A Visual Framework for Spatial Data Mining

The unique properties of spatial data provide challenges and opportunities for researching new methods in spatial data mining. In this article, we propose an interoperable framework that integrates Geographic Information System (GIS) with the spatial data mining processto facilitate spatial data preparation, to extract spatial relationships that can take advantage of traditional data mining toolkits such as Weka, and to reveal significant spatial patterns. With this approach, it’svery straightforward to adopt spatial access methods and spatial query processing algorithms foran efficient data mining technique. Moreover, our framework visually supports the complete spatial data mining process.

AN OVERVIEW OF SIMILARITY SEARCH IN TIME SERIES DATA

Time series data occur in many real life applications, ranging from science and engineering to business. In many of these applications, searching through large time series database based on query sequence is often desirable. Such similarity-based retrieval is also the basic subroutine in several advanced time series data mining tasks such as clustering, classification, finding motifs, detecting anomaly patterns, rule discovery and visualization. Although several different approaches have been developed, most are based on the common premise of dimensionality reduction and spatial access methods. This survey gives an overview of recent research and shows how the methods fit into a general framework of feature extraction.

Object-Relational Spatial Indexing

In order to generate efficient execution plans for queries comprising spatial data types and predicates, the database system has to be equipped with appropriate index structures, query processing methods and optimization rules. Although available extensible indexing frameworks provide a gateway for seamless integration of spatial access methods into the standard process of query optimization and execution, they do not facilitate the actual implementation of the spatial access method. An internal enhancement of the database kernel is usually not an option for database developers. The embedding of a custom, block-oriented index structure into concurrency control, recovery services and buffer management would cause extensive implementation efforts and maintenance cost, at the risk of weakening the reliability of the entire system. The server stability can be preserved by delegating index operations to an external process, but this approach induces severe performance bottlenecks due to context switches and inter-process communication. Therefore, we present the paradigm of object-relational spatial access methods that perfectly fits to the common relational data model, and is highly compatible with the extensible indexing frameworks of existing object-relational database systems, allowing the user to define application-specific access methods.

Indexing Regional Objects in High-Dimensional Spaces

Many spatial access methods, such as the R-tree, have been designed to support spatial search operators (e.g., overlap, containment, and enclosure) over both points and regional objects in multi-dimensional spaces. Unfortunately, contemporary spatial access methods are limited by many problems that significantly degrade the query performance in high-dimensional spaces. This chapter reviews the problems of contemporary spatial access methods in spaces with many dimensions and presents an efficient approach to building advanced spatial access methods that effectively attack these problems. It also discusses the importance of high-dimensional spatial access methods for the emerging database applications, such as location-based services.