scholarly journals Towards a Knowledge-Based Recommendation System for Genomics Visualization

2020 ◽  
Author(s):  
Aditeya Pandey ◽  
Sehi L’Yi ◽  
Nils Gehlenborg
Keyword(s):  
Data Visualization ◽  
Biomedical Research ◽  
Recommender System ◽  
Recommendation System ◽  
Formal Training ◽  
Preliminary Design ◽  
Knowledge Based ◽  
Visualization Tools ◽  
Tools And Techniques ◽  
Selection Of

Analysis and interpretation of genomics data are the backbones of breakthroughs and discoveries in biomedical research. Visualization tools and techniques play a significant role in the workflow of genomics researchers, and they are regularly employed in the interpretation of genomics data. However, the vast majority of genomics researchers have little or no formal training in data visualization design. Therefore, they require guidance on designing effective visualizations for a given set of data and analysis tasks. In this poster, we present the methodology behind a recommender system for genomics data and our preliminary design of a knowledge-based recommendation system. The system allows genomics researchers to navigate through a selection of visualization options and identify the techniques that meet their preferences and requirements.

Big Data Visualization Tools and Techniques

2022 ◽  
pp. 590-621
Author(s):  
Obinna Chimaobi Okechukwu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Visualization ◽  
Data Sets ◽  
New Paradigm ◽  
Volume Velocity ◽  
Big Data Visualization ◽  
Visualization Tools ◽  
Tools And Techniques ◽  
Data Analysis Methods

In this chapter, a discussion is presented on the latest tools and techniques available for Big Data Visualization. These tools, techniques and methods need to be understood appropriately to analyze Big Data. Big Data is a whole new paradigm where huge sets of data are generated and analyzed based on volume, velocity and variety. Conventional data analysis methods are incapable of processing data of this dimension; hence, it is fundamentally important to be familiar with new tools and techniques capable of processing these datasets. This chapter will illustrate tools available for analysts to process and present Big Data sets in ways that can be used to make appropriate decisions. Some of these tools (e.g., Tableau, RapidMiner, R Studio, etc.) have phenomenal capabilities to visualize processed data in ways traditional tools cannot. The chapter will also aim to explain the differences between these tools and their utilities based on scenarios.

scholarly journals Conversational Recommender System with Explanation Facility Using Semantic Reasoning

2016 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Zk Abdurahman Baizal ◽  
Nur Rahmawati
Keyword(s):  
Recommender System ◽  
Recommendation System ◽  
User Model ◽  
Functional Requirements ◽  
Inference Method ◽  
Semantic Reasoning ◽  
Filtering Method ◽  
Knowledge Based ◽  
The Relationship ◽  
Number Of Iterations

<p>Conversational recommender system is system that provides dialogue as user guide to obtain information from the user, in order to obtain preference for products needed. This research implements conversational recommender system with knowledge-based in the smartphone domain with an explanation facility. The recommended products are obtained based on the functional requirements of the user. Therefore, this study use ontology model as a knowledge to be more flexible in finding products that is suitable with the functional requirements of the user that is by tracing a series of semantic based on relationships in order to obtain the user model. By exploiting the relationship between instances of user models, the explanation facility generated can be more natural. Our filtering method uses semantic reasoning with inference method to avoid overspecialization. The evaluation show that the performance of our recommender system with explanation facilities is more efficient than the recommendation system without explanation facility, that can be seen from the number of iterations. We also notice that our system has accuracy of 84%.</p>

Recommendation and visualization of similar movies using minimum spanning dendrograms

2012 ◽  
Vol 12 (1) ◽  
pp. 85-101 ◽  
Author(s):  
Michail Vlachos ◽  
Daniel Svonava
Keyword(s):  
Data Mining ◽  
Bipartite Graph ◽  
Data Visualization ◽  
Recommender System ◽  
Recommendation System ◽  
Two Dimensions ◽  
Distance Functions ◽  
Semantic Features ◽  
Novel Technique ◽  
Metric Distance

Exploration of graph structures is an important topic in data mining and data visualization. This work presents a novel technique for visualizing neighbourhood and cluster relationships in graphs; we also show how this methodology can be used within the setting of a recommendation system. Our technique works by projecting the original object distances onto two dimensions while carefully retaining the ‘backbone’ of important distances. Cluster information is also overlayed on the same projected space. A significant advantage of our approach is that it can accommodate both metric and non-metric distance functions. Our methodology is applied to a visual recommender system for movies to allow easy exploration of the actor–movie bipartite graph. The work offers intuitive movie recommendations based on a selected pivot movie and allows the interactive discovery of related movies based on both textual and semantic features.

