Data Analytics Pipeline for Prediction and Decision Making in Complex Products and Systems Development

2015 ◽  
Author(s):  
Eliab Z. Opiyo
Keyword(s):  
Decision Making ◽  
Data Analytics ◽  
System Development ◽  
Systems Development ◽  
Engineering Product ◽  
Raw Data ◽  
Complex Products ◽  
Specific Objective ◽  
Iot Devices ◽  
Support Decision Making

Facilitating data analytics for effective prediction in complex products or systems development is the focus of the research described in this paper. The specific objective was to develop strategies and a data analytics pipeline with a view to supporting exploration of the design space of complex products or systems upfront. The underlying challenges tackled included how to acquire and store raw data gathered by using both the traditional methods and advanced Internet of Things (IoT) devices, how to preprocess and transform raw data into a form suited for data analytics, and how to deal with analytics. A pipeline for data analytics to support decision making in complex products or systems development is proposed and its applicability illustrated with a practical example. The incorporation of advanced analytics techniques into the proposed pipeline allows users to acquire data and to insightfully and intelligently predict aspects such as cost and assembly time early on, and to make decisions based on data that may otherwise deemed to be inaccessible or unusable. This work contributes to the efforts directed toward applying data analytics techniques in a way that can have a profound impact on an engineering product or system development process.

Developing a data analytics platform to support decision making in emergency and security management

2019 ◽  
Vol 120 ◽  
pp. 167-184 ◽  
Author(s):  
Carlos J. Pérez-González ◽  
Marcos Colebrook ◽  
José L. Roda-García ◽  
Carlos B. Rosa-Remedios
Keyword(s):  
Decision Making ◽  
Data Analytics ◽  
Security Management ◽  
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.

Casey's Collections: A Strategic Decision-Making Case Using the Systems Development Lifecycle—Planning and Analysis Phases

2016 ◽  
Vol 13 (2) ◽  
pp. 231-245
Author(s):  
Kimberly S. Church ◽  
Pamela J. Schmidt ◽  
Georgia Smedley
Keyword(s):  
Decision Making ◽  
Life Cycle ◽  
Information Systems ◽  
System Development ◽  
Systems Development ◽  
Strategic Decision ◽  
Strategic Decision Making ◽  
Strategic Decisions ◽  
Analysis And Design ◽  
Development Lifecycle

ABSTRACT The Systems Development Life Cycle (SDLC) model, developed in the 1980s, remains the foundational model for strategic decision making regarding the development or acquisition of new information systems (IS). The SDLC model proposes five system development phases—planning, analysis, design, implementation, and maintenance—using a waterfall theory. These early phases of the SDLC require strategic decisions to be made regarding information systems. Strategic decision making is a model of multi-attribute utility theory, which helps promote decisions that maximize utility among multiple alternatives. This case provides students with experience in making reasoned strategic IT decisions by executing the planning and analysis phases in a new system acquisition life cycle. This educational case is structured to be the first of several cases covering the SDLC involving the same small fictitious public corporation, Casey's Collections. Upon completion of the case, students should have a deeper understanding and appreciation for the process of IS strategic decision making. In addition, students should better understand how to identify an information system's needs and prepare system proposals based on the generation and analysis of alternative solutions. This case is suitable for students in an introductory or graduate accounting systems course; it is also appropriate for use in a IS course on systems analysis and design.

The Human-IoT Ecosystem

2020 ◽  
pp. 1132-1156
Author(s):  
Vaughan Michell ◽  
James Olweny
Keyword(s):  
Decision Making ◽  
Real World ◽  
Human Perception ◽  
Smart Device ◽  
Context Information ◽  
Situation Theory ◽  
Powerful Approach ◽  
Iot Devices ◽  
World Environment ◽  
Support Decision Making

IoT devices offer a cheap and powerful approach to identifying real world states and situations and acting on this real world environment to change these states and the environment. Augmenting real world things with IoT technology enables the capture of real world context to support decision making and actions in the real world via powerful smart objects in a human- IoT ecosystem. Increasingly we will have to understand the Human-IoT or smart device ecosystem interaction in order to optimise and integrate the design of human and IoT systems. This chapter explores the design and categorisation of IoT devices in terms of their functionality and capability to support context to add to human perception. It then proposes how we can model the context information of both IoT devices and humans in a way that may help progress Human-IoT Ecosystem design using situation theory.

