Archive for August 2008
Conversation with Luis Suarez
I have got a conversation with Luis Suarez, Knowledge Manager, Community Builder & Social Computing Evangelist in the IBM Software Group division (www.elsua.net/), through ITToolBox network.
I have received the right to publish it in my blog.
TO Luis Suarez FROM Garo Garabedyan
http://garabedyan.wordpress.com/2008/05/26/migrate-e-mail-conversation-to-a-wiki/
Above is a blog post, where I try to dream about a new automatic way to migrate from long e-mail conversations to a Wiki.
Based on a service, which creates a wiki page for every conversation (by default recognizes a bunch of e-mails as a part of a conversation) and automatically gives rights of Read&Write or only Read to the collaborators according the TO, FROM and CC tags.
I have downloaded your presentation: Тhinking out of the inbox more collaboration through less email.
4th slide- a map of people in a collaboration. I think that in IBM was a social tool which tries to present the relations in a such graphical diagram.
15th slide- e-mail and wiki. Using attached documents in e-mails is not very well presented in wiki. Which particular engine you picture, is there a such engine which provides Document revisions (like the Wiki) for Office files. I will
be very interested in such a service.
I am a student and I will be happy to hear advise from you. Do you share some of my views?
Garo Garabedyan
Sofia, Bulgaria
TO Garo Garabedyan FROM Luis Suarez
Hi Garo,
Thanks a lot for the feedback comments and for the information details! Very insightful, indeed, and glad I am not the only one thinking along these terms as well. Very very good!
I think your idea of an automated process that would convert e-mails into wiki pages with the right level of access and interactions is something that would prove really really useful and I hope to be able to see it at some point in time. Alas, we are not there yet, at least, from what I know of the various wiki engines that I have been exposed to so far, but, like I said, it would be really nice to see it at some point! Because it clearly indicates the way we would need to follow!
With regards to your comments on the slide deck, yes, inside IBM we do have that tool that provides such visualisations and it surely provides a really nice functionality of identifying your experts from a specific community, as well as the weak links from that social network, which will prove to be very handy, specially when you are looking for the right people for the right job!
On the slide of the e-mail and the wiki, I must say that the slide is not from me, the graphic is from Wikinomics and it doesn’t represent a specific wiki engine that supports extremely well attachments, in fact, very few wiki engines do. Wikis are more down to earth towards building content on top of each other’s content but more down to text and hyperlinks than attachments, so I doubt they would ever provide such excellence of support for attachments that you are asking for. If you look into Wikimatrix you will see how not many of them place the focus on attachments, which I think is the right approach. It is more about building the content linearly and in a single interface, instead of having to force people to attach, detach, download, upload, that specific file. I think that was not the purpose wikis were designed for in the first place, I am afraid.
Thanks again for the really nice feedback! Greatly appreciated!
Cheers!
Luis Suarez
TO Luis Suarez FROM Garo Garabedyan
Hello,
Thank you for your answer and that you have spent time reading my message.
I want to keep a conversation with you and with a project manager of a close to this topic product of IBM.
I think that Wiki pages over E-mail conversations is possible. I can give you short examples of some conversations which will look beautiful if are processed over Wiki.
When I have saw that diagram about sending e-mails with attachments I thought that it is already implemented to have a Document revisions of Office Docs (which I am passioned of). I think that there are a lot of things to be done in this approach. Recognizing the user behavior with Ajax in order to conclude if the user is Adding, Erasing or Editing content, and on the base of this analysis to form a very successful diagram of revisions of every piece from the document and for the whole document. [ http://garabedyan.wordpress.com/2007/11/18/ajax-wiki-editing/ # Document revisions]. This way solving a big problem of IT theory, recognizing document revisions.
I believe that my dream of a wiki over e-mail conversation is possible and I think that eventBased Algorithms and Data ( http://garabedyan.wordpress.com/2008/03/04/data-flow-processing-eventbased-algorithms-and-data/ , http://garabedyan.wordpress.com/2008/04/27/event-based-content-editor/ ) are able to do this job.
I truly thank you and I ask you to contact me with a specialist in the practice field, I want to share my thoughts with both of you.
You as a philosopher with a wide range of ideas.
He/She as a practical implementor.
PS: Social networking can truly change the nowadays web, turning it into more safer and healthy world (http://garabedyan.wordpress.com/2008/03/08/virtual-world-as-a-place-to-meet-and-interact-with-people-on-web-page/).
