300 - Rationalism vs Empiricism - Summary and History Essay Example for Free

300 Rationalism vs Empiricism Summary and History Essay What is reality really like? A current running through much of the philosophical thinking around the time of Socrates and Plato was that there is a difference between how the world appears and how it is. Our senses reveal one layer of reality but it is our minds that penetrate deeper. The world of appearances is a world in flux but underneath there must be a stable reality. For there is much that is unchanging. We recognise kinds of things – badgers, daffodils, mountains – and whilst members of these kinds are born, change and die, and differ from one another in ever so many ways, the kind-defining essence doesnt change. We see here the key rationalist idea that knowledge is a priori knowledge of necessary truths Plato said that kinds were defined by the transcendental forms. He presented a number of arguments for the existence of these things. Prior to our incarnation, our souls existed in the realm of forms where we learned about these essences. In our terrestrial state, we cannot recall what we know. Socrates considered himself a midwife to knowledge instead of a teacher, helping his interlocutors to draw out what they dont know that they know. The example of Meno and the slave-boy shows this idea clearly. Like many philosophers, Plato was also fascinated by mathematics. We are able to tap into a universe of truths that are non-sensible: we do not see numbers and we do not see the perfect geometric forms. Once again, we see the difference between the powers of the mind and the powers of the senses. It was in the 17th century that the debate between the rationalists and the empiricists came to a head. Philosophers such as Descartes and Leibniz emphasised the power of reason over the senses. Descartes argued that our senses were fallible and that we could not rule out the possibility of the demon deception hypothesis on the basis of sensory evidence alone. Descartes argued that he knew he existed, as a mind, on the basis of reflection alone: when I think, I cannot fail to be aware of myself as existing as that thinker (cogito, ergo sum). Having proved that he exists, Descartes argued that God exists. Since God is no deceiver, he would not have given us senses that systematically mislead. But let us not overemphasise the powers of the senses. Descartes argued that even with material things, it is reason that exposes their essences. In his piece of wax reasoning, he argued that the senses merely reveal a succession of impressions: it is reason that grasps the underlying and enduring substance as extended (and filled space). Plato and Descartes believed that we are born with concepts and knowledge. In Descartes case, there was a religious motive: we are all born in the image of God. We discover more about the world primarily through metaphysical reflection. The philosopher Francis Bacon, an early empiricist, famously dismissed this rationalist approach to knowledge. He compared rationalists to spiders who spin complex metaphysical systems out of their entrails. Empiricists get their hands dirty: like bees gathering pollen, they gather knowledge about the world and only then reflect on it. Around the same time as Bacon, many new discoveries were being made that shook the prevailing views of reality. The Earth was dethroned from its position at the centre of the universe by Copernicus. A new star (a supernova) was observed by Tycho Brahe in 1572 – yet the heavens were supposed to be timeless and unchanging. Galileo discovered the moons of Jupiter – again, everything clearly didnt revolve around the Earth. Later in the 17th century, scientist-philosophers such as Newton, Boyle, Gassendi and Huygens would revolutionise our understanding of reality. The original empiricist manifesto was written by John Locke. In his Essay Concerning Human Understanding, he sought to show how a mind that was blank at birth – a tabula rasa or blank slate – could come to be filled. His first targets were the innate concepts and knowledge (ideas) of the rationalists. There are no such things. There are no truths everyone agrees on. Many people fail to grasp the supposed metaphysical truths. Instead, our senses deliver ideas to us. We store them, abstract from them to form general ideas, and compound and mix them to generate new ideas. Like Lego bricks, we build the meagre sensory data into ever more complex structures. Even Leibniz thought Locke was onto something here. He claimed that our minds were like blocks of marble that had to be carefully chiselled at to reveal the hidden structure (the innate truths). It is hard work and not everyone will end up well-chiselled. Hume took empiricism to its limit. Where Locke talked indifferently of ideas, Hume distinguished impressions and ideas. Impressions are the direct deliverances of the senses and are forceful and vivid in comparison to ideas, which are the copies our minds makes. (He also agreed with the Empiricist Berkeley that Lockes theory of general ideas was wrong. We do not abstract from particular ideas to a general idea but use a particular idea in a general way via a general name. ) What about the precious necessary truths philosophy is supposed to study? Locke argued that once we have ideas in our mind, our mind will perceive the necessary connections between them – e. g. that a triangle has internal angles that add to 180o? But where does the idea of necessity come from? Hume provided an answer. He distinguished statements into two categories: those expressing relations of ideas (analytic) and those expressing matters of fact (synthetic). The analytic truths express mere definitions: we simply are aware of an association between terms. The synthetic truths are the contingent truths. So what happens to interesting necessary truths, such as God exists or nothing exists without being caused to exist? Hume argued that if these werent analytic – and they arent – they arent necessary. We feel that they are necessary and this is all necessity is: a psychological property. When we say that X caused Y, we think we have said something about the universe. We think we have seen an example of a law of nature (e. g. the water in the bucket froze because it was cold exemplifies the law water freezes at 0oC). Science investigates these laws. Hume said that causation was all in the mind. We see one thing after another and when weve seen