Relating Business Intelligence and Enterprise Architecture — A Method for Combining Operational Data with Architectural Metadata

2016 ◽  
Vol 25 (02) ◽  
pp. 1650007 ◽  
Author(s):  
R. K. M. Veneberg ◽  
M.-E. Iacob ◽  
M. J. van Sinderen ◽  
L. Bodenstaff
Keyword(s):  
Decision Making ◽  
Business Intelligence ◽  
Data Model ◽  
Enterprise Architecture ◽  
Business Analytics ◽  
Raw Data ◽  
Architecture Model ◽  
Model Based ◽  
Operational Data ◽  
Support Decision Making

Combining enterprise architecture and operational data is complex (especially when considering the actual ‘matching’ of data with enterprise architecture elements), and little has been written on how to do this. In this paper we aim to fill this gap, and propose a method to combine operational data with enterprise architecture to better support decision-making. Using such a method may result in either an enriched enterprise architecture model (which is very suitable as basis for model-based architecture analyses) or a warehouse data model where operational data is enriched with enterprise architecture metadata (which leads to more traceability by easing the retrieval and interpretation of raw data and of business analytics results). The method is illustrated by means of a case and evaluated by experts. Also, a model for mapping enterprise architecture, operational data, and time is proposed, which allows the model-based execution of new types of analyses.

Edge Analytics With Machine Learning Technique for Medical IoT Applications

2022 ◽  
pp. 131-142
Author(s):  
Jeya Mala D. ◽  
Pradeep Reynold A.
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Data Analytics ◽  
Machine Learning Technique ◽  
Iot Applications ◽  
Wide Range ◽  
Learning Technique ◽  
Internal Storage ◽  
Iot Devices

Edge analytics are tools and algorithms that are deployed in the internal storage of IoT devices or IoT gateways that collect, process, and analyze the data locally rather than transmitting it to the cloud for analysis. Edge analytics is applied in a wide range of applications in which immediate decision making is required. In the case of general IoT data analytics on the cloud, the data need to be collected from the IoT devices and to be sent to the cloud for further processing and decision making. In life-critical applications such as healthcare, the time taken to send the data to the cloud and then getting back the processed data to take decisions will not be acceptable. Hence, in these kinds of MIoT applications, it is essential to have analytics to be done on the edge in order to avoid such delays. Hence, this chapter is providing an abstract view on the application of machine learning in MIoT so that the data analytics provides fruitful results to the stakeholders.

Information Systems Development Outcomes

2011 ◽  
pp. 48-62 ◽  
Author(s):  
M. Gordon Hunter
Keyword(s):  
Decision Making ◽  
Information System ◽  
Information Systems ◽  
System Development ◽  
Systems Development ◽  
Information Systems Development ◽  
Information System Development ◽  
Development Outcomes ◽  
Positive Agreement

When is an information system development outcome considered a success and when is it considered a failure? What factors contribute to a conclusion of either success or failure? How does the situation arise to create the environment which contributes to the above conclusions? Generally, an information system is considered a success when it does what it is supposed to and/or the user is satisfied with the system’s performance in support of the information-providing and decision-making responsibilities. Naturally, this area is fraught with the problems inherent in divergent interpretations of “what it is supposed to do,” “satisfaction,” and “systems performance.” Suffice it to say, when the systems developer and user are in positive agreement about these interpretations, the information system development outcome may be considered successful.

The Human-IoT Ecosystem

2017 ◽  
pp. 223-248 ◽  
Author(s):  
Vaughan Michell ◽  
James Olweny
Keyword(s):  
Decision Making ◽  
Real World ◽  
Human Perception ◽  
Smart Device ◽  
Context Information ◽  
Situation Theory ◽  
Powerful Approach ◽  
Iot Devices ◽  
World Environment ◽  
Support Decision Making

IoT devices offer a cheap and powerful approach to identifying real world states and situations and acting on this real world environment to change these states and the environment. Augmenting real world things with IoT technology enables the capture of real world context to support decision making and actions in the real world via powerful smart objects in a human- IoT ecosystem. Increasingly we will have to understand the Human-IoT or smart device ecosystem interaction in order to optimise and integrate the design of human and IoT systems. This chapter explores the design and categorisation of IoT devices in terms of their functionality and capability to support context to add to human perception. It then proposes how we can model the context information of both IoT devices and humans in a way that may help progress Human-IoT Ecosystem design using situation theory.

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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