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teaching:projh402 [2020/10/01 11:23] ezimanyi [Projects in Mobility Databases] |
teaching:projh402 [2020/10/01 11:34] ezimanyi [Implementing TSBS on MobilityDB] |
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Mobility databases (MOD) are database systems that can store and manage moving object geospatial trajectory data. A moving object is an object that changes its location over time (e.g., a car driving on the road network). Using a variety of sensors, the location tracks of moving objects can be recorded in digital formats. A MOD, then, helps storing and querying such data. A couple of prototype systems have been proposed by research groups. Yet, a mainstream system is by far still missing. By mainstream we mean that the development builds on widely accepted tools, that are actively being maintained and developed. A mainstream system would exploit the functionality of these tools, and would maximize the reuse of their ecosystems. As a result, it becomes more closer to end users, and easily adopted in the industry. | Mobility databases (MOD) are database systems that can store and manage moving object geospatial trajectory data. A moving object is an object that changes its location over time (e.g., a car driving on the road network). Using a variety of sensors, the location tracks of moving objects can be recorded in digital formats. A MOD, then, helps storing and querying such data. A couple of prototype systems have been proposed by research groups. Yet, a mainstream system is by far still missing. By mainstream we mean that the development builds on widely accepted tools, that are actively being maintained and developed. A mainstream system would exploit the functionality of these tools, and would maximize the reuse of their ecosystems. As a result, it becomes more closer to end users, and easily adopted in the industry. | ||
- | Towards filling this gap, our group is building the [[https://github.com/MobilityDB/MobilityDB|MobilityDB]] system. It builds on [[https://postgis.net/|PostGIS]], which is a spatial database extension of PostgreSQL. MobilityDB extends the type system of PostgreSQL and PostGIS with ADTs for representing moving object data. It defines, for instance, the tgeompoint type for representing a time dependant geometry point. MobilityDB types are well integrated into the platform, to achieve maximal reusability, hence a mainstream development. For instance, the tgeompoint type builds on the PostGIS geometry(point) type. Similarly MobilityDB builds on existing operations, indexing, and optimization framework. | + | Towards filling this gap, our group is building the [[https://github.com/MobilityDB/MobilityDB|MobilityDB]] system. It builds on [[https://postgis.net/|PostGIS]], which is a spatial database extension of [[https://www.postgresql.org/|PostgreSQL]]. MobilityDB extends the type system of PostgreSQL and PostGIS with ADTs for representing moving object data. It defines, for instance, the tgeompoint type for representing a time dependant geometry point. MobilityDB types are well integrated into the platform, to achieve maximal reusability, hence a mainstream development. For instance, the tgeompoint type builds on the PostGIS geometry(point) type. Similarly MobilityDB builds on existing operations, indexing, and optimization framework. |
MobilityDB supports SQL as query interface. Currently it is quite rich in terms of types and functions. It is incubated as community project in [[https://www.osgeo.org/projects/mobilitydb/|OSGeo]], which certifies high technical quality. | MobilityDB supports SQL as query interface. Currently it is quite rich in terms of types and functions. It is incubated as community project in [[https://www.osgeo.org/projects/mobilitydb/|OSGeo]], which certifies high technical quality. | ||
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===== Implementing TSBS on MobilityDB ===== | ===== Implementing TSBS on MobilityDB ===== | ||
- | which includes devising a spatio-temporal bucket function equivalent to time_bucket from TimescaleDB. | ||
- | <TBD> | + | The Time Series Benchmark Suite ([[https://github.com/timescale/tsbs|TSBS]]) is a collection of Go programs that are used to generate datasets and then benchmark read and write performance of various time series databases. This bechmark has been developed by [[https://www.timescale.com/|TimescaleDB]], which is a time series extension of PostgreSQL. |
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+ | A significant addition of TimescaleDB to PosgreSQL is the addition of the [[https://blog.timescale.com/blog/simplified-time-series-analytics-using-the-time_bucket-function/|time_bucket]] function. This function allows to partition the time line in user-defined interval units that are used for aggregating data. | ||
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+ | The project consists in implementing a multidimensional generalization of the time_bucket function that allows the user to partition the spatial and/or temporal domain of a table in units (or tiles) that can be used for aggregating data. Then, the project consists of performing a benchmark comparison of TimescaleDB and MobilityDB. | ||