毕业于美国佛罗里达大学硕士，就职于Facebook的GeoAPI组担任技术领导。带领团队开发了Geospatial Indexing平台，将地理大数据和实时数据流检索导入以提供实时的查询和计算，该平台已经用于多个面向用户的产品。他还参与了Facebook搜索引擎中的地理查询的设计和开发。
此前就职于Qualcomm，任Qualcomm在标准制定组织Khronos Group的代表，参与OpenCL标准制定。
ArchSummit采访了宾理涵老师，详见《聊聊Facebook的大规模时空数据处理技术栈》
Big Spatial Data @ Facebook
Big Geospatial Data at scale has all the challenges of data at scale along with some quirks very specific to spatio-temporal data. However, these very quirks (like the bounds of latitude/longitude, Euclidean vs. great circle distances, the "true" shape of the earth and the extremely skewed distribution of geospatial features) can be leveraged into interesting and productive trade-offs to offset and address these challenges. With more and more mobile devices thrown into the mix (both as producers and consumers of spatio-temporal data), realtime and accurate lookup of points and polygons based on GPS locations and queries about k-nearest and Top-K based on geospatial contexts are a very common and relevant problem. At the same time, providing scalable offline aggregation and query capabilities of spatio-temporal data for analytics use cases becomes vital to making sense of it.
The Facebook Location Infrastructure team handles spatio-temporal data at Facebook scale (using a mix of in-house and open source technologies and pragmatic trade-offs/decisions). This presentation will cover various design decisions and architectural choices taken to ramp up Trillions of operations per day on a heterogeneous mix of spatio-temporal data (for both online and analytics oriented use cases).
大规模空间数据除了在规模上的挑战以外，还有一些独特的问题需要解决：例如经纬度，直线距离和弧面距离的区别，以及同样的经纬度差在两极和赤道的面积差等。这些特性既是挑战也是性能优化上的机遇。随着越来越多的移动设备，物联网设备产生海量的时空数据，如何有效存储，检索，实时的k-nearest查询，关联性排序，如何高效的解决在离线大规模空间数据分析中常用到的Spatial Join都是我们要解决的问题。
Facebook Location Infrastructure团队处理大规模时空数据过程中，在内部技术和开源技术之间采取折中而务实的办法。本次演讲将会涵盖经过验证的，在处理每天万亿级混合复杂操作的时空数据背后的多个设计决策，和架构选型内容（包括在线和面向分析的用户案例）。
译版：
地点：爱晚亭
