Rife Information Pollution (Infollution) and Virtual Organizations in Industry 4.0

Information pollution, which usually refers to the overabundance of irrelevant, unsolicited, unwanted messages, is a major cause of concern for practitioners and academic researchers. Advances in the information and communication technologies has proliferated the production of information. Consequently, people are suffering from information pollution. Information pollution has made it difficult for employees and individuals to find the quality information quickly and conveniently from diverse information sources including print and electronic sources. This chapter sheds light on the relevant literature of information pollution and analyzes its causes in the Industry 4.0 era and puts forward suggestions for tackling this problem. This chapter emphasizes the significance of concrete efforts from computer scientists, academic professionals, and information professionals to devise strategies and techniques for refuting the effects of information pollution.

Integrating Heterogeneous Enterprise Data Using Ontology in Supply Chain Management

Many industries prefer worldwide business operations due to the economic advantage of globalization on product design and development. These industries increasingly operate globalized multi-tier supply chains and deliver products and services all over the world. This global approach produces huge amounts of heterogeneous data residing at various business operations, and the integration of these data plays an important role. Integrating data from multiple heterogeneous sources need to deal with different data models, database schema, and query languages. This chapter presents a semantic web technology-based data integration framework that uses relational databases and XML data with the help of ontology. To model different source schemas, this chapter proposes a method based on the resource description framework (RDF) graph patterns and query rewriting techniques. The semantic translation between the source schema and RDF ontology is described using query and transformational language SPARQL.

Knowledge Management and Business Analytics

Knowledge management (KM) is a systematic way of managing the organization's assets for creating valuable knowledge that can be used across the organization to achieve the organization's success. A broad category of technologies that allows for gathering, storing, accessing, and analyzing data to help business users make better decisions, business intelligence (BI) allows analyzing business performance through data-driven insight. Business analytics applies different methods to gain insight about the business operations and make better fact-based decisions. Big data is data with a huge size. In the chapter, the authors have tried to emphasize the significance of knowledge management, business intelligence, business analytics, and big data to justify the role of them in the existence and development of an organization and handling big data for a virtual organization.

Clustering Earthquake Data

Seismology, which is a sub-branch of geophysics, is one of the fields in which data mining methods can be effectively applied. In this chapter, employing data mining techniques on multivariate seismic data, decomposition of non-spatial variable is done. Then k-means clustering, density-based spatial clustering of applications with noise (DBSCAN), and hierarchical tree clustering algorithms are applied on decomposed data, and then pattern analysis is conducted using spatial data on the resulted clusters. The conducted analysis suggests that the clustering results with spatial data is compatible with the reality and characteristic features of regions related to earthquakes can be determined as a result of modeling seismic data using clustering algorithms. The baseline metric reported is clustering times for varying size of inputs.

What Are Basketball Fans Saying on Twitter?

Social media receives growing interest from sports executives. Yet, very little is known about how to make use of such user-generated, unstructured data. By exploring tweets generated during Turkish Airlines Euroleague's Final Four event, which broadcasted the four tournaments of championship among four finalist teams, the authors studied how fans respond to gains and losses and how engaged they were during games through the course of the event. The authors found that favorable reactions were received when teams won, but the magnitude of unfavorable reaction was larger when teams lost. When it came to the organizer rather than the teams, the organizer of the event received most of the positive feedback. The authors also found that main source of tweets was smartphones while tablets were not among real-time feedback devices.

Big Data, Privacy, and Healthcare

In the era of big data, large amounts of data are generated from different areas like education, business, stock market, healthcare, etc. Most of the available data from these areas are unstructured, which is large and complex. As healthcare industries become value-based from volume-based, there is a need to have specialized tools and methods to handle it. The traditional methods for data storage and retrieval can be used when data is structured in nature. Big data analytics provide technologies to store large amounts of complex healthcare data. It is believed that there is an enormous opportunity to improve lives by applying big data in the healthcare industry. No industry counts more than healthcare as it is a matter of life and death. Due to rapid development of big data tools and technologies, it is possible to improve disease diagnosis more efficiently than ever before, but security and privacy are two major issues when dealing with big data in the healthcare industry.

Generating Big Data

Big data is characterized as huge datasets generated at a fast rate, in unstructured, semi-structured, and structured data formats, with inconsistencies and disparate data types and sources. The challenge is having the right tools to process large datasets in an acceptable timeframe and within reasonable cost range. So, how can social media big datasets be harnessed for best value decision making? The approach adopted was site scraping to collect online data from social media and other websites. The datasets have been harnessed to provide better understanding of customers' needs and preferences. It's applied to design targeted campaigns, to optimize business processes, and to improve performance. Using the social media facts and rules, a multivariate value creation decision model was built to assist executives to create value based on improved “knowledge” in a hindsight-foresight-insight continuum about their operations and initiatives and to make informed decisions. The authors also demonstrated use cases of insights computed as equations that could be leveraged to create sustainable value.

About Digital Avatars for Control Systems Using Big Data and Knowledge Sharing in Virtual Industries

The authors in this chapter show the essence, dignity, current state, and development prospects of avatar-based management using blockchain technology for improving implementation of economic solutions in the digital economy of Russia. The purpose of this chapter is not to review the existing published work on avatar-based models for policy advice but to try an assessment of the merits and problems of avatar-based models as a solid basis for economic policy advice that is mainly based on the work and experience within the recently finished projects Triple H Avatar, an avatar-based software platform for HHH University, Sydney, Australia. The agenda of this project was to develop an avatar-based closed model with strong empirical grounding and micro-foundations that provides a uniform platform to address issues in different areas of digital economic and creating new tools to improve blockchain technology using the intelligent visualization techniques for big data analytics.

Data Imputation Methods for Missing Values in the Context of Clustering

Missing data is a common problem for data clustering quality. Most real-life datasets have missing data, which in turn has some effect on clustering tasks. This chapter investigates the appropriate data treatment methods for varying missing data scarcity distributions including gamma, Gaussian, and beta distributions. The analyzed data imputation methods include mean, hot-deck, regression, k-nearest neighbor, expectation maximization, and multiple imputation. To reveal the proper methods to deal with missing data, data mining tasks such as clustering is utilized for evaluation. With the experimental studies, this chapter identifies the correlation between missing data imputation methods and missing data distributions for clustering tasks. The results of the experiments indicated that expectation maximization and k-nearest neighbor methods provide best results for varying missing data scarcity distributions.

Developing a Big-Data-Based Model to Study and Analyze Network Traffic

Network is a connection of devices in either a wired or wireless manner. Networking has become a part and parcel of computing in the present world. They form the backbone of the modern-day computing business. Hence, it is important for networks to remain alive, up, and reliable all the time. A way to ensure that is network traffic analysis. Network traffic analysis mainly deals with a study of bandwidth utilization, transmission and reception rates, error rates, etc., which is important to keep the network smooth and improve economic efficiency. The proposed model approaches network traffic analysis in a way to collect network information and then deal with it using technologies available for big data analysis. The model aims to analyze the collected information to calculate a factor called reliability factor, which can guide in effective network management. The model also aims to assist the network administrator by informing him whether network traffic is high or low, and the administrator can then take targeted steps to prevent network failure.