Abstract The k-Nearest Neighbors (kNN) method, established in 1951, has since evolved
into a pivotal tool in data mining, recommendation systems, and Internet of Things (IoT) …

Over the past decade, we have witnessed an enormous amount of research effort dedicated
to the design of efficient outlier detection techniques while taking into consideration …

k nearest neighbours (kNN) queries are fundamental in many applications, ranging from
data mining, recommendation system and Internet of Things, to Industry 4.0 framework …

Abstract The k-Nearest Neighbors classifier is a simple yet effective widely renowned
method in data mining. The actual application of this model in the big data domain is not …

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

大数据管理:概念,技术与挑战 Page 1 大数据管理:概念,技术与挑战 孟小峰慈祥 (**人民大学信息
学院北京100872) Big Data Management: Concepts, Techniques and Challenges Meng …

Large spatial data becomes ubiquitous. As a result, it is critical to provide fast, scalable, and
high-throughput spatial queries and analytics for numerous applications in location-based …

We present LocationSpark, a spatial data processing system built on top of Apache Spark, a
widely used distributed data processing system. LocationSpark offers a rich set of spatial …

The K-nearest neighbors (KNN) machine learning algorithm is a well-known non-parametric
classification method. However, like other traditional data mining methods, applying it on big …

This paper presents in detail our fast semistochastic heat-bath configuration interaction
(SHCI) method for solving the many-body Schrödinger equation. We identify and eliminate …