why do we denormalise when building a data ware house?
437448
The main reasons anybody denormalizes is to improve speed and reduce the complexity of SQL transactions. It is possible that your mining software cannot handle complex SQL as well. \n\nThe trade off for denormalization is more disk space. Denormalize too far and you actually slow transactions down. The same will happen if you over normalize a database. I once saw a schema that normalized gender ROFL. Gender can you believe it? An entire join for a one char field, which had a trigger on top of it to prevent entering anything but m or f. That is an example of how over normalizing can actually wreck performance and create extremely complicated SQL. \n\nAnother possible reason for denormalizing is that your mining software is unable to percieve entity relationships. \n\nIn short if your data mining software requires you to denormalize everything get better data mining software. It is cheaper and easier than trying to maintain a completely denormalized database of any size. If it requires you to mildly denormalize then your database was overnormalized most likely. Solution is to redo your schema and you'll find your entry software running faster than ever. Contact me I'd be happy to go over specifics with you.
1046204
I would suggest start with the high level dfd (level 0) diagram where you have all your entities and just one large process in the middle. What you'll show in this level of dfd is all the inputs going into the system and and all the outputs generated by the system. From here you can determine what other processes you'll need. Also you have to do a functional decomposition diagram to determine what systems you need. \n\nI haven't used these for a while now but i hope this gives you some idea.
199797
The tradeoffs are always the same when implementing types or subtypes when creating anything..logical or not, schema or not.....it's all a means to an end that is an effect based on cause...
1008551
Not sure the question but it is set up (generally) as a program that has includes (one for data declarations, one for the main program, one to hold the subroutines). The main can then reference the includes decreasing impact issues when changes are required.
580641
If the data is roughly symmetric with no outliers (values that are unlike the rest of the data), then yes, SD should be fine.  However, if the data is skewed (having a tail to the left or right) or there are outliers, then I would use IQR to describe the spread of the data instead.
365266
it's like a cluttered room or a disorganized libary.  Everything is there, it's just not in order, so it takes longer for the PC to find all the pieces.  Defragment it and then it puts it all in order.
244188
to own several houses in various shapes and sizes
389410
should we do your homework, make a drawing, send it to you?\nhere's a hint, tie 2 inputs of a nand gate together you get an inversion. MIKE
1119693
I think you might need to elucidate more on that question.\n\nPerhaps you mean how do you store multiple values in a data structure.\n\nPerhaps you mean how do you modify a "final" variable. In other languages this is called a constant. The point of this is that it cannot be changed at runtime.
490979
less elements, so less complicated maths.  \n\nYou get a less accurate result, which I don't consider an advantage.
1147783
In relational databases, normalization is a process that eliminates redundancy, organizes data efficiently, and reduces the potential for anomalies during data operations and improves data consistency. The formal classifications used for quantifying "how normalized" a relational database are called normal forms (abbrev. NF).\n\ndatabase can be normalized using the following normal forms\nFirst normal form (1NF) \nSecond normal form (2NF) \nThird normal form (3NF) \nThe first normal form basically states that an attribute can only store one value. The second and third normal forms deal with the relationship of non-key attributes to the primary key.\n\nIn practice, most applications in 3NF are fully normalized. However, later research identified potential update anomalies in 3NF databases. BCNF is one such further refinement.\n\nThe fourth and fifth normal forms (4NF and 5NF) deal specifically with the relationship of attributes comprising a multi-attribute primary key. Sixth normal form (6NF) only applies to temporal databases.\n\n\n\nDefinition: A functional dependency occurs when one attribute in a relation uniquely determines another attribute. This can be written A -> B which would be the same as stating "B is functionally dependent upon A."\nExamples: In a table listing employee characteristics including Social Security Number (SSN) and name, it can be said that name is functionally dependent upon SSN (or SSN -> name) because an employee's name can be uniquely determined from their SSN. However, the reverse statement (name -> SSN) is not true because more than one employee can have the same name but different SSNs.
75064
Tables/Objects/Classes just represent real world scenerios. We dont create these things depending on our design. Its other way around.\n\nSo depending on the real world scenerio you will have mostly one to many. This is from the third Normal form. we generally remove the most repeated columns that have a primary key for themselves. And minimize the storage.\n\nIf you have one to one tables then its same as keeping all of records together in one table. If you have many to many then you can always break this relation ship into many to one and one to many ( 2 relationships ), by adding a new table in between these 2 existing tabes, just by applying cartesian product rule. This middle extra table may just contain only primary keys of 2 tables.\n\nEnjoy.
