Machine Learning and Data Science Project Management From an Agile Perspective

2022 ◽  
pp. 73-88
Author(s):  
Murat Pasa Uysal
Keyword(s):  
Machine Learning ◽  
Project Management ◽  
Data Science ◽  
Business Models ◽  
Background Information ◽  
Future Research ◽  
Initial Attempt ◽  
Agile Project Management ◽  
Experimental Implementation ◽  
Management Method

Successful implementations of machine learning (ML) and data science (DS) applications have enabled innovative business models and brought new opportunities for organizations. On the other hand, research studies report that organizations employing ML and DS solutions are at a high risk of failure and they can easily fall short of their objectives. One major factor is to adopt or tailor a project management method for the specific requirements of ML and DS applications. Therefore, agile project management (APM) may be proposed as a solution. However, there is significantly less study that explores ML and DS project management from an agile perspective. In this chapter, the authors discuss methods and challenges according to the background information and practice areas of ML, DS, and APM. This study can be viewed as an initial attempt to enhance these knowledge and practice domains in view of APM. Therefore, future research efforts will focus on the challenges as well as the experimental implementation of APM methods in real industrial case studies of ML and DS.

Machine Learning

2012 ◽  
pp. 13-22 ◽  
Author(s):  
João Gama ◽  
André C.P.L.F. de Carvalho
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Text Processing ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Background Information ◽  
Future Research ◽  
Personal View ◽  
Learning Techniques ◽  
Future Research Directions

Machine learning techniques have been successfully applied to several real world problems in areas as diverse as image analysis, Semantic Web, bioinformatics, text processing, natural language processing,telecommunications, finance, medical diagnosis, and so forth. A particular application where machine learning plays a key role is data mining, where machine learning techniques have been extensively used for the extraction of association, clustering, prediction, diagnosis, and regression models. This text presents our personal view of the main aspects, major tasks, frequently used algorithms, current research, and future directions of machine learning research. For such, it is organized as follows: Background information concerning machine learning is presented in the second section. The third section discusses different definitions for Machine Learning. Common tasks faced by Machine Learning Systems are described in the fourth section. Popular Machine Learning algorithms and the importance of the loss function are commented on in the fifth section. The sixth and seventh sections present the current trends and future research directions, respectively.

Machine Learning

2011 ◽  
pp. 2462-2468 ◽  
Author(s):  
João Gama ◽  
André C.P.L.F. de Carvalho
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Text Processing ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Background Information ◽  
Future Research ◽  
Personal View ◽  
Learning Techniques ◽  
Future Research Directions

Machine learning techniques have been successfully applied to several real world problems in areas as diverse as image analysis, Semantic Web, bioinformatics, text processing, natural language processing,telecommunications, finance, medical diagnosis, and so forth. A particular application where machine learning plays a key role is data mining, where machine learning techniques have been extensively used for the extraction of association, clustering, prediction, diagnosis, and regression models. This text presents our personal view of the main aspects, major tasks, frequently used algorithms, current research, and future directions of machine learning research. For such, it is organized as follows: Background information concerning machine learning is presented in the second section. The third section discusses different definitions for Machine Learning. Common tasks faced by Machine Learning Systems are described in the fourth section. Popular Machine Learning algorithms and the importance of the loss function are commented on in the fifth section. The sixth and seventh sections present the current trends and future research directions, respectively.

Accountable algorithms? The ethical implications of data-driven business models

2020 ◽  
Vol 31 (2) ◽  
pp. 163-185 ◽  
Author(s):  
Christoph F. Breidbach ◽  
Paul Maglio
Keyword(s):  
Machine Learning ◽  
Business Models ◽  
Analytical Framework ◽  
Data Driven ◽  
Service Research ◽  
Future Research ◽  
Cost Models ◽  
Ethical Challenges ◽  
Content Type ◽  
Ethical Implications

