offering courses on Spark. Spark has evolved significantly since the 2010 research paper: its foundational APIs are becoming more relational and structural with the introduction of the Catalyst relational optimizer, and its execution engine is developing quickly to adopt the latest research advances in database systems such as whole-stage code generation. This tutorial is designed for database researchers (graduate students, faculty members, and industrial researchers) interested in a brief hands-on overview of Spark. This tutorial covers the core APIs for using Spark 2.0, including DataFrames, Datasets, SQL, streaming and machine learning pipelines. Each topic includes slide and lecture content along with hands-on use of a Spark cluster through a web-based notebook environment. In addition, we will dive into the engine internals to discuss architectural design choices and their implications in practice. We will guide the audience to "hack" Spark by extending its query optimizer to speed up distributed join execution.
{"title":"Introduction to Spark 2.0 for Database Researchers","authors":"Michael Armbrust, Doug Bateman, Reynold Xin, M. Zaharia","doi":"10.1145/2882903.2912565","DOIUrl":"https://doi.org/10.1145/2882903.2912565","url":null,"abstract":"Originally started as an academic research project at UC Berkeley, Apache Spark is one of the most popular open source projects for big data analytics. Over 1000 volunteers have contributed code to the project; it is supported by virtually every commercial vendor; many universities are now offering courses on Spark. Spark has evolved significantly since the 2010 research paper: its foundational APIs are becoming more relational and structural with the introduction of the Catalyst relational optimizer, and its execution engine is developing quickly to adopt the latest research advances in database systems such as whole-stage code generation. This tutorial is designed for database researchers (graduate students, faculty members, and industrial researchers) interested in a brief hands-on overview of Spark. This tutorial covers the core APIs for using Spark 2.0, including DataFrames, Datasets, SQL, streaming and machine learning pipelines. Each topic includes slide and lecture content along with hands-on use of a Spark cluster through a web-based notebook environment. In addition, we will dive into the engine internals to discuss architectural design choices and their implications in practice. We will guide the audience to \"hack\" Spark by extending its query optimizer to speed up distributed join execution.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88411008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Graphics Processing Units (GPUs) have evolved as a powerful query co-processor for main memory On-Line Analytical Processing (OLAP) databases. However, existing GPU-based query processors adopt a kernel-based execution approach which optimizes individual kernels for resource utilization and executes the GPU kernels involved in the query plan one by one. Such a kernel-based approach cannot utilize all GPU resources efficiently due to the resource underutilization of individual kernels and memory ping-pong across kernel executions. In this paper, we propose GPL, a novel pipelined query execution engine to improve the resource utilization of query co-processing on the GPU. Different from the existing kernel-based execution, GPL takes advantage of hardware features of new-generation GPUs including concurrent kernel execution and efficient data communication channel between kernels. We further develop an analytical model to guide the generation of the optimal pipelined query plan. Thus, the tile size of the pipelined query execution can be adapted in a cost-based manner. We evaluate GPL with TPC-H queries on both AMD and NVIDIA GPUs. The experimental results show that 1) the analytical model is able to guide determining the suitable parameter values in pipelined query execution plan, and 2) GPL is able to significantly outperform the state-of-the-art kernel-based query processing approaches, with improvement up to 48%.
{"title":"GPL: A GPU-based Pipelined Query Processing Engine","authors":"Johns Paul, Jiong He, Bingsheng He","doi":"10.1145/2882903.2915224","DOIUrl":"https://doi.org/10.1145/2882903.2915224","url":null,"abstract":"Graphics Processing Units (GPUs) have evolved as a powerful query co-processor for main memory On-Line Analytical Processing (OLAP) databases. However, existing GPU-based query processors adopt a kernel-based execution approach which optimizes individual kernels for resource utilization and executes the GPU kernels involved in the query plan one by one. Such a kernel-based approach cannot utilize all GPU resources efficiently due to the resource underutilization of individual kernels and memory ping-pong across kernel executions. In this paper, we propose GPL, a novel pipelined query execution engine to improve the resource utilization of query co-processing on the GPU. Different from the existing kernel-based execution, GPL takes advantage of hardware features of new-generation GPUs including concurrent kernel execution and efficient data communication channel between kernels. We further develop an analytical model to guide the generation of the optimal pipelined query plan. Thus, the tile size of the pipelined query execution can be adapted in a cost-based manner. We evaluate GPL with TPC-H queries on both AMD and NVIDIA GPUs. The experimental results show that 1) the analytical model is able to guide determining the suitable parameter values in pipelined query execution plan, and 2) GPL is able to significantly outperform the state-of-the-art kernel-based query processing approaches, with improvement up to 48%.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90944021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Collecting and processing provenance, i.e., information describing the production process of some end product, is important in various applications, e.g., to assess quality, to ensure reproducibility, or to reinforce trust in the end product. In the past, different types of provenance meta-data have been proposed, each with a different scope. The first part of the proposed tutorial provides an overview and comparison of these different types of provenance. To put provenance to good use, it is essential to be able to interact with and present provenance data in a user-friendly way. Often, users interested in provenance are not necessarily experts in databases or query languages, as they are typically domain experts of the product and production process for which provenance is collected (biologists, journalists, etc.). Furthermore, in some scenarios, it is difficult to use solely queries for analyzing and exploring provenance data. The second part of this tutorial therefore focuses on enabling users to leverage provenance through adapted visualizations. To this end, we will present some fundamental concepts of visualization before we discuss possible visualizations for provenance.
