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We are addressing the problem of query processing in large sensor networks. Each sensor produces a large amount of streaming data and it may not be possible to store all the data. By caching some of the data in aggregated format, we will be able to answer queries referring to past data. We are investigating a hierarchical caching model where summarized data is cached. The granularity of aggregation becomes coarser as we move up the levels, starting from the actual data of the immediate past stored at the lowest level. We categorize queries into two types: queries that can be answered exactly and those that can be answered approximately. We have provided an analysis of the conditions under which the exact and approximate answers can be provided for a given query. When the query answer is approximate, an estimation of the error is provided.