Support multiple dimensional index in HDFS on the FutureGrid platform

MapReduce is a patented software framework introduced by Google to support distributed computing on large data sets on clusters of computers. The framework is inspired by the map and reduce functions commonly used in functional programming, although their purpose in the MapReduce framework is not the same as their original forms. Hadoop is an open source frame that implements the MapReduce paradigm. Hadoop File System (HDFS) currently use pair as index for data processing. The multiple requirements to process large voluminous high-dimensional data sets have emerged in many different application domains, such as Geographic Information System (GIS). In such systems there are complex data processing, such as range query, nearest neighbor query, and distance join query. Hierarchical index structures, like R-tree, is used for organize multi-dimensional data. This independent study develops a research strategy how to support R-tree liked multi-dimensional index in HDFS. It is planned to developed a HDFS upperware that can map R-tree index to pair index and efficiently handle complex data processing requirements.