It's a dynamic world! Ubiquitous streams and the Linked Data Web from Manfred Hauswirth (pooh42)

Streams: Any time dependant data / changes over time.

Has done a paper about P2P stuff.

Data silo - "natural enemy of SW scientists"

Massive exponential growth of global data.

Still have to integrate dynamic data with static data.
Multiway joins are domintion operator.  Need to be efficient.

Everything/body is a sensor.

Various research challenges:

• Query framework.
• Efficient evaluation algorithm.
• Optimise queries.
• Organisation of data.

CoAP ~= http for sensors.

Stuff about sensor networks and context - useful for Michael.

• Common abstraction levels for understanding.
• SSN-XG ontology
• Application: SPITFIRE
• You can buy a sensor off the shelf that runs a binary RDF store and can be queried.  So possible to use SW tech with resource constrained devices.
• RESTful sensor interfaces stuff being standardised - CoRE, CoAP.
• Linked Stream Model
• CQELS-QL (extension to SPARQL 1.1; already legacy)

Rewrite query to spit out static and dynamic - lots of overhead.
But need to optimise between these.
Neither existing stream processing systems nor existing databases could be efficient enough.
So the built own LD stream processing system.  (Optimised and adopted existing database stuff).

HyperWave - didn't succeed.  Didn't listen to customers and wasn't open source (license fees).
But better than hypertext was back in the day.
Performance important for success/uptake.

Just putting it on cloud infrastructure doesn't mean it scales.

• Need to parallelize algorithm.
• Took it to a point where adding more hardware did help.
• Problems!  Inconsistent results, engines don't support all query patterns.. very early, don't fully understand yet.
• Long way to go.  How to prove what is a correct result?
• Needs to be easy to use - dumb it down.
• Linked Stream Middleware (available):
• Flights, _live trains_ - SPARQL endpoint!, traffic cams.
• SuperStreamCollider.org
• Current Tomcat problem with twitter streams.

To do?

• Scaleability
• Stream reasoning (only processing, pattern matching, so far.  Want to infer conclusions).

World is:
... uncertain, fuzzy, contradictory.
So combine statistics and logics.
Hard to scale logical reasoning, so use statistics to shoot in the right direction.

Privacy?

• Build systems! Can't do thought experiments about the Web.

Don't get hung up on approaches / labels.

Semantic Web questions we couldn't ask 10 years ago from Frank Van Harmelen

Semantic Web & Web of data = a more manageable mission.
Metaweb movie - got bought by Google and incorporated into Knowledge Graph.

SW Principles:
1. Give everything a name (entities).
2. Relations form graph between things.
3. Names are addresses on the Web (so we inherit properties of Web like AAA).

This becomes Giant Global Graph.  (Maybe SW should be called Giant Global Graph?)

4. Add semantics.

• Types of things, relationships.
• Hierarchy, constraints:
• Inferences.  Bounding shared beliefs by sharing ontological information.  Space for confusion gets smaller and we begin to agree on interpretation of information.

Semantics = predictable inference.

Google: from just links to results, to information boxes (last May).  Can't directly address Google Knowledge Graph.
NXP (microprocessors): 26,000 products. Integrated all databases into triplestore.  Exposing subset of triplestore to customers.
BBC: 125 million triples.  Many data sources.  APIs to website.  Own ontologies.

All have the same triple-layer architecture:

Raw data
   |
SW layer
   |
Output / API / UI etc

DataGov: eg. air quality in cities, campaign money, if policies work.

Companies don't care about SW, but are using these technologies for their own IRL purposes.

These are all different types of use cases of SW technologies:

• search;
• data integration;
• content re-use;
• SEO;
• data publishing.

It's important that the SW graph is so big.

• More questions to ask.
• Good that we no longer know how big, or how fast it is growing... Tens of billions of facts.
• How many are really permanent?
• Some are stable, some will disappear - just like the 'regular' Web.
• "...it being a mess is the only reason why it scales."

We need to get used to the idea of SW being a mess - aka "a system so large you can no longer enforce central control" (complex system).

The LD cloud is still poorly interconnected, but good graph properties.

SameAs.org

Heterogeneity is unavoidable.
Socio-economic, first to market - why certain systems/ontologies get used, eg. schema.org, dbpedia.

Self-organisation.
LD cloud grew, nobody designed it.
Knowledge follows power curve.  This has an impact on mapping and reasoning, storage and indexing.

Distribution.
Web not geared for distributed SPARQL queries.  Everyone pulls in all data and queries local copy.  Not very 'webby', disadvantageous.  So subgraphs?  Query planning?  Caching?  Payload priority?

Provenance.
Representation, (re)construction.  Metametadata (knowledge about knowledge; uncertainty; problems with vocabs for this).
How to get from provenance to trust.

Dynamics (change).
Cool Web in 60 seconds graphic.
SW not changing this fast, but soon..

Errors and noise.
Sometimes we disagree.
Deal with by: avoid, repair or contain.  Or just deal with it - allow argumentation.
Fuzzy, rough semantics - almost, maybe.

Lots of research questions.  But not ones we could ask 10 years ago.

Information universe - "algorithms exist without us looking at them".

We should ask if things work in theory.
Scientists vs. engineers.
Discovering vs. building.

• Is this incidental or universal?

OWL is our microscope.
We can see structure well in some domains, but not so well in others.  Maybe it's our tool that distorts, rather than a property of the domain.

Says we should change our mindset from building stuff to hypothesising and falsifying.