scholarly journals APPLICATION OF TRANSPORTATION BIG DATA TO SUPPORT DECISION-MAKING FOR ARCHITECTURE TEAMS: PROCESSES AND EXPERIENCES FROM TWO CASE STUDIES

2019 ◽  
Author(s):  
LADISLAVA FIALKA SOBKOVÁ ◽  
MICHAL ČERTICKÝ ◽  
ŠIMON JIRÁČEK
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Case Studies ◽  
Support Decision Making

scholarly journals Characterizing Big Data Management

10.28945/2192 ◽  
2015 ◽  
Author(s):  
Rogério Rossi ◽  
Kechi Hirama
Keyword(s):  
Decision Making ◽  
Information Technology ◽  
Big Data ◽  
Data Management ◽  
Process Management ◽  
Three Dimensions ◽  
Storage And Retrieval ◽  
Human Aspects ◽  
Business Approach ◽  
Support Decision Making

[The final form of this paper was published in the journal Issues in Informing Science and Information Technology.] Considering that big data is a reality for an increasing number of organizations in many areas, its management represents a set of challenges involving big data modeling, storage and retrieval, analysis and visualization. However, technological resources, people and processes are crucial dimensions to facilitate the management of big data in any organization, allowing information and knowledge from a large volume of data to support decision-making. Big data management must be supported by technology, people and processes; hence, this article discusses these three dimensions: the technologies for storage, analysis and visualization of big data; the human aspects of big data; and, in addition, the process management involved in a technological and business approach for big data management.

A Conceptual Model for Describing the Integration of Decision Aspect into Big Data

2022 ◽  
pp. 294-318
Author(s):  
Fatma Chiheb ◽  
Fatima Boumahdi ◽  
Hafida Bouarfa
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Competitive Advantage ◽  
Conceptual Model ◽  
Big Data Analytics ◽  
Decision Makers ◽  
Modern Business ◽  
Definition Of ◽  
A Company ◽  
Support Decision Making

Big Data is an important topic for discussion and research. It has gained this importance due to the meaningful value that could be extracted from these data. The application of Big Data in the modern business allows enterprises to take faster and smarter decisions, achieving a real competitive advantage. However, a lot of Big Data projects provide disappointing results that don't address the decision-makers' needs due to many reasons. The main reason for this failure can be summarized in neglecting the study of the decision-making aspect of these projects. In light of this challenge, this study proposes the integration of decision aspect into Big Data as a solution. Therefore, this article presents three main contributions: 1) Clarify the definition of Big Data; 2) Presents BD-Da model, a conceptual model describes the levels that should be considered to develop a Big Data project aiming to solve a problem that calls a decision; 3) Describes a particular, logical, requirements-like approach that explains how a company develops a Big Data analytics project to support decision-making.

A Conceptual Model for Describing the Integration of Decision Aspect into Big Data

2019 ◽  
Vol 10 (4) ◽  
pp. 1-23
Author(s):  
Fatma Chiheb ◽  
Fatima Boumahdi ◽  
Hafida Bouarfa
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Conceptual Model ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Decision Makers ◽  
Modern Business ◽  
Definition Of ◽  
A Company ◽  
Support Decision Making

Big Data is an important topic for discussion and research. It has gained this importance due to the meaningful value that could be extracted from these data. The application of Big Data in the modern business allows enterprises to take faster and smarter decisions, achieving a real competitive advantage. However, a lot of Big Data projects provide disappointing results that don't address the decision-makers' needs due to many reasons. The main reason for this failure can be summarized in neglecting the study of the decision-making aspect of these projects. In light of this challenge, this study proposes the integration of decision aspect into Big Data as a solution. Therefore, this article presents three main contributions: 1) Clarify the definition of Big Data; 2) Presents BD-Da model, a conceptual model describes the levels that should be considered to develop a Big Data project aiming to solve a problem that calls a decision; 3) Describes a particular, logical, requirements-like approach that explains how a company develops a Big Data analytics project to support decision-making.

New spatial decision support systems for sustainable urban and regional development

2015 ◽  
Vol 4 (1) ◽  
pp. 45-66 ◽  
Author(s):  
Patrizia Lombardi ◽  
Valentina Ferretti
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Case Studies ◽  
Support Systems ◽  
Spatial Decision Support ◽  
Content Type ◽  
New Generation ◽  
Support Decision Making ◽  
Regeneration Processes

Purpose – Policy makers are frequently challenged by the need to achieve sustainable development in cities and regions. Current decision-making processes are based on evaluation support systems which are unable to tackle the problem as they cannot take a holistic approach or a full account of actors. The purpose of this paper is to present a new generation of evaluation systems to support decision making in planning and regeneration processes which involve expert participation. These systems ensure network representation of the issues involved and visualization of multiple scenarios. Design/methodology/approach – A literature review is used for both revising existing evaluation tools in urban planning and the built environment and highlighting the need to give stakeholders (industry, cities, operators, etc.) new tools for collaborative or individual decisions and to facilitate scaling up solutions. An overview of the new generation of decision support systems, named Multicriteria Spatial Decision Support Systems (MC-SDSS) is provided and real case studies are analyzed to show their ability to tackle the problem. Findings – Recent research findings highlight that decisions in urban planning should be supported by collaborative and inclusive processes. Otherwise, they will fail. The case studies illustrated in this study highlight the usefulness of MC-SDSS for the successful resolution of complex problems, thanks to the visualization facilities and a network representation of the scenarios. Research limitations/implications – The case studies are limited to the Italian context. Practical implications – These SDSS are able to empower planners and decision makers to better understand the interaction between city design, social preferences, economic issues and policy incentives. Therefore, they have been employed in several case studies related to territorial planning and regeneration processes. Originality/value – This study provides three case studies and a review of the new MC-SDSS methodology which involve the Analytic Network Process technique to support decision-making in urban and regional planning.