instances of a regularity enough, we develop the feeling that one thing must be followed by the other. Hume, like Locke, emphasised how all we can be certain of are our impressions – how the world seems. Scientists are really investigating how the world appears: they can never be certain that the world really is the way it appears. So, empiricism seems to lead straight to scepticism about the external world. Kant objected strongly to this. Science really is studying the external world and there really is an external world for it to investigate. Kant brought about a revolution in philosophy (he called it a Copernican revolution). He argued that the empiricists and rationalists were both right and wrong. The Empiricists were right: science requires the study of the world and the world is brought to us via the senses. The Rationalists were right: our mind is not blank but contains structures that enable us to interpret the stream of data from the senses. We may liken the mind to a mould and the data to jelly: one only has something structured by combining both. Or: the mind is a computer with an operating system and the data is the input from the user. A computer with just an operating system is inert. A computer into which data is inputted but which has no operating system is just data: it cannot be interpreted. Only when you combine both do you get something useful. Our minds contain the structures for space, time, objects and causation, for example. (In Kants terminology, space and time are the pure forms of intuition whereas the structures for objects and causation are pure concepts of the understanding. ) This means that we experience a world of spatio-temporally located objects in which causation happens because this is how our minds make it appear. Does this mean that the world as such is all in the mind? Or is the mind somehow tuned to the structure of reality, so that our pre-programmed minds mirror the structures of reality? This is a very difficult question over which there is no agreement amongst experts. The Empiricist movement came back with a vengeance in the 20th century. Philosophers such as Bertrand Russell agreed with Hume that our knowledge begins with our knowledge of sense-data (classical empirical foundationalism). Armed with new discoveries in mathematics and logic, and backed by the successes of science, the logical positivists argued that the only proper way to investigate the world was the scientific way. If I say p and p is synthetic and there is no objective, scientific way to verify my claim that p, then my claim is meaningless. (This is the celebrated verification principle). So, if it is true that there atoms, we should be able to find empirical – sensory – evidence of them. If it is true that nothing happens without being caused to happen, then we likewise need scientific evidence for this. We cannot discover whether it is true by pure reason. The Logical Positivist movement failed. There is much that seems meaningful that is not objectively verifiable by the senses, such as the occurrence of private sensations. The principle makes it impossible for general claims such as all mammals are warm-blooded to be true, as we cannot verify all of them. The very verification principle itself fails its own test! The Logical Positivists responded by watering down their principle: a meaningful claim is one we could gather some evidence for in principle and the principle itself is special – exempt from this rule. But it was not enough. (* Then Quine argued that the fundamental division between analytic and synthetic sentences was incorrect. Analytic sentences cannot be false. But no sentence enjoys this privilege. As we learn more and more, truths we thought were beyond doubt are rejected. Once upon a time, we would have thought it analytic that no object can be in two places at once or that there is no fastest velocity. Quantum physics and general relativity theory show that they are not true. Instead, we should have a web of belief. At the centre are those sentences least likely to be revised – our core beliefs. As we move out, we find those sentences that would be easier and easier to accept as false – that would cause less and less disruption to the rest of what we believe. ) In the 1950s, Chomsky became famous for suggesting that we are not born as blank slates when it comes to language. We are born knowing the fundamental structures of human language. When we are young, we hear our mother tongue and use our knowledge of language to pick up our language very quickly. (At 24 months, the average child understands 500-700 words; at 36 months, 1000; at 48 around 2500-3000; at 60 around 5000 words: thats around 7 words a day between 3 and 6). More recently, studies have shown that children are born with brains structured to expect the world to behave in certain way. Very young children expect objects to persist over time: not to disappear and reappear at two different places, for example. Is this a revival of rationalism? Not according to many people. Rationalists argued that we had innate concepts and knowledge. By reflection, we can discover them and manipulate them to gain new knowledge. But our knowledge of language is altogether different. None of us can easily articulate the rules we follow in generating syntactically-correct English. (And certainly none of us at all can articulate the common structure rules to all human languages. ) Our brains are certainly pre-programmed, but only perhaps in the same way that a computer is pre-wired: clearly something has to be there but nothing as advanced as software. So where are we today? No side is victorious: this would be to grossly over-simplify the debate between the empiricists and the rationalists. We definitely have minds in some way ready to receive the world – hardly surprising, perhaps, given the time it has taken for us to evolve. But when it comes to working out what is true? Few philosophers are rationalists in the old-fashioned way. There is no sharp division between metaphysics and science: our study of reality cannot be done from the armchair alone. But our capacity to grasp abstract mathematical truths has always been difficult to explain from an empiricist perspective. We seem to have an access to a mathematical realm and a cognitive or intuitive access instead of a sensory one. You cant see numbers, after all, and it is not easy to say what we could see that would lead us to generate the ideas of numbers.

