Computer Science
Advanced Data Structures
Advanced data structures are complex data structures that are designed to solve specific problems efficiently. They are used to store and manipulate large amounts of data in a way that allows for fast access and retrieval. Examples of advanced data structures include trees, graphs, and heaps.
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7 Key excerpts on "Advanced Data Structures"
No longer available |Learn moreData Structures and Program Design Using C
A Self-Teaching Introduction
- D. Malhotra, N. Malhotra(Authors)
- 2018(Publication Date)
- Mercury Learning and Information(Publisher)
C H A P T E R 1 INTRODUCTION TO DATA STRUCTURES In This Chapter ● ● Introduction ● ● Types of data structures ● ● Operations on data structures ● ● Algorithms ● ● Approaches for designing an algorithm ● ● Analyzing an algorithm ● ● Abstract data types ● ● Big O notation ● ● Summary 1.1 Introduction A data structure is an efficient way of storing and organizing the data elements in the computer memory. Data means a value or a col- lection of values. Structure refers to a method of organizing the data. The mathematical or logical representation of data in the memory is referred as a data structure. The objective of a data structure is to store, retrieve, and update the data efficiently. A data structure can be referred 2 • DATA STRUCTURES AND PROGRAM DESIGN USING C to as a group of elements grouped under one name. The group of data elements is called members, and they can be of different types. Data structures are used in almost every program and software system. There are various kinds of data structures that are suited for different types of applications. Data structures are the building blocks of a program. For a program to run efficiently, a programmer must choose appropriate data structures. A data structure is a very crucial part of data management. As the name suggests, data management is a task which includes differ- ent activities like collection of data, an organization of data into struc- tures, and much more. Some examples where data structures are used include stacks, queues, arrays, binary trees, linked lists, hash tables, and so forth. A data structure helps us to understand the relationship of one element to another element and organize it within the memory. It is a mathematical or logical representation or organization of data in memory.
eBook - PDF- Anil Kumar Yadav, Vinod Kumar Yadav(Authors)
- 2019(Publication Date)
- Arcler Press(Publisher)
DATA STRUCTURES 1 CHAPTER CONTENTS 1.1. Introduction ........................................................................................ 2 1.2. Data Structure Basic Terminology ....................................................... 2 1.3. Data Structure ..................................................................................... 3 1.4. Introduction To Algorithm ................................................................... 8 1.5. Basic Concept of Function ................................................................ 13 1.6. Basic Concept of Pointers ................................................................. 19 1.7. Introduction To Structure ................................................................... 22 1.8. Dynamic Memory Allocation In Data Structure ................................. 28 Data Structures with C Programming 2 1.1. INTRODUCTION In the Computer Programming or Software development, Data Structures is one of the most valuable roles for computer engineers. Use of appropriate data structures enables a computer system to perform its task more efficiently, by influencing the ability of computers to store and retrieve data from any location in its memory. A data structure is a method of how storing data on a computer so that it can be used efficiently. In Computer Programming we are using different types of data to perform, store, access and sent data and information. Computers cannot do without data, so organizing that data is very important. If that data is not organized effectively, it is very difficult to perform any task on that data. If it is organized effectively then any operation can be performed easily on that data. 1.2. DATA STRUCTURE BASIC TERMINOLOGY 1.2.1. Data and Data Types Data are a group of specific facts, numbers, character, and figures. A data item refers to a single unit of values. Data is an unprocessed form of information.
eBook - ePubEngineering Informatics
Fundamentals of Computer-Aided Engineering
- Benny Raphael, Ian F. C. Smith(Authors)
- 2013(Publication Date)
- Wiley(Publisher)
In some engineering fields, work has concentrated on standardizing data structures. Data structure standardization helps contribute to better communication between engineers during complex engineering tasks. Such standardization also increases compatibility between software products. Increases in productivity that are linked to such efforts could result in better, faster and cheaper engineering activities.This chapter presents and describes fundamental types of data structures. Knowledge of these types is important for understanding the material presented in subsequent chapters on object-oriented programming (Chapter 4), database concepts (Chapter 5) and constraint-based reasoning (Chapter 7), as well as optimization and search (Chapter 8).3.2 Definitions
The term data structure refers to the organization of data within software applications. Properly organized data is easy to understand and maintain. Consider the list of variables shown in Figure 3.1a . Compare it with the structured form of the same data shown in Figure 3.1b . In structured form, logically related data are grouped together. Such structure helps understanding, thereby increasing the reliability of appropriate data use and modifications.Figure 3.1Data structures group related information together: (a) a flat list of unorganized data; (b) structured dataData structures often involve hierarchies, called decomposition hierarchies, where data are organized into attributes of systems, components and subcomponents. In this chapter, the term object will be used informally to refer to a collection of data that is organized into attributes. The term attribute is used in a generic sense to denote characteristic aspects of objects (properties) as well as components of objects. Thus an attribute might itself be an object consisting of subattributes. Figure 3.2 illustrates the decomposition of the data structure in Figure 3.1b
eBook - PDFFoundations of Data Intensive Applications
Large Scale Data Analytics under the Hood
- Supun Kamburugamuve, Saliya Ekanayake(Authors)
- 2021(Publication Date)
- Wiley(Publisher)
C H A P T E R 107 4 Every program that does meaningful work deals with data structures. Whether it is a large-scale distributed application or a serial program running in the cluster, we can find data structures everywhere. The simplest data types we use are primitive types such as integers and doubles. Computers are designed to work on such fundamental data by loading them into special hardware units inside CPUs called registers wherein they make decisions and perform arithmetic operations. Utilizing simple primitive types in computing is an efficient and well- documented endeavor these days. Compilers supply excellent support to opti-mize operations involving them. But a complex program cannot rely solely on just simple types to meet its data needs. To represent complex data, composite types are created from their less complicated brethren. Examples of such are present in any programming language. In C we program with structs, while in Java or C++, we program classes to create composite types. An array is another composite data structure widely used in computing. An array has multiple values of the same type in a contiguous space in memory. The individual value of an array can be accessed using an index and can be of multiple dimensions, making it extremely efficient. When we deal with substantial amounts of data, we need to use data struc-tures that can hold them as composite forms. It all boils down to how efficient a data structure is in terms of access speed and storage. Different application classes have found certain data structures to be best suited for their use cases. Data Structures 108 Chapter 4 ■ Data Structures Application frameworks designed to work on these application classes natu-rally choose the most productive data representations. For example, big data frameworks prefer table data structures to store the data, while deep learning frameworks tend to use tensors.
