III:Small: Towards Cross-Model Query Optimizations for Multi-model Heterogeneous Data Analytics

III:Small：面向多模型异构数据分析的跨模型查询优化

• 批准号: 1909875
• 负责人: Amarnath Gupta
• 金额: $ 44.32万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1909875&HistoricalAwards=false
• 关键词: III; Small; Towards; Cross; Model

Large-scale analysis of complex, heterogeneous datasets is now an integral part of various social and natural sciences, digital journalism, law, enterprises, and numerous other application domains. Users in such fields are increasingly grappling with the need to perform holistic integrated analytics spanning a variety of data models beyond just structured or semi-structured data to include graph data, text data, etc. Such multi-model data repositories are also growing in volume due to the widespread availability of online data sources such as social media and news media, which have opened up new avenues for insight in various domains. To take advantage of these opportunities, it is necessary to develop joint understanding and processing of at least three data models - relations, graphs, and text - including their evolution over time. This project aims to enable faster and scalable cross-model data analytics.An emerging information architecture for such heterogeneous data problem is the "polystore" approach that uses multiple "uni-model" backend engines such as RDBMSs, graph DBMSs, Solr, etc., and provides a translation layer in the middle to farm out different parts of a cross-model query to different engines. This approach is gaining popularity because it allows us to exploit the full functionality and native performance of uni-model engines for the corresponding parts of the queries. Amongst polystores, there are loosely-coupled solutions that have a very thin processing layer whose task is to "stitch the parts" together, and primarily provide support for data placement, movement and transformation. This project will focus on the query architecture and optimization principles for a tighter-coupled polystore. A usable, efficient, and scalable data analytics platform for queries spanning three data models, viz., relations, graphs, and text (including temporal evolution), that arise from social media and other sources, will be designed. A cross-model dataflow optimizer will be created for this "tri-store" setting to study fundamental systems optimization principles and will be implemented within the AWESOME polystore system. Further, several novel cross-model query optimization techniques will be devised to exploit the semantics of these three data models. Special attention will be paid to the temporality of data such that the optimizations treat temporal evolution of the data as a first-class primitive and support such queries efficiently on top of the existing engines even though they may lack native support for temporal queries.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

对复杂、异构数据集的大规模分析现已成为各种社会和自然科学、数字新闻、法律、企业和许多其他应用领域不可或缺的一部分。这些领域的用户越来越需要满足执行全面集成分析的需求，这些数据模型不仅限于结构化或半结构化数据，还包括图形数据、文本数据等。由于社交媒体和新闻媒体等在线数据源的广泛可用性，此类多模型数据存储库的数量也在不断增长，这为各个领域的洞察开辟了新的途径。为了利用这些机会，有必要对至少三种数据模型（关系、图形和文本）进行联合理解和处理，包括它们随时间的演变。 该项目旨在实现更快、可扩展的跨模型数据分析。解决此类异构数据问题的一种新兴信息架构是“polystore”方法，它使用多个“单模型”后端引擎，如 RDBMS、图形 DBMS、Solr 等，并在中间提供一个转换层，将跨模型查询的不同部分分包给不同的引擎。这种方法越来越受欢迎，因为它允许我们针对查询的相应部分利用单模型引擎的完整功能和本机性能。在 Polystore 中，有一些松散耦合的解决方案，它们具有非常薄的处理层，其任务是将各个部分“缝合”在一起，并且主要为数据放置、移动和转换提供支持。该项目将重点关注紧密耦合的 Polystore 的查询架构和优化原则。将设计一个可用、高效且可扩展的数据分析平台，用于跨越来自社交媒体和其他来源的三种数据模型（即关系、图形和文本（包括时间演变））的查询。将为这个“三存储”设置创建一个跨模型数据流优化器，以研究基本的系统优化原理，并将在 AWESOME Polystore 系统中实施。此外，将设计几种新颖的跨模型查询优化技术来利用这三种数据模型的语义。我们将特别关注数据的时态性，以便优化将数据的时态演化视为一流的原语，并在现有引擎之上有效地支持此类查询，即使它们可能缺乏对时态查询的本机支持。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

An Algebraic Approach for High-level Text Analytics

高级文本分析的代数方法

• DOI: 10.1145/3400903.3400926
• 发表时间: 2020
• 期刊: 32nd International Conference on Scientific and Statistical Database Management
• 作者: Zheng, Xiuwen;Gupta, Amarnath
• 通讯作者: Gupta, Amarnath

PK2G - Declarative Construction and Quality Evaluation of Knowledge Graphs from Polystores

PK2G - Polystores 知识图的声明式构建和质量评估

• 发表时间: 2023
• 期刊: Workshop on Knowledge Graphs Analysis on a Large Scale
• 作者: Zheng, X;Dasgupta S;Gupta, A
• 通讯作者: Gupta, A