related to the sirius disclosure of ending illegal secrecy on the BITS and Pieces we are unable to define for ourselves.
About "BIG DATA" and META DATA and the DEEP BLACK illegal secrecy of our writing traditions as various writing
styles in space in the future.
About "BIG DATA" and META DATA and the DEEP BLACK illegal secrecy of our writing traditions as various writing
styles in space in the future.
What are the similarities of the
question
answer
and
result.?
the theory goes into BIG data and the "WHY?" of the "META DATA" as the link between the data that is collected about people, not for intrusive data but as the best way to exchange values of various needs we have for spending our time.
from
meta.stackexchange.com/
When I type a new question in SO, it displays the list of similar questions almost instantly? I imagine there must be some kind of distance computation between the asked question and the questions in the database. How do they implement this? Does the algorithm scale when there are more questions?
You may want to read a "Collective Intelligence" book, like link text.
Lucene can be used, or more commonly something that builds on Lucene like Solr (link text)
Basically it's just matter of calculating approximate distance between questions, usually implemented by using vector spaces. A simplification is to think of each unique word from either query being a value; if query has it, value is 1.0, if not, 0.0. And then create ordered vector of values, calculate cartesian distance (both have the word, 0 distance in one dimension, only one has it, 1; sum differences, take Nth root etc). But after that, it's better to scale words in order of relevancy, usually using TF/IDF (or similar). That's close to what Lucene does.
With Lucene you could just index all old questions, use the question being asked as the query, and choose highest ranking entry or entries. That's simple, fast, and possibly good enough. But there are many many ways to improve this obviously; so using Solr is a logical next step.
on the BIG DATA "WHY?"
there are some good books on the BIG DATA and how it functions with the human brain.The way that the Brain remains centered and the way that the left and right hemispheres are related to the centered THALAMUS positional projection into space as the value of SPACE having it's special value as being shifted into the spectral shades and how to give oneself the "WHY?" answer to oneself as "WHY"?.So there is some similarities in space of the WHY? existing in a larger space.If you like storing "WHY?" questions, than it might be true that the more there are ,then the greater the amount of space they fill.
question
answer
and
result.?
the theory goes into BIG data and the "WHY?" of the "META DATA" as the link between the data that is collected about people, not for intrusive data but as the best way to exchange values of various needs we have for spending our time.
from
meta.stackexchange.com/
When I type a new question in SO, it displays the list of similar questions almost instantly? I imagine there must be some kind of distance computation between the asked question and the questions in the database. How do they implement this? Does the algorithm scale when there are more questions?
You may want to read a "Collective Intelligence" book, like link text.
Lucene can be used, or more commonly something that builds on Lucene like Solr (link text)
Basically it's just matter of calculating approximate distance between questions, usually implemented by using vector spaces. A simplification is to think of each unique word from either query being a value; if query has it, value is 1.0, if not, 0.0. And then create ordered vector of values, calculate cartesian distance (both have the word, 0 distance in one dimension, only one has it, 1; sum differences, take Nth root etc). But after that, it's better to scale words in order of relevancy, usually using TF/IDF (or similar). That's close to what Lucene does.
With Lucene you could just index all old questions, use the question being asked as the query, and choose highest ranking entry or entries. That's simple, fast, and possibly good enough. But there are many many ways to improve this obviously; so using Solr is a logical next step.
on the BIG DATA "WHY?"
there are some good books on the BIG DATA and how it functions with the human brain.The way that the Brain remains centered and the way that the left and right hemispheres are related to the centered THALAMUS positional projection into space as the value of SPACE having it's special value as being shifted into the spectral shades and how to give oneself the "WHY?" answer to oneself as "WHY"?.So there is some similarities in space of the WHY? existing in a larger space.If you like storing "WHY?" questions, than it might be true that the more there are ,then the greater the amount of space they fill.
from
www.amazon.in/Deviate-Reality-Differently-Beau-Lotto/dp/1474607241
Perception is the foundation of human experience, but few of us understand how our own perception works. By revealing the startling truths about the brain and perception, Beau Lotto shows that the next big innovation is not a new technology: it is a new way of seeing. In his first major book, Beau Lotto draws on over a decade of pioneering research to show how our brains play tricks on us. With an innovative combination of case studies and optical and perception illusion exercises, Deviate will revolutionise the way you see the world. With this new understanding of how the brain works and its perceptive trickery, we can apply these insights to every aspect of life and work. Deviate is not just an engaging look into the neuroscience of thought, behaviour and creativity: it is a call to action, enlisting readers in their own journey of self-discovery.
