구글, 미래를 '클라우드컴퓨팅'에 걸다 Mike Ricciuti, 정리=박효정 기자   2008/06/16

클라우드컴퓨팅 구글은 기업 트렌드가 ‘클라우드컴퓨팅’이라고 보고 있다. ‘클라우드컴퓨팅’은 기업 유저가 원격 데이터센터의 웹 애플리케이션을 이용해 IT 운용비용을 절감하는 것을 뜻한다. 구글의 리시 찬드라 엔터프라이즈 제품 담당 매니저는 “다음 10년의 혁신은 클라우드로 일어날 것”이라며 “엔터프라이즈 소프트웨어가 사라지진 않겠지만, 분명 변화가 일어나고 있다”고 주장했다. 지난9일~12일(현지시간) 보스턴에서 열린 ‘엔터프라이즈 2.0’ 컨퍼런스에서 강연자로 나선 찬드라 매니저는 구글이 앞으로 보다 많은 기업 고객을 획득하려는 이유에 대해, 가장 중요한 점은 개인 유저 시장에서의 구글의 강점이며 장래는 비즈니스 컴퓨팅에서의 거점이 될 것이라고 강조했다. 그는 “클라우드의 시대가 도래하고 있다. 문제는 언제가 될지가 아니라 얼마나 빨리 올까이다”라며 “구글은 ‘구글앱스’로 그에 대응하고 있다”고 말했다. 찬드라 매니저는 마이크로소프트(MS), 아마존닷컴, 세일즈포스닷컴 등의 대기업이 기존 업무용 시스템과 같은 신뢰성과 안전성을 보유한 비즈니스 애플리케이션을 웹에서 제공하는 시장으로 모여들고 있다고 말했다. 찬드라 매니저는 MS와의 경쟁을 중요시하지 않았다. 그는 “MS와는 경쟁관계지만, 경쟁할 생각은 없다. 구글은 새로운 애플리케이션 사용법을 시장에 투입하려고 있고, 최종사용자에 초점을 맞추고 있다”고 강조했다. 물론 MS에도 클라우드를 바탕으로 한 독자적인 계획이 있다. 레이 오지 수석 소프트웨어 아키텍트가 가장 걱정하고 있는 것은 오픈소스와 구글의 야망이다. MS는 ‘라이브메시(Live Mesh)’로 클라우드컴퓨팅 계획 일부에 도착했다. 올해 안에 더 자세한 내용이 드러날 것이다. 찬드라 매니저는 업계의 4가지 트렌드가 구글의 강점을 지지하고 있다고 말했다. 첫 번째는 구글은 기술 혁신을 추진하고 있는 것은 개인 유저 시장이라고 보고 있다는 점이다. 개인 유저의 세계는 엔터프라이즈의 세계보다 다윈의 ‘진화론’에 따른 세계다. 유저는 뒤떨어진 제품을 참지 않는다. 찬드라 매니저는 “개인 유저의 세계에서는 환승 비용이 없다. 개인 유저 세계의 테스터 수억명이 엔터프라이즈 시장을 돕고 있다. 따라서 개인 유저는 엔터프라이즈 세계보다 뛰어난 기술을 손에 넣고 있다. 인스턴트 메시징(IM), 검색, VoIP는 모두 개인 유저 세계에 바탕한다”라고 설명했다. 그는 이어 구글은 개인 유저 시장에서 다양한 일을 배웠다고 말했다. “단순함이 이긴다. 개인 유저 시장에서 태어난 기술이 머지않아 엔터프라이즈 시장의 기술이 된다”고 강조했다. 두 번째 트렌드는 사내에서의 ‘파워 협력자(공동 작업자)’의 대두라고 찬드라 매니저는 말했다. 그는 “엔터프라이즈 소프트웨어는 전문가에 의해서, 전문가를 위해 만들어져 있다. 그러나 팀별 업무가 늘어나고 있는 지금, 신세대 직원들에 대응해 처음부터 다시 생각할 필요가 있다. 어떤 OS가 사용되고 있는지, 어디서 일하는지가 문제가 돼선 안된다. 소프트웨어는 ‘개방 표준’을 바탕으로 한다. 이것이 클라우드컴퓨팅의 비전이며, 구글이 클라우드야말로 차세대 엔터프라이즈 컴퓨팅의 비전이라고 생각하는 이유”라고 말했다. 또 엔터프라이즈 컴퓨팅의 경제적 측면도 변화하고 있다고 찬드라 매니저는 지적했다. 기업은 증가하는 콘텐츠·동영상·사진 처리 때문에 골머리를 앓고 있다. 찬드라 매니저는 하루 700만매 사진을 처리하는 구글의 사진 공유 사이트 ‘피카사’를 예를 들었다. “클라우드로 구글이 시장과 공유할 수 있는 큰 이점이 있다. 구글의 ‘앱엔진’은 기본적으로는 측정할 수 있는 호스팅 플랫폼이며, 거의 무한한 공간을 제공한다. 기회가 매우 크다”는 것이다. 마지막으로 찬드라 매니저는 엔터프라이즈의 클라우드컴퓨팅 도입 장해가 없어지고 있다고 말했다. 그는 기업의 큰 관심사로서 신뢰성을 들었다. 그는 “지금 구글은 멈출 수 없다. 구글이 멈추면 유저는 구글로부터 떨어져 버린다. 그래서 구글은 클라우드컴퓨팅에 투자하고 있는 것”이라고 말했다.

