Data centric applications often use heterogeneous datasets: some very large while others of moderate size, some highly structured (e.g., relations) while others complex structured (e.g., graphs) or little structured (e.g., log data). Facing them is a variety of storage systems but none of which is the best for all, at all times.#R##N#We present Estocada, an architecture we are currently developing to efficiently handle highly heterogeneous datasets based on a dynamic set of potentially very different data stores. Estocada provides to the ap- plication layer access to each dataset in its native format, while hosting them internally in a set of potentially overlapping fragments, possibly distributed across heterogeneous stores. At the core of Estocada lie powerful view-based rewriting and view selection algorithms to marry correctness with high performance.
['Francesca Bugiotti', 'Damian Bursztyn', 'Alin Deutsch', 'Ioana Ileana', 'Ioana Manolescu']
Toward Scalable Hybrid Stores