{"title":"Provenance: On and Behind the Screens","authors":"Melanie Herschel, Marcel Hlawatsch","doi":"10.1145/2882903.2912568","DOIUrl":"https://doi.org/10.1145/2882903.2912568","url":null,"abstract":"Collecting and processing provenance, i.e., information describing the production process of some end product, is important in various applications, e.g., to assess quality, to ensure reproducibility, or to reinforce trust in the end product. In the past, different types of provenance meta-data have been proposed, each with a different scope. The first part of the proposed tutorial provides an overview and comparison of these different types of provenance. To put provenance to good use, it is essential to be able to interact with and present provenance data in a user-friendly way. Often, users interested in provenance are not necessarily experts in databases or query languages, as they are typically domain experts of the product and production process for which provenance is collected (biologists, journalists, etc.). Furthermore, in some scenarios, it is difficult to use solely queries for analyzing and exploring provenance data. The second part of this tutorial therefore focuses on enabling users to leverage provenance through adapted visualizations. To this end, we will present some fundamental concepts of visualization before we discuss possible visualizations for provenance.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":"112 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88910514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The CPU cache performance is one of the key issues to efficiency in database systems. It is reported that cache miss latency takes a half of the execution time in database systems. To improve the CPU cache performance, there are studies to support searching including cache-oblivious, and cache-conscious trees. In this paper, we focus on CPU speedup for graph computing in general by reducing the CPU cache miss ratio for different graph algorithms. The approaches dealing with trees are not applicable to graphs which are complex in nature. In this paper, we explore a general approach to speed up CPU computing, in order to further enhance the efficiency of the graph algorithms without changing the graph algorithms (implementations) and the data structures used. That is, we aim at designing a general solution that is not for a specific graph algorithm, neither for a specific data structure. The approach studied in this work is graph ordering, which is to find the optimal permutation among all nodes in a given graph by keeping nodes that will be frequently accessed together locally, to minimize the CPU cache miss ratio. We prove the graph ordering problem is NP-hard, and give a basic algorithm with a bounded approximation. To improve the time complexity of the basic algorithm, we further propose a new algorithm to reduce the time complexity and improve the efficiency with new optimization techniques based on a new data structure. We conducted extensive experiments to evaluate our approach in comparison with other 9 possible graph orderings (such as the one obtained by METIS) using 8 large real graphs and 9 representative graph algorithms. We confirm that our approach can achieve high performance by reducing the CPU cache miss ratios.
