Time series is an important class of temporal data objects and it can be easily obtained from
scientific and financial applications. A time series is a collection of observations made …

Anomaly or outlier detection is consider as one of the vital application of data mining, which
deals with anomalies or outliers. Anomalies are considered as data points that are …

Data world is ruling us since last decade. The rapidity at which heterogeneous data is being
generated from various bases such as social networks and media, mobile devices, e …

This paper describes SpatialHadoop; a full-fledged MapReduce framework with native
support for spatial data. SpatialHadoop is a comprehensive extension to Hadoop that injects …

In spatial query processing, the popular index R-tree may incur large storage consumption
and high IO cost. Inspired by the recent learned index [17] that replaces B-tree with machine …

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 …

The explosive growth in volume, velocity, and diversity of data produced by mobile devices
and cloud applications has contributed to the abundance of data or 'big data.'Available …

Clustering, in data mining, is useful for discovering groups and identifying interesting
distributions in the underlying data. Traditional clustering algorithms either favor clusters …

We present an efficient indexing method to locate 1-dimensional subsequences within a
collection of sequences, such that the subsequences match a given (query) pattern within a …

Individual-based models are an exciting and widely used new tool for ecology. These
computational models allow scientists to explore the mechanisms through which population …