论文标题
关于神经网络的适合性，作为设计高效学习索引的基础
On the Suitability of Neural Networks as Building Blocks for The Design of Efficient Learned Indexes
论文作者
论文摘要
为了获得经典数据结构的时间/空间改进，新兴趋势是将机器学习技术与数据结构的适当方法相结合。 This new area goes under the name of Learned Data Structures.其研究的动机是，计算机架构中的范式会改变范式，这些范式将有利于使用图形处理单元和张量处理单元而不是传统的中央处理单元。 In turn, that would favour the use of Neural Networks as building blocks of Classic Data Structures.的确，博学的Bloom过滤器是学到的数据结构的主要支柱之一，它广泛使用神经网络来改善经典过滤器的性能。 However, no use of Neural Networks is reported in the realm of Learned Indexes, which is another main pillar of that new area.在这项贡献中，我们提供了有关使用神经网络作为学习索引的构建基础的第一个，急需的比较实验分析。此处报道的结果强调了设计非常专业的神经网络的需求，该网络量身定制为学习的索引，并为这些发展建立了坚实的基础。从方法上讲，我们的发现对神经网络设计和实施中工作的科学家和工程师都感兴趣，鉴于所涉及的应用领域（例如计算机网络和数据基础）的重要性。
With the aim of obtaining time/space improvements in classic Data Structures, an emerging trend is to combine Machine Learning techniques with the ones proper of Data Structures. This new area goes under the name of Learned Data Structures. The motivation for its study is a perceived change of paradigm in Computer Architectures that would favour the use of Graphics Processing Units and Tensor Processing Units over conventional Central Processing Units. In turn, that would favour the use of Neural Networks as building blocks of Classic Data Structures. Indeed, Learned Bloom Filters, which are one of the main pillars of Learned Data Structures, make extensive use of Neural Networks to improve the performance of classic Filters. However, no use of Neural Networks is reported in the realm of Learned Indexes, which is another main pillar of that new area. In this contribution, we provide the first, and much needed, comparative experimental analysis regarding the use of Neural Networks as building blocks of Learned Indexes. The results reported here highlight the need for the design of very specialized Neural Networks tailored to Learned Indexes and it establishes a solid ground for those developments. Our findings, methodologically important, are of interest to both Scientists and Engineers working in Neural Networks Design and Implementation, in view also of the importance of the application areas involved, e.g., Computer Networks and Data Bases.