The Horror of War Exposed in Slaughterhouse Five :: Slaughterhouse-Five Essays

The Horror of War Exposed in Slaughterhouse Five When one begins to analyze a military novel it is important to first look at the historical context in which the book was written. On the nights of February 13-14 in 1944 the city of Dresden, Germany was subjected to one of the worst air attacks in the history of man. By the end of the bombing 135,000 to 250,000 people had been killed by the combined forces of the United States and the United Kingdom. Dresden was different then Berlin or many of the other military targets which were attacked during World War II because it was never fortified or used for strategic purposes and, therefore, was not considered a military target. Because of it's apparent safety, thousands of refugees from all over Europe converged on Dresden for protection (Klinkowitz 2-3). Dresden's neutrality was broken and the resulting attacks laid waste, what Vonnegut called, "the Florence of the Elbe." Kurt Vonnegut was a witness to this event and because of fate, had been spared. He wrote Slaughterhouse Five to answer the questi on that resounded through his head long after the bombs could no longer be heard. "Why me?"- a frequent question asked by survivors of war. Vonnegut was tormented by this question and through Billy Pilgrim, the protagonist in Slaughterhouse Five, he attempts to reconcile the guilt which one feels when one is randomly saved from death, while one's friends and loved ones perish. Billy Pilgrim's own life was spared, but was never able to live with himself knowing that so many others had died. The feelings of guilt which emerged from his having survived the bombing of Dresden and from Billy's fortunate escape from death under the shelter of the fifth Slaughterhouse haunted Billy through much of his life. Billy Pilgrim did not consider his survival a blessing, but a curse. A curse to be forced to live on with the guilt of survival. Billy Pilgrim faced such tremendous guilt, that he spent his entire life after Dresden trying to alleviate himself of it. His guilt is in many ways comparable to the guilt felt by the survivors of the Holocaust. Many Holocaust survivors had to face their own "Why me?" question. However, many Holocaust survivors w ere

Logical and Physical Data Models

The Physical Data Model (PDM) describes how the information represented in the Logical Data Model is actually implemented, how the information-exchange requirements are implemented, and how the data entities and their relationships are maintained. There should be a mapping from a given Logical Data Model to the Physical Data Model if both models are used. The form of the Physical Data Model can vary greatly, as shown in Figure 31. For some purposes, an additional entity-relationship style diagram will be sufficient.The Data Definition Language (DDL) may also be used. References to message format standards (which identify message types and options to be used) may suffice for message-oriented implementations. (Getting information from the LDM in form of file) Descriptions of file formats may be used when file passing is the mode used to exchange information. Interoperating systems may use a variety of techniques to exchange data, and thus have several distinct partitions in their Physi cal Data Model with each partition using a different form.The figure illustrates some options for expressing the Physical Data Model and an other table (in the original document) provides a listing of the types of information to be captured. A physical data model (or database design) is a representation of a data design which takes into account the facilities and constraints of a given database management system. In the lifecycle of a project it typically derives from a logical data model, though it may be reverse-engineered from a given database implementation.A complete physical data model will include all the database artifacts required to create relationships between tables or to achieve performance goals, such as indexes, constraint definitions, linking tables, partitioned tables or clusters. Analysts can usually use a physical data model to calculate storage estimates; it may include specific storage allocation details for a given database system. As of 2012 seven main databas es dominate the commercial marketplace: Informix, Oracle, Postgres, SQL Server, Sybase, DB2 and MySQL.Other RDBMS systems tend either to be legacy databases or used within academia such as universities or further education colleges. Physical data models for each implementation would differ significantly, not least due to underlying operating-system requirements that may sit underneath them. For example: SQL Server runs only on Microsoft Windows operating-systems, while Oracle and MySQL can run on Solaris, Linux and other UNIX-based operating-systems as well as on Windows.This means that the disk requirements, security requirements and many other aspects of a physical data model will be influenced by the RDBMS that a database administrator (or an organization) chooses to use. Overview Logical data models represent the abstract structure of a domain of information. They are often diagrammatic in nature and are most typically used in business processes that seek to capture things of im portance to an organization and how they relate to one another. Once validated and approved, the logical data model can become the basis of a physical data model and inform the design of a database.Logical data models should be based on the structures identified in a preceding conceptual data model, since this describes the semantics of the information context, which the logical model should also reflect. Even so, since the logical data model anticipates implementation on a specific computing system, the content of the logical data model is adjusted to achieve certain efficiencies. The term ‘Logical Data Model' is sometimes used as a synonym of ‘Domain Model' or as an alternative to the domain model.While the two concepts are closely related, and have overlapping goals, a domain model is more focused on capturing the concepts in the problem domain rather than the structure of the data associated with that domain. History The ANSI/SPARC three level architecture, which â⠂¬Å"shows that a data model can be an external model (or view), a conceptual model, or a