Creation of a Data Structure - Case Study Example

Data Structure and Algorithm Introduction Data structures implementation requires the help of programs. Algorithm is written before compiling any program. Algorithm is just collection of instructions, which are executed in consecutive manners. Study of the data structure and algorithm is an important component in the engineering curricula. Computer engineers do not only use the standard of computer packages but also know how to implement the data structure, which is the heart of software. Contrary to traditional ways of understanding the data structures, which rely upon the static examples and textbook diagrams provide the some aspects of execution of a program. More dynamic and program walkthroughs are still not much emphasized for students learning of multiple aspects of the program state that changes from one step to another step. There is a need of a tool, which can assist the learners to visualize those changes occurring from step by step execution of a program. In computer science, a significant way of information organization is the data structure. It is learned that how data structures are created by using the computers. Before the reading of data structure and algorithm, a mental picture about storing the data structure is perceived differently from real storing of data structures. Creation of a data structure in different ways has been learned. Most common organizations used in data structure are stack, linked list, records, trees, queues, graphs, priority graphs, etc. Data structures have been used mainly for three reasons as abstraction, efficiency and reusability. Abstraction More understanding is provided by data structures to view the data, and the level of abstraction is provided to solve the problems. Stacks as a data structure have pop and push properties rather than know thoroughly about each operation performed for push and pop of elements. Efficiency Algorithms’ efficiency is achieved through organizing data in a way by putting the data in an array and then searched out. Another way of data searching is the transverse of data element by element and finding the desired element. However, later way of searching the data is inefficient, because in most cases the transverse of all elements is necessary that is time-consuming and memory loss. By using the Hash Tree or binary tree this inefficiency of the previous approach has been overcome, and data search is faster than earlier approaches. Reusability It is found that algorithms are reusable in various conditions and situations. Some of the algorithms only solve general issues but also work for complicated ones (Louden, 1999). Sorting and Searching During the coursework, programming subject are clearly stated and understood from several lab exercises. Lecture material was completely absorbed, which was sometimes provided in the form of implementation of the algorithm. An important algorithm that was clearly understood is the sorting and searching. This algorithm is aimed to solve the issue of elements’ placement in the sequence and lying difference that how these elements moved around. Working of sorting and searching is achieved through an abstract viewing instead of looking a block of memory that contains the strings or numbers (Kavar and Parmar, 2013). Insertion operation on stack or array is also clearly understood. Best possible condition is that when an element is inserted at the last position in the array or stack. In such condition, there is no down movement of an element. If an element is inserted at the first position is called as the worse case occurrence. Similar to insertion operation on a stack or array, search operation for finding the location of an element in the array is determined by using either linear search or binary search. Linear search is applied when no information about array is given and each element is compared and searched for an item. Best case occurs when target element is found at the first position of an array and last position for a worst case. Data structure and algorithm course has been found a base in the computer science engineering. Labs attended in the undergraduate program helped to understand the complex nature of the algorithm. Both theory and practical work have been introduced parallel for understanding. Practical sessions proved to be more useful as compared to the theoretical part. In the traditional way of practicing, only predefined problem statements with data structure were covered through lab exercises. Students are restricted to utilize their limited abilities on these specific applications of data structure and algorithm. An approach is required to conduct the labs exercises, teach the data structure, and learn the concepts of data structure. This approach should help the students to formulate the problems and apply the programming skills for finding the solution of these real world problems. Another thing that is learned during this course is about giving only concept and think about problem statement and a solution or an algorithm. It motivated me to apply the data structures in other domains (Sujatha et al., 2012). It is learned that how a rooted tree describes the directed tree having a node root without any incoming edges. Outgoing edges of a node is called as test node or internal. Except this node, all other nodes are known as leaves. Data