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While data analysis is a common term for data modeling, the activity actually has more in common with the ideas and methods of synthesis (inferring general concepts from particular instances) than it does with analysis (identifying component concepts from more general ones). {Presumably we call ourselves systems analysts because no one can say systems synthesis .} Data modeling strives to bring the data structures of interest together into a cohesive, inseparable, whole by eliminating unnecessary data redundancies and by relating data structures with relationships .
A different approach is through the use of adaptive systems such as artificial neural networks that can autonomously create implicit models of data.
(From Wikipedia, the free encyclopedia)
In computer science , a data structure is a way of storing data in a computer so that it can be used efficiently. Often a carefully chosen data structure will allow the most efficient algorithm to be used. The choice of the data structure often begins from the choice of an abstract data structure . A well-designed data structure allows a variety of critical operations to be performed, using as few resources, both execution time and memory space, as possible. Data structures are implemented using the data types , references and operations on them provided by a programming language .
Different kinds of data structures are suited to different kinds of applications, and some are highly specialized to certain tasks. For example, B-trees are particularly well-suited for implementation of databases, while routing tables rely on networks of machines to function.
In the design of many types of programs, the choice of data structures is a primary design consideration, as experience in building large systems has shown that the difficulty of implementation and the quality and performance of the final result depends heavily on choosing the best data structure. After the data structures are chosen, the algorithms to be used often become relatively obvious. Sometimes things work in the opposite direction - data structures are chosen because certain key tasks have algorithms that work best with particular data structures. In either case, the choice of appropriate data structures is crucial.
This insight has given rise to many formalized design methods and programming languages in which data structures, rather than algorithms, are the key organizing factor. Most languages feature some sort of module system , allowing data structures to be safely reused in different applications by hiding their verified implementation details behind controlled interfaces. Object-oriented programming languages such as C++ and Java in particular use classes for this purpose.
Since data structures are so crucial, many of them are included in standard libraries of modern programming languages and environments, such as C++'s Standard Template Library , the Java Collections Framework, and the Microsoft .NET Framework .
The fundamental building blocks of most data structures are arrays , records , discriminated unions , and references . For example, the nullable reference, a reference which can be null, is a combination of references and discriminated unions, and the simplest linked data structure, the linked list , is built from records and nullable references.
Data structures represent implementations or interfaces : A data structure can be viewed as an interface between two functions or as an implementation of methods to access storage that is organized according to the associated data type .
(From Wikipedia, the free encyclopedia)
To analyze an algorithm is to determine the amount of resources (such as time and storage) necessary to execute it. Most algorithms are designed to work with inputs of arbitrary length. Usually the efficiency or complexity of an algorithm is stated as a function relating the input length to the number of steps (time complexity) or storage locations (space complexity).
Algorithm analysis is an important part of a broader computational complexity theory , which provides theoretical estimates for the resources needed by any algorithm which solves a given computational problem. These estimates provide an insight into reasonable directions of search of efficient algorithms.
In theoretical analysis of algorithms it is common to estimate their complexity in asymptotic sense, i.e., to estimate the complexity function for reasonably large length of input. Big O notation , omega notation and theta notation are used to this end. For instance, binary search is said to run an amount of steps proportional to a logarithm, or in O(log(n)), colloquially "in logarithmic time". Usually asymptotic estimates are used because different implementations of the same algorithm may differ in efficiency. However the efficiencies of any two "reasonable" implementations of a given algorithm are related by a constant multiplicative factor called hidden constant.
Exact (not asymptotic) measures of efficiency can sometimes be computed but they usually require certain assumptions concerning the particular implementation of the algorithm, called model of computation . A model of computation may be defined in terms of an abstract computer , e.g., Turing machine , and/or by postulating that certain operations are executed in unit time. For example, if the sorted set to which we apply binary search has N elements, and we can guarantee that a single binary lookup can be done in unit time, then at most log2 N + 1 time units are needed to return an answer.
Exact measures of efficiency are useful to the people who actually implement and use algorithms, because they are more precise and thus enable them to know how much time they can expect to spend in execution. To some people (e.g. game programmers), a hidden constant can make all the difference between success and failure.
Time efficiency estimates depend on what we define to be a step. For the analysis to make sense, the time required to perform a step must be guaranteed to be bounded above by a constant. One must be careful here; for instance, some analyses count an addition of two numbers as a step. This assumption may not be warranted in certain contexts. For example, if the numbers involved in a computation may be arbitrarily large, addition no longer can be assumed to require constant time (compare the time you need to add two 2-digit integers and two 1000-digit integers using a pen and paper).
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