SPEAR-board: cross-platform interactive spatio-temporal big data analytics

With the widespread use of mobile and sensing devices, there has been an explosion of high velocity, transient data having spatial and temporal characteristics. Interactive analysis at such scale and speed require support for highly efficient query processing backend frameworks coupled with lightweight yet powerful frontend interfaces. While existing in-memory distributed stream processing frameworks are perfect candidates for scalable big data processing, spatio-temporal systems in this domain are mostly dominated by specify-once-apply-continuously query model. Any modification in query state requires query restart limiting system responsiveness and producing outdated or in the worst case erroneous results. Furthermore, most of the contemporary spatio-temporal big data systems are designed to operate in a single execution environment limiting their applicability to users accustomed to other similar frameworks with different APIs. In this paper, we demon-strate SPEAR-Board; an interactive web-based interface integrated with cross-platform stream processing engine; SPEAR, capable of seamlessly handling spatio-temporal query state changes in real-time. We demonstrate working of SPEAR-Board with respect to spatio-temporal Range and Nearest Neighbor queries backed by Apache Spark and Apache Flink deployed over cloud resources.