E-Scan: Consuming Contextual Data with Model Plugins

Extracting value and insights from increasingly heterogeneous data sources involves multiple systems combining and consuming the data. With multi-modal and context-rich data such as strings, text, videos, or images, the problem of standardizing the data model and format for interchangeable use is further exacerbated by a non-uniform way of processing, extracting, and preserving content and context from the data. This makes the data movement, reuse, and exchange between different systems a non-composable, manual process. On the other hand, increasingly powerful and popular machine learning-driven data representation models map the input data into uniform high-dimensional vector embeddings for further processing, informed by particular models. However, using models is expensive, and the manual integration effort might exacerbate unnecessary costs. Thus, we propose E-Scan, a contextual data exchange plugin for using, exchanging, and caching context-rich data. We outline the need for a common interface that separates the concerns and allows smooth and cost-effective data exchange. First, while vector embeddings are context-less, the model information is saved to preserve the context and preprocessing steps. Next, a lightweight vector engine caches and stores the uniform intermediate data representation in a lazy way to lower the transformation and data access, exchange, and retrieval cost. Finally, a pull-based interface allows uniform data consumption between components under a common plugin interface. This way, various context-rich data types are stored, processed, and exchanged in a standardized way while allowing plugin-based customization for subsequent context interpretation.