Evaluation of IoT Data Visualization Tools and Techniques

2020 ◽  
pp. 115-139 ◽  
Author(s):  
Suresh K. Peddoju ◽  
Himanshu Upadhyay
Keyword(s):  
Data Visualization ◽  
Visualization Tools ◽  
Tools And Techniques

Big Data Visualization Tools and Techniques

2018 ◽  
pp. 465-496
Author(s):  
Obinna Chimaobi Okechukwu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Visualization ◽  
Data Sets ◽  
New Paradigm ◽  
Volume Velocity ◽  
Big Data Visualization ◽  
Visualization Tools ◽  
Tools And Techniques ◽  
Data Analysis Methods

In this chapter, a discussion is presented on the latest tools and techniques available for Big Data Visualization. These tools, techniques and methods need to be understood appropriately to analyze Big Data. Big Data is a whole new paradigm where huge sets of data are generated and analyzed based on volume, velocity and variety. Conventional data analysis methods are incapable of processing data of this dimension; hence, it is fundamentally important to be familiar with new tools and techniques capable of processing these datasets. This chapter will illustrate tools available for analysts to process and present Big Data sets in ways that can be used to make appropriate decisions. Some of these tools (e.g., Tableau, RapidMiner, R Studio, etc.) have phenomenal capabilities to visualize processed data in ways traditional tools cannot. The chapter will also aim to explain the differences between these tools and their utilities based on scenarios.

scholarly journals ALGORITHMIC SUPPORT OF THE WEB SERVICE RECOMMENDATION SYSTEM FOR LEARNING FOREIGN LANGUAGES

2021 ◽  
pp. 100-106
Author(s):  
Liliia Bodnar ◽  
Kateryna Shulakova ◽  
Liudmyla Gryzun
Keyword(s):  
Foreign Language ◽  
Web Service ◽  
Recommender System ◽  
Recommendation System ◽  
Foreign Languages ◽  
Multimedia Content ◽  
Functional Requirements ◽  
Algorithmic Support ◽  
User Ratings ◽  
Selection Of

This work is devoted to the analysis of algorithmic support of multimedia content recommender systems and the development of a web service toincrease the efficiency of learning foreign languages using a recommender system that personalized the selection of educational content for the user.To form a list of necessary multimedia content, the main criteria of the recommender system were selected, the basic needs of users were identified,which the system should solve, since increasing the efficiency of learning a foreign language is achieved not only by choosing teaching methods, butalso by watching multimedia content, namely news, films, educational videos, clips, etc. Therefore, in order to form a list of the necessary multimediacontent, the main criteria of the recommender system were formed, the main needs of users were identified, which the system must solve. From theside of the method for implementing algorithmic support, various types of data filtering were considered, from modern technical methods to librariesto ensure the functionality of the system, and the algorithm based on hybrid filtering was chosen, in which known user ratings are used to predict thepreferences of another user. Functional requirements have been developed and a web service has been proposed that allows a comprehensive impact onuser learning when learning a foreign language, software implementation of which is made using Java Script, Python and additional libraries. Thisimplementation allows you to build a process for tracking changes in user requirements and transfer information to the database (DB) and, afteranalyzing the input data, change the proposed multimedia content to the user. In the course of further research, it is planned to conduct practicalexperiments, taking into account the specifics of certain methods of teaching foreign languages and the use of statistical data to assess the effectivenessof the algorithm of the proposed recommender system.

scholarly journals Feature analysis of ontology visualization methods and tools

2020 ◽  
Vol 1 (2) ◽  
pp. 61-77
Author(s):  
Merlin Florrence Joseph ◽  
Ravi Lourdusamy
Keyword(s):  
Feature Analysis ◽  
Knowledge Based ◽  
Ontology Visualization ◽  
Semantic Concepts ◽  
Visualization Tools ◽  
Tools And Techniques

Visualization is a technique of creating images, graphs or animations to share knowledge. Different kinds of visualization methods and tools are available to envision the data in an efficient way. The visualization tools and techniques enable the user to understand the knowledge in an easy manner. Nowadays most of the information is presented semantically which provides knowledge based retrieval of the information. Knowledge based visualization tools are required to visualize semantic concepts. This article analyses the existing semantic based visualization tools and plug-ins. The features and characteristics of these tools and plug-ins are analyzed and tabulated.

