Machine learning, especially deep learning, is used increasingly to enable better solutions
for data management tasks previously solved by other means, including database indexing …

In recent years, with the widespread use of location-aware handheld devices and the
development of wireless networks, trajectory data have shown a trend of rapid growth in …

The emerging class of instance-optimized systems has shown potential to achieve high
performance by specializing to a specific data and query workloads. Particularly, Machine …

Subtrajectory query has been a fundamental operator in mobility data management and
useful in the applications of trajectory clustering, co-movement pattern mining and contact …

R-tree is a popular index which supports efficient queries on multi-dimensional data. The
performance of R-tree mostly depends on how the tree structure is built if new data instances …

With the rapid development of the Internet of Things and cloud computing, HBase has
become a good choice for massive data storage, and is efficient in reading and writing data …

Although several spatial indexes achieve fast query processing, they are ineffective for
highly dynamic data sets because of costly updates. On the other hand, simple structures …

Learned indices can leverage the high prediction accuracy and efficiency of modern deep
learning techniques. They are capable of delivering better query performance than …

Similarity search is getting increasingly useful in real applications. This paper focuses on the
in-memory similarity search, ie, the range query and k nearest neighbor (kNN) query, under …

Efficient indexing is fundamental to managing and analyzing multi-dimensional data. A
growing trend is to directly learn the storage layout of multi-dimensional data using simple …