terça-feira, 27 de janeiro de 2009

Systems - A Journey Along the Way

Systems - A Journey Along the Way: "Systems

A Journey Along theWay

Welcome to a journey in the realm of systems. The journey is still unfolding as this web site continues to evolve over time. Yet, even with the endless changes, there continues to be a connection, in one fashion or another, with systems. And, I continue to find that the lens which provides a systems perspective is the most revealing of understanding found to date.

The real intent here is not to study systems as a discipline, though more an intent to study lots of things and employ a systems perspective to foster understanding. Agreed, this requires some understanding of systems. As such, information is provided to enable one to develop a level of understanding sufficient to delve into the rest of what resides at this web site.

Every attempt will be made to avoid the major failing of 'system science.' In the words of Ludwig von Bertalanffy, 'The student in 'system science' receives a technical training which makes systems theory -- originally intended to overcome current overspecialization -- into another of the hundreds of academic specialities.'

Enjoy the journey!

System

The word system probably has more varied meanings than any other word in use today. The definition I have become comfortable with I owe to the late Austrian Biologist Ludwig von Bertalanffy.

A system is an entity which maintains its existence
through the mutual interaction of its parts.

The key emphasis here is 'mutual interaction,' in that something is occurring between the parts, over time, which maintains the system. A system is different than a heap or a collection, mostly.


This definition of a system implies something beyond cause and effect. Rather than simply A affects B, there is an implication that B also affects A. Examples of systems are particle, atom, molecule, cell, organ, person, community, state, nation, world, solar system, galaxy, and universe, in increasing levels of complexity. In truth there is only one system, 'the Universe,' and all other systems are really just sub-systems of this larger system. The relevant question has to do with where one chooses to draw boundaries.
Emergence

Associated with the idea of system is a principle called emergence. From the mutual interaction of the parts of a system there arise characteristics which can not be found as characteristic of any of the individual parts.

Stumbling across this as I did was most enlightening. It was probably in high school that I was first acquainted with the idea of synergy; the idea that the whole was greater than the sum of its parts. And, for all the examples ever used, emergence never really hit me until I ran into the right example. The right example just happened to be water! Amazing it took so long since there's so much of it around.

One could study hydrogen and oxygen in isolation from each other forever and never discover the characteristic of wetness. Wetness is an emergent characteristic of the mutual interaction of hydrogen and oxygen when combined to produce the molecular form called water. One has to study the system to get a true understanding of wetness. Studying the parts will not provide an appropriate understanding.

A systems view is somewhat in contradiction to the concept of analysis, which is breaking things down into smaller pieces to simplify the study. Analysis brings with it the risk of potentially loosing the most relevant characteristics of the system, and possibly developing a less than complete understanding. Yes, analysis is an important technique, and at the same time another method of study is also warranted, something I have seen called anasynthis. Anasynthis being the study of the whole, and the parts, in the hopes of developing an appropriate level of understanding.
Classes of Systems

There are multiple ways of characterizing systems. Of those I have come to understand to date, several of the most useful are as follows.
Isolated, Open and Closed Systems

Systems may be characterized as either closed or open. A closed system is one that does not need to interact with its environment to maintain its existence. Examples are atoms and molecules. Mechanical systems are closed systems. Open systems are organic and must interact with their environment in order to maintain their existence. People are open systems in that they must interact with their environment in order to take in food, water, and obtain shelter. People provide waste products to the environment in return.

The examples of the furnace, filling the water glass, adjusting the shower tap are all open systems as there are elements outside the system which are considered to have an effect yet are not elaborated.

An open system may interact with its environment in a growth or balancing fashion. Often the time of influence of the open system on the environment or the environment on the system may be of such lengthy duration or of such minimal nature as to limit its need to be considered. In 1927 Ludwig von Bertalanffy first proposed that the human organism should be treated as an open system.

Any system taken in a large enough context can be considered a closed system. It is often more appropriate to consider a system as a subsystem of some larger system with which it must interact in some way. Taking the larger system into account is unnecessary for understanding the operation of the subsystem. All systems are both subsystems of larger systems and composed of subsystems at the same time.
Boulding's Classification

Economist Kenneth Boulding, one of the founders of The Society for General Systems Theory, in his book, 'The World as a Total System,' defined 5 generalized classes of systems which encompasses all other systems. These provide a means of understanding some general characteristics of systems. These systems are arranged in what is considered an evolutionary hierarchy.

