Despite two decades of research in moving object databases and a few research prototypes that have been proposed, there is not yet a mainstream system targeted for industrial use. In this article, we present MobilityDB, a moving object database that extends the type system of PostgreSQL and PostGIS with abstract data types for representing moving object data. The types are fully integrated into the platform to reuse its powerful data management features. Furthermore, MobilityDB builds on existing operations, indexing, aggregation, and optimization framework. This is all made accessible via the SQL query interface.

This paper demonstrates the MobilityDB moving object database system. It is an extensive implementation on top of PostgreSQL and PostGIS with multiple novel aspects. MobilityDB defines multiple spatiotemporal types for moving geometry and geography points, as well as for temporal integers, reals, Booleans, and strings. It also defines a rich set of operations on these types. The types are supported with spatiotemporal index access methods by extending GiST (Generalized Search Tree) and SP-GiST (Space Partitioning GiST). The query interface is SQL. MobilityDB thus extends the PostgreSQL optimizer with statistics collectors and selectivity estimation functions. It is available as open source. The demonstration includes a scenario with multiple queries, and a publicly accessible query interface on the Web.

The development in computational processing has driven towards distributed processing frameworks performing tasks in parallel setups. The recent advances in Cloud Computing have widely contributed to this tendency. The MapReduce model proposed by Google is one of the most popular despite the well-known limitations inherent to the model which constrain the types of jobs that can be expressed. On the other hand models based on Data Flow Graphs (DFG) for the processing and the definition of the jobs, while more complex to express, are more general and suitable for a wider range of tasks, including iterative and pipelined tasks. In this paper we present AROM, a framework for large scale distributed processing based on DFG to express the jobs and which uses paradigms from functional programming to define the operators. The former leads to more natural handling of pipelined tasks while the latter enhances genericity and reusability of the operators, as shown by our tests on a parallel and pipelined job performing the calculation of PageRank.