eBook - PDF- Michael Tooley(Author)
- 2013(Publication Date)
- Newnes(Publisher)
Chapter 6 Data structures and algorithms 6.1 Introduction The design and analysis of data structures and algorithms play a very important role in software engineering. If the first two main goals in software development are reliability and maintainability, the third one is definitely efficiency. It is well known that in several applications, such as real-time systems and defence, efficiency is a critical issue. But it is worth stressing that efficiency is also a key issue in several other areas where users need fast and powerful interaction with complex systems (very large databases, expert systems, computer-aided design (CAD) and computer-aided software engineering (CASE) systems etc.). This chapter is describes the design of efficient algorithms and data structures. According to the most widespread view of software design, based on the principles of modularity and abstraction, a software system can be viewed as a collection of data types, each one composed of a set of data items together with operations on such items. This approach has several advantages, the main one being the provision of a certain degree of independence between the logical specification of the operations and their physical imple-mentation by means of different data structures. This allows the adoption of the particular implementation of a data type that is most suited to meeting the efficiency requirements. Starting from this point of view, efficiency considerations should not be seen as being in contrast to other design principles. No clear difference can be established between designing algo-rithms and designing data structures. On the one hand, the effi-cient performance of an algorithm may derive from the appropri-ate choice of the implementation of the supporting data type, while on the other, ingenious algorithm design techniques may be exploited to provide efficient implementation complex opera-tions on a data structure.
eBook - PDFAlgorithms and Theory of Computation Handbook, Volume 1
General Concepts and Techniques
- Mikhail J. Atallah, Marina Blanton(Authors)
- 2009(Publication Date)
- Chapman and Hall/CRC(Publisher)
However, efficiency is not the only quality measure of a data structure. Simplicity and ease of implementation should be taken into account when choosing a data structure for solving a practical problem. 4.1.2 Abstract Data Types Data structures are concrete implementations of abstract data types (ADTs). A data type is a collection of objects. A data type can be mathematically specified (e.g., real number, directed graph) or concretely specified within a programming language (e.g., int in C, set in Pascal). An ADT is a mathematically specified data type equipped with operations that can be performed on the objects. Object-oriented programming languages, such as C++, provide support for expressing ADTs by means of classes. ADTs specify the data stored and the operations to be performed on them. 4.1.3 Main Issues in the Study of Data Structures The following issues are of foremost importance in the study of data structures. Static vs. dynamic: A static data structure supports only queries, while a dynamic data structure supports also updates. A dynamic data structure is often more complicated than its static counterpart supporting the same repertory of queries. A persistent data structure (see, e.g., [9]) is a dynamic data structure that supports operations on past versions. There are many problems for which no efficient dynamic data structures are known. It has been observed that there are strong similarities among the classes of problems that are difficult to parallelize and those that are difficult to dynamize (see, e.g., [32]). Further investigations are needed to study the relationship between parallel and incremental complexity [26]. Implicit vs. explicit: Two fundamental data organization mechanisms are used in data struc-tures. In an explicit data structure, pointers (i.e., memory addresses) are used to link the elements and access them (e.g., a singly linked list, where each element has a pointer to the next one).
eBook - PDF- Markus Knasmüller(Author)
- 2003(Publication Date)
- Morgan Kaufmann(Publisher)
4 Data Structures and Algorithms Now that we have introduced static data structures as well as dynamic data structures (also called pointers; see Section 3.7), this chapter deals more intensively with that subject. Dynamic data structures are important, because the only way to create objects in object-oriented programming is to create them dynamically. This chapter begins with a discussion of data abstraction, which will help you understand the concept of an object-oriented class easily, then discusses algorithms that use dynamic data structures. Although such algo-rithms can also be used in COBOL, experience has shown that they rarely are, especially by programmers who made their way into the COBOL world through a crash course from a different discipline, where they did not get a chance to work with any programming language intensively. This chapter is particularly important for those who feel addressed by this statement. 4.1 Abstract Data Types The tutorial shows a few preparatory steps for how programmers can create their own data types. Our discussion concentrates less on syntactic details (which have been dealt with in Section 3.4.1) than on how this operation works efficiently from the contents point of view. We know that data types are normally made available to various other programs. Therefore, the Intensive dealing with pointers 108 Chapter 4 Data Structures and Algorithms client needs to know only the essential things about the data type. In most cases, no detailed knowledge is required. Behind this concept is the term abstraction. The abstract data structure and abstract data type build on this abstraction. 4.1.1 Term: Abstraction The term abstraction is interpreted as a generalization that allows us to better handle the complexity of a program. We can neglect unnecessary details and concentrate more on the essentials. The following two exam-ples explain this concept. Our first example uses a CD player, which all of us are familiar with.
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