Distributing Text Mining tasks with librAIry

Resumen

We present librAIry, a novel architecture to store, process and an- alyze large collections of textual resources, integrating existing algorithms and tools into a common, distributed, high-performance work ow. Available text mining techniques can be incorporated as independent plug&play modules working in a collaborative manner into the framework. In the absence of a pre-de ned ow, librAIry leverages on the aggregation of operations executed by di erent components in response to an emergent chain of events. Extensive use of Linked Data (LD) and Representational State Transfer (REST) principles are made to provide individually addressable resources from textual documents. We have described the architecture design and its implementation and tested its e ectiveness in real-world scenarios such as collections of research papers, patents or ICT aids, with the objective of providing solutions for decision makers and experts in those domains. Major advantages of the framework and lessons-learned from these experiments are reported.