physical model. This is not the only way to look at data models, but it is a useful way, particularly when comparing models†. [1] When ANSI first laid out the idea of a logical schema in 1975,[2] the choices were hierarchical and network.The relational model – where data is described in terms of tables and columns – had just been recognized as a data organization theory but no software existed to support that approach. Since that time, an object-oriented approach to data modelling – where data is described in terms of classes, attributes, and associations – has also been introduced. Logical data model topics Reasons for building a logical data model * Helps common understanding of business data elements and requirements * Provides foundation for designing a database Facilitates avoidance of data redundancy and thus prevent data & business transaction inconsisten cy * Facilitates data re-use and sharing * Decreases development and maintenance time and cost * Confirms a logical process model and helps impact analysis. Modeling benefits * Facilitates business process improvement * Focuses on requirements independent of technology * Facilitates data re-use and sharing * Increases return on investment * Centralizes metadata * Fosters seamless communication between applications * Focuses communication for data analysis and project team members * Establishes a consistent naming schemeLogical & Physical Data Model A logical data model is sometimes incorrectly called a physical data model, which is not what the ANSI people had in mind. The physical design of a database involves deep use of particular database management technology. For example, a table/column design could be implemented on a collection of computers, located in different parts of the world. That is the domain of the physical model. Logical and physical data models are very different in their objectives, goals and content. Key differences noted below. Logical Data Model| Physical Data Model|Includes entities (tables), attributes (columns/fields) and relationships (keys)| Includes tables, columns, keys, data types, validation rules, database triggers, stored procedures, domains, and access constraints| Uses business names for entities & attributes| Uses more defined and less generic specific names for tables and columns, such as abbreviated column names, limited by the database management system (DBMS) and any company defined standards| Is independent of technology (platform, DBMS)| Includes primary keys and indices for fast data access. Is normalized to fourth normal form(4NF)| May be de-normalized to meet performance requirements based on the nature of the database. If the nature of the database is Online Transaction Processing(OLTP) or Operational Data Store (ODS) it is usually not de-normalized. De-normalization is common in Datawarehouses. | A logical data m odel describes the data in as much detail as possible, without regard to how they will be physical implemented in the database. Features of a logical data model include: * Includes all entities and relationships among them. All attributes for each entity are specified. * The primary key for each entity is specified. * Foreign keys (keys identifying the relationship between different entities) are specified. * Normalization occurs at this level. The steps for designing the logical data model are as follows: 1. Specify primary keys for all entities. 2. Find the relationships between different entities. 3. Find all attributes for each entity. 4. Resolve many-to-many relationships. 5. Normalization. The figure below is an example of a logical data model.Logical Data Model Comparing the logical data model shown above with the conceptual data model diagram, we see the main differences between the two: * In a logical data model, primary keys are present, whereas in a conceptual data model, no primary key is present. * In a logical data model, all attributes are specified within an entity. No attributes are specified in a conceptual data model. * Relationships between entities are specified using primary keys and foreign keys in a logical data model.In a conceptual data model, the relationships are simply stated, not specified, so we simply know that two entities are related, but we do not specify what attributes are used for this relationship. Logical Model Design Physical Model Design Figure 5. A logical data model (Information Engineering notation). You also need to identify the cardinality and optionality of a relationship (the UML combines the concepts of optionality and cardinality into the single concept of multiplicity). Cardinality represents the concept of â€Å"how many† whereas optionality represents the concept of â€Å"whether you must have something. For example, it is not enough to know that customers place orders. How many orders can a custome r place? None, one, or several? Furthermore, relationships are two-way streets: not only do customers place orders, but orders are placed by customers. This leads to questions like: how many customers can be enrolled in any given order and is it possible to have an order with no customer involved? Figure 5 shows that customers place zero or more orders and that any given order is placed by one customer and one customer only.It also shows that a customer lives at one or more addresses and that any given address has zero or more customers living at it. Although the UML distinguishes between different types of relationships – associations, inheritance, aggregation, composition, and dependency – data modelers often aren’t as concerned with this issue as much as object modelers are. Subtyping, one application of inheritance, is often found in data models, an example of which is the is a relationship between Item and it’s two â€Å"sub entities† Service and Product.Aggregation and composition are much less common and typically must be implied from the data model, as you see with the part of role that Line Item takes with Order. UML dependencies are typically a software construct and therefore wouldn’t appear on a data model, unless of course it was a very highly detailed physical model that showed how views, triggers, or stored procedures depended on Logical Data Models (LDMs) represent data table (Entity Type) relationships. Logical Data Model Notations Entity Type Entity Type refers to a group of related data placed in an RDBMS (Relational Database Management Systems) table.An entity is an instance of an entity type represented as a single row in a data table. Relationships and Multiplicity Relationships illustrate how two entity types are related. Cardinality specifies how many instances of an entity relate to one instance of another entity. Physical data model represents how the model will be built in the database. A phy sical database model shows all table structures, including column name, column data type, column constraints, primary key, foreign key, and relationships between tables. Features of a physical data model include: * Specification all tables and columns. Foreign keys are used to identify relationships between tables. * Denormalization may occur based on user requirements. * Physical considerations may cause the physical data model to be quite different from the logical data model. * Physical data model will be different for different RDBMS. For example, data type for a column may be different between MySQL and SQL Server. Steps For Physical Data Model * Convert entities into tables. * Convert relationships into foreign keys. * Convert attributes into columns. * Modify the physical data model based on physical constraints / requirements. Physical v/s logical Entity names are now table names. * Attributes are now column names. * Data type for each column is specified. Data types can be different depending on the actual database being used. Data modeling is the act of exploring data-oriented structures. Like other modeling artifacts data models can be used for a variety of purposes, from high-level conceptual models to physical data models (PDMs). Physical data modeling is conceptually similar to design class modeling, the goal being to design the internal schema of a database, depicting the data tables, the data columns of those tables, and the relationships between the tables.      | presents a partial PDM for the university – you know that it isn’t complete by the fact that the Seminar table includes foreign keys to tables that aren’t shown, and quite frankly it’s obvious that many domain concepts such as course and professor are clearly not modeled. All but one of the boxes represent tables, the one exception is UniversityDB which lists the stored procedures implemented within the database. Because the diagram is given the stereo type Physical Data Model you know that the class boxes represent tables, without the diagram stereotype I would have needed to use the stereotype Table on each table.Relationships between tables are modeled using standard UML notation, although not shown in the example it would be reasonable to model composition and inheritance relationships between tables. Relationships are implemented via the use of keys (more on this below). Figure 1. A partial PDM for the university. When you are physical data modeling the following tasks are performed in an iterative manner: * Identify tables. Tables are the database equivalent of classes; data is stored in physical tables. As you can see in Figure 1 the university has a Student table to store student data, a Course table to store course data, and so on.Figure 1 uses a UML-based notation (this is a publicly defined profile which anyone can provide input into). If you have a class model in place a good start is to do a one-to-one mapping of your classes to data tables, an approach that works well in â€Å"greenfield† environments where you have the luxury of designing your database schema from scratch. Because this rarely happens in practice you need to be prepared to be constrained by one or more legacy database schemas which you will then need to map your classes to.In these situations it is unlikely that you will need to do much data modeling, you will simply need to learn to live with the existing data sources, but you will need to be able to read and understand existing models. In some cases you may need to perform legacy data analysis and model the existing schema before you can start working with it. * Normalize tables. Data normalization is a process in which data attributes within a data model are organized to increase the cohesion of tables and to reduce the coupling between tables. The fundamental goal is to ensure that data is stored in one and only one place.This is an important consideration for applic ation developers because it is incredibly difficult to stores objects in a relational database if a data attribute is stored in several places. The tables in Figure 1 are in third normal form (3NF). * Identify columns. A column is the database equivalent of an attribute, and each table will have one or more columns. For example, the Student table has attributes such as FirstName and StudentNumber. Unlike attributes in classes, which can either be primitive types or other objects, a column may only be a primitive type such as a char (a string), an int (integer), or a float. Identify stored procedures. A stored procedure is conceptually similar to a global method implemented by the database. In Figure 1 you see that stored procedures such as averageMark() and studentsEnrolled() are modeled as operations of the class UniversityDB. These stored procedures implement code that work with data stored in the database, in this case they calculate the average mark of a student and count the nu mber of students enrolled in a given seminar respectively.Although some of these stored procedures clearly act on data contained in a single table they are not modeled as part of the table (along the lines of methods being part of classes). Instead, because stored procedures are a part of the overall database and not a single table, they are modeled as part of a class with the name of the database. * Apply naming conventions. Your organization should have standards and guidelines applicable to data modeling, and if not you should lobby to have some put in place.As always, you should follow AM’s practice of Apply Modeling Standards. * Identify relationships. There are relationships between tables just like there are relationships between classes. The advice presented relationships in UML class diagrams applies. * Apply data model patterns. Some data modelers will apply common data model patterns, David Hay’s (1996) book Data Model Patterns is the best reference on the s ubject. Data model patterns are conceptually closest to analysis patterns