DOLAR: Virtualizing heterogeneous information spaces to support their expansion

Users expect applications to successfully cope with the expansion of information as necessitated by the continuous inclusion of novel types of content. Given that such content may originate from 'not-seen thus far' data collections and/or data sources, the challenging issue is to achieve the return of investment on existing services, adapting to new information without changing existing business-logic implementation. To address this need, we introduce DOLAR (Data Object Language And Runtime), a service-neutral framework which virtualizes the information space to avoid invasive, time-consuming, and expensive source-code extensions that frequently break applications. Specifically, DOLAR automates the introduction of new business-logic objects in terms of the proposed virtual 'content objects'. Such user-specified virtual objects align to storage artifacts and help realize uniform 'store-to-user' data flows atop heterogeneous sources, while offering the reverse 'user-to-store' flows with identical effectiveness and ease of use. In addition, the suggested virtual object composition schemes help decouple business logic from any content origin, storage and/or structural details, allowing applications to support novel types of items without modifying their service provisions. We expect that content-rich applications will benefit from our approach and demonstrate how DOLAR has assisted in the cost-effective development and gradual expansion of a production-quality digital library. Experimentation shows that our approach imposes minimal overheads and DOLAR-based applications scale as well as any underlying datastore(s).