III: Medium: Longview: Querying the Future Now

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。传统的数据库系统旨在为用户提供数据库中所代表的世界过去状态的清晰画面。最近，流处理系统被引入，为那些需要最新信息的应用程序产生近实时的答案。因此，我们看到一个趋势towardshrinking？现实差距？为零但对于某些应用，即使是实时的也不够好。人们往往希望通过预测未来事件来把握机遇或避免灾难，从而走在当前的前面。安全应用程序就是一个很好的例子，因为它们通常对防止违规行为感兴趣，而不是简单地报告发生了违规行为。目前还没有一个数据库系统可以有效地作为一个通用的平台来支持这样的预测应用。本项目旨在通过设计和构建一个名为“Longview”的原型数据库系统来填补这一空白，以实现以数据为中心的预测分析。Longview促进了对历史和当前数据的分析，并对未来的数据值和事件进行预测。用户可以沿着少量的元数据将新的预测模型插入系统，系统使用这些模型来有效地评估预测查询。Longview将预测模型视为一等公民，在数据管理和查询优化过程中对其进行智能管理。这涉及到自动构建模型，并确定何时以及使用哪些模型来回答预测问题。这也涉及到创建和使用适当的物理数据结构，以促进有效的模型构建，选择和解释。Longview处理流和历史查询。事实上，许多流查询需要有效访问过去值的存档，因此有必要将流和历史处理无缝地联合收割机结合起来。最后，Longview研究了“白盒”模型支持，其中数据库利用模型的操作语义和表示来提高性能。Longview的目标是使在数据密集型情况下构建预测分析应用程序变得更加容易。将数据和模型管理无缝地结合起来，是使预测计算过程比当前的ad-hoc应用程序级方法更容易表达和更有效的关键。结果技术也允许更好地理解和支持数据库系统中的用户自定义功能。Longview最初用于现实世界中基于传感器的跟踪应用程序和预测门户网站，以便轻松实验不同的模型和数据集。有关该项目的更多信息，请访问项目网页：http://database.cs.brown.edu/projects/longview/

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).Traditional database systems are designed to provide the user with aclear picture of past states of the world as is represented in thedatabase. Recently, stream processing systems are introduced toproduce near real-time answers for those applications applicationsthat require up-to-date information. Thus, we see a trend towardshrinking the ?reality gap? to zero. But for some applications, evenreal-time is not good enough. There is often a desire to get out infront of the present by delivering predictions of future events totake advantage of opportunities or to avert calamity. Securityapplications are a good example since they are typically interested inpreventing a breach rather than simply reporting that one hashappened. There is currently no database system that can effectivelyserve as a generic platform to support such predictive applications.This project aims to fill this gap by designing and building aprototype database system called "Longview" to enable data-centricpredictive analytics. Longview facilitates the use of statisticalmodels to analyze historical and current data and make predictionsabout future data values and events. Users can plug new predictivemodels into the system along with a modest amount of meta-data, andthe system uses those models to efficiently evaluate predictivequeries.Longview treats predictive models as first-class citizens byintelligently managing them in the process of data management andquery optimization. This involves automatically building models anddetermining when and which model(s) to apply to answer predictivequeries. This also involves creating and using the proper physicaldata structures to facilitate efficient model building, selection, andexecution. Longview handles both streaming and historical queries. Infact, many streaming queries need efficient access to an archive ofpast values, making it necessary to seamlessly combine both stream andhistorical processing. Finally, Longview investigates "white-box"model support, in which the database leverages the operationalsemantics and representation of models to improve performance.Longview's goal is to make it much easier to build predictiveanalytics applications in data intensive situations. Seamlesslycombining data and model management is key to make the process ofcomputing with predictions far easier to express and more efficientthan the current ad-hoc application-level approaches. The resultingtechnology also allows for a better understanding and support foruser-defined functions in database systems.Longview is initially used for a real-world sensor-based trackingapplication and a predictive web portal for easy experimentation withdifferent models and data sets. Further information on the project canbe found on the project web page: http://database.cs.brown.edu/projects/longview/