because they describe solutions to common domain issues.Hay’s book is a very good reference for anyone involved in analysis-level modeling, even when you’re taking an object approach instead of a data approach because his patterns model business structures from a wide variety of business domains. * Assign keys. A key is one or more data attributes that uniquely identify a row in a table. A key that is two or more attributes is called a composite key. A primary key is the preferred key for an entity type whereas an alternate key (also known as a secondary key) is an alternative way to access rows within a table.In a physical database a key would be formed of one or more table columns whose value(s) uniquely identifies a row within a relational table. Primary keys are indicated using the stereotype and foreign keys via . Read here for more about keys. Although similar notation is used it is interes ting to note the differences between the PDM of Figure 21 and the UML class diagram from which is ti based: 1. Keys. Where it is common practice to not model scaffolding properties on class models it is common to model keys (the data equivalent of scaffolding). 2. Visibility. Visibility isn’t modeled for columns because they’re all public.However, because most databases support access control rights you may want to model them using UML constraints, UML notes, or as business rules. Similarly stored procedures are also public so they aren’t modeled either. 3. No many-to-many associations. Relational databases are unable to natively support many-to-many associations, unlike objects, and as a result you need to resolve them via the addition of an associative table. The closest thing to an associative table in is WaitList which was introduced to resolve the on waiting list many-to-many association depicted in the class diagram.A pure associative table is comprised of the primary key columns of the two tables which it maintains the relationship between, in this case StudentNumber from Student and SeminarOID from Seminar. Notice how in WaitList these columns have both a PK and an FK stereotype because they make up the primary key of WaitList while at the same time are foreign keys to the other two tables. WaitList isn’t truly an associative table because it contains non-key columns, in this case the Added column which is used to ensure that the first people on the waiting list are the ones that are given the opportunity to enroll if a seat becomes available.Had WaitList been a pure associative table I would have applied the associative table stereotype to it. Logical Versus Physical Database Modeling * March 14, 2001 * By Developer. com Staff * Bio  » * Send Email  » * More Articles  » After all business requirements have been gathered for a proposed database, they must be modeled. Models are created to visually represent the propose d database so that business requirements can easily be associated with database objects to ensure that all requirements have been completely and accurately gathered.Different types of diagrams are typically produced to illustrate the business processes, rules, entities, and organizational units that have been identified. These diagrams often include entity relationship diagrams, process flow diagrams, and server model diagrams. An entity relationship diagram (ERD) represents the entities, or groups of information, and their relationships maintained for a business. Process flow diagrams represent business processes and the flow of data between different processes and entities that have been defined.Server model diagrams represent a detailed picture of the database as being transformed from the business model into a relational database with tables, columns, and constraints. Basically, data modeling serves as a link between business needs and system requirements. Two types of data mode ling are as follows: * Logical modeling * Physical modeling If you are going to be working with databases, then it is important to understand the difference between logical and physical modeling, and how they relate to one another.Logical and physical modeling are described in more detail in the following subsections. * Post a comment * Email Article * Print Article * Share Articles Logical Modeling Logical modeling deals with gathering business requirements and converting those requirements into a model. The logical model revolves around the needs of the business, not the database, although the needs of the business are used to establish the needs of the database. Logical modeling involves gathering information about business processes, business entities (categories of data), and organizational units.After this information is gathered, diagrams and reports are produced including entity relationship diagrams, business process diagrams, and eventually process flow diagrams. The diagr ams produced should show the processes and data that exists, as well as the relationships between business processes and data. Logical modeling should accurately render a visual representation of the activities and data relevant to a particular business. Note| Logical modeling affects not only the direction of database design, but also indirectly affects the performance and administration of an implemented database.When time is invested performing logical modeling, more options become available for planning the design of the physical database. | The diagrams and documentation generated during logical modeling is used to determine whether the requirements of the business have been completely gathered. Management, developers, and end users alike review these diagrams and documentation to determine if more work is required before physical modeling commences. Typical deliverables of logical modeling include * Entity relationship diagrams An Entity Relationship Diagram is also referred t o as an analysis ERD.The point of the initial ERD is to provide the development team with a picture of the different categories of data for the business, as well as how these categories of data are related to one another. * Business process diagrams The process model illustrates all the parent and child processes that are performed by individuals within a company. The process model gives the development team an idea of how data moves within the organization. Because process models illustrate the activities of individuals in the