{"title":"Speedup Graph Processing by Graph Ordering","authors":"Hao Wei, J. Yu, Can Lu, Xuemin Lin","doi":"10.1145/2882903.2915220","DOIUrl":"https://doi.org/10.1145/2882903.2915220","url":null,"abstract":"The CPU cache performance is one of the key issues to efficiency in database systems. It is reported that cache miss latency takes a half of the execution time in database systems. To improve the CPU cache performance, there are studies to support searching including cache-oblivious, and cache-conscious trees. In this paper, we focus on CPU speedup for graph computing in general by reducing the CPU cache miss ratio for different graph algorithms. The approaches dealing with trees are not applicable to graphs which are complex in nature. In this paper, we explore a general approach to speed up CPU computing, in order to further enhance the efficiency of the graph algorithms without changing the graph algorithms (implementations) and the data structures used. That is, we aim at designing a general solution that is not for a specific graph algorithm, neither for a specific data structure. The approach studied in this work is graph ordering, which is to find the optimal permutation among all nodes in a given graph by keeping nodes that will be frequently accessed together locally, to minimize the CPU cache miss ratio. We prove the graph ordering problem is NP-hard, and give a basic algorithm with a bounded approximation. To improve the time complexity of the basic algorithm, we further propose a new algorithm to reduce the time complexity and improve the efficiency with new optimization techniques based on a new data structure. We conducted extensive experiments to evaluate our approach in comparison with other 9 possible graph orderings (such as the one obtained by METIS) using 8 large real graphs and 9 representative graph algorithms. We confirm that our approach can achieve high performance by reducing the CPU cache miss ratios.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":"11 1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85118617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Shaikhha, Yannis Klonatos, L. Parreaux, Lewis Brown, Mohammad Dashti, Christoph E. Koch
This paper studies architecting query compilers. The state of the art in query compiler construction is lagging behind that in the compilers field. We attempt to remedy this by exploring the key causes of technical challenges in need of well founded solutions, and by gathering the most relevant ideas and approaches from the PL and compilers communities for easy digestion by database researchers. All query compilers known to us are more or less monolithic template expanders that do the bulk of the compilation task in one large leap. Such systems are hard to build and maintain. We propose to use a stack of multiple DSLs on different levels of abstraction with lowering in multiple steps to make query compilers easier to build and extend, ultimately allowing us to create more convincing and sustainable compiler-based data management systems. We attempt to derive our advice for creating such DSL stacks from widely acceptable principles. We have also re-created a well-known query compiler following these ideas and report on this effort.
{"title":"How to Architect a Query Compiler","authors":"A. Shaikhha, Yannis Klonatos, L. Parreaux, Lewis Brown, Mohammad Dashti, Christoph E. Koch","doi":"10.1145/2882903.2915244","DOIUrl":"https://doi.org/10.1145/2882903.2915244","url":null,"abstract":"This paper studies architecting query compilers. The state of the art in query compiler construction is lagging behind that in the compilers field. We attempt to remedy this by exploring the key causes of technical challenges in need of well founded solutions, and by gathering the most relevant ideas and approaches from the PL and compilers communities for easy digestion by database researchers. All query compilers known to us are more or less monolithic template expanders that do the bulk of the compilation task in one large leap. Such systems are hard to build and maintain. We propose to use a stack of multiple DSLs on different levels of abstraction with lowering in multiple steps to make query compilers easier to build and extend, ultimately allowing us to create more convincing and sustainable compiler-based data management systems. We attempt to derive our advice for creating such DSL stacks from widely acceptable principles. We have also re-created a well-known query compiler following these ideas and report on this effort.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86194786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We demonstrate PerfEnforce, a dynamic scaling engine for analytics services. PerfEnforce automatically scales a cluster of virtual machines in order to minimize costs while probabilistically meeting the query runtime guarantees offered by a performance-oriented service level agreement (SLA). The demonstration will show three families of dynamic scaling algorithms --feedback control, reinforcement learning, and online machine learning--and will enable attendees to change tuning parameters, performance thresholds, and workloads to compare and contrast the algorithms in different settings.
{"title":"PerfEnforce Demonstration: Data Analytics with Performance Guarantees","authors":"Jennifer Ortiz, Brendan Lee, M. Balazinska","doi":"10.1145/2882903.2899402","DOIUrl":"https://doi.org/10.1145/2882903.2899402","url":null,"abstract":"We demonstrate PerfEnforce, a dynamic scaling engine for analytics services. PerfEnforce automatically scales a cluster of virtual machines in order to minimize costs while probabilistically meeting the query runtime guarantees offered by a performance-oriented service level agreement (SLA). The demonstration will show three families of dynamic scaling algorithms --feedback control, reinforcement learning, and online machine learning--and will enable attendees to change tuning parameters, performance thresholds, and workloads to compare and contrast the algorithms in different settings.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90152143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ronald Barber, Matthew Huras, G. Lohman, C. Mohan, René Müller, Fatma Özcan, H. Pirahesh, Vijayshankar Raman, Richard Sidle, O. Sidorkin, Adam J. Storm, Yuanyuan Tian, Pınar Tözün
We demonstrate Hybrid Transactional and Analytics Processing (HTAP) on the Spark platform by the Wildfire prototype, which can ingest up to ~6 million inserts per second per node and simultaneously perform complex SQL analytics queries. Here, a simplified mobile application uses Wildfire to recommend advertising to mobile customers based upon their distance from stores and their interest in products sold by these stores, while continuously graphing analytics results as those customers move and respond to the ads with purchases.
