This is continuation of IP1 and IP 2

For this IP 3, you will provide a detailed, full-sentence outline of your paper. In APA format, the full-sentence outline is essentially the same as the alphanumeric outline. The main difference (as the title suggests) is that full sentences are required at each level of the outline (Purdue University Online Writing Lab, n.d.b). Your outline should be comprised of complete sentences. Bullet points and sentence fragments are not acceptable. Your full sentence outline should detail the key sections of your paper.

The following is an example of a full-sentence outline:

• Man-made pollution is the primary cause of global warming.

  Greenhouse gas emissions are widely identified by the scientific community to be harmful.

  The burning of coal and fossil fuels are the primary releasers of hazardous greenhouse gases.

Your full-sentence outline should include the introduction, conclusion, transitions, main sections and paragraphs, and the main points and subpoints.

Your full-sentence outline should also include an APA reference list on a separate page. Quotes within the outline must also utilize APA in-text citations (Purdue University Online Writing Lab, n.d.a).

References

Purdue University Online Writing Lab. (n.d.a). The Purdue OWL: Sample outlines. http://owl.english.purdue.edu/media/pdf/20081113013048_544

Purdue University Online Writing Lab. (n.d.b). Types of outlines and samples. 

http://owl.english.purdue.edu/owl/resource/544/03/

Hybrid Scalable Researcher Recommendation System Using Page Rank Algorithm

1

Hybrid Scalable Researcher Recommendation System Using Page Rank Algorithm 5

Hybrid Scalable Researcher Recommendation System Using Page Rank Algorithm

Dinesh Kalla

Colorado Technical University

CS 857: Business Intelligence

Dr D Revenaugh

3rd March 2021

Problem Statement

Page rank algorithm is used by Google search engine to rank websites based on their importance. Google scholar is the commonly used recommender system for research papers. The increasing number of research papers makes it difficult for novice academic researchers to obtain relevant scholarly articles with ease. The current recommender system does not have scalable functionality to cater to the increasing number of journals and articles published daily in the global arena. Other recommender systems such as Microsoft Academic, Web of Science, and PubMed databases use the collaborative approach, hindered by data protection legislation. Additionally, the said academic search engines domicile in one particular field of study. My research paper will focus on resolving this problem by recommending a hybrid scalable search engine that will utilize a page rank algorithm to determine the relevance of academic documents and display them in the order of importance. It will combine content-based and co-citation recommendations to produce related documents, including the recently published ones, without compromising the quality of recommendations.

Justification / Literature Review

Recommender systems have been applied in a variety of applications such as Twitter and Netflix. For instance, Twitter uses PageRank to suggest new followers to its users. To generate this suggestion, the application is designed in such a way it performs personalized PageRank by establishing two copies of each active user; a content producer and a content consumer. In this sense, a user can post a tweet or, in other cases, read a tweet by other users. A content produced by the user can be retweeted, further-reaching a new audience. This developing network can leverage on PageRank algorithm to suggest new followers.

Another application scenario is in movie rental sites such as Netflix, where a user considers a movie to be relevant if other users like the movie. The second approach to like a movie is when a mutual friend likes movies that we both have interests. With these two approaches, Netflix can create a personalized ranking for users on the film they want using the PageRank algorithm. The ‘behavior’ of other mutual users on the platform enables the system to determine what other users might like.

Data mining systems also utilize a similar approach where user behaviors can predict what products they might like (Bathrinath, 2019). In Business intelligence systems can track user preferences and suggest new products to the user in priority. Data mining is categorized into three groups, namely, web content mining, Web usage mining, and web structure mining. Web structure mining uses the PageRank concept to analyze and rank web pages in an internet search.

Also, a page ranking algorithm is applied to academic services such as e-libraries, e-learning, online purchasing systems, and news synthesis. In this way, users are provided a personalized recommendation based on their liking behavior of a certain service or product. In this paper, I will focus on offering an alternative solution to the problematic collaborative filtering (CF) by taking into account the freshness of contents, scalability feature, improved user satisfaction, and turnaround time (computational time ) to generate a search list.

Methodology

I propose to use a hybrid recommender system that utilizes both content-based (CB)s and co-citation-based (CcB)to generate a search of items. CcB is computationally time-consuming and generates low-quality recommendations, whereas CB uses metadata, keywords, articles, and abstracts to create fresh content and huge coverage. CcB uses reference information in a paper to make a recommendation. This empirically resembles human behavior when it’s searching for related documents. The referencing feature of CcB is a major drawback considering most old papers were published in hard copies, and their reference is not available digitally

On the other hand, CB resolves some of the inefficacies of CcB, i.e., complexity issues, privacy concerns, and cold start problems. CB uses information available on the web pages, such as metadata, keywords, title, and abstract. Cluster paper embedding and generating paper embedding shall be used to determine the relevance of a paper in the search domains in larger data sets (Xing et al., 2018). This augments well with my research that seeks to provide a scalable recommender system that can utilize content-based and co-citation-based to improve on quality and hierarchical clustering based on relevance. Combining both CB and CcB generates unified search items by ordering related papers from both lists based on congruency to the document in question.

References

Bathrinath (2019). PageRank Algorithm-Based Recommender System Using Uniformly Average Rating Matrix. https://www.igi-global.com/chapter/pagerank-algorithm-based-recommender-system-using-uniformly-average-rating-matrix/216694

Xing, W., & Ghorbani, A. (2018, May). It weighted the PageRank algorithm. In Proceedings. Second Annual Conference on Communication Networks and Services Research, 2004. (pp. 305-314). IEEE.

