Large-Scale Few-Shot Learning via Multi-modal Knowledge Discovery

2020 ◽  
pp. 718-734
Author(s):  
Shuo Wang ◽  
Jun Yue ◽  
Jianzhuang Liu ◽  
Qi Tian ◽  
Meng Wang
Keyword(s):  
Knowledge Discovery ◽  
Large Scale

Effective Statistical Methods for Big Data Analytics

2017 ◽  
pp. 280-299 ◽  
Author(s):  
Cheng Meng ◽  
Ye Wang ◽  
Xinlian Zhang ◽  
Abhyuday Mandal ◽  
Wenxuan Zhong ◽  
...  
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Knowledge Discovery ◽  
Statistical Methods ◽  
Large Scale ◽  
Big Data Analytics ◽  
Divide And Conquer ◽  
Data Driven ◽  
The Past ◽  
Large Scale Dataset

With advances in technologies in the past decade, the amount of data generated and recorded has grown enormously in virtually all fields of industry and science. This extraordinary amount of data provides unprecedented opportunities for data-driven decision-making and knowledge discovery. However, the task of analyzing such large-scale dataset poses significant challenges and calls for innovative statistical methods specifically designed for faster speed and higher efficiency. In this chapter, we review currently available methods for big data, with a focus on the subsampling methods using statistical leveraging and divide and conquer methods.

Fad or Here to Stay: Predicting Product Market Adoption and Longevity Using Large Scale, Social Media Data

2013 ◽  
Author(s):  
Suppawong Tuarob ◽  
Conrad S. Tucker
Keyword(s):  
Social Media ◽  
Knowledge Discovery ◽  
Large Scale ◽  
Product Information ◽  
Product Market ◽  
Knowledge Discovery In Databases ◽  
Product Reviews ◽  
Product Model ◽  
Social Media Data ◽  
Media Data

The authors of this work propose a Knowledge Discovery in Databases (KDD) model for predicting product market adoption and longevity using large scale, social media data. Social media data, available through sites such as Twitter® and Facebook®, have been shown to be leading indicators and predictors of events ranging from influenza spread, financial stock market prices, and movie revenues. Being ubiquitous and colloquial in nature allows users to honestly express their opinions in a unified, dynamic manner. This makes social media a relatively new data gathering source that can potentially appeal to designers and enterprise decision makers aiming to understand consumers response to their upcoming/newly launched products. Existing design methodologies for leveraging large scale data have traditionally relied on product reviews available on the internet to mine product information. However, such web reviews often come from disparate sources, making the aggregation and knowledge discovery process quite cumbersome, especially reviews for poorly received products. Furthermore, such web reviews have not been shown to be strong indicators of new product market adoption. In this paper, the authors demonstrate how social media can be used to predict and mine information relating to product features, product competition and market adoption. In particular, the authors analyze the sentiment in tweets and use the results to predict product sales. The authors present a mathematical model that can quantify the correlations between social media sentiment and product market adoption in an effort to compute the ability to stay in the market of individual products. The proposed technique involves computing the Subjectivity, Polarity, and Favorability of the product. Finally, the authors utilize Information Retrieval techniques to mine users’ opinions about strong, weak, and controversial features of a given product model. The authors evaluate their approaches using the real-world smartphone data, which are obtained from www.statista.com and www.gsmarena.com.

scholarly journals Robustifying Scholia: paving the way for knowledge discovery and research assessment through Wikidata

2019 ◽  
Vol 5 ◽  
Author(s):  
Lane Rasberry ◽  
Egon Willighagen ◽  
Finn Nielsen ◽  
Daniel Mietchen
Keyword(s):  
Knowledge Discovery ◽  
Large Scale ◽  
Knowledge Workers ◽  
Open Data ◽  
Linked Open Data ◽  
Data Curation ◽  
Research Assessment ◽  
Knowledge Graph ◽  
Incomplete Datasets ◽  
Active Contributor

Knowledge workers like researchers, students, journalists, research evaluators or funders need tools to explore what is known, how it was discovered, who made which contributions, and where the scholarly record has gaps. Existing tools and services of this kind are not available as Linked Open Data, but Wikidata is. It has the technology, active contributor base, and content to build a large-scale knowledge graph for scholarship, also known as WikiCite. Scholia visualizes this graph in an exploratory interface with profiles and links to the literature. However, it is just a working prototype. This project aims to "robustify Scholia" with back-end development and testing based on pilot corpora. The main objective at this stage is to attain stability in challenging cases such as server throttling and handling of large or incomplete datasets. Further goals include integrating Scholia with data curation and manuscript writing workflows, serving more languages, generating usage stats, and documentation.

