scholarly journals Software Processes Analysis with Provenance

2018 ◽  
Author(s):  
Gabriella C. B. Costa ◽  
Humberto L. O. Dalpra ◽  
Eldânae N. Teixeira ◽  
Cláudia M. L. Werner ◽  
Regina M. M. Braga ◽  
...  
Keyword(s):  
Decision Making ◽  
Process Management ◽  
Process Analysis ◽  
Real Data ◽  
Management Tool ◽  
Process Data ◽  
Novel Approach ◽  
Provenance Data ◽  
Data Capturing ◽  
Storage Technologies

Companies have been increasing the amount of data that they collect from their systems and processes, considering the decrease in the cost of memory and storage technologies in recent years. The emergence of technologies such as Big Data, Cloud Computing, E-Science, and the growing complexity of information systems made evident that traceability and provenance are promising approaches. Provenance has been successfully used in complex domains, like health sciences, chemical industries, and scientific computing, considering that these areas require a comprehensive semantic traceability mechanism. Based on these, we investigate the use of provenance in the context of Software Process (SP) and introduce a novel approach based on provenance concepts to model and represent SP data. It addresses SP provenance data capturing, storing, new information inferencing and visualization. The main contribution of our approach is PROV-SwProcess, a provenance model to deal with the specificities of SP and its ability in supporting process managers to deal with vast amounts of execution data during the process analysis and data-driven decision-making. A set of analysis possibilities were derived from this model, using SP goals and questions. A case study was conducted in collaboration with a software development company to instantiate the PROV-SwProcess model (using the proposed approach) with real-word process data. This study showed that 87.5% of the analysis possibilities using real data was correct and can assist in decision-making, while 62.5% of them are not possible to be performed by the process manager using his currently dashboard or process management tool.

Project Time-Cost-Quality Trade-off Problem: A Novel Approach Based on Fuzzy Decision Making

2020 ◽  
Vol 28 (04) ◽  
pp. 545-567
Author(s):  
Esmaeil Keshavarz ◽  
Abbas Shoul
Keyword(s):  
Decision Making ◽  
Completion Time ◽  
Real Data ◽  
Research Problem ◽  
Fuzzy Decision Making ◽  
Time Cost ◽  
Fuzzy Decision ◽  
Trade Off ◽  
Novel Approach

Trade-off problems concentrate on balancing the main parameters of a project as completion time, total cost and quality of activities. In this study, the problem of project time-cost-quality trade-off is formulated and solved from a new standpoint. For this purpose, completion time and crash cost of project are illustrated as fuzzy goals, also the dependency of implementing time of each activity and its execution-quality is described by a fuzzy number. The overall quality of the project execution is defined as the minimum execution-quality of the project activities that should be maximized. Based on some real assumptions, a three-objective programming problem associated with the time-cost-quality trade-off problem is formulated; then with the aim of identifying a fair and appropriate trade-off, the research problem is reformulated as a single objective linear programming by utilizing a fuzzy decision-making methodology. Generating a final preferred solution, rather than a set of Pareto optimal solutions, and having a reasonable interpretation are two most important advantages of the proposed approach. To explain the practical performance of the proposed models and approach, a time-cost-quality trade-off problem for a project with real data is solved and analyzed.

Advanced Learning Analytics in Academic Education

2020 ◽  
Vol 16 (3) ◽  
pp. 70-87
Author(s):  
Ayman G. Fayoumi ◽  
Amjad Fuad Hajjar
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Saudi Arabia ◽  
Academic Performance ◽  
Knowledge Society ◽  
Real Data ◽  
Added Value ◽  
Academic Education ◽  
Ann Model ◽  
Novel Approach