Structure and algorithm learning helps one to manage resource usage for programs. Some of the important points that how a simple algorithm saves time and uses less memory for its execution that is determined only through skills of writing an algorithm are discussed. How pointers and referencing concept help the programmer for formulate the program that consumes low memory during the execution. Some very important page replacement algorithms are studied like Fist-in-First-out (FIFO), least frequently used (LFU), least recently used (LRU)and last in first out (LIFO). These algorithms are available for purposes of memory management. Page replacement performance is proved by using the augmented skip list, link list, doubly circular, hash table and splay tree on LRU algorithm of page replacement (Kavar and Parmar, 2013). A programming language is the core for understanding and implementation of data structures. Programming language C was preferred for program implementation as C is a programming language of a high level. Data structure is easily implemented in C programming language. In Data structure and Algorithm, various concerning aspects have been not emphasized so for on algorithms and programming logic. In past, various testers and visualizers have been used for teaching the data structure but processing of program execution has been remained an unsolved issue. Competitive programming has been used for motivating the learners to practice and understand the C programming in coursework. Other approaches used for learning the data structure adopted included the graphic based, active learning and active teaching approaches for engaging the learners. In some extent, the interactive games and graphical representation have appealed for classmates. Classmates learn while they play games developed from C Programming. Game playing helps the student to view the queue and stack behavior in data structures. Many of classmates showed their interest as computer games helped them to know the concepts of data structure. Similar exposure of computer games has a positive attitude for learning of data structures and creates confidence to enhance the programming skills. Healthy learning environment is also cultivated through peer learning and teaching. Data structure and algorithm course requires programming skills for application development, because more projects are developed at the undergraduate graduate level. A new approach of increasing the learning of programming concepts and development skills is required from researchers to be presented. Understanding development on data structures, algorithms and programs are challenging, but advantages of this understanding also can be noticed in software engineering courses. This importance is also evidenced by Smith et al., (2010) as they emphasized on the development of a tool for data structure visualization designed in a way that can animate the standards for manipulation of several data structures. Purpose of this application development is to practice the algorithm covered in classes. One of the advantages of using the visualization or animation tool is the exchange of ideas in an interactive environment. Algorithm is invoked from developed data structure to animate it. Various computation algorithms are executed by developing a data structure model (Adamchick, 2011). Conclusion In this, a colloquium paper, importance of data structure and algorithm for the undergraduate program has been discussed. To support the students of data structure and algorithm for a comprehensive understanding of programs developed from data structure by a visualization tool has been an issue for students as well as instructor. This course mainly divides into theoretical as well as practical parts. Theoretical base is only achieved if students are shown the execution of a program step by step with the help of a visualization tool. Main concepts covered in data structure and algorithm class include the understanding about data structures and operation performed on these data structures. Array, stack, queue, record, trees, graphs, priority graphs are main data structures covered during progress of this course. Overall learning from Data structure and algorithm has enabled learners to expand the use of data structures in other areas of computer science i.e. software engineering. References Adamchick V (2011). Data structures and algorithms in pen-based computing environments. In Global Engineering Education Conference (EDUCON), 2011 IEEE, on 4-6 April 2011, in Amman, pp. 1211-1214. Kavar and Parmar (2013). Improve the performance of LRU page replacement algorithm using augmentation of data structure, In Computing, Communications and Networking Technologies (ICCCNT), 2013 Fourth International Conference IEEE on 4-6 July 2013 in Tiruchengode, pp.1-5. Louden K. (1999). Mastering Algorithms with C, O’Reilly Media Inc. Sujatha C., Jatalaxmi G.N., and Suvarna, G.K (2012). An innovative approach carried out in data Structures and algorithms lab, In Engineering Education: Innovative Practices and Future Trends (AICERA), 2012 IEEE International Conference on 19-21 July 21012 in Khayyam. pp. 1-4. Smith C., Strauss J., and Maher P. (2010). Data structure visualization: the design and implementation of an animation tool, In ACM SE, 10 Proceedings of the 48th Annual Southeast Regional Conference, in New York in 2010. Read More