Architectural styles and the design of network-based software architectures
Architectural styles and the design of network-based software architectures
An infrastructure for context-awareness based on first order logic
Personal and Ubiquitous Computing
A Metamodel Approach to Context Information
PERCOMW '05 Proceedings of the Third IEEE International Conference on Pervasive Computing and Communications Workshops
Learning Contextual Dependency Network Models for Link-Based Classification
IEEE Transactions on Knowledge and Data Engineering
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
When is self-training effective for parsing?
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Adapting boosting for information retrieval measures
Information Retrieval
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Data retrieval architectures are designed with various types of criteria. Most focus on matching the input request parameters to the target content. Inherent emphasis is on static data model underneath. Data retrieval from a static data model has some strong assumptions incorporated into the applications. Non-static data models have the obvious issue of application guessing/formulating a query to retrieve data from the repository. The mere fact that the underlying data model cannot be predicted in terms of its structure and format becomes a big issue. CADRA approaches these key aspects in its research. The component-based software architecture presented by CADRA creates an innovative methodology that can be applied to solve the non-deterministic data model problem. CADRA assimilates the heterogeneity of data sources within its model. CADRA addresses data retrieval within a heterogeneous environment absorbing the differences and performing dynamic data resolution inside the platform.