—This paper describes our efforts in refining k-nearest neighbor forest attributes classification using U.S. Department of Agriculture Forest Service Forest Inventory and Analysis plot data and Landsat 7 Enhanced Thematic Mapper Plus imagery. The analysis focuses on FIA-defined forest type classification across St. Louis County in northeastern Minnesota. We outline three steps in the classifica...

Classification of spatial data has become important due to the fact that there are huge volumes of spatial data now available holding a wealth of valuable information. In this paper we consider the classification of spatial data streams, where the training dataset changes often. New training data arrive continuously and are added to the training set. For these types of data streams, building a ...

A k-nearest neighbor (kNN) query, which retrieves nearest k points from a database is one of the fundamental query types in spatial databases. An all k-nearest neighbor query (AkNN query), a variation of a kNN query, determines the k-nearest neighbors for each point in the dataset in a query process. In this paper, we propose a method for processing AkNN queries in Hadoop. We decompose the give...

Currently, there has been an increasing development in the area of location-based service. An important type of query in this area is k nearest neighbor (kNN) query, which retrieves the top k nearest neighbors based on the user's position. Although a wide spectrum of work has been conducted on this query type, most of these studies focus on the ideal Euclidean plane without obstacles considered...

Data Mining in non-stationary data streams is particularly relevant in the context of the Internet of Things and Big Data. Its challenges arise from fundamentally different drift types violating assumptions of data independence or stationarity. Available methods often struggle with certain forms of drift or require unavailable a priori task knowledge. We propose the Self-Adjusting Memory (SAM) ...