'똑똑하고 매력적' 구글러들의 일상은 어떤 모습일까

구글 ‘검색 알고리즘’ 베일 벗는다

웹2.0 시대에 살고 있다.
그럼 웹3.0 시대는 어떤 모습일까? 어떤 모습으로 다가올까? 그것이 궁금해서 찾아봤다.

웹2.0 대표적인 것이 UCC 일것이다. 우리가 하고 있는 블로그도 그중에 하나라고 보며 이런 수많은 정보들이 하루에도 꾸준하게 생겨나고 있다.  그런 웹2.0 은 정보 교류가 늘어났지만 정작 비즈니스 모델은 명확하지 않아서 그 의미는 거창하지만 수익성을 확보하지 못하고 있어서 고민이다.

중국 최대 검색업체 바이두닷컴의 로빈 리 회장은 이렇게 말한다. 검색엔진이나 인터넷 경매, 광고, 포털사이트 등의 비즈니스 모델과 비교할 때, 웹2.0 기업은 아직 기술 진보나 안정성 측면에서 갈길이 멀다는 지적이다. 그는 "웹2.0 기업은 아직까지 명확한 수익 모델이 정비되지 않아 불확실성이 많다" 며 "명확한 비즈니스 모델을 정립하는 게 최우선 과제" 라고 강조한다.


웹3.0은 어떤 모습으로 우리에게 다가올까?
세계적인 IT 거두들은 웹3.0 을 열 두 가지의 커다란 패러다임을 제시한다.

한 가지는 이용자 개인이 언제 어디서나 미디어에 연결 될 수 있도록 해주는 유비쿼티(Ubiquity)다.
또 다른 한 가지는 언어 표준화다.

이 두 가지가 인터넷의 장벽을 해소하고 웹3.0 시대를 열 것이라는 전망이다.


어떤 것이 웹3.0 이 될까?
전문가들은 모바일이 될 것이라고 본다.
빠르고 효율적인 방식으로 어떤 장비에서도 콘텐츠를 이용할 수 있는 통합 연결망이 바로 웹3.0 이 될 것이라고 구글 CEO 에릭슈미트는 말했다.

웹2.0 을 모바일에서도 가능하게끔 만드는 통신 기술의 발전이 웹3.0 을 이끌 것이라고 한다.

결구 '웹의 유비쿼티' 가 미래 성공의 열쇠라는 진단이다.
세계에서 2억명이 인터넷을 서핑하는 반면 휴대폰은 20~30억 명이 사용한다.
웹2.0 이 모바일의 힘을 얻는다면 그것이 유비쿼터스를 구현한 웹3.0 이 된다는 거다.

휴대폰과 가상세계가 웹3.0 을 이끌 가장 가능성 높은 후보라고 판단 된다.
서로 다른 미디어에 이용자들이 '한번의 클릭' 만으로 접속 할 수 있도록 미디어를 통합하는 것이 웹3.0 이 될 것이라고 전문가들은 예측한다.

