Due to the surge of spatio-temporal data volume, the popularity of location-based services
and applications, and the importance of extracted knowledge from spatio-temporal data to …

As more and more mobile devices and IoT terminals are connected to the Internet, a huge
amount of spatio-temporal data is generated. In order to cope with the pressure of storing …

The construction of systems supporting spatial data has experienced great enthusiasm in
the past, due to the richness of this type of data and their semantics, which can be used in …

Spatial data is ubiquitous. Massive amounts of data are generated every day from a plethora
of sources such as billions of GPS-enabled devices (eg, cell phones, cars, and sensors) …

Data scientists and researchers utilize enormous spatio-temporal data and build machine
learning models to solve practical problems in diverse domains including intelligent …

Mainstream LSM-tree-based key-value stores face challenges in optimizing performance for
point lookup, range lookup, and update operations concurrently due to their constrained …

Modern data analytics applications prefer to use column-storage formats due to their
improved storage efficiency through encoding and compression. Parquet is the most popular …

Big spatial data has become ubiquitous, from mobile applications to satellite data. In most of
these applications, data is continuously growing to huge volumes. Existing systems for big …

Non-point spatial objects (eg, polygons, linestrings, etc.) are ubiquitous. We study the
problem of indexing non-point objects in memory for range queries and spatial intersection …

The importance and complexity of spatial join resulted in many join algorithms, some of
which run on big-data platforms such as Hadoop and Spark. This paper proposes the first …