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

对复杂、异构数据集的大规模分析现在是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多地需要执行跨各种数据模型的整体集成分析，而不仅仅是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，这种多模型数据存储库的数量也在增长，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和可扩展的跨模型数据分析。针对这种异构数据问题的新兴信息架构是“polystore”方法，该方法使用多个“uni-model”后端引擎，如RDBMS，graph DBMS，Solr等，并在中间提供转换层以将跨模型查询的不同部分外包给不同的引擎。这种方法越来越受欢迎，因为它允许我们为查询的相应部分利用统一模型引擎的全部功能和本地性能。在polystores中，存在松散耦合的解决方案，其具有非常薄的处理层，其任务是将部件“缝合”在一起，并主要为数据放置，移动和转换提供支持。这个项目将集中在查询体系结构和优化原则的紧耦合polystore。一个可用、高效和可扩展的数据分析平台，用于跨三种数据模型的查询，即，将设计从社交媒体和其他来源产生的关系、图形和文本（包括时间演变）。将为这种“三存储”设置创建一个跨模型的优化器，以研究基本的系统优化原则，并将在AWESOME多存储系统中实施。此外，一些新的跨模型查询优化技术将被设计来利用这三个数据模型的语义。特别关注的是数据的时间性，这样的优化处理作为一个一流的原始数据的时间演变，并支持这种查询有效地在现有的引擎，即使他们可能缺乏对时间查询的本地支持。这个奖项反映了NSF的法定使命，并已被认为是值得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的支持。

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.