A lot of my friends are becoming zombies while using their computers, I think that by presenting social connections while (i.e.) browsing web page surfers will be protected by this modern disease of IT workers.
Would you be so kind to paste some pieces from your message as comments to the blog post ( http://garabedyan.wordpress.com/2008/05/26/migrate-e-mail-conversation-to-a-wiki/ ). Now I am studying in Bachelor program in Technical University of Sofia- Bulgaria for 1st year. I work on the blog in order to find valuable remarks and use them in order to continue my education in Europe.
Garo Garabedyan
TO Garo Garabedyan FROM Luis Suarez
Hi Garo,
Thanks for the follow up and for the feedback comments. Appreciated. RE: “I think that Wiki pages over E-mail conversations is possible. I can give you short examples of some conversations which will look beautiful if are processed over Wiki.” > Oh yeah, I know PLENTY of those, too! Starting with this one! As far as I can see almost everything that would not be consider a one-on-one conversation discussing a subject of a sensitive or confidential nature would be considered possible to go into a wiki. And even in the latter example, it could still go into it, if it would be a fully protected wiki, which I have seen far too many. So from what I can see plenty of e-mails could have their space in the wiki-sphere. What I am just trying to say is that as soon as you work with attachments in e-mail that becomes more difficult to manage in a wiki, since wikis have not been built to store attachments, but more to build content ad-hoc amongst a group of people. That’s all what I meant.
With regards to document revisions, I am thinking that a similar thing is what you would get with Recent Changes, right? I mean, they are not as fancy and sophisticated as Office documents, but they surely get the job telling you who made those updates, when, and what content changed, which for the basic purposes of document revisioning may be good enough to get things going. Nothing fancy, nothing more complex. Just gets the job done. And you can syndicate that content, something Office documents don’t offer. So I would find them to have a bit of an advantage in that space, for sure.
Hummm, interesting that you say that e-mail is a collaboration tool, when it is not. Don’t confuse communication vs. collaboration. There are two completely different things. The fact that all of us have abused e-mail as a way to spread information does not mean it will make it as a collaboration tool. In fact, it doesn’t. It does such a poor job at helping collaboration flow in a natural way that it becomes a nightmare, after a few instances, so let’s just try to consider how wikis vs. e-mail, as collaboration tools just don’t compare. Issues like openness, public, awareness, co-creation, co-authorship, etc. etc. just won’t happen in e-mail whereas they really thrive in wiki systems.
With regards to your final comments on contacting a specialist in the practice field, I am sorry to disappoint you, but you won’t find any, main reason being that distinction I mentioned above where e-mail and wiki are two completely different beasts, one to communicate and the other to collaborate, so doubt you would ever find anyone out there in this area. And I think if you are trying to move forward in that direction to make that distinction as well, it would help you save a few headaches along the way.
And with regards to your comments about quoting some of the interactions through e-mail into your blog, I would be more than happy, in fact, I would be more than happy to carry out the conversation through the blog as it would help everyone else out there benefit from such exchange. So, by all means, go ahead and quote those bits and pieces and use a link to my main blog: http://www.elsua.net (I can’t track the links to ITtoolbox’s blog at the moment, trying to fix it as we speak) and will be chiming in accordingly.
Hope that helps and thanks again for the helpful comments and insightful feedback!
Regards
Luis Suarez
TO Luis Suarez FROM Garo Garabedyan
About Document Revisions:
http://alumni.media.mit.edu/~fviegas/papers/history_flow.pdf
Studying Cooperation and Conflict between Authors with history flow Visualizations
by
Fernanda B. Viégas
MIT Media Lab
Cambridge, MA 02139 USA
fviegas@media.mit.edu
Martin Wattenberg
IBM Research
Cambridge, MA 02142 USA
mwatten@us.ibm.com
Kushal Dave
IBM Research
Cambridge, MA 02142 USA
kdave@us.ibm.com
eventBased Content Editor, + eventBased Philosophy
http://garabedyan.wordpress.com/2008/04/27/event-based-content-editor/
http://garabedyan.wordpress.com/2008/03/04/data-flow-processing-eventbased-algorithms-and-data/
By using a declarative approach about the relations between pieces of the content which are fast (and asynchronously) edited by many authors (in example wiki, no one can know how many authors what exactly will edit) to: a) trace changes and inform the interested readers (authors), b) present an excellent data flow diagram of document revisions
Scenario about a):
You add an information about some event and present the needed stuff and declare relations of this stuff to the content about the weather forecast. Some people declare interest of the event.
