CSR: Small: Materialized Views over Heterogeneous Structured Data Sources in a Distributed Event Stream Processing Environment

职务名称：企业社会责任：小号：分布式事件流处理环境中异构结构化数据源上的物化视图。Dietrich机构：Arizona State UniversityProposal #：0915325项目摘要：软件系统正变得越来越复杂，需要在一个松散耦合的分布式环境中协调异构的结构化数据源，并支持处理事件和流数据.一些示例系统包括国土安全、刑事司法、供应链管理、医疗保健和消费者监控。这样的软件系统涉及用于检测事件、监视条件、处理流和查询数据的许多查询表达式。本研究分析了这些查询表达式之间的依赖关系，在关系或以数据为中心的XML不同的语言组件定义的结构化数据源，以检测常见的子表达式作为候选人的物化视图。当视图被物化时，计算视图的结果被存储，以便后续引用有效地访问物化视图，避免重新计算的成本。对于分布式数据源，这种性能改进甚至更为关键。但是，如果实体化视图所依赖的任何数据源发生了更改，则必须更新实体化视图。为了避免代价高昂的重新计算，增量视图维护算法使用该变化来增量地计算对物化视图的更新。这项研究的一个独特的方面是有效地维护物化视图，同时尊重底层松耦合，异构数据源的本地格式。使用最先进的商业和开源组件，支持分布式事件流处理框架的原型环境提供了一个研究和评估平台，用于探索异构，分布式结构化数据的物化视图的识别，规范和增量评估。这种环境还为本科生研究和课程改进提供了共享的基础设施。

Title: CSR: Small: Materialized Views over Heterogeneous Structured Data Sources in a Distributed Event Stream Processing EnvironmentInvestigator: Suzanne W. DietrichInstitution: Arizona State UniversityProposal #: 0915325Project Abstract: Software systems are becoming increasingly complex, requiring the coordination of heterogeneous structured data sources in a loosely-coupled distributed environment with support for handling events and streaming data. Some sample systems include homeland security, criminal justice, supply chain management, health care, and consumer monitoring. Such software systems involve numerous query expressions for detecting events, monitoring conditions, handling streams, and querying data. This research analyzes the dependencies among these query expressions over structured data sources defined in different language components over relational or data-centric XML to detect common subexpressions as candidates for materialized views. When views are materialized, the results of the computed view are stored so that subsequent references efficiently access the materialized view, avoiding the cost of recomputation. This performance improvement is even more critical with distributed data sources. However, the materialized view must be updated if any data source that it depends on has changed. To avoid costly recomputation, an incremental view maintenance algorithm uses the change to incrementally compute updates to the materialized view. A unique aspect of this research is the efficient maintenance of the materialized views while respecting the native format of the underlying loosely-coupled, heterogeneous data sources. Using state-of-the-art commercial and open-source components, a prototype environment that supports a distributed event stream processing framework provides a research and evaluation platform for the exploration of the identification, specification, and incremental evaluation of materialized views over heterogeneous, distributed structured data. This environment also provides a shared infrastructure for undergraduate research and curriculum enhancement.