Parasitic System

This is a system in which a positive influence from one element to another provides a negative influence in return to the first element.

I get positive things from you and provide you a negative return in response. Essentially I subsist on you.

Prey/Predator System

In this type of system the elements are essentially dependent on each other from the perspective that the quantity of one element determines the quantity of the other element. The Foxes/Rabbits example is a prey/predator system. Even though the fox may be detrimental to the continuation of an individual rabbit, the fox is instrumental in maintaining the health of the overall rabbit population.

I will feed upon you even though my existence is dependent upon your existence.

Threat System

A threat system is one in which one element doesn't do something if the other element doesn't do something else. The U.S./Soviet Arms Race was a specific example. This particular example lead to escalation since each side said to the other, 'If you start a war I will destroy you.' Yet to continue to validate the threat each side had to continue building arms. It has been said this is a fine example of two countries racing headlong to where neither of them wanted to be.

If you don't do something I don't want you to do then I won't do something you don't want me to do. This may also be formed as, if you do something I want you to do, then I won't do something you don't want me to.

Exchange System

The capitalist economy is a very good example of an exchange system. Elements of the system provide goods and services to other elements in exchange for money or other goods and services.

If you do something I want you to do, then I will do something you want me to do. This may also be stated as, if I do something you want me to do then I expect you will do something I want you to do.

Our buy now pay later economy has a tendency to change an exchange system into a threat system. Initially we purchase something and in exchange we provide a promise, a promise to pay more later. Once we have received what we wanted the system changes and the bank says if you pay your bills then I won't take the stuff away from you, which is essentially a threat systems.

Employer/Employee systems are often transformed from and exchange systems to a threat system. The employee is hired under an exchange premise. I will pay you (what you want) if you do this work (what I want). Once the employee is hired the situation changes and becomes, if you do what I want I won't fire you.

Integrative System

Examples of an integrative system are charitable organizations or business endeavors where individuals ban together to accomplish some common desired objective or goal.

Where you and I do something together because of what we both want to accomplish.

The greatest leverage is found in integrative systems, where all the individuals are motivated by what they are endeavoring to create. This will be addressed in more detail when we get into building shared vision and team learning.

Generative System

During discussions on the Learning Organization list sometime in late 1995 Michael McMaster proposed another category beyond the Integrative System. Michael proposed what he called the Generative System, which might be represented by a situation where two people come together to create something neither of them had any idea of when they began.

Another Classification

At one time I happened across another definition of a systems hierarchy which seemed to make a lot of sense, yet at present I can't recall the reference from whence it came.

Protection System - act when events occur (reactive)

Regulating System - single loop - continuously measure or sample control variables and compare with pre-set desired values and adjust accordingly to regulate control variables (responsive)

Optimizing System - double loop - regulates selected variables in accordance with desired values and also ascertains what the desired values should be to satisfy pre-determined goals (systemic)

Adaptive System - multi-loop/structural - system changes its internal structure in order to optimize its behavior in spite of continuous changes in the environment... (evolutionary)

Introduction to Systems Thinking

theWay of Systems * Feedback * Musings
Copyright © 2004-2005 Gene Bellinger"

Data, Information, Knowledge, & Wisdom

Data, Information, Knowledge, & Wisdom: "Data, Information, Knowledge, and Wisdom

by Gene Bellinger, Durval Castro, Anthony Mills

There is probably no segment of activity in the world attracting as much attention at present as that of knowledge management. Yet as I entered this arena of activity I quickly found there didn't seem to be a wealth of sources that seemed to make sense in terms of defining what knowledge actually was, and how was it differentiated from data, information, and wisdom. What follows is the current level of understanding I have been able to piece together regarding data, information, knowledge, and wisdom. I figured to understand one of them I had to understand all of them.

According to Russell Ackoff, a systems theorist and professor of organizational change, the content of the human mind can be classified into five categories:

1. Data: symbols

2. Information: data that are processed to be useful; provides answers to 'who', 'what', 'where', and 'when' questions

3. Knowledge: application of data and information; answers 'how' questions

4. Understanding: appreciation of 'why'

5. Wisdom: evaluated understanding.

Ackoff indicates that the first four categories relate to the past; they deal with what has been or what is known. Only the fifth category, wisdom, deals with the future because it incorporates vision and design. With wisdom, people can create the future rather than just grasp the present and past. But achieving wisdom isn't easy; people must move successively through the other categories.