휴대폰이 발전되면서 다양한 애플리케이션이 가능해졌고, 이젠 웹 PC에서만 가능하던 것이 휴대인터넷에서도 가능하게 되었으며, 웹의 가치를 이용자 수 증가에서만 찾을 것이 아니라 사람들 개인의 삶에 더 많은 혜택을 줄 수 있는가의 여부에서 찾아야 한다고 말한다.


참고로, 앞으로 지금의 정보가 2배로 늘어나는 기간이 멀지 않아서 단 11시간으로 짧아질 거다.
짐 데이비스 SAS 부회장은 "정보가 2배로 늘어나는 기간이 2005년에는 3년이었고, 2007년에는 11개월이었다. 2010년이면 단 11시간으로 짧아질 것이다" 라고 전망한다.
이와 같이 앞으로 정보량은 엄청난 빠른 속도로 늘어날 것이며 그러한 정보를 어떻게 관리해야 할지도 고민해야 할 부분이다.


관련 내용은 '부의 창조' 웹3.0 관련 부분을 참조했다.

Associative Search and the Semantic Web: The Next Step Beyond Natural Language Search

 

Our present day search engines are a poor match for the way that our brains actually think and search for answers. Our brains search associatively along networks of relationships. We search for things that are related to things we know, and things that are related to those things. Our brains not only search along these networks, they sense when networks intersect, and that is how we find things. I call this associative search, because we search along networks of associations between things.

 

Human memory -- in other words, human search -- is associative. It works by "homing in" on what we are looking for, rather than finding exact matches. Compare this to the the keyword search that is so popular on the Web today and there are obvious differences. Keyword searching provides a very weak form of "homing in" -- by choosing our keywords carefully we can limit the set of things which match. But the problem is we can only find things which contain those literal keywords.

 

There is no actual use of associations in keyword search, it is just literal matching to keywords. Our brains on the other hand use a much more sophisticated form of "homing in" on answers. Instead of literal matches, our brains look for things things which are associatively connected to things we remember, in order to find what we are ultimately looking for.

 

For example, consider the case where you cannot remember someone's name. How do you remember it? Usually we start by trying to remember various facts about that person. By doing this our brains then start networking from those facts to other facts and finally to other memories that they intersect.  Ultimately through this process of "free association" or "associative memory" we home in on things which eventually trigger a memory of the person's name.

 

Both forms of search make use of the intersections of sets, but the associative search model is exponentially more powerful because for every additional search term in your query, an entire network of concepts, and relationships between them, is implied. One additional term can result in an entire network of related queries, and when you begin to intersect the different networks that result from multiple terms in the query, you quickly home in on only those results that make sense. In keyword search on the other hand, each additional search term only provides a linear benefit -- there is no exponential amplification using networks.

 

Keyword search is a very weak approximation of associative search because there really is no concept of a relationship at all. By entering keywords into a search engine like Google we are simulating an associative search, but without the real power of actual relationships between things to help us. Google does not know how various concepts are related and it doesn't take that into account when helping us find things. Instead, Google just looks for documents that contain exact matches to the terms we are looking for and weights them statistically. It makes some use of relationships between Web pages to rank the results, but it does not actually search along relationships to find new results.

 

Basically the problem today is that Google does not work the way our brains think. This difference creates an inefficiency for searchers: We have to do the work of translating our associative way of thinking into "keywordese" that is likely to return results we want. Often this requires a bit of trial and error and reiteration of our searches before we get result sets that match our needs.

 

A recently proposed solution to the problem of "keywordese" is natural language search (or NLP search), such as what is being proposed by companies like Powerset and Hakia. Natural language search engines are slightly closer to the way we actually think because they at least attempt to understand ordinary language instead of requiring keywords. You can ask a question and get answers to that question that make sense.

 

Natural language search engines are able to understand the language of a query and the language in the result documents in order to make a better match between the question and potential answers. But this is still not true associative search. Although these systems bear a closer resemblance to the way we think, they still do not actually leverage the power of networks -- they are still not as powerful as associative search.

 

A natural language search can understand the meaning of a query like "books about Harry Potter" and it knows this is not the same as "Books by Harry Potter." But ultimately what is happening is that a linguistic expression is being converted into a more sophisticated keyword search. The language in the query is being mapped to documents that contain text that answers a question, or to data objects that match the thing being asked for. This is certainly better than keyword search but it is still ultimately just a smarter form of literal matching. It is not really making use of associative search along networks of semantic relationships in the data (other than linguistic relationships between words in the query) or any sort of sophisticated reasoning.