If this forecast changes, the system traces which content is related to the changed one and informs the authors of the related content about the change.
Next, the authors update the related content by applying the change in the forecast.
You have two choices, to update the information or to not, depending what you think about the importance of the change in the forecast.
System informs the interested readers that a change occurs in the page about the event, if you have updated it.
Garo Garabedyan
TO Garo Garabedyan FROM Luis Suarez
Hi Garo, and me thinking that every single change that you do to most wiki engines would get registered into the system and therefore easy to track. Your comments below suggest otherwise. I am thinking as well that apart from Wikipedia I doubt there would be other wikis at that level and with level of complexity that you are mentioning and as such I doubt it would be even worth while tracking all of that activity. For what purpose, to track active contributors? Wouldn’t that be obvious already? To track doc revisioning? Hummm… interesting but I thought that doc revisioning would be done for documents, in most cases, considered critical or essential to the project or business, and for that I bet there are better tools than wikis, specially when you get involved with Intellectual Capital, Intellectual Property and IP Law.
I am certainly not disagreeing here with your point of view. I think it is a fascinating interesting new aspect of the complexity aspects of a wiki. I am just saying that perhaps a good majority of the folks who regularly use wikis don’t care, and therefore would not want to complicate their user experience.
Thanks for sending the links along! Will keep digging into your research, although with the travelling I am doing at the moment, I am not sure I would be able to get much done before end of June, but will try in between now and then and see how much I can get through.
Thanks again for very enlightening conversation. Take care and have a good one!
(Need to catch a flight to Munich)
Regards
Luis Suarez
TO Luis Suarez FROM Garo Garabedyan
Hello,
I was busy this month with the final exams in my university.
I want to ask you about your opinion on using ontology in wiki pages.
You know how Wikipedia presents a lot of tags which indicates to the visitors that the information presented here can be a little bit old and not complete. If calling this as an ontology mechanism where people are free to write their opinions in natural language, but are free to mark some pieces of it as related somehow to something out there. Is this kind of collaboration commercially interesting.
I want to give you an example. Imagine a business application which handles all the tasks of the workers and keeps tracking what percent of the work they have done according their own reports. Is this replaceable by a custom designed ontology Wiki. Such a wiki which has tags (like XML) which lets editors to edit task page wikis by setting the percent of the done work.
Is the common application user interface tag presentable (with all the calls up to the database about types in check boxes and list boxes) and in this sense is editing a wiki page become more powerful? Editors can be free to bind this discrete values in such a way like they are in one DataBase and use them around the all wiki pages, when the letter is aimed to a business tasks.
I respect your professional view. What you thing?
Garo Garabedyan,
Sofia, Bulgaria
TO Garo Garabedyan FROM Luis Suarez
Hi Garo,
Thanks a lot for the message and for the feedback details. I think you are on to something when you are proposing to put together a tagging / folksonomy infrastructure added further add to the contributions of a wiki. More than anything else because you are making the content of the wiki itself much easier to search & find it at the same time that you give people the opportunity to build a second entry of knowledge based on such tagclouds, as well as the overall content from the wiki itself. If you combine that with a potential fixed taxonomy, a limited, but original one, that a wiki may well have you are putting together some really good advantages towards encouraging knowledge workers to collaborate further having that massive index, or tagcloud, of key terms spread around the wiki.
Interesting challenge would be though how to put such folksonomy to the test of proving useful to the business and not just the knowledge workers. I can imagine that the business would be interested, but it would need to see the buy-in. It would work on an individual basis for each of the knowledge workers contributing, but not sure the business will buy it. In fact, there aren’t many businesses out there exploting the power of the tag in a business context to create dynamic taxonomies combining them with folksonomies and I guess that’s mostly due to the fact that people who have been managing those taxonomies in the past may not feel very comfortable with letting that control go.
Not sure whether you would be aware of this or not, but Thomas van der Wal, the guy who coined the term folksonomy, has done some fascinating research around the world of tagging and how it can improve the way people share information as well as find it at a later time. You may want to contact him and see if he would have some other ideas he could throw on the table on the kind of impact it could have on a wiki…
Hope that helps get some discussion going…
Thanks!
Luis Suarez
Intelligent Feeds
Internet feeds present updates and information about some topic. On the base of the feeds’ articles that you read it can be synthesized a list of tags that you prefer more than others. Newly sent feeds can be ordered in respect this list of most preferred topics.
