  Machine Learning (ML) is continuously permeating a growing amount of
application domains. Generative AI such as Large Language Models (LLMs) also
sees broad adoption to process multi-modal data such as text, images, audio,
and video. While the trend is to use ever-larger datasets for training,
managing this data efficiently has become a significant practical challenge in
the industry-double as much data is certainly not double as good. Rather the
opposite is important since getting an understanding of the inherent quality
and diversity of the underlying data lakes is a growing challenge for
application-specific ML as well as for fine-tuning foundation models.
Furthermore, information retrieval (IR) from expanding data lakes is
complicated by the temporal dimension inherent in time-series data which must
be considered to determine its semantic value. This study focuses on the
different semantic-aware techniques to extract embeddings from mono-modal,
multi-modal, and cross-modal data to enhance IR capabilities in a growing data
lake. Articles were collected to summarize information about the
state-of-the-art techniques focusing on applications of embedding for three
different categories of data modalities.