 

By comparison, associative search doesn't merely understand the meaning of the query, it understands and can reason about relationships in the data. This is an important distinction.

 

An associative search returns documents that represent things that are related, via various forms of associations (semantic links), to the things in the query. An associative search looks through a network of associations for the things that are most connected to the items in the query. By specifying more specific starting points, the set of things which are connected to all those starting points is narrowed. Thus an associative search is an intersection of multiple networks. The items that are most strongly intersected are the results that are most likely to matter.

 

Associative search is a very different approach to search from keyword search (which merely looks for things with the keywords in them) and natural language search (which merely looks for things that contain content that matches the meaning of the question). It also happens to be more similar to how our brains actually think.

 

On its own, associative search represents an important advance in the way we search. But by adding some simple reasoning to an associative search it becomes even more powerful. Reasoning adds the ability to generalize or get more specific, and to weight various paths through the network of relationships in more sophisticated ways, such as based on logical relationships or inferences through the network.

 

A simple example of reasoning is transitivity -- for example, if A is a part of B and B is a part of C, then A is a part of C. If we know that the "part of" relationship is transitive, then whenever we see chains of "part of" links between things we can make transitive inferences. In an associative search these inferences are quite useful. For example, we can search for all the parts of a 747 jet. Using transitive reasoning along networks of relationships we can find all the parts, even those things that are "parts of parts." Similarly we could find "all products of Sony" including products of subsidiaries and business units of Sony. Transitive inferences across transitive links is just one type of reasoning; there are many other variations that are possible, which when combined together become even more useful.

 

Our current search tools -- whether they are keyword based or natural language based do not support true associative search, let alone reasoning. But we do see associative search starting to appear in a very different breed of application: social networks. A search in LinkedIn for example, is an associative search. Will social networks do an end-run around traditional search engines to provide the next-generation of search? It's quite possible. Facebook and LinkedIn are far better positioned than Google today for associative search. In fact, I would venture that this is how Facebook could give Google some serious competition. But they have to hurry if they are going to do this -- Google has clearly realized the power of "social search" and is rapidly moving to leverage it in their own search results.

 

Ultimately associative search is more than just social search however. To be really effective, associative search engines need to understand and leverage the full spectrum of relationships between things, not just social relationships. They need to see and understand more types of relationships between more types of things. In order to accomplish this, associative search engines need the Semantic Web.

 

The Semantic Web provides exactly what is needed to enable associative search, with reasoning, on the Web-at-large. Using RDF and OWL, content can be marked up with metadata that specifies not only its intended meaning and structure, but also the various kinds of semantic relationships it has to other content and to other concepts. In other words, these standards provide a way to add a new network of semantically defined associations to the data on the Web. For example a document about Microsoft can be linked to the concepts "Software Company," "Software Manufacturer," and "Redmond." It can also be linked to a data record that represents "Microsoft" and the properties that define it as a company. The "Microsoft" object can then link to companies that are "suppliers" and "customers" and "competitors" as well as to things which are connected as "products" or "services."

 

This rich network of relationships between things goes far beyond documents. It contains relationships to people, places, other organizations, products, events, services, etc. It's similar to a social network, but instead of just containing people and social relationships, it contains more types of things and relationships between them. This is really what the Semantic Web enables. One can imagine that as this new semantic data becomes visible on the Web (which is rapidly happening in fact), the power of search will be dramatically improved. Associative search is coming soon to a Web near you!

 

With that in mind, here is an example of how Semantic Web enabled associative search will work in the future.

 

PROBLEM: I am trying to remember name of the organizer of a conference I once attended.

 

WHAT I ALREADY  KNOW:

 

l       I know this person and have corresponded with them in the past.

l       The conference was related to government and the Internet.

l       It took place in a town near Big Sur, but I can't remember the name of the town.

l       The organizer of the conference once introduced me to a male celebrity, but I can't remember the celebrity's name.

l       I gave a talk at the Conference about Web 3.0.

l       My friend, Sue Smith, also spoke at the conference.

l       The conference I attended took place in the Spring, but I am not sure if it was last year or two years ago.