The integration of innovative data mining and decision-making techniques in the context of higher education is a bold initiative towards enhanced performance. Predictive and descriptive analytics add interesting insights for significant aspects the education. The purpose of this article is to summarize a novel approach for the adoption of artificial intelligence (AI) techniques towards forecasting of academic performance. The added value of applying AI techniques for advanced decision making in education is the realization that the scientific approach to standard problems in academia, like the enhancement of academic performance is feasible. For the purpose of this research the authors promote a research in Saudi Arabia. The vision of the Knowledge Society in the Kingdom of Saudi Arabia is a critical milestone towards digital transformation. The human capital and the integration of industry and academia has to be based on holistic approaches to skills and competencies management. One of the main objectives of an academic decision maker is to ensure that academic resources are adequately planned and that students are properly advised. To achieve such an objective, an extensive analysis of large volumes of data may be required. This research develops a decision support system (DSS) that is based on an artificial neural network (ANN) model that can be deployed for effective academic planning and advising. The system is based on evaluating academic metrics against academic performance for students. The model integrates inputs from relevant academic data sources into an autonomous ANN. A simulation of real data on an ANN is conducted to validate the system's accuracy. Moreover, an ANN is compared with different mathematical approaches. The system enables the quality assurance of planning, advising, and the monitoring of academic decisions. The overall contribution of this work is a novel approach to the deployment of Artificial Intelligent for advanced decision making in higher education. In future work this model is integrated with big data and analytics research for advanced visualizations

Data-Driven Methods to Create Knowledge Maps for Decision Making in Academic Contexts

2017 ◽  
Vol 16 (01) ◽  
pp. 1750008 ◽  
Author(s):  
Rizan Moradi ◽  
Khalil Taheri ◽  
Maryam S. Mirian
Keyword(s):  
Decision Making ◽  
Knowledge Management ◽  
Real Data ◽  
Research Management ◽  
Management Tool ◽  
Knowledge Map ◽  
Functional Evaluation ◽  
Knowledge Maps ◽  
The Real ◽  
Associate Dean

Knowledge is the primary asset of today’s organisations; thus, knowledge management has been focused on discovery, representation, modification, transformation, and creation of knowledge within an enterprise. A knowledge map is a knowledge management tool that makes organisational processes more visible, feasible, and practicable. It is a graphical representation of decision-related information. What happens, how various events can be managed, and why they happened: all can be demonstrated very precisely by a well-designed knowledge map. There are diverse knowledge-related roles; for example, each university dean’s office — as an instance of a knowledge-based organisation — usually relies upon their institutional memory to make daily decisions. However, utilising a knowledge map greatly facilitates any individual’s or group’s decision-making process, by proposing or establishing key required information. In this study, two important managerial roles — Associate Deans of Research and Education — were selected; then we reviewed their key managerial decisions and proposed three different techniques for supporting their decisions. The chief superiority of the approach offered here was in the creation of role-based knowledge maps, including an expertness map and a collaboration map for the Associate Dean of Research, which were formed using clustering, taxonomy formation, and information retrieval methods. A third map was created for the Associate Dean of Education, including a Bayesian reasoning map based on an Improved PC (IPC) algorithm, which learned the structure and the parameters of a Bayesian network to describe decision-making in the domain of education. To evaluate the proposed approaches, structural and functional evaluation measures and standard datasets (in the available cases) were chosen. The results found that the approaches were comparable to the selected benchmarks within the real data; even after considering the challenging nature of the real data, which included problems such as incomplete and unclean data extracted from the University of Tehran’s education and research management information systems.

scholarly journals Deep Learning for Transient Image Reconstruction from ToF Data

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1962
Author(s):  
Enrico Buratto ◽  
Adriano Simonetto ◽  
Gianluca Agresti ◽  
Henrik Schäfer ◽  
Pietro Zanuttigh
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Light Response ◽  
Real Data ◽  
Depth Image ◽  
Learning Approach ◽  
Multiple Reflections ◽  
Noisy Input ◽  
Novel Approach ◽  
Incoming Light

In this work, we propose a novel approach for correcting multi-path interference (MPI) in Time-of-Flight (ToF) cameras by estimating the direct and global components of the incoming light. MPI is an error source linked to the multiple reflections of light inside a scene; each sensor pixel receives information coming from different light paths which generally leads to an overestimation of the depth. We introduce a novel deep learning approach, which estimates the structure of the time-dependent scene impulse response and from it recovers a depth image with a reduced amount of MPI. The model consists of two main blocks: a predictive model that learns a compact encoded representation of the backscattering vector from the noisy input data and a fixed backscattering model which translates the encoded representation into the high dimensional light response. Experimental results on real data show the effectiveness of the proposed approach, which reaches state-of-the-art performances.

