Only provides the list of supported operations, type of parameters they can accept and return type of these operations.

provides the internal representation of a data structure. also provides the definition of the algorithms used in the operations of the data structure.

Data structure implementation should implement its interface correctly.

Running time or the execution time of operations of data structure must be as small as possible.

Memory usage of a data structure operation should be as little as possible.

Consider an inventory of 1 million(106) items of a store. If the application is to search an item, it has to search an item in 1 million(106) items every time slowing down the search. As data grows, search will become slower.

although being very high, falls limited if the data grows to billion records.

As thousands of users can search data simultaneously on a web server, even the fast server fails while searching the data.

This is the scenario where a particular data structure operation takes maximum time it can take.

This is the scenario depicting the average execution time of an operation of a data structure.

This is the scenario depicting the least possible execution time of an operation of a data structure.

Data are values or set of values.

Data item refers to single unit of values.

Data items that are divided into sub items are called

Data items that cannot be divided are called

An entity is that which contains certain attributes or properties, which may be assigned values.

Entities of similar attributes form an entity set.

is a single elementary unit of information representing an attribute of an entity.

is a collection of field values of a given entity.

collection of records of the entities in a given entity set.

Each of its steps (or phases), and their inputs/outputs should be clear and must lead to only one meaning.

An algorithm should have 0 or more well-defined inputs.

An algorithm should have 1 or more well-defined outputs,

Algorithms must terminate after a finite number of steps.

Should be feasible with the available resources.

An algorithm should have step-by-step directions, which should be___of any programming code.

This is a theoretical analysis of an algorithm. Efficiency of an algorithm is measured by assuming that all other factors, for example, processor speed, are constant and have no effect on the implementation.

This is an empirical analysis of an algorithm. The selected algorithm is implemented using programming language. This is then executed on target computer machine. In this analysis, actual statistics like running time and space required, are collected.

Time is measured by counting the number of key operations such as comparisons in the sorting algorithm.

Space is measured by counting the maximum memory space required by the algorithm.

an algorithm refers to defining the mathematical foundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case, and worst case scenario of an algorithm.

Minimum time required for program execution.

Average time required for program execution.

Maximum time required for program execution

is the formal way to express the upper bound of an algorithm's running time.

is the formal way to express the lower bound of an algorithm's running time.

is the formal way to express both the lower bound and the upper bound of an algorithm's running time.

An algorithm is designed to achieve optimum solution for a given problem. In greedy algorithm approach, decisions are made from the given solution domain.

This problem is to count to a desired value by choosing the least possible coins and the greedy approach forces the algorithm to pick the largest possible coin.

This step involves breaking the problem into smaller sub-problems. Sub-problems should represent a part of the original problem.

This step receives a lot of smaller sub-problems to be solved. Generally, at this level, the problems are considered 'solved' on their own.

When the smaller sub-problems are solved, this stage recursively combines them until they formulate a solution of the original problem. When the smaller sub-problems are solved, this stage recursively combines them until they formulate a solution of the original problem.

is similar to divide and conquer in breaking down the problem into smaller and yet smaller possible sub-problems.

In contrast to greedy algorithms, where local optimization is addressed, dynamic algorithms are motivated for an overall optimization of the problem.

defines a particular data with the following characteristics.

Definition should define a single concept.

Definition should be able to be mapped to some data element.

Definition should be unambiguous.

represents an object having a data.

is a way to classify various types of data such as integer, string, etc. which determines the values that can be used with the corresponding type of data, the type of operations that can be performed on the corresponding type of data.

Those data types for which a language has built-in support are known as Built-in Data types. For example, most of the languages provide the following built-in data types. • Integers • Boolean (true, false) • Floating (Decimal numbers) • Character and Strings

Those data types which are implementation independent as they can be implemented in one or the other way are known as derived data types. These data types are normally built by the combination of primary or built-in data types and associated operations on them. For example − • List • Array • Stack • Queue

The data in the data structures are processed by certain operations. The particular data structure chosen largely depends on the frequency of the operation that needs to be performed on the data structure. • Traversing • Searching • Insertion • Deletion • Sorting • Merging

is a container which can hold a fix number of items and these items should be of the same type.

Each item stored in an array is called an element.

Each location of an element in an array has a numerical