4.3 Module Review Questions
Question 1
Search engine crawlers are the scripts that automatically and systematically browse pages on the web. An example is Google (Turban, Pollard & Wood, 2018). These engines work in three steps: crawling, indexing, and ranking. In the crawling level, the search engines scour the internet for content over the code for each URL they come across and progress to the progress to the indexing step. Here, search engines organize and store the content found during crawling for display as a solution to relevant questions. In the ranking phase, results are odder from the most relevant to the least appropriate.
Question 2
The use of mobile technologies has positively affected the search engine optimization. First, most websites have been mobile optimized because they want to be ranked high the search engines. The reason is that search engines such as Google detect high levels of traffic of mobile technologies; thus, it cannot permit organizations or individuals to operate a website that can only run on a desktop if they wish to be rated highly (Bhandari & Bansal, 2018). Second, search results have been altered because people type differently when searching on desktop and when typing on the desktop. On mobile, users type shorter inquiries than they would on a desktop.
Question 3
Mashups enhance new functionality and create benefits from operating data in that; end-users can identify trends in data and develop new insights. Data smashups can repurpose information such as Lego blocks and introduce new data that brings self-service to a higher level for entrepreneurs who study sifts or dashboards using reports with underlying data (Blichmann, Rümpel, Schrader, Radeck & Meißner, 2017). As a result, the end-users can immediately analyze and visualize the resulting data blocks by combining his data with that which is available in the platform or by combining data from sources subjected to the platform and not projected in the schema.
References
Bhandari, R. S., & Bansal, A. (2018). Impact of search engine optimization as a marketing tool. Jindal Journal of Business Research, 7(1), 23–36. doi: 10.1177/2278682117754016.
Blichmann, G., Rümpel, A., Schrader, M., Radeck, C., & Meißner, K. (2017). Private data in collaborative web mashups. Proceedings of the 13th International Conference on Web Information Systems and Technologies. doi: 10.5220/0006374201740183.
Turban, E., Pollard, C., & Wood, G. R. (2018). Information technology for management: on-demand strategies for performance, growth, and sustainability. Hoboken,