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ABSTRACT:High resolution level in satellite imagery came with its fundamental problem as big amount of telemetry data which is to be stored after the downlink operation. Moreover, later the post-processing and image enhancement steps after the image is acquired, the file sizes increase even more and then it gets a lot harder to store and consume much more time to transmit the data from one source to another; hence, it should be taken into account that to save even more space with file compression of the raw and various levels of processed data is a necessity for archiving stations to save more space. Lossless data compression algorithms that will be examined in this study aim to provide compression without any loss of data holding spectral information.

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Data warehouses have been considered to be the key aspect of success for any Decision Support System (DSS). Temporal database research has produced important results in this field. Data warehouses store historical data, and therefore could clearly benefit from the research on temporal databases. Temporal data warehouses join the two fields of temporal databases and data warehouse research. This paper introduces a bitemporal data warehouse model that both valid time and transaction time are attached to attributes. Data warehouse objects and cubes are created with multidimensional bitemporal relational database. Performance of available well-known relational database and bitemporal extension for data warehouse is evaluated and compared in terms of execution time and disk space consumption with the set of queries.