Novel reactive moving object applications require solutions to support object reactive behaviors as a way to query and update dynamic data. While moving object scenarios have long been researched in the context of spatio-temporal data management, reactive behavior is usually left to complex end-user implementations. However, it is not just a matter of hardwiring reactive constraints: the required solutions need to satisfy tight low-latency computation requirements and be scalable. This paper explores a novel approach to enrich a distributed actor-based framework with reactive functionality and complex spatial data management along with concurrency semantics. Our approach relies on a proposal of the moving actor abstraction, which is a conceptual enhancement of the actor model with reactive sensing, movement, and spatial querying capabilities. This enhancement helps developers of reactive moving object applications avoid the significant burden of implementing application-level schemes to balance performance and consistency. Based on moving actors, we define a reactive moving object data management platform, named Moving Actor-Oriented Databases (M-AODBs), and build Dolphin -- an implementation of M-AODBs. Dolphin embodies a non-intrusive actor-based design layered on top of the Microsoft Orleans distributed virtual actor framework. In a set of experimental evaluations with realistic reactive moving object scenarios, Dolphin exhibits scalability on multi-machines and provides near-real-time reaction latency.

翻译：新型反应式移动对象应用需要支持对象反应行为作为查询和更新动态数据方式的解决方案。尽管移动对象场景在时空数据管理领域已得到长期研究，但反应行为通常留给复杂的终端用户实现。然而，这不仅仅是硬编码反应约束的问题：所需解决方案必须满足严格的低延迟计算要求并具备可扩展性。本文探索了一种新颖方法，通过增强分布式基于行动者的框架，使其具备反应功能、复杂空间数据管理及并发语义。我们的方法依赖于移动行动者抽象提案，这是对行动者模型的概念性增强，增加了反应感知、移动和空间查询能力。这一增强有助于反应式移动对象应用开发者避免为实现应用级方案以平衡性能与一致性而承担巨大负担。基于移动行动者，我们定义了名为移动行动者导向数据库（M-AODBs）的反应式移动对象数据管理平台，并构建了Dolphin——M-AODBs的一个实现。Dolphin体现了基于Microsoft Orleans分布式虚拟行动者框架之上的非侵入式行动者分层设计。在一系列真实反应式移动对象场景的实验评估中，Dolphin在多机环境下展现出可扩展性，并提供近实时的反应延迟。