Clustering is an efficient way to group data into different classes on basis of the internal and
previously unknown schemes inherent of the data. With the development of the location …

Compact data structures help represent data in reduced space while allowing it to be
queried, navigated, and operated in compressed form. They are essential tools for efficiently …

In order to handle spatial data efficiently, as required in computer aided design and geo-
data applications, a database system needs an index mechanism that will help it retrieve …

A large-scale multi-object tracker based on the generalised labeled multi-Bernoulli (GLMB)
filter is proposed. The algorithm is capable of tracking a very large, unknown and time …

The problem of interpolating between discrete fields arises frequently in computational
physics. The obvious approach, consistent interpolation, has several drawbacks such as …

We present the priority R-tree, or PR-tree, which is the first R-tree variant that always
answers a window query using O ((N/B) 1− 1/d+ T/B) I/Os, where N is the number of d …

Big Data has driven the need for datastores that can scale horizontally leading to the
development of many different NoSQL database implementations, each with different …

Indexes are special purpose data structures, designed to facilitate and speed up the access
to the contents of a file. Indexing has been actively and extensively investigated in DBMSes …

From environmental management to land planning and geo-marketing, the number of
application domains that may greatly benefit from using data enriched with spatio-temporal …

Geometric range search is a fundamental primitive for spatial data analysis in SQL and
NoSQL databases. It has extensive applications in location-based services, computer-aided …