In this first part of the six-part series, we'll look at why data structures are important, and their effect on the performance of an algorithm. To determine a data structure's effect on performance, we'll need to examine how the various operations performed by a data structure can be rigorously analyzed. Finally, we'll turn our attention to two data structures present in the .NET Framework—the Array and ArrayList. Chances are you've used both of these data structures in past projects. In this article, we'll examine what operations they provide and the efficiency of these operations.
In Part 2, we'll explore the ArrayList class in more detail and examine its counterparts, the Queue class and Stack class. Like the ArrayList, both the Queue and Stack classes store a contiguous collection of data and are data structures available in the .NET Framework Base Class Library. However, unlike an ArrayList from which you can retrieve any data item, Queues and Stacks only allow data to be accessed in a predetermined sequential order. We'll examine some applications of Queues and Stacks, and see how to implement both of these classes by extending the ArrayList class. After examining Queues and Stacks, we'll look at HashTables, which allow for direct access like an ArrayList, but store data indexed by a string key.
While ArrayLists are ideal for directly accessing and storing contents, they are suboptimal candidates when the data needs to be searched. In Part 3, we'll examine the binary search tree data structure, which provides a much more efficient means for searching than the ArrayList. The .NET Framework does not include any built-in binary search tree data structures, so we will have to build our own.
The efficiency of searching a binary search trees is sensitive to the order with which the data was inserted into the tree. If the data was inserted in sorted or near-sorted order, the binary search tree loses virtually all of its efficiency advantages over the ArrayList. To combat this issue, in Part 4 we'll examine an interesting randomized data structure—the SkipList. SkipLists provide the efficiency of searching a binary search tree, but without the sensitivity to the order with which data is entered.
In Part 5 we'll turn our attention to data structures that can be used to represent graphs. A graph is a collection of nodes, with a set of edges connecting the various nodes. For example, a map can be visualized as a graph, with cities as nodes and the highways between them as edged between the nodes. Many real-world problems can be abstractly defined in terms of graphs, thereby making graphs an often-used data structure.
Finally, in Part 6 we'll look at data structures to represent sets and disjoint sets. A set is an unordered collection of items. Disjoint sets are a collection of sets that have no elements in common with one another. Both sets and disjoint sets have many uses in everyday programs, which we'll examine in detail in this final part.
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