Identifying Attack Models for Securing Cluster-Based Recommendation System

2020 ◽  
Vol 14 ◽  
Author(s):  
Amreen Ahmad ◽  
Tanvir Ahmad ◽  
Ishita Tripathi
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Relevant Information ◽  
System Function ◽  
Recommendation Algorithm ◽  
Underlying Network ◽  
Entire Dataset ◽  
Attack Models ◽  
Wide Usage

: The immense growth of information has led to the wide usage of recommender systems for retrieving relevant information. One of the widely used methods for recommendation is collaborative filtering. However, such methods suffer from two problems, scalability and sparsity. In the proposed research, the two issues of collaborative filtering are addressed and a cluster-based recommender system is proposed. For the identification of potential clusters from the underlying network, Shapley value concept is used, which divides users into different clusters. After that, the recommendation algorithm is performed in every respective cluster. The proposed system recommends an item to a specific user based on the ratings of the item’s different attributes. Thus, it reduces the running time of the overall algorithm, since it avoids the overhead of computation involved when the algorithm is executed over the entire dataset. Besides, the security of the recommender system is one of the major concerns nowadays. Attackers can come in the form of ordinary users and introduce bias in the system to force the system function that is advantageous for them. In this paper, we identify different attack models that could hamper the security of the proposed cluster-based recommender system. The efficiency of the proposed research is validated by conducting experiments on student dataset.

scholarly journals A Hybrid Knowledge-Based Recommender for Product-Service Systems Mass Customization

Information ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 296
Author(s):  
Laila Esheiba ◽  
Amal Elgammal ◽  
Iman M. A. Helal ◽  
Mohamed E. El-Sharkawi
Keyword(s):  
Recommender System ◽  
Mass Customization ◽  
Constraint Satisfaction Problem ◽  
Service Systems ◽  
Prototype System ◽  
Knowledge Based ◽  
Product Service ◽  
Wide Range ◽  
Hybrid Knowledge ◽  
Product Service Systems

Manufacturers today compete to offer not only products, but products accompanied by services, which are referred to as product-service systems (PSSs). PSS mass customization is defined as the production of products and services to meet the needs of individual customers with near-mass-production efficiency. In the context of the PSS mass customization environment, customers are overwhelmed by a plethora of previously customized PSS variants. As a result, finding a PSS variant that is precisely aligned with the customer’s needs is a cognitive task that customers will be unable to manage effectively. In this paper, we propose a hybrid knowledge-based recommender system that assists customers in selecting previously customized PSS variants from a wide range of available ones. The recommender system (RS) utilizes ontologies for capturing customer requirements, as well as product-service and production-related knowledge. The RS follows a hybrid recommendation approach, in which the problem of selecting previously customized PSS variants is encoded as a constraint satisfaction problem (CSP), to filter out PSS variants that do not satisfy customer needs, and then uses a weighted utility function to rank the remaining PSS variants. Finally, the RS offers a list of ranked PSS variants that can be scrutinized by the customer. In this study, the proposed recommendation approach was applied to a real-life large-scale case study in the domain of laser machines. To ensure the applicability of the proposed RS, a web-based prototype system has been developed, realizing all the modules of the proposed RS.

scholarly journals Comparison of blockchain platforms: a systematic review and healthcare examples

2019 ◽  
Vol 26 (5) ◽  
pp. 462-478 ◽  
Author(s):  
Tsung-Ting Kuo ◽  
Hugo Zavaleta Rojas ◽  
Lucila Ohno-Machado
Keyword(s):  
Systematic Review ◽  
Biomedical Research ◽  
Health Informatics ◽  
Clinical Informatics ◽  
Healthcare Applications ◽  
Software Engineers ◽  
Key Features ◽  
Technical Features ◽  
Meta Analyses ◽  
Selection Of

Abstract Objectives To introduce healthcare or biomedical blockchain applications and their underlying blockchain platforms, compare popular blockchain platforms using a systematic review method, and provide a reference for selection of a suitable blockchain platform given requirements and technical features that are common in healthcare and biomedical research applications. Target audience Healthcare or clinical informatics researchers and software engineers who would like to learn about the important technical features of different blockchain platforms to design and implement blockchain-based health informatics applications. Scope Covered topics include (1) a brief introduction to healthcare or biomedical blockchain applications and the benefits to adopt blockchain; (2) a description of key features of underlying blockchain platforms in healthcare applications; (3) development of a method for systematic review of technology, based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement, to investigate blockchain platforms for healthcare and medicine applications; (4) a review of 21 healthcare-related technical features of 10 popular blockchain platforms; and (5) a discussion of findings and limitations of the review.

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