scholarly journals BIG DATA AND DECISION-MAKING SUPPORT

2021 ◽  
Vol 2 ◽  
pp. 87-92
Author(s):  
Peter Procházka
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Internet Of Things ◽  
Methodological Approach ◽  
Timely Manner ◽  
Domestic Literature ◽  
Risk Definition ◽  
Similar Solutions ◽  
Support Decision Making ◽  
Big Data Technology

INTRODUCTION: Nowadays, Big Data is created in previously unimaginable quantities. Newly generated data from various Internet of Things (IoT) sensors and their use have never reached their current dimensions. Along with this trend, the availability of devices capable of collecting this data increases, the time for their evaluation is reduced and the volume of data collected at the same time increases. The most important task of research and development in this area is to bring solutions suitable for processing large amounts of data because our current storage and processing capabilities are limited and unable to compete with the storage, processing and publication of the resulting data. OBJECTIVES: Point out the importance of implementing Big Data technology. METHODS: To achieve the goal, the following methodological approach was chosen: study and processing of foreign and domestic literature, acquaintance with similar solutions for data processing, definition of Big Data and IoT, proposal for using Big Data solution to support decision-making, risk definition and evaluation. RESULTS: With the growing amount of disparate and incoherent data and the further growth of the Internet of Things, it is now almost impossible to evaluate all available information correctly and in a timely manner. Without this knowledge, the company loses its competitive advantage and is unable to respond in a timely manner to client requests. CONCLUSION: Implementing a solution for processing Big Data to support decision-making in the company is a complex process. As part of the implementation and use of the Big Data solution to support decision-making, the company must be prepared for the emergence of various problems. We can assume that Big Data technology will constantly be evolving in terms of streamlining analytical tools for obtaining information from large volumes of generated data. Therefore, it is appropriate to create space for the implementation of Big Data technology.

The big data regime shift in real estate

2020 ◽  
Vol 38 (4) ◽  
pp. 363-395 ◽  
Author(s):  
James R. DeLisle ◽  
Brent Never ◽  
Terry V. Grissom
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Real Estate ◽  
Urban Areas ◽  
Regime Shift ◽  
Design Thinking ◽  
Future Research ◽  
Content Type ◽  
Data Movement ◽  
Support Decision Making

PurposeThe paper explores the emergence of the “big data regime” and the disruption that it is causing for the real estate industry. The paper defines big data and illustrates how an inductive, big data approach can help improve decision-making.Design/methodology/approachThe paper demonstrates how big data can support inductive reasoning that can lead to enhanced real estate decisions. To help readers understand the dynamics and drivers of the big data regime shift, an extensive list of hyperlinks is included.FindingsThe paper concludes that it is possible to blend traditional and non-traditional data into a unified data environment to support enhanced decision-making. Through the application of design thinking, the paper illustrates how socially responsible development can be targeted to under-served urban areas and helps serve residents and the communities in which they live.Research limitations/implicationsThe paper demonstrates how big data can be harnessed to support decision-making using a hypothetical project. The paper does not present advanced analytics but focuses aggregating disparate longitudinal data that could support such analysis in future research.Practical implicationsThe paper focuses on the US market, but the methodology can be extended to other markets where big data is increasingly available.Social implicationsThe paper illustrates how big data analytics can be used to help serve the needs of marginalized residents and tenants, as well as blighted areas.Originality/valueThis paper documents the big data movement and demonstrates how non-traditional data can support decision-making.

scholarly journals Characterizing Big Data Management

10.28945/2204 ◽  
2015 ◽  
Vol 12 ◽  
pp. 165-180 ◽  
Author(s):  
Rogério Rossi ◽  
Kechi Hirama
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Management ◽  
Large Volume ◽  
Process Management ◽  
Three Dimensions ◽  
Storage And Retrieval ◽  
Human Aspects ◽  
Business Approach ◽  
Support Decision Making

Big data management is a reality for an increasing number of organizations in many areas and represents a set of challenges involving big data modeling, storage and retrieval, analysis and visualization. However, technological resources, people and processes are crucial to facilitate the management of big data in any kind of organization, allowing information and knowledge from a large volume of data to support decision-making. Big data management can be supported by these three dimensions: technology, people and processes. Hence, this article discusses these dimensions: the technological dimension that is related to storage, analytics and visualization of big data; the human aspects of big data; and, in addition, the process management dimension that involves in a technological and business approach the aspects of big data management.

Big Data Analytics in Financial Statement Audits

2015 ◽  
Vol 29 (2) ◽  
pp. 423-429 ◽  
Author(s):  
Min Cao ◽  
Roman Chychyla ◽  
Trevor Stewart
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Financial Statement ◽  
Practical Implementation ◽  
Efficiency And Effectiveness ◽  
Advanced Analytics ◽  
Support Decision Making

SYNOPSIS Big Data analytics is the process of inspecting, cleaning, transforming, and modeling Big Data to discover and communicate useful information and patterns, suggest conclusions, and support decision making. Big Data has been used for advanced analytics in many domains but hardly, if at all, by auditors. This article hypothesizes that Big Data analytics can improve the efficiency and effectiveness of financial statement audits. We explain how Big Data analytics applied in other domains might be applied in auditing. We also discuss the characteristics of Big Data analytics, which set it apart from traditional auditing, and its implications for practical implementation.

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