A further elaboration of Ackoff's definitions follows:

Data... data is raw. It simply exists and has no significance beyond its existence (in and of itself). It can exist in any form, usable or not. It does not have meaning of itself. In computer parlance, a spreadsheet generally starts out by holding data.

Information... information is data that has been given meaning by way of relational connection. This 'meaning' can be useful, but does not have to be. In computer parlance, a relational database makes information from the data stored within it.

Knowledge... knowledge is the appropriate collection of information, such that it's intent is to be useful. Knowledge is a deterministic process. When someone 'memorizes' information (as less-aspiring test-bound students often do), then they have amassed knowledge. This knowledge has useful meaning to them, but it does not provide for, in and of itself, an integration such as would infer further knowledge. For example, elementary school children memorize, or amass knowledge of, the 'times table'. They can tell you that '2 x 2 = 4' because they have amassed that knowledge (it being included in the times table). But when asked what is '1267 x 300', they can not respond correctly because that entry is not in their times table. To correctly answer such a question requires a true cognitive and analytical ability that is only encompassed in the next level... understanding. In computer parlance, most of the applications we use (modeling, simulation, etc.) exercise some type of stored knowledge.

Understanding... understanding is an interpolative and probabilistic process. It is cognitive and analytical. It is the process by which I can take knowledge and synthesize new knowledge from the previously held knowledge. The difference between understanding and knowledge is the difference between 'learning' and 'memorizing'. People who have understanding can undertake useful actions because they can synthesize new knowledge, or in some cases, at least new information, from what is previously known (and understood). That is, understanding can build upon currently held information, knowledge and understanding itself. In computer parlance, AI systems possess understanding in the sense that they are able to synthesize new knowledge from previously stored information and knowledge.

Wisdom... wisdom is an extrapolative and non-deterministic, non-probabilistic process. It calls upon all the previous levels of consciousness, and specifically upon special types of human programming (moral, ethical codes, etc.). It beckons to give us understanding about which there has previously been no understanding, and in doing so, goes far beyond understanding itself. It is the essence of philosophical probing. Unlike the previous four levels, it asks questions to which there is no (easily-achievable) answer, and in some cases, to which there can be no humanly-known answer period. Wisdom is therefore, the process by which we also discern, or judge, between right and wrong, good and bad. I personally believe that computers do not have, and will never have the ability to posses wisdom. Wisdom is a uniquely human state, or as I see it, wisdom requires one to have a soul, for it resides as much in the heart as in the mind. And a soul is something machines will never possess (or perhaps I should reword that to say, a soul is something that, in general, will never possess a machine).

Personally I contend that the sequence is a bit less involved than described by Ackoff. The following diagram represents the transitions from data, to information, to knowledge, and finally to wisdom, and it is understanding that support the transition from each stage to the next. Understanding is not a separate level of its own.





Data represents a fact or statement of event without relation to other things.

Ex: It is raining.

Information embodies the understanding of a relationship of some sort, possibly cause and effect.

Ex: The temperature dropped 15 degrees and then it started raining.

Knowledge represents a pattern that connects and generally provides a high level of predictability as to what is described or what will happen next.

Ex: If the humidity is very high and the temperature drops substantially the atmospheres is often unlikely to be able to hold the moisture so it rains.

Wisdom embodies more of an understanding of fundamental principles embodied within the knowledge that are essentially the basis for the knowledge being what it is. Wisdom is essentially systemic.

Ex: It rains because it rains. And this encompasses an understanding of all the interactions that happen between raining, evaporation, air currents, temperature gradients, changes, and raining.

Yet, there is still a question regarding when is a pattern knowledge and when is it noise. Consider the following:

* Abugt dbesbt regtc uatn s uitrzt.
* ubtxte pstye ysote anet sser extess
* ibxtedstes bet3 ibtes otesb tapbesct ehracts

It is quite likely this sequence represents 100% novelty, which means it's equivalent to noise. There is no foundation for you to connect with the pattern, yet to me the statements are quite meaningful as I understand the translation with reveals they are in fact Newton's 3 laws of motion. Is something knowledge if you can't understand it?

Now consider the following:

* I have a box.
* The box is 3' wide, 3' deep, and 6' high.
* The box is very heavy.
* The box has a door on the front of it.
* When I open the box it has food in it.
* It is colder inside the box than it is outside.
* You usually find the box in the kitchen.
* There is a smaller compartment inside the box with ice in it.
* When you open the door the light comes on.
* When you move this box you usually find lots of dirt underneath it.
* Junk has a real habit of collecting on top of this box.

