Labeled Data

Reallaer embraces the daunting contemporary challenge of transforming constantly expanding masses of data into knowledge. Reallaer is fully engaged in the quest to transform unstructured data such as texts, visual data, social media, public data farms, and government reports into meaningful, actionable information. Reallaer has succeeded in labeling data efficiently, accurately and flexibly to magnify data's utility to build knowledge. Reallaer's research and development into data analytics focuses on multi-modal data and deducing data associations to capitalize on the growing availability of data.

Segmenting Data

Reallaer creates effective approaches to organizing data. Reallaer's metadata tagging support optimizes data organization solutions based on business operations. We apply systems engineering principles to structure and label data abstractions to assure accuracy in recorded data and speed in retrieval. Reallaer creates custom segmented multi-media data solutions. Reallaer designs and delivers user interfaces (UIs) to facilitate intuitive interaction with very large data stores.

Case Study


Reallaer has developed prototypes for providing structure and discovery to an enterprise consisting of multiple resources such as sensors, databases, and personnel. Users are able to semantically markup their resources using freeform vocabulary, which is then expanded using Semantic Web axioms and a vocabulary ontology to enable discovery through text searches or graph-based queries. Semantic Web provides the extensible implementation for aligning different user's interpretations, allowing for continued discovery of new resources when building an integrated solution.


Semantic Web

Reallaer uses Semantic Web standards as the building blocks in structuring data for the transformation into knowledge. While the approaches to provide order to unstructured data continue to evolve, a need to provide a standardized approach increases, leading to increased compatibility in understanding of one's data, and the use of one's tools. Reallaer advocates extensibility and repeatability in its software tools and techniques used in labeling its data semantically, enabling a streamlined approach for non-experts to get a handle on their knowledge needs and solutions. Reallaer's methods continue to adapt to evolving views of data and how the world uses and generates data, such as advances in the use of social media and collaborating between multiple sources of data.

Reallaer makes use of this collaborative work to provide flexible structure in an enterprise environment, fusing metadata between disparate hardware, software, and human resources. We transform semantically enabled data sources into knowledge that is leveraged by a range of applications.

Case Study


Reallaer built an intuitive data interface for social science multimedia data sets including audio, video and physiological inputs.