{"title":"Wildfire: Concurrent Blazing Data Ingest and Analytics","authors":"Ronald Barber, Matthew Huras, G. Lohman, C. Mohan, René Müller, Fatma Özcan, H. Pirahesh, Vijayshankar Raman, Richard Sidle, O. Sidorkin, Adam J. Storm, Yuanyuan Tian, Pınar Tözün","doi":"10.1145/2882903.2899406","DOIUrl":"https://doi.org/10.1145/2882903.2899406","url":null,"abstract":"We demonstrate Hybrid Transactional and Analytics Processing (HTAP) on the Spark platform by the Wildfire prototype, which can ingest up to ~6 million inserts per second per node and simultaneously perform complex SQL analytics queries. Here, a simplified mobile application uses Wildfire to recommend advertising to mobile customers based upon their distance from stores and their interest in products sold by these stores, while continuously graphing analytics results as those customers move and respond to the ads with purchases.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":"179 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75957237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this extended abstract, we explore the use of MinHash Locality Sensitive Hashing (MinHash LSH) to address the problem of indexing and searching Web data. We discuss a statistical tuning strategy of MinHash LSH, and experimentally evaluate the accuracy and performance, compared with inverted index. In addition, we describe an on-line demo for the index with real Web data.
{"title":"Searching Web Data using MinHash LSH","authors":"B. Rao, Erkang Zhu","doi":"10.1145/2882903.2914838","DOIUrl":"https://doi.org/10.1145/2882903.2914838","url":null,"abstract":"In this extended abstract, we explore the use of MinHash Locality Sensitive Hashing (MinHash LSH) to address the problem of indexing and searching Web data. We discuss a statistical tuning strategy of MinHash LSH, and experimentally evaluate the accuracy and performance, compared with inverted index. In addition, we describe an on-line demo for the index with real Web data.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84433642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Ulbricht, Claudio Hartmann, M. Hahmann, H. Donker, Wolfgang Lehner
The role of precise forecasts in the energy domain has changed dramatically. New supply forecasting methods are developed to better address this challenge, but meaningful benchmarks are rare and time-intensive. We propose the ECAST online platform in order to solve that problem. The system's capability is demonstrated on a real-world use case by comparing the performance of different prediction tools.
{"title":"Web-based Benchmarks for Forecasting Systems: The ECAST Platform","authors":"R. Ulbricht, Claudio Hartmann, M. Hahmann, H. Donker, Wolfgang Lehner","doi":"10.1145/2882903.2899399","DOIUrl":"https://doi.org/10.1145/2882903.2899399","url":null,"abstract":"The role of precise forecasts in the energy domain has changed dramatically. New supply forecasting methods are developed to better address this challenge, but meaningful benchmarks are rare and time-intensive. We propose the ECAST online platform in order to solve that problem. The system's capability is demonstrated on a real-world use case by comparing the performance of different prediction tools.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83395200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An in-memory indexing tree is a critical component of many databases. Modern many-core processors, such as GPUs, are offering tremendous amounts of computing power making them an attractive choice for accelerating indexing. However, the memory available to the accelerating co-processor is rather limited and expensive in comparison to the memory available to the CPU. This drawback is a barrier to exploit the computing power of co-processors for arbitrarily large index trees. In this paper, we propose a novel design for a B+-tree based on the heterogeneous computing platform and the hybrid memory architecture found in GPUs. We propose a hybrid CPU-GPU B+-tree, "HB+-tree," which targets high search throughput use cases. Unique to our design is the joint and simultaneous use of computing and memory resources of CPU-GPU systems. Our experiments show that our HB+-tree can perform up to 240 million index queries per second, which is 2.4X higher than our CPU-optimized solution.
{"title":"A Hybrid B+-tree as Solution for In-Memory Indexing on CPU-GPU Heterogeneous Computing Platforms","authors":"Amirhesam Shahvarani, H. Jacobsen","doi":"10.1145/2882903.2882918","DOIUrl":"https://doi.org/10.1145/2882903.2882918","url":null,"abstract":"An in-memory indexing tree is a critical component of many databases. Modern many-core processors, such as GPUs, are offering tremendous amounts of computing power making them an attractive choice for accelerating indexing. However, the memory available to the accelerating co-processor is rather limited and expensive in comparison to the memory available to the CPU. This drawback is a barrier to exploit the computing power of co-processors for arbitrarily large index trees. In this paper, we propose a novel design for a B+-tree based on the heterogeneous computing platform and the hybrid memory architecture found in GPUs. We propose a hybrid CPU-GPU B+-tree, \"HB+-tree,\" which targets high search throughput use cases. Unique to our design is the joint and simultaneous use of computing and memory resources of CPU-GPU systems. Our experiments show that our HB+-tree can perform up to 240 million index queries per second, which is 2.4X higher than our CPU-optimized solution.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83571702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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