Gain insights into array and linked list-based implementations, delve into advanced structures like skiplists and trees, and explore template-based collections for efficient data optimization and retrieval.

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Data structures and algorithms are essential in computer science since they play a crucial role in efficient information retrieval and processing, dealing with files, storing contacts on phones, social networks and web searches.

In this course, you’ll learn about the array-based implementation of various linear data structures, stack, and queues. You’ll also learn about linked list-based implementation. Next, you’ll explore advanced data structures like skiplists and hashing. You’ll learn how to implement a variety of trees and graphs, and data structures related to bits of an integer. Toward the end of the course, you’ll learn the implementation of structures based on external storage.

After completing this course, you’ll be able to create reusable programs with template-based collections that can efficiently analyze how to optimize the storage and retrieval of very large amounts of data. Overall, this course will enhance your productivity and performance as a software developer.

Data Structures with Generic Types in C++

# Additional notes
The external memory model of computation was introduced by #key# Aggarwal and Vitter: A. Aggarwal and J. S. Vitter. The input/output complexity of sorting and related problems. Communications of the ACM, 31(9):1116– 1127, 1988. #key#. It is sometimes also called the I/O model or the disk access
model.

B-Trees are to external memory searching what binary search trees are to internal memory searching. B-trees were introduced by #key# Bayer and McCreight: R. Bayer and E. M. McCreight. Organization and maintenance of large ordered indexes. In SIGFIDET Workshop, pages 107–141. ACM, 1970. #key# in 1970 and, less than ten years later, the title of #key# Comer’s ACM Computing Surveys article: D. Comer. The ubiquitous B-tree. ACM Computing Surveys, 11(2):121–137, 1979. #key# referred to them as ubiquitous.

Like binary search trees, there are many variants of B-trees, including
$B^+$–trees, $B^∗$–trees, and counted B-trees. B-trees are indeed ubiquitous and are the primary data structure in many file systems, including Apple’s
HFS+, Microsoft’s NTFS, and Linux’s Ext4; every major database system; and key-value stores used in cloud computing. #key# Graefe’s recent survey: G. Graefe. Modern B-tree techniques. Foundations and Trends in Databases, 3(4):203–402, 2010. #key# provides a $200+$ page overview of the many modern applications, variants, and optimizations of B-trees.


B-trees implement the `SSet` interface. If only the `USet` interface is needed, then external memory hashing could be used as an alternative to B-trees. External memory hashing schemes do exist; see, for example, #key# Jensen and Pagh: M. S. Jensen and R. Pagh. Optimality in external memory hashing. Algorithmica, 52(3):403–411, 2008. #key#. These schemes implement the `USet` operations in $O(1)$ expected time in the external memory model. However, for a variety of reasons, many applications still use B-trees even though they only require `USet` operations.


Discover more aspects regarding external memory searching.

Overview

Array-Based Lists

Linked Lists

Skiplists

Hash Tables

Binary Trees

Random Binary Search Trees

Scapegoat Trees

Red-Black Trees

Heaps

Sorting Algorithms

Graphs

Data Structures for Integers

External Memory Searching

Wrap Up

Discussion on External Memory Searching

Additional notes