 

In the above example, I cannot remember the specific keywords that will help me generate a query to find the answer. Instead, I remember a number of relationships and generalizations about the answer. Present day search engines cannot see these relationships, and they have no ability to understand a generalization and look at things it contains.

 

The ability to intersect the sets formed by relationships and generalizations is a fundamental feature of human memory and search. But our present day tools don't have these capabilities. Thus we have to spend time translating our questions into keywordese, rather than just asking our questions in the actual language of human thought.

 

There are two ways to approach solving this.

The first way is to create artificial intelligence which, given a question in natural language English, can understand it and reason about the question as well as understand and reason about the information in the set of documents being searched, in order to intelligently arrive at candidate answers. This is computationally intensive, and very hard to program. This is why AI hasn't quite happened yet on this scale.

 

A perhaps easier approach is to use the Semantic Web. In the Semantic Web approach, metadata is embedded into content that describes the meaning of the content, it's various important properties, and its relationships to other concepts. On the basis of this metadata, the problem becomes much simpler to solve. Instead of doing high-level AI it becomes essentially a statistical search.

 

Now let's look at how using the Semantic Web could help us solve the above problem via an associative search:

 

Items are connected to more general or specific concepts by virtue of semantic linkages between concepts. For example, the conference I am looking for is related to the concepts "Government" and "Technology." If I can at least remember that then I can find conferences related to government and technology. Furthermore, since the concept "Policy" is a subset of government it may be related to that topic as well.

 

Likewise, things are connected to things that are "near" them via geographic links. Because the conference was near Big Sur it is in Northern California, along the coast. It is probably in a town that is geographically close, ror example Carmel-by-the-Sea is a town that is near the Big Sur area.

 

The organizer of the conference introduced me to a male celebrity. There are several celebrities in my social network. If the fact that I met certain people via introductions from other people was stored using semantic links, then this too would be searchable. For example, "find all celebrities I was introduced to by my connections" would be a solvable query. Similarly, "find people who introduced me to celebrities" would also be solvable.

 

The fact that I gave a talk at the conference could also be semantically represented on a data record describing the conference, as well as on my own profile. Thus there could exist a link such as "speaker at" which links me to various conferences I have spoken at. I could then get a list of all the conference I have spoken at. I could also look for all the conferences where both myself and Sue Smith were speakers.

 

Or, better yet, there could be a link called "Gave talk about" which links me to an instance describing each talk I have given. From such an instance there could then be "Gave talk at" links to all the events where I have given that talk. So I could look up my "Web 3.0" talk and then see all the conferences where I gave that talk.

 

Temporal relations can also be generalized and semantically represented. For example, the conference I am looking for took place in the spring. Therefore only look for conferences that took place in or near months that are considered to be in the spring season.

 

By intersecting the results of the above searches we narrow down very precisely to a set of people I might be looking for, or just to a single qualifying person.

 

For example the answer I was seeking for was that the organizer was named Robert Jones, and the conference was about Government and Technology Policy in Carmel-by-the-Sea last spring.

 

This result should be easily findable via associative search starting from the above set of things I remember. But if for some reason the answer is still not there, there is another capability which the brain uses that we need to add to our search engines:

 

1단계 : Perturbation, or what could be called "prospecting."

Perturbation = 섭동 : 수학에서 해가 알려진 다른 비슷한 문제와 비교하여 문제를 푸는 방법. 보통 이 방법으로 구한 해는 근사값일 뿐이다.

The query I entered is comprised of a question and a set of facts related to the answer I am seeking. But there is a possiblity that I asked the question incorrectly, or some of the facts I added were incorrect, or insufficient. Perturbation can correct for this by introducing variations into the question and the facts in order to explore the space of answers that are "near" them as well.

 

There are many ways to go about adding perturbation to the system -- for example, we can search more than one hop out from every link, or we can search for other types of relationships that are highly correlated with relationships we are asking for explicitly, or we can include results for things which are strongly connected to things that are found.

 

From a user-interface standpoint perturbation can be controlled with a simple "sliding lever" in the user interface for "Precision." If the user sets very high Precision as a requirement then there is no perturbation -- the results are exact matches to the query and facts. If there is low Precision as a requirement then there can be more perturbation, thus the results are fuzzy and may include things that are near what I asked for but not exactly what I specified, enabling me to discover things via relevant relationships that I could not even remember to mention as facts.

 

2단계 : Reasoner