company, the process model can be used to determine how a database application interface is design. * User feedback documentationPhysical Modeling Physical modeling involves the actual design of a database according to the requirements that were established during logical modeling. Logical modeling mainly involves gathering the requirements of the business, with the latter part of logical modeling directed toward the goals and requirements of the database. Ph ysical modeling deals with the conversion of the logical, or business model, into a relational database model. When physical modeling occurs, objects are being defined at the schema level. A schema is a group of related objects in a database. A database design effort is normally associated with one schema.During physical modeling, objects such as tables and columns are created based on entities and attributes that were defined during logical modeling. Constraints are also defined, including primary keys, foreign keys, other unique keys, and check constraints. Views can be created from database tables to summarize data or to simply provide the user with another perspective of certain data. Other objects such as indexes and snapshots can also be defined during physical modeling. Physical modeling is when all the pieces come together to complete the process of defining a database for a business.Physical modeling is database software specific, meaning that the objects defined during phy sical modeling can vary depending on the relational database software being used. For example, most relational database systems have variations with the way data types are represented and the way data is stored, although basic data types are conceptually the same among different implementations. Additionally, some database systems have objects that are not available in other database systems. Implementation of the Physical Model| The implementation of the physical model is dependent on the hardware and software being used by the company.The hardware can determine what type of software can be used because software is normally developed according to common hardware and operating system platforms. Some database software might only be available for Windows NT systems, whereas other software products such as Oracle are available on a wider range of operating system platforms, such as UNIX. The available hardware is also important during the implementation of the physical model because da ta is physically distributed onto one or more physical disk drives. Normally, the more physical drives available, the better the performance of the database after the implementation.Some software products now are Java-based and can run on virtually any platform. Typically, the decisions to use particular hardware, operating system platforms, and database software are made in conjunction with one another. | A logical data model describes your model entities and how they relate to each other. A physical data model describes each entity in detail, including information about how you would implement the model using a particular (database) product. In a logical model describing a person in a family tree, each person node would have attributes such as name(s), date of birth, place of birth, etc.The logical diagram would also show some kind of unique attribute or combination of attributes called a primary key that describes exactly one entry (a row in SQL) within this entity. The physical model for the person would contain implementation details. These details are things like data types, indexes, constraints, etc. The logical and physical model serve two different, but related purposes. A logical model is a way to draw your mental roadmap from a problem specification to an entity-based storage system.The user (problem owner) must understand and approve the logical model. A physical model is the roadmap from the logical model to the hardware. The developer (software owner) must understand and use the physical model. ERD Consider a hospital: Patients are treated in a single ward by the doctors assigned to them. Usually each patient will be assigned a single doctor, but in rare cases they will have two. Heathcare assistants also attend to the patients, a number of these are associated with each ward. Initially the system will be concerned solely with drug treatment.Each patient is required to take a variety of drugs a certain number of times per day and for varying leng ths of time. The system must record details concerning patient treatment and staff payment. Some staff are paid part time and doctors and care assistants work varying amounts of overtime at varying rates (subject to grade). The system will also need to track what treatments are required for which patients and when and it should be capable of calculating the cost of treatment per week for each patient (though it is currently unclear to what use this information will be put).

Personal Narrative Are Best Friends - 805 Words

Were Best Friends When I step into the room, everyone turns to me and says hello. People call me one of the most popular girl in my high school, everyone wants to approach me. I hardly trust people, because I know people changes, so I act like a different me in front of people. Apparently, there are only two people that are my true friends that I could be myself. One is my boyfriend Alex and one is Tina my friend since I was a child. We are always together no matter where we go, I will tell Tina all my concerns and my feelings toward people. Unlike me, Tina is an introvert girl and not really good at expressing herself in front of people but somehow we are best friends. When I tell my feelings of people and even Alex†¦show more content†¦I didn’t even know the post existed, and I definitely did not write it. â€Å"What are you saying? Who told you I wrote it? How could you believe such a rumor? You know I won’t do such a thing!† â€Å"How could I believe it? I started it!† I was so shocked I almost collapsed. â€Å"What did I ever do to you to make you did such a horrible thing?† I muttered weakly. Tina’s smile changed to a pure anger. â€Å"How could I? You didn’t even know how I felt! Being next to you, all these years, while you were so popular. I feel so small! You have everything and I had nothing! But now things will change. It’s your turn to stand in my shadow!