PurposeThe purpose of this study is to identify, analyze and explain the ethical implications that can result from the datafication of service.Design/methodology/approachThis study uses a midrange theorizing approach to integrate currently disconnected perspectives on technology-enabled service, data-driven business models, data ethics and business ethics to introduce a novel analytical framework centered on data-driven business models as the general metatheoretical unit of analysis. The authors then contextualize the framework using data-intensive insurance services.FindingsThe resulting midrange theory offers new insights into how using machine learning, AI and big data sets can lead to unethical implications. Centered around 13 ethical challenges, this work outlines how data-driven business models redefine the value network, alter the roles of individual actors as cocreators of value, lead to the emergence of new data-driven value propositions, as well as novel revenue and cost models.Practical implicationsFuture research based on the framework can help guide practitioners to implement and use advanced analytics more effectively and ethically.Originality/valueAt a time when future technological developments related to AI, machine learning or other forms of advanced data analytics are unpredictable, this study instigates a critical and timely discourse within the service research community about the ethical implications that can arise from the datafication of service by introducing much-needed theory and terminology.

Big Data Methods

2016 ◽  
Vol 21 (3) ◽  
pp. 525-547 ◽  
Author(s):  
Scott Tonidandel ◽  
Eden B. King ◽  
Jose M. Cortina
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Data Analytics ◽  
Data Science ◽  
Data Sources ◽  
Future Research ◽  
Organizational Science ◽  
Associated Data ◽  
Organizational Sciences

Advances in data science, such as data mining, data visualization, and machine learning, are extremely well-suited to address numerous questions in the organizational sciences given the explosion of available data. Despite these opportunities, few scholars in our field have discussed the specific ways in which the lens of our science should be brought to bear on the topic of big data and big data's reciprocal impact on our science. The purpose of this paper is to provide an overview of the big data phenomenon and its potential for impacting organizational science in both positive and negative ways. We identifying the biggest opportunities afforded by big data along with the biggest obstacles, and we discuss specifically how we think our methods will be most impacted by the data analytics movement. We also provide a list of resources to help interested readers incorporate big data methods into their existing research. Our hope is that we stimulate interest in big data, motivate future research using big data sources, and encourage the application of associated data science techniques more broadly in the organizational sciences.

The promise of implementing machine learning in earthquake engineering: A state-of-the-art review

2020 ◽  
Vol 36 (4) ◽  
pp. 1769-1801 ◽  
Author(s):  
Yazhou Xie ◽  
Majid Ebad Sichani ◽  
Jamie E Padgett ◽  
Reginald DesRoches
Keyword(s):  
Machine Learning ◽  
Earthquake Engineering ◽  
Structural Control ◽  
Data Science ◽  
State Of The Art ◽  
Hazard Analysis ◽  
Future Research ◽  
Main Stream ◽  
Topic Area ◽  
Data Resource

Machine learning (ML) has evolved rapidly over recent years with the promise to substantially alter and enhance the role of data science in a variety of disciplines. Compared with traditional approaches, ML offers advantages to handle complex problems, provide computational efficiency, propagate and treat uncertainties, and facilitate decision making. Also, the maturing of ML has led to significant advances in not only the main-stream artificial intelligence (AI) research but also other science and engineering fields, such as material science, bioengineering, construction management, and transportation engineering. This study conducts a comprehensive review of the progress and challenges of implementing ML in the earthquake engineering domain. A hierarchical attribute matrix is adopted to categorize the existing literature based on four traits identified in the field, such as ML method, topic area, data resource, and scale of analysis. The state-of-the-art review indicates to what extent ML has been applied in four topic areas of earthquake engineering, including seismic hazard analysis, system identification and damage detection, seismic fragility assessment, and structural control for earthquake mitigation. Moreover, research challenges and the associated future research needs are discussed, which include embracing the next generation of data sharing and sensor technologies, implementing more advanced ML techniques, and developing physics-guided ML models.

