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Bitmap indices have been widely used in several domains such as data warehousing and scientific applications due to their efficiency in answering certain query types over large data sets. However, their utilization has been largely limited to read-only data sets or to static snapshots of data due to the cost associated with the update and append of new data. Typically, several bitmaps are associated with each indexed attribute in a table, i.e. one for each attribute value, bin, or range. Each one of these bitmaps needs to be updated to reflect a new, appended row. Since a given table could be represented by hundreds or even thousands of bitmaps, the insertion of a single record can be prohibitively costly. In order to transfer the fast query response times offered by bitmap indices to dynamic database domains, we propose an update conscious bitmap index that provides a mechanism to quickly update bitmaps to reflect dynamic database changes. For an insert operation only the bitmaps that represent the values being inserted need to be updated. We formalize the insert and delete operations of the proposed technique and provide a cost model for bitmap updates. We compare the update conscious bitmaps to traditional bitmaps in terms of storage space, update performance, and query execution time.