What is it?

A refrigerator. You knew that, right? At some point in the sequence you connected with the pattern and understood it was a description of a refrigerator. From that point on each statement only added confirmation to your understanding.

If you lived in a society that had never seen a refrigerator you might still be scratching your head as to what the sequence of statements referred to.

Also, realize that I could have provided you with the above statements in any order and still at some point the pattern would have connected. When the pattern connected the sequence of statements represented knowledge to you. To me all the statements convey nothing as they are simply 100% confirmation of what I already knew as I knew what I was describing even before I started.
References:

* Ackoff, R. L., 'From Data to Wisdom', Journal of Applies Systems Analysis, Volume 16, 1989 p 3-9.
* Gadomski, Adam Maria, Information, Preferences and Knowledge, An Interesting Evolution in Thought
* Sharma, Nikhil, The Origin of the Data Information Knowledge Wisdom Hierarchy

theWay of Systems * Feedback * Musings
Copyright © 2004 Gene Bellinger"

segunda-feira, 29 de dezembro de 2008

Northgate Reality


Northgate Reality: "Welcome to your Reality by NorthgateThe original MultiValue SQL-enabled database, designed to assist your organisation to run seamlessly... the choice for a wide range of organisations and businesses, from start-ups to large enterprise 24x7 mission critical environments, returning great value and exceptional resilience."

segunda-feira, 8 de dezembro de 2008

ConservationEconomy.net -- The Pattern Map


ConservationEconomy.net -- The Pattern Map: "The Pattern Map offers a visual guide to the sustainability patterns that provide a frame-work for developing a Conservation Economy."

segunda-feira, 27 de outubro de 2008

Projeto de Banco de Dados (PBD) - Modelagem de Banco de Dados (MBD)

image

0 - Visão Geral de PBD (MBD)

a - Material da Aula 1 (PDF e PPSX) (New)

b - Projeto De Banco De Dados (Ufrgs) (134 Slides) - Heuser (New)

c - Apostila de Projeto De Banco De Dados (New)

1 - Fundamentos de Banco de Dados

a - Material da Aula 2 (PDF e PPSX) (New)

b - Livro de Matemática para profissionais de BD (New)

2 - Fundamentos de Gestão do Conhecimento

3 - Fundamentos de Projeto de Banco de Dados (PBD) - Modelagem de Banco de Dados - MBD

4 - Projeto Conceitual de Banco de Dados

4.1 - Critérios de Qualidade de Projeto Conceitual de BD

4.2 - Modelo Entidade Relacionamento (MER)

4.3 - Modelo de Classes da UML

5 - Projeto Lógico de Banco de Dados

5.1 - MDR - Modelo de Dados Relacional

5.2 - Transformação do DER em Tabelas

5.3 - Normalização

5 - Projeto Físico de Banco de Dados - PFBD

6 - Engenharia Reversa de Banco de Dados

7 - Administração de Dados e Administração de Banco de Dados

Apostila de Projeto de Banco de Dados

Livro de Matemática para profissionais de BD

image

MBD (PBD) - Aula 1 - Material

domingo, 26 de outubro de 2008

Projeto De Banco De Dados (Ufrgs) (134 Slides) - Heuser

Iluminando os Meandros do Cérebro

Iluminando_Meandros_Cérebro

Construção da Inteligência

Construção_da_inteligência

O Despontar da Ciência da WEB

Ciência_da_WEB

Algoritmo dá a computadores uma capacidade quase humana

Redação do Site Inovação Tecnológica
25/09/2008

Nós temos uma capacidade inata, e uma tendência quase inconsciente em utilizar essa capacidade, de encontrar padrões em grandes volumes de dados e informações. Foi assim que nossos antepassados traçaram as constelações na infinidade de estrelas que eles observavam no céu, e é assim que nós localizamos um grupo de amigos no meio de um salão lotado.

Algoritmo dá aos computadores a capacidade de encontrar padrões em dados brutos

Novo algoritmo consegue encontrar padrões em dados brutos, identificando a estrutura - ordens lineares, anéis ou clusters - que melhor descreve os dados.[Imagem: Charles Kemp]

Capacidade de ordenação

Seria muito útil se conseguíssemos replicar essa capacidade nos computadores - a Era da Informação está gerando uma quantidade de dados maior do que tudo o que a humanidade gerou ao longo de milênios. Sabemos que essa montanha de dados contém informações valiosas, mas só conseguiremos tirar proveito delas se os próprios computadores forem capazes de capturá-las para nós.

