Database And Brain

Vizoo, YouTube for graphs, got TechCranched. I have led the db design from scrach over a year.

Not yet in English. But coming soon!

Haruki Murakami is one of the most interesting writer in the world today.

As he often mentions, he likes Raymond Carver.

Once you read Ray's works, you never forget his taste. It is strange. In the begining, it looks normal, but in the end, it becomes totally scary world as if you got through to the other world.

Ray puts a little difference one by one. You can move on without paying too much attention. You can just put an ordinary image on the gap.

But if you do, you find it's hard to picture such a scene. So you have to imagine.

Then, in the end, you made up the story with bunch of images you had.

That is a blank or difference effect, I think. So you find something new whenever you read.

With this hypothesis in your mind, take a look at popular novels. They are filled with pictures, no blanks left.

You can enjoy for the first time, second?, third?, boring, boring, boring...

They are made to be consumed. While something with lots of blanks or differences is to be created in users.

Here, I see a significant difference. And it is a key, I think, to the coming world.

With such a concept, I made a search system, which will give you "How come? Ah-ha!" search experience. Available in 4 languages.

cSearch

Examples are blogged here in English. Just try, and tell me what it is.

This world is changing. From the one "how to do" matters, to the other "what to do" does.

Then, problems people have are changing accordingly.

Answering to "what to do" is not something others can do or should do. But to make a decision on "what to do" makes sense when there's any change on its field.

"Chigai" is Japanese that means "difference". An interesting property of "Chigai" is not the difference itself, but the gap that tells A is different from B. So I could say, "Chigai" holds something new information that A and B do not hold.

A change is recognized as "Chigai". And then perceived or interpreted to get accepted.

With this "Chigai" in mind, to deal with the "what to do" era, Database that tells "Chigai" is making more sense. An interesting fact is that such a database can tell a new information that the database doesn't store.

Okay, so how we recognize "Chigai"? Some recent brain sciences answer to this question. By expecting what'll come next, and to which a reality is compared.

I love the concept "Chigai", and its interesting property that says a gap, 無(emptiness), holds the source of creativity.

Probabilistic Graph Model

This is rushed to complete for the submission to "Common Persistent Model Patterns for Performance and/or Scalability Optimization: Call for Submissions" by ODBMS.ORG.

This is just a brief idea to recognize how concepts are formed on the Web. It is not static, but dynamic. And it is shown in numbers, not in abstract way, to enjoy statistical techniques on it.

Invariant

At first, let me introduce how our brain recognizes things. You may want to take a look at this video by Jeff Hawkins to catch up the latest brain science. "Invariant", which is the term Jeff takes, is less changing, mostly static concept considered residing in our neocortex at levels.

Our brain translates things into these invariants. That's why we can tell you're human, so am I. Otherwise, every thing is different from each other. Then, your life will get wild.

"Invariant" *expects* what will happen next based on its context when actuall input is coming up. Emergency annunciator will get on if any differences are obserbed between the expectation and actuall inputs. Then, your consciousness gets involved to watch out.

It is natural to wonder if we refine Information Retrieval with such concepts.

Invariant representations for document

Let's consider document as usuall in IR.

What is "Invatiant" for document? As you can guess, and actually see in various researches about clustering, factoring, topic estimation, "Topic" looks the most natural choise to take.

Y = AX
Y: Document/Word(probability) matrix
X: Document/Topic(probability) matrix
A: Topic/Word(probability) matrix

Think about such a matrix X, which shows topical aspects of documents. You can get relax, don't need to get bothered here by the exact meaning of matrix. It is actually the equation used in GaP by Canny.

We'll get "Invatiant"s if we can find such A and X. Please not that it is just one way to get "Invatiant", and I think, is the best way to picture the idea.

Difference, Earthquake, Concept Map

We can apply this idea to Information Retrieval, not for search, but to see its statistical aspects of underlying document collection.

These will form X in the equation above.

Now you have "Invariant" representation by numbers for a query, a concept.

You can measure differences, deviations, means, whatever you like.

If we measure deviations shown in concepts like doing for "Earthquake", isn't it safe to say that similar measures mean they are close to each other geometically? Then, we'll get dynamic "Concept Map" on the web.