Data Space FAQ
What is a Data Space?
A Data Space is an atomic point of presence in cyberspace (Internet, Web, Blogosphere, Wikisphere etc.) for interacting with Data, Information, Knowledge, and Services.
Its content may be imported from, or simply point to data in, other Data Spaces.
In all cases the content is either transient (generated “on the fly”) or persistent (supported by cache synchronization schemes).
What would you typically find in a Data Space?
- Raw Data - SQL, HTML, XML (raw), XHTML, RDF etc.
- Information (Data In Context) - XHTML (various microformats), Blog Posts (in RSS, Atom, RSS-RDF formats), Subscription Lists (OPML, OCS, etc), Social Networks (FOAF), and many other forms of applied XML.
- Knowledge - Domain specific Concepts and Terms available in transient or persistent forms expressed in RDF with Domain, Schema, and Instance Data serialized using formats such as RDF-XML, N3/Turtle etc.
FOAF, SIOC, and Atom OWL amongst others
- Web Services - REST or SOAP based invocation of application logic for context sensitive and controlled interaction with Data Space content
- Web Services - REST or SOAP based invocation of application logic for context sensitive and controlled interaction with Data Space content.
- Knowledge - Information in actionable context that is available in transient or persistent forms expressed in a Graph Data Model.
Increasingly, RDF will provide the Data Language, RDFS its Schema Language, and OWL its Domain Rules Definition (Ontology) Language . Domain, Schema, and Instance Data can be serialized using formats such as RDF-XML, N3/Turtle etc).
How do Data Spaces and Databases differ?
Data Spaces are fundamentally problem-domain-specific database applications.
They offer functionality that you would instinctively expect of a database (e.g.
AICD data management) with the additional benefit of being data model and query language agnostic.
Data Spaces are for the most part DBMS Engine and Data Access Middleware hybrids in the sense that ownership and control of data is inherently loosely-coupled.
How do Data Spaces and Content Management Systems differ?
Data Spaces are inherently more flexible, they support multiple data models and data representation formats.
Content management systems do not possess the same degree of data model and data representation dexterity.
How do Data Spaces and Knowledgebases differ?
A Data Space cannot dictate the perception of its content.
For instance, what I may consider as knowledge relative to my Data Space may not be the case to a remote client that interacts with it from a distance, Thus, defining my Data Space as Knowledgebase, purely, introduces constraints that reduce its broader effectiveness to third party clients (applications, services, users etc..).
A Knowledgebase is based on a Graph Data Model resulting in significant impedance for clients that are built around alternative models.
To reiterate, Data Spaces support multiple data models.
What Architectural Components make up a Data Space?
- ORDBMS Engine - for Data Modeling agility (via complex purpose specific data types and data access methods), Data Atomicity, Data Concurrency, Transaction Isolation, and Durability (aka ACID).
- Virtual Database Engine - for creating unified views of heterogeneous RDF, SQL, XML, Free Text, and other data.
This is all about Virtualization at the Data Access Level.
- Web Services Platform - enabling controlled access and manipulation (via application, service, or protocol logic) of Virtualized or Disparate Data.
This layer handles the decoupling of functionality from monolithic wholes for function specific invocation via Web Services using either the SOAP or REST approach.
Where do Data Spaces fit into the Web's rapid evolution?
They are an essential part of the burgeoning Data Web / Semantic Web.
In short, they will take us from data “Mash-ups” (combining web accessible data that exists without integration and repurposing in mind) to “Mesh-ups” (combining web accessible data that exists with integration and repurposing in mind).
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