Keynote

Ten years into Linked Data there are still many unresolved challenges towards arriving at a truly machine-readable and decentralized stage that would make the promised vision of a Web of Data come true. In this talk we will review the current state of affairs and highlight the key technical and non-technical challenges to the success of LOD.

How serious can a missing document on a search result list be? For certain people that conduct search as part of their profession, a missing search result could lead to litigation or death. This talk will examine search tasks in two such professional domains: Intellectual Property and Medicine, covering the challenges inherent in search in these domains.

How do you convince a well-run IT department to expand its horizon into the world of Semantics? How can you explain to business users the need to incorporate Semantics? What are the types of investments one has to make to start? What are the tools/techniques that best support Semantics and fit withing our organization?

Technology innovations aren't all created equal. Most are incremental improvements (Type I), layers of a new stack, for example, that accumulate over time. Stacks become towers of Babel, triggering struggles with complexity. Migrating to the cloud simply hands the complexity problem over to service providers.

We live in a time of data. Every day, tools for creating, storing, communicating, and manipulating data grow more sophisticated and ubiquitous. Yet, the cultural and intellectual frameworks that underlie our data-saturated world are old, and their histories illuminate important aspects of our present.

Watson, the computer that achieved prominence in 2011 by defeating the all time best players of the American TV quiz show, Jeopardy!, was a herald of a new age.  Suddenly it was possible for machines to understand us, in our own language.  In the six years since, new AI and cognitive capabilities for machines are being announced at a dizzying pace, yet in the 60 years before Watson, AI moved co

Millions of players consume or create content related to EA’s video games every day. For EA, managing this content using traditional content management systems and databases had become increasingly difficult. The environments in which this content was created, stored and consumed kept growing in number and complexity.

Current metadata and information about data lineage are crucial for understanding and interpreting data in a Hadoop data warehouse. At the same time, Hadoop data warehouse projects sink or swim with the ability to continuously add new data sources and views as business requirements evolve.