Ordering content on the base of the users activity on the letter is something not new. All web based applications and content at all can have such option.
Feature like this is implementable only if the content object of statistics is tagged in a such precise way that articles have to have a lot of tags which are shared by the rest but in such a way to not make more than one article with identical list of tags.
It will be very interesting if the place in the list of tags is important to the value of the tag over the article and the system lets publishers to arrange the list of tags. Of course, it is possible tags to be an associative array and publishers to set values to every tag (in percents in example), but I do not find it useful.
While using a collaborative software, each attender is informed by mail or feed about changes made by the rest collaborators. I think that a list of recent changes or containing the changes since your last view or edit is an useful user interface feature placed next to the concrete editor and attached to the document information while browsing collaborative projects/ documents.
Possible implementations:
- GDocs
- Wiki
Querying Tables in the Web
I am glad to announce that after a lot of work now I am ready with a new work over how search engines should understand and search in HTML tables.
Printable version as a PDF file: querying-tables-in-the-web
Querying Tables in the Web
Information technology research
Here is expressed a new way of crawling and searching information stored in HTML tables. The results of this search are new tables (like results of SQL queries).
When I have started working on this topic I was not aware of Google’s MapReduce, now I find it very useful to the topic of this paper.
Contents:
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Abstract. Where tables are used
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The Algorithm in a few words. Understanding how tables care information
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Addressing cells. Setting calling names of cells in order to extract information
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Semantics and value of secondary cells
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The Algorithm in deep. Theoretical translation of tables into natural text statements
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Constructing new tables (using MapReduce’s idea). Comparison with SQL queries
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Legal Drinking Age in Asia. Table to natural text translations
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Is Semantic web a table like model
Understanding meaning of main cells and secondary cells in a table and deriving meanings in order to present tables as natural language sentences (search-able by machines). Querying this data and receiving as a result a composed table by the search engine.
Free text to table is a possible future option.
Abstract. Where tables are used
No matter if you read a restaurant menu with no table borders (content is which arranged like a table with no borders) or a bordered table, the way content is placed is like it is filled in columns and rows.
I believe that the table is able to be translated in natural language sentences in order to understand and index the information as a natural text by search engines. Browsing the web we find tables explaining time schedules, steps, categories and so on. May be the table is the second most popular form of expressing information next to the simple text (array of characters and symbols).
The Algorithm in a few words. Understanding how tables care information
Data in tables is presented in two main cell groups, the first group is the main cells which carry the general attributes of the data and the second is the group about the concrete category/ case (in variation tables). This two groups are in most of the real life tables separated by their formatting, data type of content and place in the table’s rows and columns.
Understanding the way this cells content relates to each other and translating this relations into natural text sentences like data structure which is able to be queried like a relational database table.
By using of a MapReduce (http://labs.google.com/papers/mapreduce.html) like algorithm, new tables can be created from the database of many tables to present search result by mapping equal cell names or values according to a complicated user query search.
Addressing cells. Setting calling names of cells in order to extract information
Lets address cells of a table like a mathematical matrix’s cells.
This is an example of a table aiming to present all possible combinations of a structure of a table.
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R1; C1 |
R1; C2 |
R1; C3,4 |
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R2; C1 |
R2; C2 |
R2; C3 |
R2; C4 |
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R3; C1 |
R3; C2 |
R3; C3 |
R3; C4a |
R3; C4b |
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R4; C1 |
R4; C2,3 |
R4; C4 |
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R5,6; C1 |
R5; C2 |
R5; C3 |
R5; C4 |
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R6; C2 |
R6; C3 |
R6; C4 |
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R7; C1 |
R7; C2 |
R7; C3 |
R7; C4 |
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Table 1. Theoretical table presenting all important cell arrangements (carrying information about the content of the cell according its arrangement)
One of the possible ways to address cells is to begin setting names from the upper left cell, but the other which I find as more practical is to start from the most lower and most right cell. I prefer the second way because the most common tables have main cells on first (and second) row and/ or first row and first column, so it is very possible if you start with the upper and most left cells to find a cell, which is separating going deep in the table (if table contains more than one main cells row or column). For this reason in order to not name and later address cells like: R3a, C4a. Lets begin from the most low and right cell.
The above consideration does not reflect to the algorithm itself but rather to the more rational naming of cells which rationalization enables easy white box testing and debugging.
Highlighted cell -addressing starting point, this cell’s width and height gives the width of the last column and height of the last row.