Development of a Digital ESP Performance Monitoring System Based on Artificial Intelligence

2021 ◽  
Author(s):  
Göktug Diker ◽  
Herwig Frühbauer ◽  
Edna Michelle Bisso Bi Mba
Keyword(s):  
Machine Learning ◽  
Project Management ◽  
Data Science ◽  
Performance Monitoring ◽  
Intelligent System ◽  
Operating Conditions ◽  
Oil Field ◽  
Training Dataset ◽  
Detailed Knowledge ◽  
Concept Evaluation

Abstract Wintershall Dea is developing together with partners a digital system to monitor and optimize electrical submersible pump (ESP) performance based on the data from Mittelplate oil field. This tool is using machine learning (ML) models which are fed by historic data and will notify engineers and operators when operating conditions are trending beyond the operating envelope, which enables an operator to mitigate upcoming performance problems. In addition to traditional engineering methods, such a system will capture knowledge by continuous improvement based on ML. With this approach the engineer has a system at hand to support the day-to-day work. Manual monitoring and on demand investigations are now backed up by an intelligent system which permanently monitors the equipment. In order to create such a system, a proof of concept (PoC) study has been initiated with industry partners and data scientists to evaluate historic events, which are used to train the ML-systems. This phase aims to better understand the capabilities of machine learning and data science in the subsurface domain as well as to build up trust for the engineers with such systems. The concept evaluation has shown that the intensive collaboration between engineers and data scientist is essential. A continuous and structured exchange between engineering and data science resulted in a mutual developed product, which fits the engineer's needs based on the technical capabilities and limits set by ML-models. To organize such a development, new project management elements like agile working methods, sprints and scrum methods were utilized. During the development Wintershall Dea has partnered with two organizations. One has a pure data science background and the other one was the data science team of the ESP manufacturer. After the PoC period the following conclusions can be derived: (1) data quality and format is key to success; (2) detailed knowledge of the equipment speeds up the development and the quality of the results; (3) high model accuracy requires a high number of events in the training dataset. The overall conclusion of this PoC is that the collaboration between engineers and data scientists, fostered by the agile project management toolkit and suitable datasets, leads to a successful development. Even when the limits of the ML-algorithms are hit, the model forecast, in combination with traditional engineering methods, adds significant value to the ESP performance. The novelty of such a system is that the production engineer will be supported by trusted ML-models and digital systems. This system in combination with the traditional engineering tools improves monitoring of the equipment and taking decisions leading to increased equipment performance.

scholarly journals Examining the Values and Principles of Agile Construction Management in Iraqi Construction Projects

2018 ◽  
Vol 24 (7) ◽  
pp. 114
Author(s):  
Sawsan Rasheed Mohammed ◽  
Asmaa Jebur Jasim
Keyword(s):  
Project Management ◽  
Construction Industry ◽  
Construction Projects ◽  
Relevant Literature ◽  
Construction Project ◽  
New Methods ◽  
Agile Project Management ◽  
The World ◽  
Management Method ◽  
The Cost

The avoidance of failure in construction projects is not an easy task, which makes the failure of the construction project to achieve its objectives a major problem experienced by all countries in the world, especially Iraq. Where nearly two-thirds of the construction projects in the world have been suffered by significant problems as an increase in the cost of the project, delay in the specified duration for execution, and stopping the project. Therefore it is required to study and apply new methods for managing the construction project to ensure its success and achieve its objectives. The aim of this study is to study the Agile project management method and its impact on the construction project. In addition, to identify the values and principles of Agile project management, which can be applied in the Iraqi construction industry to be adopted it as a new method to manage the construction projects in Iraq. The researcher reviewed the relevant literature to define the method of Agile project management and its methods and impact on the construction project. Then, the researcher conducted a questionnaire survey of a sample of engineers' experts who work in four main parties in the construction project: (beneficiary, supervising, designer, and contractor). The results of this survey showed that it is possible to apply the four values of Agile project management for managing the Iraqi construction projects, and can apply eleven of the twelve principles of Agile project management for managing the Iraqi construction projects.  

scholarly journals Cloud computing survey on services, enhancements and challenges in the era of machine learning and data science