So what then, are the other relations of the function of questions and answers as related to the results?.
from
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0071511
AbstractWith the blooming of Web 2.0, Community Question Answering (CQA) services such as Yahoo! Answers (http://answers.yahoo.com), WikiAnswer (http://wiki.answers.com), and Baidu Zhidao (http://zhidao.baidu.com), etc., have emerged as alternatives for knowledge and information acquisition. Over time, a large number of question and answer (Q&A) pairs with high quality devoted by human intelligence have been accumulated as a comprehensive knowledge base. Unlike the search engines, which return long lists of results, searching in the CQA services can obtain the correct answers to the question queries by automatically finding similar questions that have already been answered by other users. Hence, it greatly improves the efficiency of the online information retrieval. However, given a question query, finding the similar and well-answered questions is a non-trivial task. The main challenge is the word mismatch between question query (query) and candidate question for retrieval (question). To investigate this problem, in this study, we capture the word semantic similarity between query and question by introducing the topic modeling approach. We then propose an unsupervised machine-learning approach to finding similar questions on CQA Q&A archives. The experimental results show that our proposed approach significantly outperforms the state-of-the-art methods.
source and writer.
Citation: Zhang W-N, Liu T, Yang Y, Cao L, Zhang Y, Ji R (2014) A Topic Clustering Approach to Finding Similar Questions from Large Question and Answer Archives. PLoS ONE9(3): e71511. https://doi.org/10.1371/journal.pone.0071511
Editor: Derek Abbott, University of Adelaide, Australia
Received: May 3, 2013; Accepted: June 27, 2013; Published: March 4, 2014
Copyright: © 2014 Zhang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
The next question may relate to the bits and pieces as related to space and the question WHY? are people interested in the filling of the space?.
by variables of the space size as cause and effect and the way to define it's result as ones own?.
What is space made of? Space that we can see is mainly very very empty. If you ignore the galaxies and stars, then the rest of space is mainly a vacuum, so there's no particles at all. The particles that are there are mainly hydrogen and helium, which form a plasma called the Intergalactic Medium.Nov 26, 2012
Some of the most stimulating thinkers of our age have long questioned this question of the "WHY?!
there is space and why it is dark as ending the illegal secrecy of the DEEP BLACK.It's probably the
types of letter styles that you use to write data with and how this would effect the future.
www.amazon.in/Deviate-Reality-Differently-Beau-Lotto/dp/1474607241
Perception is the foundation of human experience, but few of us understand how our own perception works. By revealing the startling truths about the brain and perception, Beau Lotto shows that the next big innovation is not a new technology: it is a new way of seeing. In his first major book, Beau Lotto draws on over a decade of pioneering research to show how our brains play tricks on us. With an innovative combination of case studies and optical and perception illusion exercises, Deviate will revolutionise the way you see the world. With this new understanding of how the brain works and its perceptive trickery, we can apply these insights to every aspect of life and work. Deviate is not just an engaging look into the neuroscience of thought, behaviour and creativity: it is a call to action, enlisting readers in their own journey of self-discovery.