Designing Business Intelligence System with PageRank Algorithm 5

Designing Business Intelligence System with PageRank Algorithm

Dinesh Kalla

CS 857 : Business Intelligence : IP 2

Colorado Technical University

Dr D Revenaugh

17th March 2021

Problem Statement

Organizations are continuously bombarded with vast amounts of data. Managers must gain the expertise needed to fully utilize the benefits that come with business intelligence systems (Olszak, 2016). Data mining has become an essential tool for managers that provide insights about their daily operations and leverage the information provided by decision support systems to improve customer relationships (Visinescu, 2017). Additionally, managers require business intelligence systems that can rank the output in the order of priority. PageRank algorithm can replace the traditional data mining algorithms that will be discussed in-depth in the literature review (Florescu, 2017). The purpose of this research paper is to demonstrate how the PageRank algorithm can be implemented in business intelligence (BI) systems to support managers in decision-making in hiring active authors or researchers belongs to computer science field of study by displaying query results in order of their significance in supporting hiring processes (Kanakia, 2019).

Justification / Literature Review

Algorithms are used in business intelligence systems to unearth patterns otherwise challenging to deduce from a data warehouse. The algorithms can generate several relationships between data and another variable. Most BI systems utilize statistical techniques to analyze and create reports used by managers to make decisions (Arnott, 2017). Examples of these algorithms include; regression analysis, decision trees, association, and cluster analysis.

Companies such as Oracle, Microsoft dynamic, and SAP ERP software have business intelligence functionality (Gounder et al., 2016). SAS, a statistical tool, currently has business intelligence functionalities. Additionally, SQL Server 2005/2008 also contains business intelligence functionality to help users sieve specific information from the database server. Business intelligence systems offer more advanced reporting features than traditional statistical software; therefore, managers find it more intriguing to use data mining tools to provide reports in sequential order to support their decision-making process (Tvrdikova, 2017). As managers grapple with massive data that they use to make a decision, there is a need to rank the reports generated by BI systems based on their significant impact on business processes (Kasemsap, 2016). To have a competitive advantage and effectively run a business, there a need to rely on automated data mining tools that can support their daily activities (Oussous, 2018)

Methodology

In my research, I will use quantitative measures to establish which algorithms are used in various business intelligence systems and how effective they are in supporting managers’ decision-making (Arnott, 2017). Face-to-face interviews will be conducted and questionnaires to managers of various organizations to ascertain their satisfaction and the general performance of the BI systems. I will study, compare and analyze different algorithms and their effectiveness in the decision-making in hiring process.

References

Arnott, D., Lizama, F., & Song, Y. (2017). Patterns of business intelligence systems use in organizations. Decision Support Systems, 97, 58-68.

Florescu, C., & Caragea, C. (2017, February). A position-biased pagerank algorithm for keyphrase extraction. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 31, No. 1).

Fortunato, S., Bergstrom, C. T., Börner, K., Evans, J. A., Helbing, D., Milojević, S., … Uzzi, B. (2018). Science of science. Science, 359(6379).

Gounder, M. S., Iyer, V. V., & Al Mazyad, A. (2016, March). A survey on business intelligence tools for university dashboard development. In 2016 3rd MEC International Conference on Big Data and Smart City (ICBDSC) (pp. 1-7). IEEE.

Kasemsap, K. (2016). The fundamentals of business intelligence. International Journal of Organizational and Collective Intelligence (IJOCI), 6(2), 12-25.

Kanakia, A., Shen, Z., Eide, D., & Wang, K. (2019). A Scalable Hybrid Research Paper Recommender System for Microsoft Academic. In The World Wide Web Conference on (pp. 2893–2899).

Olszak, C. M. (2016). Toward better understanding and use of Business Intelligence in organizations. Information Systems Management, 33(2), 105-123.

Oussous, A., Benjelloun, F. Z., Lahcen, A. A., & Belfkih, S. (2018). Big Data technologies: A survey. Journal of King Saud University-Computer and Information Sciences, 30(4), 431-448.

Tvrdikova, M. (2017, June). Support of decision-making by business intelligence tools. In 6th International Conference on Computer Information Systems and Industrial Management Applications (CISIM’07) (pp. 364-368). IEEE.

Visinescu, L. L., Jones, M. C., & Sidorova, A. (2017). Improving decision quality: the role of business intelligence. Journal of Computer Information Systems, 57(1), 58-66.

http://owl.english.purdue.edu/owl/resource/544/01/

The Purdue OWL: Sample Outlines

Alphanumeric Outline

THE COLLEGE APPLICATION PROCESS

I. CHOOSE DESIRED COLLEGES

A. Visit and evaluate college campuses

B. Visit and evaluate college websites

1. Look for interesting classes

2. Note important statistics

II. PREPARE APPLICATION

A. Write personal statement

1. Choose interesting topic

a. Describe an influential person in your life

(1) Favorite high school teacher

(2) Grandparent

b. Describe a challenging life event

2. Include important personal details

a. Volunteer work

b. Participation in varsity sports

B. Revise personal statement

III. COMPILE RÉSUMÉ

A. List relevant coursework

B. List work experience

C. List volunteer experience

1. Tutor at foreign language summer camp

2. Counselor for suicide prevention hotline

http://owl.english.purdue.edu/owl/resource/544/01/

Full Sentence Outline

I. Man-made pollution is the primary cause of global warming.

A. Greenhouse gas emissions are widely identified by the scientific community to be

harmful.

1. The burning of coal and fossil fuels are the primary releasers of hazardous

greenhouse gases.

Full sentence outlines are often accompanied with an APA reference list on a separate

page. Quotes within the outline must also utilize APA in-text citations.

Decimal Outline

1.0 Choose Desired College

1.1 Visit and evaluate college campuses

1.2 Visit and evaluate college websites

1.2.1 Look for interesting classes

1.2.2 Note important statistics