A Graph Query Processor for Queries of Class CRPQagg

用于 CRPQagg 类查询的图查询处理器

• 批准号: 265596218
• 负责人: Professor Dr. Michael Grossniklaus
• 金额: --
• 依托单位: Arbeitsgruppe Datenbanken und Informationssysteme
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2015
• 资助国家: 德国
• 起止时间: 2014-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/265596218?language=en
• 关键词: Graph; Query; Processor; Queries; Class

In this project, we will address these requirements in the tradition of database research by designing and developing a general query processor for graph databases. The research conducted in this project will be structured into two streams of work. The first stream of work will approach the problem "top-down" by starting from concrete graph query languages that belong to a well-defined class of query languages. The functionality of popular query languages in this class will be analyzed in order to formally define a common graph data model and a unified algebra of operators. The second stream of work will address the problem "bottom-up" by starting from existing algorithms for graph operations and index structures. The dependencies between the characteristics of a graph and the possible algorithms to accomplish a certain processing task will be studied systematically. Based on this empirical study, an analytical cost model will be derived that will tie the results of both streams of work together in order to build a query processor that translates logical graph processing tasks into optimized physical execution plans.

在这个项目中，我们将通过设计和开发图形数据库的通用查询处理器来解决传统数据库研究中的这些需求。在这个项目中进行的研究将分为两个工作流程。第一个工作流将从属于一个定义良好的查询语言类的具体图形查询语言开始，“自上而下”地解决问题。本课程将分析常用查询语言的功能，以便正式定义通用的图数据模型和统一的运算符代数。第二部分工作将从图操作和索引结构的现有算法开始，“自下而上”地解决问题。将系统地研究图的特征与完成特定处理任务的可能算法之间的依赖关系。基于这一实证研究，将推导出一个分析成本模型，该模型将两种工作流的结果联系在一起，以便构建一个查询处理器，将逻辑图形处理任务转换为优化的物理执行计划。

项目成果

Experiences with Implementing Landmark Embedding in Neo4j

• DOI: 10.1145/3327964.3328496
• 发表时间: 2019-06
• 期刊: Proceedings of the 2nd Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA)
• 作者: Manuel Hotz;Theodoros Chondrogiannis;Leonard Wörteler;Michael Grossniklaus