Multipledata

Multipledata is a term used in data science to describe datasets that combine information from more than one source or modality for analysis. It does not refer to a single formal standard, but rather a family of practices and representations that handle heterogeneity across data types.

The concept encompasses multi-source data, multi-modal data, multi-relational data, and multi-view data. Examples include sensor networks that collect temperature, humidity, and location; multimedia corpora with text, images, and audio; electronic health records that merge lab results, imaging, and genomic data; and social media data that merges user posts with metadata and network relations.

Key challenges include data heterogeneity, inconsistent schemas, different data quality levels, missing values, provenance and privacy concerns, and scalable processing. Approaches often involve data integration and fusion, schema alignment, entity resolution, ontology-based modeling, and standardized representations such as common data models or feature-level alignment.

In practice, multipledata underpins tasks such as multi-modal machine learning, multi-view learning, and cross-source analytics. It is closely related to, but distinct from, fields like data fusion, data integration, and cross-source databases. The term emphasizes the combined analytical value derived from diverse sources rather than any single data type.