Big Data and Information Theory

Jiuping Xu, Syed Ejaz Ahmed, Zongmin Li, Jiuping Xu, Syed Ejaz Ahmed, Zongmin Li

• Publisher: Routledge
• Year: 2022
• ISBN:
  9781000591781
• Edition: 1
• Topic: Betriebswirtschaft
• Subtopic: Business allgemein

Big Data and Information Theory are a binding force between various areas of knowledge that allow for societal advancement. Rapid development of data analytic and information theory allows companies to store vast amounts of information about production, inventory, service, and consumer activities. More powerful CPUs and cloud computing make it possible to do complex optimization instead of using heuristic algorithms, as well as instant rather than offline decision-making.

The era of "big data" challenges includes analysis, capture, curation, search, sharing, storage, transfer, visualization, and privacy violations. Big data calls for better integration of optimization, statistics, and data mining. In response to these challenges this book brings together leading researchers and engineers to exchange and share their experiences and research results about big data and information theory applications in various areas. This book covers a broad range of topics including statistics, data mining, data warehouse implementation, engineering management in large-scale infrastructure systems, data-driven sustainable supply chain network, information technology service offshoring project issues, online rumors governance, preliminary cost estimation, and information system project selection.

The chapters in this book were originally published in the journal, International Journal of Management Science and Engineering Management.

Engineering management: new advances and three open questions

Jiuping Xu

ABSTRACT

Big data has changed engineering management, which has consequently opened up many current and future scientific and technological challenges. This paper investigates engineering management in large scale infrastructure systems, which are regarded as giant data-driven open complex systems with system complexities that encompass openness, human involvement, society, and the emergence of intelligence. With the assistance of big data technologies and information theories, large scale infrastructure systems can be described as synergetic systems made up of ‘Data Streams–Information Sets–Problem Groups–Model Fields–Algorithmic Clusters’, so a 3 M-based engineering management based on meta-intelligence, meta-synthesis, and meta-technology is proposed as the methodological framework. Adjustability is explained as a precondition for the construction of large scale infrastructure systems, and three open questions on the engineering management of infrastructure systems are proposed based on this precondition. (1) Is there an effective 3 M-based engineering management methodology for all large scale infrastructure systems? (2) How can the morphism between problem groups and model fields in large scale infrastructure systems be proven? (3) How can the morphism between model fields and algorithmic clusters in large scale infrastructure systems be proven?

Introduction

Large scale infrastructure construction, such as oil and gas field development projects (Senouci, EI-Abbasy, & Zayed, 2014; Xu & Wu, 2015), water conservancy and hydropower engineering projects (Xu & Zeng, 2014, 2011; Zeng, Xu, Wu, & Shen, 2014) and urban transportation networks (Chang & Kendall, 2011), involves massive information, large investments, long construction periods, high risks, organizational complexity, and significant environmental impacts, all of which require complex systems engineering. The impact of human activities on natural systems has grown significantly, meaning that engineers now need consciously to employ engineering management systems. Realistically, engineering is at the heart of all infrastructure developments, so must be included when seeking to develop new theories and tools for complex engineering systems (Ottino, 2004). Improving construction efficiency and quality and reducing environmental impacts are the three core demands of current infrastructure development practice. With significant recent advances in information and engineering technologies, it is possible to move beyond traditional infrastructure construction engineering management practices. However, during this evolution, there are significant challenges, which this paper summarizes by focusing on three open questions that raise future research possibilities to determine effective solutions.

Opportunities within engineering development advances

Meta-intelligence through advances in big data analysis technology

Big data has become a current and future research frontier (Fung, Tse, & Fu, 2015; Lazer, Kennedy, King, & Vespignani, 2014; Lynch, 2008; Marx, 2013; Servick, 2015; Shneiderman, 2014; Wren, 2014) and has attracted significant research attention from the information sciences, governments and enterprise policy and decision makers, and technological development engineers (Chen & Zhang, 2014). It has also changed engineering management as it has opened up an ability to deal with current and future scientific and technological challenges. This is especially true in large scale infrastructure systems, which can be regarded as complex open data-driven giant systems with system complexities such as openness, human involvement, societal characteristics, and intelligence emergence.

Advances in information theory and computing technologies are reshaping the geographic scale of infrastructure developments from the local to the international, which can have both positive and negative impacts. For example, the merging of cyberspace with traditional infrastructure has created new functionalities and opportunities while simultaneously exposing the vulnerabilities of cyberspace. From an engineering perspective, understanding the interdependencies within infrastructure systems continues to be a major challenge both in terms of defining the appropriate theoretical constructs and in terms of defining and implementing the appropriate interventions given the fiscal realities. Infrastructure system engineering management under a big data environment emphasizes that meta-intelligence methodology should take an engineering management perspective with the support of advanced data acquisition, data storage, data management, and network intelligences. Meta-intelligence formation through the provision of more comprehensive focused information can support infrastructure construction and allow for intelligent construction.

