An Extensible Digital Library Service to Support Network Science

Network science research aims to understand the underlying properties of complex networks. Large-scale modeling and simulation is the core of network science research. Existing systems take a long time to run large network science experiments with high performance computing resources. Scientific data management systems currently lack the performance efficiency needed to support this type of computation and data-intensive research. Memoization provides the ability to index, archive, and reuse frequently requested and expensive to re-compute datasets. In this paper, a domain independent memoization service to increase the computational execution process performance within cyberinfrastructure-based systems is described. The service is formulated as an extensible memoization framework for the computational and simulation network science domains that is built on top of well-defined metadata objects. We present extensible concepts, discuss the proposed algorithm and framework architecture, and examine the flexible nature of the framework. The framework was utilized as a part of the CINET cyberinfrastructure-based digital library (DL). Our experimental results indicate an increase in the efficiency of the system and recommendation of the service's inclusion in scientific DLs.