SIOC Post profile for "AI3:::Adaptive Information"
type: Document
description: A SIOC profile describes the structure and contents of a weblog in a machine readable form. For more information please refer to http://sioc-project.org/.
primary topic: What is Big Structure?
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What is Big Structure?
type: Post
has container: weblog
has creator: Mike Bergman
maker: Mike Bergman
created: 2014-08-12T15:34:21Z
content: Defining the Guideposts for Big Data Download PDF In our recent two-part series we described a decade of experience working in the semantic Web (Part I) and our view that Big Structure, which resides at the nexus of the semantic Web, knowledge bases and artificial intelligence, was a key component of making sense of Big Data going forward (Part II). We are at a time when multiple advances are conjoining to create new opportunities and excitement. Data without context and relationships is meaningless. The idea of Big Data is powerful, but it is often presented as either a "good thing" in and of itself, or a mantra for something that is rather undefined. There is no doubt that with the Internet and the Web we are now able to generate and access data at unprecedented scale. There is also no question that tracking mechanisms and cheap storage -- and simpler, large-scale databases and Web services -- mean that we can also capture data and structure of natures previously unseen. Everyone knows the remarkable growth in exabytes and more. The prospect of data everywhere -- some useful with important context and some not -- has clearly captured the current discussion. Heck, if we claim Big Data, we even make more in wage or consulting charge-out fees. Who can argue with that? Well, actually, anyone interested in meaningful data or cross-dataset interoperability can argue with that. Big Data is great, except it means little if we can not combine that data across multiple sources for potentially multiple purposes. (Remember, one of the "V's" of Big Data is variability.) Once the question of what data means gets brought to the fore, it is now time for context and relationships. Structure in an information context means that which situates or describes data in an interpretable way. Big Data needs a Big Structure complement to make sense of it all. What is a Big Structure? Big Structure is data relationships and context that can be combined into a coherent framework to enable dat…
encoded: Defining the Guideposts for Big Data Download PDF In our recent two-part series we described a decade of experience working in the semantic Web (Part I) and our view that Big Structure, which resides at the nexus of the semantic Web, knowledge bases and artificial intelligence, was a key component of making sense of Big Data going forward (Part II). We are at a time when multiple advances are conjoining to create new opportunities and excitement. Data without context and relationships is meaningless. The idea of Big Data is powerful, but it is often presented as either a “good thing” in and of itself, or a mantra for something that is rather undefined. There is no doubt that with the Internet and the Web we are now able to generate and access data at unprecedented scale. There is also no question that tracking mechanisms and cheap storage — and simpler, large-scale databases and Web services — mean that we can also capture data and structure of natures previously unseen. Everyone knows the remarkable growth in exabytes and more. The prospect of data everywhere — some useful with important context and some not — has clearly captured the current discussion. Heck, if we claim Big Data, we even make more in wage or consulting charge-out fees. Who can argue with that? Well, actually, anyone interested in meaningful data or cross-dataset interoperability can argue with that. Big Data is great, except it means little if we can not combine that data across multiple sources for potentially multiple purposes. (Remember, one of the “V’s” of Big Data is variability.) Once the question of what data means gets brought to the fore, it is now time for context and relationships. Structure in an information context means that which situates or describes data in an interpretable way. Big Data needs a Big Structure complement to make sense of it all. What is a Big Structure? Big Structure is data relationships and context that can be combined into a coherent framework to enable dataset…
topic: Adaptive Information
topic: Big Structure
topic: Ontologies
topic: Structured Web
topic: UMBEL
links to: Download PDF
links to: Part I
links to: Big Data
links to: Part II
links to: [1]
links to: ontologies
links to: [2]
links to: [3]
links to: Data model
links to: RDF
links to: [4]
links to: [5]
links to: Concept maps
links to: [6]
links to: Schema
links to: [7]
links to: Mappings
links to: [8]
links to: Taxonomies
links to: [9]
links to: Facets
links to: Categories
links to: [10]
links to: Tables
links to: Synsets
links to: Metadata
links to: Thesauri
links to: Gazetteers
links to: Controlled vocabularies
links to: Reference lists
links to: [11]
links to: Dictionaries
links to: Glossaries
links to: Nested lists
links to: Ordered lists
links to: Clusters
links to: Unordered lists
links to: [12]
links to: Values
links to: QUDT
links to: symbol grounding
links to: referent
links to: [13]
links to: Greenwich meridian
links to: Structured Dynamics
links to: MDM
links to: An Intrepid Guide to Ontologies
links to: UMBEL
links to: upper level ontologies
links to: 0129.html
links to: Domain ontologies
links to: mind maps
links to: UML diagrams
links to: XML schema
links to: directories
links to: folksonomies
links to: subject trees
links to: tags
links to: Sources and Classification of Semantic Heterogeneities

Mike Bergman
type: User

Mike Bergman
type: Person

ontologies
label: Domain ontologies

Data model
label: Data models

RDF
label: RDF discussion

Schema
label: database schema

Facets
label: topic maps

Metadata
label: classification