Fashion informatics and the network of fashion knockoffs

First Monday ◽  
2019 ◽  
Author(s):  
Lauren Copeland ◽  
Giovanni Luca Ciampaglia ◽  
Li Zhao
Keyword(s):  
Knowledge Discovery ◽  
Large Scale ◽  
Multimedia Data ◽  
Fashion Industry ◽  
Structure And Dynamics ◽  
Computer Scientists ◽  
History Of ◽  
Fashion Apparel ◽  
Use Of Knowledge

Knowledge discovery techniques have a long history of application to fields of practice such as marketing and business intelligence. Fashion and other manufacturing compartments have comparably enjoyed little attention from computer scientists. With the increasing availability of multimedia data from the Web and social media, our understanding of the fashion apparel industry could be significantly enhanced through the use of knowledge discovery methods and of large scale datasets obtained from places such as Twitter and Instagram. Here, we are interested in one of the issues at the center of the contemporary structure and dynamics of the fashion industry: the practice of knockoffs. We combine Web scraping and network science techniques to give a preliminary characterization of how brands knock designs off each other. Such a study could be one of the first examples of an emergent field, which we refer to and define as “fashion informatics.”

Implicit Knowledge Discovery in Design Semantic Network by Applying Pythagorean Means on Shortest Path Searching

2017 ◽  
Author(s):  
Feng Shi ◽  
Liuqing Chen ◽  
Ji Han ◽  
Peter Childs
Keyword(s):  
Text Mining ◽  
Knowledge Discovery ◽  
Language Processing ◽  
Shortest Path ◽  
Large Scale ◽  
Semantic Network ◽  
Semantic Networks ◽  
Implicit Knowledge ◽  
Implicit Associations ◽  
Correlation Degree

With the advent of the big-data era, massive textual information stored in electronic and digital documents have become valuable resources for knowledge discovery in the fields of design and engineering. Ontology technologies and semantic networks have been widely applied with text mining techniques including Natural Language Processing (NLP) to extract structured knowledge associations from the large-scale unstructured textual data. However, most existing works mainly focus on how to construct the semantic networks by developing various text mining methods such as statistical approaches and semantic approaches, while few studies are found to focus on how to subsequently analyze and fully utilize the already well-established semantic networks. In this paper, a specific network analysis method is proposed to discover the implicit knowledge associations from the existing semantic network for improving knowledge discovery and design innovation. Pythagorean means are applied with Dijkstra’s shortest path algorithm to discover the implicit knowledge associations either around a single knowledge concept or between two concepts. Six criteria are established to evaluate and rank the correlation degree of the implicit associations. Two engineering case studies were conducted to illustrate the proposed knowledge discovery process, and the results showed the effectiveness of the retrieved implicit knowledge associations on helping providing relevant knowledge from various aspects, and provoking creative ideas for engineering innovation.

scholarly journals A Cognitive Adopted Framework for IoT Big-Data Management and Knowledge Discovery Prospective

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Nilamadhab Mishra ◽  
Chung-Chih Lin ◽  
Hsien-Tsung Chang
Keyword(s):  
Big Data ◽  
Data Management ◽  
Knowledge Discovery ◽  
Large Scale ◽  
Industrial Applications ◽  
Sensor Technology ◽  
Industrial Automation ◽  
Data Framework ◽  
Industrial Internet ◽  
Knowledge Exploration

In future IoT big-data management and knowledge discovery for large scale industrial automation application, the importance of industrial internet is increasing day by day. Several diversified technologies such as IoT (Internet of Things), computational intelligence, machine type communication, big-data, and sensor technology can be incorporated together to improve the data management and knowledge discovery efficiency of large scale automation applications. So in this work, we need to propose a Cognitive Oriented IoT Big-data Framework (COIB-framework) along with implementation architecture, IoT big-data layering architecture, and data organization and knowledge exploration subsystem for effective data management and knowledge discovery that is well-suited with the large scale industrial automation applications. The discussion and analysis show that the proposed framework and architectures create a reasonable solution in implementing IoT big-data based smart industrial applications.

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