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Software Engineering: What Makes It Run IBM Essay

Software Engineering: What Makes it Run IBM and the Institute of Electrical and Electronic Engineers (IEEE) have collaborated to create the Software Engineering Online Learning Center, a portal of educational and industry information that is very useful for any student of computer science, programming or software engineering. The intent of this paper is to evaluate this portal for its applicability and value to learning. The portal is designed around IBM-centric content as they are the primary partner; there are however many other software technology vendors also contributing content to this portal. The IEEE has divided the content into DB2 tutorials, provided linked to webcasts, Java learning tools, Linux certification tutorials, and dozens of white papers. There are also excellent sections on Amazon.com and eBay tutorials, and a balance of coverage for open source vs. proprietary operating systems. What is very useful about the content on the site is that it only takes a short opt-in process to gain access to it. The positive aspects of the portal include the active support of IBM and other technology vendors, all of which are providing excellent content, the support for online video formats which makes learning much easier through online tutorials, and the availability of downloadable software for students to experiment and learn with. The negative aspects of the portal are its heavy reliance on IBM alone, with only slight participation from other vendors. There is too much content on DB2 alone...

Finally there is a lack of deep coverage on how data structures are changing to support massive data sets including the very popular approach to data reduction from Yahoo called MapReduce (Dean, Ghemawat, 2010) and its counterpart in the open source community, hadoop (LaValle, Lesser, Shockley, Hopkins, Kruschwitz, 2011).
Discussion and Analysis

The area of database taxonomies and how they are being designed to support multiple role-based access across large data sets provides one of the most challenging aspects of computer science (Kavanagh, 2010). The portal provides insights into DB2 programming and development, yet fails to provide enough information on taxonomy-based development for large data sets. The area of large data set management including MapReduce shows significant potential for creating taxonomies that can scale to rapidly expanding areas of knowledge and learning (Dean, Ghemawat, 2010). The proliferation of content on social networking sites including Facebook, Twitter and others are fueling an exceptionally rich series of data sets that when combined with geo-demographic analysis techniques to refine and strengthen retail and supply chain strategies (Adnan, Longley, Singleton, Brunsdon, 2010). Being able to create more efficient means to launching retail operations, streamlining supply chains and creating more efficient forecasting methods are all possible using MapReduce-level data sets as part…

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References

Adnan, M., Longley, P., Singleton, A., & Brunsdon, C. (2010). Towards Real-Time Geodemographics: Clustering Algorithm Performance for Large Multidimensional Spatial Databases. Transactions in GIS, 14(3), 283-297.

Jeffrey Dean, & Sanjay Ghemawat. (2008). MapReduce: Simplified Data Processing on Large Clusters. Association for Computing Machinery. Communications of the ACM, 51(1), 107-113.

Dean, J., & Ghemawat, S.. (2010). MapReduce: A Flexible Data Processing Tool. Association for Computing Machinery. Communications of the ACM, 53(1), 72.

Eric Kavanagh. (2010). The New View: Web 3.0 Will Change Our Perspective: With semantic understanding, we'll unravel the complex world of unstructured content. Information Management, 20(5), 56.
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