Database Fundamentals

"Productivity experts say that breakthroughs come by thinking ‘nonlinearly.’ In this chapter, we discuss one of the most important nonlinear data structures in computing—trees. Tree structures are indeed a breakthrough in data organization, for they allow us to implement a host of algorithms much faster than when using linear data structures, such as list. Trees also provide a natural organization for data, and consequently have become ubiquitous structures in file systems, graphical user interfaces, databases, Web sites, and other computer systems”(Goodrich & Tamassia, 2010, p. 284). “A priority queue is an abstract data type for storing a collection of prioritized elements that supports arbitrary element insertion but supports removal of elements in order of priority, that is, the element with first priority can be removed at any time. This ADT is fundamentally different from the position-based data structures we discussed in previous chapters, such as stacks, queues, deques, lists, and even trees(Goodrich & Tamassia, 2010, p. 338). “An efficient realization of a priority queue uses a data structure called a heap. This data structure allows us to perform both insertions and removals in logarithmic time”(Goodrich & Tamassia, 2010, p. 350). I feel like I did learn data structures to help me analyze problems. I felt that on some of the assignments I got partial credit. Getting partial credit is similar to Calculus. Trees, queues, and heaps are helpful because they involve critical thinking skills. All of the data structures and algorithms in this class involve critical thinking skills. “Most programmers work on their own when writing the code, and other people are brought in only after the implementation is finished, to test or review the code” (Barnes & Kölling, 2012, p. 469). The class displayed the character of Jesus by joining together and helping one another this semester.

References

Barnes, D., & Kölling, M. (2012). Objects first with java:

A practical...