The Enterprise Ethereum Alliance (EEA) announced the formation of the blockchain-neutral Token Taxonomy Initiative. The aim of the initiative is to address the need to universally define tokens to better understand how their use and implementation can occur interchangeably across all token-enabled blockchain platforms. Furthermore, the purpose of this initiative is to define a token in non-technical and cross-industry terms clearly by leveraging real world, everyday analogies so that anyone can recognize them.
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Automating video streaming at a large scale and simultaneously maintaining editorial standards and brand safety is not a trivial task. Therefore, to remain competitive, smart players are focusing on what they accomplish best and automating the rest. However, irrespective of the AI-based video programming platform being used, algorithmic-based systems work best when video asset metadata is structured in a category taxonomy. According to Melissa Lachman, marketing communications manager at IRIS.TV, a well-constructed taxonomy supports discovery and recommendations. Additionally, it offers precise video programming controls and actionable insight from analytics.
Applying graph database capabilities to machine learning (ML) and artificial intelligence (AI) applications is a relatively new concept. This is surprising considering Google’s Knowledge Graph dates back to 2012. Also graphs are a natural fit, as they are ideal for storing, connecting, and making inferences from complex data. In this article, the authors deep dive into how graphs can help ML and how they are related to deep link graph analytics for big data.
Inability to identify intelligent metadata from within content impacts the entire content management lifecycle. This challenge is compounded by the fact that only about 20 percent of the enterprises employ effective metadata and classification of their data. Carla Mulley, Vice President of Marketing, Concept Searching, describes how intelligent knowledge or intelligent metadata can be created using adaptive technology.
Dark data, mostly unstructured, grows many times faster than structured business data. It is retained by enterprises by deploying huge storage, backup, and management infrastructure. However, enterprises find extracting value from these clutters a challenge. This article titled “Illuminate the dark data to make the big digital leap” published as part of the IDG Contributor Network explores object storage solutions. In addition, the article explains why it will help enterprises overcome the many IT and business challenges associated with dark data.