Open data management solutions for problem solving environments: application of distributed authoring and versioning to the Extensible Computational Chemistry Environment

Next-generation problem solving environments (PSEs) promise significant advances over those now available. They will span scientific disciplines and incorporate collaboration capabilities. They will host feature-detection and other agents, allow data mining and pedigree tracking, and provide access from a wide range of devices. Fundamental changes in PSE architecture are required to realize these and other PSE goals. This paper focuses specifically on issues related to data management and recommends an approach based on open, metadata-driven repositories with loosely defined, dynamic schemas. Benefits of this approach are discussed and the redesign of the Extensible Computational Chemistry Environment's (Ecce) data storage architecture to use such a repository is described, based on the distributed authoring and versioning (DAV) standard. The suitability of DAV for scientific data, the mapping of the Ecce schema to DAV, and promising initial results are presented.

1. Introduction

Scientific problem solving environments are complex computing systems that seek to integrate the activities necessary to accomplish high-level domain tasks [1] [2]. They may include components for managing scientific workflow, tracking data pedigrees, transforming and filtering data, analyzing and visualizing results, automating feature extraction, and annotating records. As described by Gallopoulos et al., they also “use the language of the target class of problems, so users can run them without specialized knowledge of the underlying computer hardware or software” [1]. Thus, at the cognitive level, a PSE encodes domain knowledge, and, to varying degrees, enforces or guides users toward best practices. This characteristic is a powerful benefit of PSEs, particularly for novices or occasional users.