† I rushed into the bathroom stall with tears all over my face. I can’t believe this was happening. Tina and I were best friends, aren’t we? How could I misunderstand everything so badly? Everyone turned on me, just because of Tina’s rumor. I am desperate for support, I tried to call my boyfriend, Alex. He picked up the call immediately and sounded worried. â€Å"Are you ok?† he asked, I was so happy to hear his voice. â€Å"I’m not ok. I didn’t even know what was going on.† â€Å"I heard the rumor, but I didn’t believe it,† he said. â€Å"Tina tries to convince me you’d done it and that I should break up with you and be with her instead, but I refused. Where are you? I’ll come and get you.† â€Å"How could she do this to me? I’m so glad you believeShow MoreRelatedPersonal Narrative : My Best Friend1210 Words   |  5 PagesSunday, my friends and I were eating a meal of thick stew and crusty bread and drinking a pitcher of hot, spiced, and very watered-down wine. We’d chosen my room because it was the biggest and therefore had the most space for practicing weaponry, our afternoon plan. My friends ate and made small talk. We saw each other most days so sometimes it seemed like we ran out of real things to talk about. I was somewhat lost in my thoughts, about us and about our futures. Koilin was my best friend. He wasRead MorePersonal Narrative : My Best Friend1052 Words   |  5 PagesI Threw my books on the bed and approached the jacket slowly, as if it were Andrew Garfield who would become my best friend. I couldn t look away, I wanted to scream. The jacket would be my new best friend. The leather black and silver studs, the belts, and best of all being popular. This jacket is no ordinary jacket, this is my jacket. I heard steps coming up stairs, my mom stopped by and asked if I like it. I yelled yes with excitement and joy. She left, I stared at the jacket like whenRead MorePersonal Narrative : My Best Friend1080 Words   |  5 Pageswe could finish the vlog, but Caleb laid on the couch unresponsive. As I called his name while walking down the stairs, I realized something was wrong. â€Å"Caleb† I yelled tears pouring out of my eyes as I found him, not breathing. He was dead. My best friend, my brother, the only person who made me really happy, was gone forever. I couldn t imagine what I was going to do without him. I loved him more than anyone in my life he was my hero. It s october 20th and we all sat at Caleb s funeral cryingRead MorePersonal Narrative : My Best Friend757 Words   |  4 Pagesperson or part of my day. It s an overall impression, that no matter how hard I try to be everyone s best friend, I still don t fit into any one group flawlessly. It wasn t always like this, it all began last summer ago when I moved from the best place on earth, Pittsburgh, Pennsylvania to this ghost town. I was a nerd at my old school, but at least I was a respected nerd with real friends, who always included me, and loved me for me. I was happy there and never wanted to leave. Then my worldRead MorePersonal Narrative : My Best Friend940 Words   |  4 Pages Looking back, I remind myself that friends are temporary, but memories are forever. This was going to be our last night together, Cesli and I. Cesli Crum was my best friend that I met in third grade. That year came and went, and though in fourth grade Cesli was held back, we still vowed to always be best friends. Then, that winter of two thousand fourteen, her family decided to move away. I felt devastated, so my mom agreed to have Cesli over one evening right before she left. Thus that is whatRead MorePersonal Narrative : My Best Friend1369 Words   |  6 Pagesmy stomach. I was on my way to visit Julia, one of my best friends at the time. The whole way there, in bumper to bumper traffic, I reflected on all of my memories with her, including playing on a fallen, rotten tree and pretending that it was milk chocolate shop. As little girls, we would dress up in glittery, razzled costumes and sing our hearts out, which continued well into our teen years when she drove me to school my freshman year. The best of times were when we were sipping on our gooey, caramelRead MorePersonal Narrative : My Best Friend1034 Words   |  5 Pagesâ€Å"No! No!,† I screamed hysterically. â€Å"Emma! Wake up, wake up, you re okay now, we’re safe now. We are safe in the Tent Cities † Ella reassured me. When I arose, I saw Ella hovering over me. Ella has been my best friend since I was deported to this horrid tent city. My spouse and daughter died in the floods of Grimsdon. Every thought of them, cripples me with grief instantly, my heart and soul ached for my precious daughter and partner bring me to tears. Thankfully, I met . Ella, who, whilstRead MorePersonal Narrative : My Best Friend1033 Words   |  5 Pagesany clothes. My face edged with tear streaks and bags under my eyes. I had mascara smudged across my right cheek and my makeup was running. At one point, I turned over and glanced up at him. I studied his face and thought to myself. This was my best friend. We used to hang out after school at my house and watch movies until late. How had things changed so suddenly? I thought of all the good moments we d had. I remembered every crease in his face, every segment of his skin, and the way his heart wouldRead MorePersonal Narrative : My Best Friend1327 Words   |  6 Pagesâ€Å"Okay sissy, don t cry,† she pleaded.   I knew the day was coming up, but I tried not to think about it.   She was my absolute best friend and I could not imagine living 1,300 miles away.    Growing up, we were resentful of each other.   We used words and actions to get our point across.   Not only did we slap each other, but also kicked and punched.   I cried even if it didn’t hurt, that was me being a baby.   My dad would scoop me in his arms and at the same time, discipline my sister.   It was satisfyingRead MorePersonal Narrative : My Best Friend1192 Words   |  5 Pagesshort frame, golden hair and curvy figure made her stand out. I wished I looked as perfect as she did on a bad day. Between the two of us, she has been the outgoing one, always popular with the fellows, always the center of attention. We have been best friends since elementary school and gone through a lot together, but we backed each other when it meant the most. While I waited for my flight at Regan International, I called Elizabeth and invited her to my place for a late dinner. I missed her and