2020 ◽  
Vol 9 (2) ◽  
pp. 117
Author(s):  
Wajid Hassan ◽  
Te-Shun Chou ◽  
Omar Tamer ◽  
John Pickard ◽  
Patrick Appiah-Kubi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Cloud Computing ◽  
Data Science ◽  
Service Level Agreement ◽  
Resource Scarcity ◽  
Cloud Services ◽  
Future Research ◽  
Intelligent Decision Making ◽  
Intelligent Decision

<p>Cloud computing has sweeping impact on the human productivity. Today it’s used for Computing, Storage, Predictions and Intelligent Decision Making, among others. Intelligent Decision Making using Machine Learning has pushed for the Cloud Services to be even more fast, robust and accurate. Security remains one of the major concerns which affect the cloud computing growth however there exist various research challenges in cloud computing adoption such as lack of well managed service level agreement (SLA), frequent disconnections, resource scarcity, interoperability, privacy, and reliability. Tremendous amount of work still needs to be done to explore the security challenges arising due to widespread usage of cloud deployment using Containers. We also discuss Impact of Cloud Computing and Cloud Standards. Hence in this research paper, a detailed survey of cloud computing, concepts, architectural principles, key services, and implementation, design and deployment challenges of cloud computing are discussed in detail and important future research directions in the era of Machine Learning and Data Science have been identified.</p>

scholarly journals Determining a novel feature-space for SARS-CoV-2 sequence data

2020 ◽  
Author(s):  
Francesco Ballesio ◽  
Ali Haider Bangash ◽  
Didier Barradas-Bautista ◽  
Justin Barton ◽  
Andrea Guarracino ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Mhc Class I ◽  
Phylogenetic Trees ◽  
Data Science ◽  
Sequence Data ◽  
Protein Sequences ◽  
Feature Space ◽  
Future Research ◽  
Alignment Free

The pandemicity &amp; the ability of the SARS-COV-2 to reinfect a cured subject, among other damaging characteristics of it, took everybody by surprise. A global collaborative scientific effort was direly required to bring learned people from different niches of medicine &amp; data science together. Such a platform was provided by COVID19 Virtual BioHackathon, organized from the 5th to the 11th of April, 2020, to ponder on the related pressing issues varying in their diversity from text mining to genomics. Under the "Machine learning" track, we determined optimal k-mer length for feature extraction, constructed continuous distributed representations for protein sequences to create phylogenetic trees in an alignment-free manner, and clustered predicted MHC class I and II binding affinity to aid in vaccine design. All the related work in available in a Github repository under an MIT license for future research.

Project Management Method Adoption

2017 ◽  
Vol 8 (2) ◽  
pp. 17-33 ◽  
Author(s):  
Mehmet N. Aydin ◽  
Ebru Dilan
Keyword(s):  
Project Management ◽  
Management Control ◽  
It Outsourcing ◽  
Future Research ◽  
Structure And Agency ◽  
Future Research Agenda ◽  
Project Management Institute ◽  
Different Types ◽  
Management Method

The purpose of this research is to understand what aspects of brand-named project management method (Project Management Institute - PMI) have been adopted in a service organization and how. The case context examined demonstrates how a weak-matrix organizational structure and agency interpretation along with project management maturity for IT outsourcing projects can affect adoption of a project management method. An interpretative case study is employed for examining the interplays among key notions underlying project management method adoption in IT outsourcing projects. The case study is framed with a research logic constituting the underlying notions of method adoption: the context, the agency, and the method and its fragments. It is found that the organization realizes 43 out of 47 processes proposed by PMI. It is also observed that the perceived project management maturity level is not aligned with the method fragments adopted. Among other discussion points, the present findings contribute to the existing literature by emphasizing the effects of management control on PM method adoption in IT outsourcing. Furthermore, this case allows us to argue that product-focused orientation in project management method adoption is evident and has several implications. The adoption and adaptation of processes in different types of projects is on the authors' future research agenda.

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