SMART(ER) DATA

TOPICS INCLUDE:
• Decision-making systems
• Web-scale data integration
• Analytics
• New meta(data) opportunities
• Human-machine decision making
• Data and context
• Visualization and imaging tools
• Revisiting AI
• Search and ad hoc queries
• Recombinant business data
CONFERENCE OVERVIEW
As Web 2.0 meets the information infrastructure, the information delivery model is being upended. New networking tools make it possible for large communities of people to rapidly contribute information, accumulate knowledge, and act on useful new data and conclusions. Better visualization tools are changing the way we see the world and our relationships. We demand that information in all its rich forms—video, audio, images, documents, text—be combined, in context, and delivered to users and applications in real time.

How can we sift through the thousands of options for defining and redefining relationships, customizing products, discovering new markets, and making better decisions when we seemingly have no clear understanding of where to start? We’ll tackle some of the nagging dilemmas we face when dealing with mixtures of data, information, and knowledge, and how we and our machines will make decisions in the future.

We're creating software that improves the ability of computers to negotiate with each other over the Internet, laying the foundation for on-the-fly information integration. If we can automate the extraction and integration of data from disparate, unstructured sources, how will we redefine our notions of acceptable information quality, governance, and accountability?

We assume that newer systems will be smarter and will capture "meaning." Ontologies provide knowledge about domains of interest better than expert systems do, but they can limit how we look at and categorize data. New data and metadata types, such as transitory, contextual, and exhaust data, might prove worthwhile, but is their value enough to justify the cost of collection and storage?

What kinds of new integration, mining, collaboration, and query tools will help improve our decision making? Should we allow mechanical savants to make more decisions? If so, which ones? Philosophical, economic, and legal issues abound, though policymaking will most likely lag technology implementation. We'll soon learn whether unearthing known and unknown opportunities buried in mountains of data can make us and our decisions smart(er).