Exploiting connector knowledge to efficiently disseminate highly voluminous data sets

Abstract

Ever-growing amounts of data that must be distributed from data providers to consumers across the world necessitate a greater understanding of the software architectural implications of choosing data movement technologies. Currently, this understanding is mired in the minds of software architects who have been there before, and who rely on past intuition and choices, failing to properly document their rationale and context. In this paper we describe a software architecture-based decision making framework called DISCO for selecting data movement technologies, or software connectors. DISCO effectively captures (traditionally undocumented) insight, observation and ultimately architectural knowledge about the connectors, demonstrating the effectiveness of using such information to accurately encode the connector selection decision making process