Semantics and value of secondary cells
Every secondary cell has value and name (semantic).
The semantics of the cells is placed in their main cells.
Main cells carry only the semantics of the rest cells as its value and does not have semantics. Main cells value is in common a string.
A table can contain from 0 (no main cells found in the table) up to 2 groups main cells (into a main cell is placed another table containing its own main cells forming the second group of letter).
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Empty/ no content |
semantic1 |
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semantic2 |
value1 |
Table 2. Scheme of semantics and values of a cell. The bottom right cell is a secondary cell with semantics: semantic1, semantic2 and value: values1
Values can be these data types:
string
number (if a space character, new line character if found it is ignored)
number range (spaces are ignored, “-” and “,”/”;” between the numbers describing the range)
currency (recognized by patterns)
time (recognized by patterns)
data (recognized by patterns)
time and date (recognized by patterns)
picture (the “alt” text if it is set can be treated as value)
href
Note: Ignore comments (i.e.:“18 (<string>)” is not string, but number). Comments are used with this patterns: “<statement> (<comment>)”; “<statement> - <comment>”; “<statement> <- <comment>”. Spaces are added only for a better visual expression. Of its own the algorithm ignores them from the very beginning, except the statement is string.
The Algorithm in deep. Theoretical translation of tables into natural text statements
All cells are addressed and main cells are recognized from the rest. The above theoretical table (Table 1) is used in order to present the all important policies of the algorithm.
R3; C4a and R3; C4b are split cells. R4; C2,3 is a merged cell (by columns). R5,6; C1 is a merged cell (by rows), too.
There is no difference in split cell by row or column. I do not find any reasonable application of this two different kinds of splitting instead of splitting by diagonal (by column and row at once) of the upper right cell.
Every secondary cell is translated into a natural text statement, by this pattern:
[R2; C2] are/is (R2; C2)
are - if main cells are more than 1
is - if there is only one main cell
R2; C2 – the cell itself
(R2; C2) – the value within cell R2; C2
[R2; C2] – the value(s) of the main cells of R2; C2. The semantics of cell R2; C2
Constructing new tables (using MapReduce’s idea). Comparison with SQL querie
MapReduce is a programming model and an associated implementation for processing and generating large data sets. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. [labs.google.com]
Having many secondary cells collected from many tables from different domains around the Internet is enough data to construct a new table as a result every time a query is executed. Secondary cells contain in themselves information about the semantics and about its value. Constructing a table as a search result is useful when the database does not contain a suitable table for the search query, but many columns and enough rows from different tables solely extracted can satisfy the search. Secondary cells’ semantics and data types of their values are not unique, many tables represent one and the same phenomena. By using some NLP policy is possible to claim is a secondary cell semantics equal to others secondary cell semantic from a different table.
User search can be treated as a query for a set of secondary cells with specified semantics and a specified value(s) for a semantic(s) which have to be bound with the presented results.
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Product type |
Vendor |
The Lowest 3 Prices |
Alternatives (on the same price as tempVariable1? ) |
Alternatives (on the same price or lower as tempVariable1? ) |
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MP3 Player |
Sony |
?Price? |
?Vendor, Product? |
?Price? |
Table 3. Search query presented in a table
Result:
The Lowest 10 Prices are: price1, price2, price3 // there are supposed to be MP3 Players on a same low price, but I have not deal with this possible case, this is just an example
Alternatives on Price price1: vendor1, product1, priceX1; vendor2, product2, priceX2
Alternatives on Price price2: vendor3, product3, priceX3; and all the above
Alternatives on Price price3: vendor4, product4, priceX4; vendor5, product5, priceX5; and all the above
As a comparison with SQL queries and the relational Database theory. I think that this are queries just like SQL ones executed on a relational DataBase but here we have no FROM clause, so the engine searches everywhere he finds such columns’ semantics and uses its rows if possible to do so. This kind of searching is made on the base of multiple searches for intermediate values associated with the same intermediate semantic around this mass storage of tables.
Examples of query type searches which can be performed better if the above is implemented:
What is the population in USA in 1979 and which countries have a similar amount of citizens?
How much does it cost a Sony mp3 player and which are the alternatives on the same or lower price?
How long does it take to travel from LA to San Francisco by car and is there other so big or bigger (in meaning of citizens) city where I can go from LA in a shorter traveling time again by car?
Of course, these are not general search queries according to the level of Natural Language Processing capabilities of search engines. This queries are at all executed by a specific domain related user interface. But I believe that if every result is serialized to tables this approach can be extremely useful.