Para que um computador ordene um conjunto de dados, nós devemos encontrar a ordem subjacente a esses dados e então dizer ao computador como ordená-los, por meio de um programa.

Encontrando padrões em dados brutos

Agora, pesquisadores do MIT elaboraram um algoritmo que é capaz de encontrar um padrão nos dados brutos, e então ordená-los segundo esse padrão. "Em vez de procurar por um tipo particular de estrutura, nós criamos um algoritmo mais amplo que é capaz de testar todas essas estruturas e pesá-las umas contra as outras," explica Joshua Tenenbaum, coordenador da pesquisa.

O algoritmo consegue entender vários tipos de estruturas de dados, como árvores, ordens lineares, anéis, hierarquias dominantes, clusters etc. Ele analisa os dados brutos até encontrar a estrutura que melhor os descreve e então ordena os dados seguindo essa estrutura.

Os humanos fazem isso o tempo todo, na vida diária, freqüentemente de forma inconsciente. Várias descobertas-chave na história da ciência também consistiram na localização desses padrões, como na elaboração da Tabela Periódica ou na criação do sistema de classificação das espécies utilizada pela biologia.

quarta-feira, 22 de outubro de 2008

Fundamentos de Banco de Dados

1 - Dado, Informação, Conhecimento

2 - Definição de Dado

2.1 - Termo (Elemento Simbólico - Símbolo - Valor - Dado)

3 - Definição de Informação

3.1 - Natureza da Informação

3.2 - Características da Informação

4 - Definição de Conhecimento

4.1 - Sociedade do Conhecimento

4.2 - Propósito do Conhecimento

4.3 - Conhecimento Organizacional

5 - Tecnologia da Informação e Banco de Dados

6 - Definição de Banco de Dados

6.1 - Banco de Dados - Exemplo

7 - Características Básicas dos Bancos de Dados

7.1 - Estrutura

7.2 - Estado

7.3 - Comportamento

7.4 - Transação

7.5 - Persistência (Durabilidade)

7.6 - Consistência (Integridade)

8 - Modelo de Dados, Esquema, Instância

9 - Tipos de Banco de Dados

9.1 - Classificação - Modelo de Dados Implementado

9.1.1 - Banco de Dados Hierárquico

9.1.1.1 - Exemplo de Banco de Dados Hierárquico

9.1.2 - Banco de Dados de Rede

9.1.2.1 - Exemplo de Banco de Dados de Rede

9.1.3 - Banco de Dados Relacional

9.1.3.1 - Exemplo de Banco de Dados Relacional

9.1.3.2 - Exemplo de Banco de Dados Relacional

9.1.4 - Banco de Dados Orientado a Objetos

9.1.5 - Banco de Dados Objeto Relacional

10 - Sistema de Banco de Dados - SBD - Database System - DBS

11 - Sistema Gerenciador de Banco de Dados - SGBD - Database Management System - DBMS

11.1 - Sistema Gerenciador de Banco de Dados - SGBD - Classificação

12 - Idependência de Dados

13 - Arquitetura ANSI/SPARC

14 - Linguagem de Banco de Dados

15 - Utilitário de Banco de Dados

Fundamentos de Gestão do Conhecimento

1 - Realidade como uma rede de fenômenos

2 - Uma Ontologia para a Realidade

3 - Rede de Conhecimento a base para o Projeto (Modelagem Conceitual de Banco de Dados)

3.1 - Conhecimento Organizacional

4 - Fenômeno da Realidade

4.1 - Elemento

4.2 - Interação

4.3 - Lei da Natureza

5- Fenômenos da Rede de Conhecimento

5.1- Termo - Símbolo - Valor - Dado

5.2- Conceito

5.3 - Conceito Associativo

5.4 - Conceito Construtivo - Construtor Semântico

5.4.1 - Classificação - Instanciação

5.4.2 - Generalização - Especialização

5.4.3 - Classificação - Generalização

5.4.4 - Agregação (Composição) - Decomposição

5.4.5 - Associação

5.4.6 - Fato - Informação

5.4.7 - Fato Associativo (Propositivo, Assertivo) - Proposição - Asserção

5.4.8 - Regra