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Prior to computers, models were created in clay to show what a design may look like when done for real. Being made of clay, they were natually 3 dimensional.  \nWith the advent of compuetrs, the modeling moved to the screen, but was only able to be done in two demensions.\nWith the advent of current 3D capable video cards and software, it is now possible to create compuetr models of a design in three dimensions
83033
The first one is used for DataMining the second for Simple/Complex/Queries...
1030688
The object oriented programming will give the impression very unnatural to a programmer with a lot of procedural programming experience. In Object Oriented programming Encapsulation is the first pace. Encapsulation is the procedure of covering up of data and functions into a single unit (called class). An encapsulated object is often called an abstract data type. In this article let us see about it in a detailed manner. \n\nThe need of encapsulation is to protect or prevent the code (data) from accidental corruption due to the silly little errors that we are all prone to make. In Object oriented programming data is treated as a critical element in the program development and data is packed closely to the functions that operate on it and protects it from accidental modification from outside functions. \n\nEncapsulation provides a way to protect data from accidental corruption. Rather than defining the data in the form of public, we can declare those fields as private. The Private data are manipulated indirectly by two ways. The first method is using a pair of conventional accessor and mutator methods. Another one method is using a named property. Whatever be the method our aim is to use the data with out any damage or change.\n\nusing system;\npublic class Department\n{\nprivate string departname;\n.......\n\n// Accessor.\npublic string GetDepartname()\n{\nreturn departname;\n}\n\n// Mutator.\npublic void SetDepartname( string a)\n{\ndepartname=a;\n}\n\n}\nLike the above way we can protect the private data from the outside world. Here we use two separate methods to assign and get the required data.\n\npublic static int Main(string[] args)\n{\nDepartment d = new Department();\nd.SetDepartname("ELECTRONICS");\nConsole.WriteLine("The Department is :"+d.GetDepartname());\nreturn 0;\n}\nIn the above example we can't access the private data departname from an object instance. We manipulate the data only using those two methods.\n\nProperties are a new language feature introduced with C#. Only a few languages support \nthis property. Properties in C# helps in protect a field in a class by reading and writing to it. The first method itself is good but Encapsulation can be accomplished much smoother with properties.\n\nusing system;\npublic class Department \n{\nprivate string departname;\npublic string Departname\n{\nget\n{\nreturn departname;\n}\nset \n{\ndepartname=value;\n}\n}\n}\npublic class Departmentmain\n{\npublic static int Main(string[] args)\n{\nDepartment d= new Department();\nd.departname="Communication";\nConsole.WriteLine("The Department is :{0}",d.Departname);\nreturn 0;\n} \n}\n\nFrom the above example we see the usage of Encapsulation by using properties. The property has two accessor get and set. The get accessor returns the value of the some property field. The set accessor sets the value of the some property field with the contents of "value”. Properties can be made read-only. This is accomplished by having only a get accessor in the property implementation.
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Normalization\n\nNormalization is a design technique that is widely used as a guide in designing relational databases. Normalization is essentially a two step process that puts data into tabular form by removing repeating groups and then removes duplicated data from the relational tables.\n\nNormalization theory is based on the concepts of normal forms. A relational table is said to be a particular normal form if it satisfied a certain set of constraints. There are currently five normal forms that have been defined. In this section, we will cover the first three normal forms that were defined by E. F. Codd. \n\nBasic Concepts\n\nThe goal of normalization is to create a set of relational tables that are free of redundant data and that can be consistently and correctly modified. This means that all tables in a relational database should be in the third normal form (3NF). A relational table is in 3NF if and only if all non-key columns are (a) mutually independent and (b) fully dependent upon the primary key. Mutual independence means that no non-key column is dependent upon any combination of the other columns. The first two normal forms are intermediate steps to achieve the goal of having all tables in 3NF. In order to better understand the 2NF and higher forms, it is necessary to understand the concepts of functional dependencies and lossless decomposition.
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There are lots of reasons why a manufacturer might be interested in this. For example, suppose that a manufacturer wants to produce a container with a given capacity that uses as little material as possible, so it is inexpensive to produce. This could easily lead to characterizing potential product designs with parameters like the ratio of volume to surface area; designs with larger ratios will potentially be more profitable.