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Data warehouses store large volumes of data according to a multidimensional model with dimensions representing different axes of analysis. OLAP systems (online analytical processing) provide the ability to interactively explore the data warehouse. Rising volumes and complexity of data favor the use of more powerful distributed computing architectures. Computing grids in particular are built for decentralized management of heterogeneous distributed resources. Their lack of centralized control however conflicts with classic centralized data warehouse models. To take advantage of a computing grid infrastructure to operate a data warehouse, several problems need to be solved. First, the warehouse data must be uniquely identified and judiciously partitioned to allow efficient distribution, querying and exchange among the nodes of the grid. We propose a data model based on "chunks" as atomic entities of warehouse data that can be uniquely identified. We then build contiguous blocks of these chunks to obtain suitable fragments of the data warehouse. The fragments stored on each grid node must be indexed in a uniform way to effectively interact with existing grid services. Our indexing structure consists of a lattice structure mapping queries to warehouse fragments and a specialized spatial index structure formed by X-trees providing the information necessary for optimized query evaluation plans.