A real-time network-based approach for analysing best–worst data types

Best–worst scaling is a widespread approach in market research used for collecting data on the needs and preferences of people. However, the current preparation of its design and the analysis of the data depends on complex statistical methods. One of the most commonly used models for estimating individual preference probabilities is the hierarchical Bayes model, which can only be applied after the data collection phase. This type of calculation needs more infrastructural background and a large sample to provide accurate estimations. Here, we introduce a new application that enables fast calculations and individual-level real-time estimations, which also has a great potential to ask additional questions depending on the respondent’s answers during live interviews. Our network-based approach (integrating the PageRank algorithm) works well for online surveys, and it supports our dynamic and adaptive, real-time evaluation (DART) of best–worst data types, and results in more relevant decision making in marketing.

Pemeringkatan Pencarian pada Buku Pedoman Akademik Filkom UB Menuju Merdeka Belajar dan Free E-Book Pembelajaran Sebagai Prototype Local Smart Micro Search Engine Menggunakan Algoritma Pagerank dan TF-IDF

<p class="Abstrak">Buku Pedoman Akademik FILKOM Universitas Brawijaya merupakan suatu kebutuhan informasi akademik yang cukup penting, dan juga buku penunjang pembelajaran seperti Free e-Book bagi para mahasiswa. Untuk memperoleh informasi yang relevan terhadap query yang diberikan seringkali belum sesuai dengan kebutuhan pencarian pengguna. Pengguna harus menguasai secara keseluruhan untuk mengetahui dokumen mana yang paling sesuai, dan proses ini akan memakan waktu yang banyak. Sistem ini mampu memberikan rekomendasi dokumen sesuai dengan hasil perhitungan pemeringkatan teks. Proses pemeringkatan teks dapat diselesaikan dengan algoritma PageRank, di mana dokumen yang memiliki bobot pemeringkatan terkecil, memiliki kata terbanyak pada dokumen tersebut. Algoritma ini telah dibuktikan mampu memeberikan feedback dokumen yang relevan melalui dua tahap pengujian. Evaluasi yang dilakukan terhadap dua buah pengujian menghasilkan rata-rata nilai recall tertinggi yaitu 80.6% pada data ke-1, dan data ke-2 didapatkan korelasi terbaik antara precision, recall dan f-measure sebesar 0,98, 0,99, 0,99.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>The Brawijaya University FILKOM Academic Handbook is an important academic information need, as well as learning support books such as Free e-Books for students. To obtain information that is relevant to the query given is often not in accordance with the wishes of the user. Users must master the whole to find out which documents are most suitable, which is where the process will take a lot of time. This system is able to provide document recommendations in accordance with the results of the text ranking calculation. The process of ranking the text can be solved by the PageRank algorithm, where documents that have the smallest ranking weight, have the most words in the document. This algorithm has been proven to be able to provide feedback on relevant documents through two stages of testing. he evaluation conducted on the two tests resulted in the highest average recall value of 80.6% on the 1st dataset, and 2nd dataset the best correlation was obtained between precision, recall and f-measure of 0.98, 0.99, 0.99.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

A New Method for Identifying Key and Common Themes Based on Text Mining: An Example in the Field of Urban Expansion

Urban land use is a core area of multidisciplinary research that involves geography, land science, and urban planning. With the rapid progress of global urbanization, urban expansion has become a research focus in recent years. Therefore, how to scientifically and accurately identify key and common themes in the urban expansion literature has become crucial for scientific research institutions in various countries. This paper proposes a new framework for identifying such themes based on an analysis of scientific literature and by using text mining and thematic evolutionary analysis. First, the latent Dirichlet allocation algorithm is used to capture the thematic clustering of scientific literature. Second, the key degree of the thematic node in the thematic evolution transfer network is used to represent the key feature of a theme, and the PageRank algorithm is employed to measure the critical score of this theme. When recognizing common themes, the common features of various themes are digitized and mapped to a specially selected quadratic function to measure the degree of commonness. Finally, the hidden Markov model is used to build a thematic prediction model. This method can efficiently identify key and common themes from the literature and provide theoretical and technical support for future research in related fields.

A Novel PageRank Based Fault Handling Strategy for Workflow Scheduling in Cloud Data Centers

Unexpected faults result in unscheduled cloud outage, which negatively affects the completion of workflow tasks in the cloud. This paper presents a novel PageRank based fault handling strategy to rescue workflow tasks at the faulty data center. The proposed approach uses a holistic view and considers the task attributes, the timeline scenario, and the overall cloud performance. A priority assignment system is developed based on the modified PageRank algorithm to prioritise workflow tasks. A Min-Max normalization method is applied to select the target data center and match the timeline at this data center. Additionally, a dynamic PageRank-constrained task scheduling algorithm is proposed to generate the task scheduling solution. The simulation results show that the proposed approach can achieve better fault handling performance, measured by task resilience ratio, workflow resilience ratio and workflow continuity ratio, in both the traditional 3-replica and the image backup cloud environment.