Many data analysis can be made on the base of such search queries.
Legal Drinking Age in Asia. Table to natural text translations
At the end is attached a table of Legal Drinking Ages in Asia.
Having in mind the above table pieces of important translations have to be presented in order to picture the algorithm of translation and its’ major policies.
“Armenia” “De jure” “Drinking Age” is “none”.
“Armenia” “De jure” “Purchase Age” is “none”.
“Armenia” “De jure” “Drinking Age” and “Purchase Age” are “none”. (the two cells are merged)
“Azerbaijan” “De jure” “Drinking Age” is “18”.
“Azerbaijan” “De jure” “Purchase Age” is “18”.
“Azerbaijan” “De jure” “Drinking Age” and “Purchase Age” are “18”. (the two cells are merged)
“Thailand” “De jure” “Drinking Age” is “18”.
“Thailand” “De jure” “Purchase Age” is “18”.
(no matter that the two columns are with identical values, they are not merged so third sentence like Azerbaijan and Armenia is not composed)
The underlined and words (phrases) in quotes are exact words/ phrases. Search engine algorithms have been always dealing with problems to distinguish is”Vegas” and “Las Vegas” identical or is “Los Angeles” and “Angeles” not identical. In the sentence “Thailand De jure Drinking Age is 18.” additional knowledge is needed in order to claim is “Thailand De jure” one phrase (or it is not) or may be is an other combination of serial words a phrase or not.
The bold-ed words are added by the algorithm and are needed for this presentation of the translation. In order to construct a human readable sentence the above words are added. In a possible technological implementation of the algorithm specific DataBase data separation bytes would be used instead of this English words.
Is Semantic web a table like model
Semantic web is a declaration between the HTML in general code and the information semantics behind the HTML (in microformats and RDFa). This can be presented as a link between the semantic and the concrete technical presentation of the semantic.
This semantic web links (semantic tags and pieces of HTML as values) are not table like, it is hard to compare the values of the semantics because they are different and are closely related to the technical representation.
Attachments to Querying Tables in the Web
Table of Legal Drinking Age in Asia
“De jure” column is the reason this table is presented as a real life example. “De jure” column contains a table with two main cells placed on its first row- “Drinking Age”, “Purchase Age”. This main cells head two columns which are sometimes merged.
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Muslims are not allowed to drink or possess alcohol, non-Muslim residents and visitors may import small amounts of alcohol for personal consumption. Most restaurants will allow non-Muslim customers to drink their own brought in wine on premises with no corking fee. Public sale of alcohol is illegal. |
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Indonesia (excluding Bali) |
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Regulated by underage drinking prohibition law. (Alcohol vending machines widely available.) |
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Legal ages are reckoned “from birth”, rather than East Asian age reckoning. South Koreans are 20 or 21 in their own reckoning when they reach legal drinking age. |
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Republic of China (Taiwan) |
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People under 18 can buy alcohol but in order to purchase alcohol that has more than 4.5 alcohol concentration, you have to be more than 25 years old. |
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I have queried Powerset (http://www.powerset.com/) and received these results:
Screenshot not available here. See the printable version of the document.
Screenshot 1. Search results from Powerset on “what is the legal drinking age in Azerbaijan”. With red color are surrounded important text snippets.
Screenshot not available here. See the printable version of the document.
Screenshot 2. Search results from Powerset on “what is the legal drinking age in Armenia”. With red color are surrounded important text snippets.
Azerbaijan: The Legal drinking age is equal to the Age of majority.
Armenia: There is no connection with the Age of majority and the Legal drinking age, the letter is not set in the legal environment of Republic of Armenia.
Social feature in Universal Search can become a powerful feedback
I want to present the possibility of letting people with close search queries to meet each other in order to discuss the topic they explore.
While using search engines people often are not satisfied with the presented results so they change the query by adding new words, removing some words, changing their order and so on. In Google developers wanted to understand when users are not satisfied with the results and if not to focus more work on this particular search spaces, but unfortunately people do not claim in any way that they haven’t found what they wanted, they just leave Google.
I think that by letting people with similar queries to meet each other in real time through the search engine service will give the needed feedback for the managers of the searching engine and will let users to speak with each others about their search and do in their own way to guess new more precise queries. Executing the newly guessed queries have to be made by the only one collaborator but all the users to see the results (idea is related to Virtual world as a place to meet and interact with people on web page).