So what then, are the other relations of the function of questions and answers as related to the results?.
from
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0071511
AbstractWith the blooming of Web 2.0, Community Question Answering (CQA) services such as Yahoo! Answers (http://answers.yahoo.com), WikiAnswer (http://wiki.answers.com), and Baidu Zhidao (http://zhidao.baidu.com), etc., have emerged as alternatives for knowledge and information acquisition. Over time, a large number of question and answer (Q&A) pairs with high quality devoted by human intelligence have been accumulated as a comprehensive knowledge base. Unlike the search engines, which return long lists of results, searching in the CQA services can obtain the correct answers to the question queries by automatically finding similar questions that have already been answered by other users. Hence, it greatly improves the efficiency of the online information retrieval. However, given a question query, finding the similar and well-answered questions is a non-trivial task. The main challenge is the word mismatch between question query (query) and candidate question for retrieval (question). To investigate this problem, in this study, we capture the word semantic similarity between query and question by introducing the topic modeling approach. We then propose an unsupervised machine-learning approach to finding similar questions on CQA Q&A archives. The experimental results show that our proposed approach significantly outperforms the state-of-the-art methods.
source and writer.
Citation: Zhang W-N, Liu T, Yang Y, Cao L, Zhang Y, Ji R (2014) A Topic Clustering Approach to Finding Similar Questions from Large Question and Answer Archives. PLoS ONE9(3): e71511. https://doi.org/10.1371/journal.pone.0071511
Editor: Derek Abbott, University of Adelaide, Australia
Received: May 3, 2013; Accepted: June 27, 2013; Published: March 4, 2014
Copyright: © 2014 Zhang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
The next question may relate to the bits and pieces as related to space and the question WHY? are people interested in the filling of the space?.
by variables of the space size as cause and effect and the way to define it's result as ones own?.
What is space made of? Space that we can see is mainly very very empty. If you ignore the galaxies and stars, then the rest of space is mainly a vacuum, so there's no particles at all. The particles that are there are mainly hydrogen and helium, which form a plasma called the Intergalactic Medium.Nov 26, 2012
Some of the most stimulating thinkers of our age have long questioned this question of the "WHY?!
there is space and why it is dark as ending the illegal secrecy of the DEEP BLACK.It's probably the
types of letter styles that you use to write data with and how this would effect the future.
my name made it to the INSIGHT MISSION to MARS so of course i would be interested what happens to the computerized bits and pieces of my name as related to the inherent question, answer and results of having my name somewhere other than "on Earth".So when I write my name in space, will it's inherent data displacement be different ? in some way ?.
Here then, the answer from NASA.
ANSWER:from
NASA
starchild.gsfc.nasa.gov/docs/StarChild/questions/question52.html
Your question, which seems simple, is actually very difficult to answer! It is a question that many scientists pondered for many centuries - including Johannes Kepler, Edmond Halley , and German physician-astronomer Wilhelm Olbers.
There are two things to think about here. Let's take the easy one first and ask "why is the daytime sky blue here on Earth?" That is a question we can answer. The daytime sky is blue because light from the nearby Sun hits molecules in the Earth's atmosphere and scatters off in all directions. The blue color of the sky is a result of this scattering process. At night, when that part of Earth is facing away from the Sun, space looks black because there is no nearby bright source of light, like the Sun, to be scattered. If you were on the Moon, which has no atmosphere, the sky would be black both night and day. You can see this in photographs taken during the Apollo Moon landings.
So, now on to the harder part - if the universe is full of stars, why doesn't the light from all of them add up to make the whole sky bright all the time? It turns out that if the universe was infinitely large and infinitely old, then we would expect the night sky to be bright from the light of all those stars. Every direction you looked in space you would be looking at a star. Yet we know from experience that space is black! This paradox is known as Olbers' Paradox. It is a paradox because of the apparent contradiction between our expectation that the night sky be bright and our experience that it is black.
Many different explanations have been put forward to resolve Olbers' Paradox. The best solution at present is that the universe is not infinitely old; it is somewhere around 15 billion years old. That means we can only see objects as far away as the distance light can travel in 15 billion years. The light from stars farther away than that has not yet had time to reach us and so can't contribute to making the sky bright.
Another reason that the sky may not be bright with the visible light of all the stars is because when a source of light is moving away from you, the wavelength of that light is made longer (which for light means more red.) This means that the light from stars that are moving away from us will become shifted towards red, and may shift so far that it is no longer visible at all. (Note: You hear the same effect when an ambulance passes you, and the pitch of the siren gets lower as the ambulance travels away from you; this effect is called the Doppler Effect).