# Web Scraping Scientific Repositories for Augmented Relevant Literature Search Using CRISP-DM
## 5 Case Study
### Results  
Interpretation and evaluation of the returned results from the earlier exercise is highly dependent upon the researcher's analysis and synthesis of the literature as well as academic domain understanding, which is linked to Steps 5 and 6 of the methodology. As explained, the iterative nature of systematic reviews and the methodology at hand puts research rigor at a high rank for an academic concept to be either proven or refuted. The results at which the researcher had discovered helps significantly support these decisions. In this case it had been initially discovered by the researcher that leading causes behind enterprise systems challenges are implementation and post-implementation bottlenecks. This required further investigation. The below results show the heatmap produced out of the X-means clustering algorithm (Figure 7). Figure 6: (**a**) Process model for Information Extraction and Similarity on all literature titles; (**b**) Process model for X-Means clustering and Random Forest. It could immediately be observed that for the top analyzed terms being analyzed, they are clustered around terms like: {_control, impact, employees, enterprise_system_implementation_, _system_implementation_, _learning, implications, projects_, _etc._}. It however is not the case for post-implementation. By examining several decision trees produced by the random forest algorithm, it could be observed from the post-implementation, despite making a small fraction of appearances across the literature scraped from the journals (Figure 8), that it still surfaces across many of these trees (Figure 9). Figure 8: Post-Implementation term is ranked the 205 th most frequent term from the generated N-Grams, with only five appearances in total. Figure 7: Visualizing X-means algorithm using a heatmap graph for the top terms representing the clusters. By implementing the above methods; results show proof towards less literature counts on \"post-implementation\" lifecycles. Despite the fact, this phase within the ERP lifecycle typically poses significant bottlenecks that could impede digital transformation initiatives as illustrated in Figure 9d. The researcher then has evidence that the post-implementation phase has high affinity to prove the initial hypothesis. Yet there might not be much literature focus on the topic, which could direct his research attention towards focusing upon that topic. Through cosine similarity (Figure 10) the researcher is also able to link the highly relevant literature to the topic of interest for which enterprise systems challenges could be rigorously researched and tackled. Correspondingly, the results did show how the proposed methodology could present a stance towards augmenting the activities of a relevant literature search. Hence, enabling researchers to easily and efficiently achieve the objectives of breadth and depth of knowledge, rigor and consistency of comprehension and application, as well as the clarity and brevity of analysis, synthesis and evaluation. To further assess the performance of the artifact at hand; the author of this scholar work conducted a literature review as part of another work in progress research activity examining the same topic of \"enterprise systems challenges\". Details of this literature review exercise are mentioned in Appendix B.  
Figure 10: Relevant Post Implementation literature titles with cosine similarity measures. Figure 9: “Post-Implementation” term appearing in several trees produced by the Random Forest algorithm. Out of the literature review table illustrated in Appendix B, the total count, as well as percentages of the total included articles, are calculated for every phase and dimension within the Enterprise System (ES) implementation lifecycle. Table 2 correspondingly shows high affinities towards Implementation as well as Use and Maintenance phases (with 49% and 66%, respectively, from the manually shortlisted content of the literature review exercise).