Meta-synthesis through advances in complex system theory

Complex systems are a critical challenge as they can seriously affect future systems, human life, and cybernetics development (Aeppli & Chandra, 1997; Boumen, de Jong, Mestrom, van de Mortel-Fronczak, & Rooda, 2009; Foote, 2007; Leleur, 2008; May, 1972; Nadis, 2003). However, open complex giant systems are very challenging due to their inherent system complexities (Boumen et al., 2009; Cao, Dai, & Zhou, 2009). For open complex giant system problem solving, it is necessary to analyse the cooperative structures existing between people and systems and to study how people can better deal with and manage these open complex giant systems. In the 1990s, qualitative-to-quantitative meta-synthesis theory was proposed as an effective breakthrough methodology for the understanding and problem solving of open complex giant systems (Qian, 1991; Qian, Yu, & Dai, 1990, 1993). This method combined quantitative methods with qualitative knowledge through a synthesis of data-driven information and expert knowledge. Since that time, there has been a continuous endeavour to put these ideas into practice (Gu & Tang, 2005; Xu & Tao, 2012; Xu & Yao, 2011; Xu & Zhou, 2011). However, to date there has been little research focused on meta-synthesis-based engineering management for infrastructure systems, a technique which could provide significant improvements to construction quality and efficiency.

Meta-technology through advances in engineering management

The development of infrastructure engineering management has had a long history. From the beginning, engineering management has required theories or at least organized frameworks for engineering calculations (Ottino, 2004). Engineering management has been defined in many ways (Lannes, 2001). In 1916, Fayol (1949) first wrote General and Industrial Management, in which management was described as a process of planning, organization, coordination, directing, and controlling. Lock (1993) called Fayol the founding father of engineering management and modern management theory. Bennett (1996) also cited Fayol’s works as the origin of the management process that has formed the basis for most other work in this area. However, despite Fayol’s pioneering work on management in the early 1900s, engineering management only emerged as a discipline in its own right in the latter part of the twentieth century (2014). Winston (2004)) stated that engineering leadership is the means to a more promising future for the engineering profession.

As one of the more important disciplines in engineering management, civil engineering management in the twenty-first century is expected to be dramatically different because of the growing and long-overdue realization that the traditional contract forms that have remained virtually unchanged since the 1860s have become obsolete (Barnes, 2000a, 2000b). During the past decade, against the background of the creation of independent business units within organizations, there have been major changes in the civil engineering management of safety, quality, and productivity (Hendrickson, 2012). Xu and Li (2012) concluded that engineering management should be based on ecological engineering, as this is an essential requirement for effective engineering management. At the same time, ecological engineering has been seen as the base and driver of comprehensive engineering management. With the above advances in engineering management, engineering management understanding has taken on a more focused system perspective, indicating that engineering technologies are becoming significantly more interdependent. Therefore, a meta-technology that establishes the technological basis for new advances in engineering management can be seen to be the most appropriate approach for the future.

Challenges in system engineering management

With the increasing demand for large scale infrastructure construction, many new challenges have emerged, some of which have remained unsolved and some of which have remained in the background. Based on the framework of a qualitative-to-quantitative meta-synthesis methodology, this paper describes large scale infrastructure systems as synergetic systems made up of ‘Data Streams–Information Sets–Problem Groups–Model Fields–Algorithmic Clusters’ as shown in Figure 1. To address this synergetic system, there are challenges that need to be overcome during the evolutionary process such as system efficiencies, accuracy, and effectiveness. The details are explained in the following.

Figure 1. Synergetic system for ‘Data Streams–Information Sets–Problem Groups–Model Fields–Algorithmic Clusters’.

In this paper, infrastructure systems are defined as networks of systems and processes that function cooperatively and synergistically to produce and distribute a continuou...

Table of contents

• Cover
• Half Title Page
• Title Page
• Copyright Page
• Table of Contents
• Citation Information
• Notes on Contributors
• Preface
• 1 Engineering management: new advances and three open questions
• 2 Bayes and big data: the consensus Monte Carlo algorithm
• 3 Measurement and analysis of quality of life related to environmental hazards: the methodology illustrated by recent epidemiological studies
• 4 Big data analytics: integrating penalty strategies
• 5 Seeking relationships in big data: a Bayesian perspective
• 6 Designing a data-driven leagile sustainable closed-loop supply chain network
• 7 Exploring capability maturity models and relevant practices as solutions addressing information technology service offshoring project issues
• 8 The evolution and governance of online rumors during the public health emergency: taking COVID-19 pandemic related rumors as an example
• 9 An empirical study of data warehouse implementation effectiveness
• 10 Developing a preliminary cost estimation model for tall buildings based on machine learning
• 11 A framework for managing uncertainty in information system project selection: an intelligent fuzzy approach
• Index