Artificial Intelligence and Human Rights: Four Realms of Discussion: Summary of Remarks

2020 ◽  
Vol 114 ◽  
pp. 242-245
Author(s):  
Jootaek Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Language Processing ◽  
Human Reasoning ◽  
The Past ◽  
Making Choices ◽  
Data Capturing ◽  
Intensive Procedures

The term, Artificial Intelligence (AI), has changed since it was first coined by John MacCarthy in 1956. AI, believed to have been created with Kurt Gödel's unprovable computational statements in 1931, is now called deep learning or machine learning. AI is defined as a computer machine with the ability to make predictions about the future and solve complex tasks, using algorithms. The AI algorithms are enhanced and become effective with big data capturing the present and the past while still necessarily reflecting human biases into models and equations. AI is also capable of making choices like humans, mirroring human reasoning. AI can help robots to efficiently repeat the same labor intensive procedures in factories and can analyze historic and present data efficiently through deep learning, natural language processing, and anomaly detection. Thus, AI covers a spectrum of augmented intelligence relating to prediction, autonomous intelligence relating to decision making, automated intelligence for labor robots, and assisted intelligence for data analysis.

scholarly journals Decision Support System for Evaluating Suitable Job Applicants

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1773
Author(s):  
Bogdan Walek ◽  
Ondrej Pektor ◽  
Radim Farana
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Real Data ◽  
Job Applicants ◽  
Inference System ◽  
Selection Procedures ◽  
Novel Approach ◽  
Fuzzy Expert Systems ◽  
Takagi Sugeno

This paper describes a novel approach in the area of evaluating suitable job applicants for various job positions, and specifies typical areas of requirement and their usage. Requirements for this decision-support system are defined in order to be used in middle-size companies. Suitable tools chosen were fuzzy expert systems, primarily the inference system Takagi-Sugeno type, which were then supplied with implementation of methods of variant multi-criteria analysis. The resulting system is a variable tool with the possibility to simply set the importance of individual selection criteria so that it can be used in various situations, primarily in repeated selection procedures for similar job positions. A strong emphasis is devoted to the explanatory module, which enables the results of the expert system to be used easily. Verification of the system on real data in cooperation with a collaborating company has proved that the system is easily usable.

scholarly journals Integrating Value Considerations in the Decision Making for the Design of Biorefineries

2020 ◽  
Vol 26 (6) ◽  
pp. 2927-2955
Author(s):  
Mar Palmeros Parada ◽  
Lotte Asveld ◽  
Patricia Osseweijer ◽  
John Alexander Posada
Keyword(s):  
Decision Making ◽  
Complex Systems ◽  
Research And Development ◽  
Design Process ◽  
Design Decision ◽  
Value Sensitive Design ◽  
Design Practice ◽  
Novel Approach ◽  
Design Alternatives ◽  
Biobased Production

AbstractBiobased production has been promoted as a sustainable alternative to fossil resources. However, controversies over its impact on sustainability highlight societal concerns, value tensions and uncertainties that have not been taken into account during its development. In this work, the consideration of stakeholders’ values in a biorefinery design project is investigated. Value sensitive design (VSD) is a promising approach to the design of technologies with consideration of stakeholders’ values, however, it is not directly applicable for complex systems like biorefineries. Therefore, some elements of VSD, such as the identification of relevant values and their connection to a technology’s features, are brought into biorefinery design practice. Midstream modulation (MM), an approach to promoting the consideration of societal aspects during research and development activities, is applied to promote reflection and value considerations during the design decision making. As result, it is shown that MM interventions during the design process led to new design alternatives in support of stakeholders' values, and allowed to recognize and respond to emerging value tensions within the scope of the project. In this way, the present work shows a novel approach for the technical investigation of VSD, especially for biorefineries. Also, based on this work it is argued that not only reflection, but also flexibility and openness are important for the application of VSD in the context of biorefinery design.

scholarly journals A Novel Approach to Fuzzy Soft Set-Based Group Decision-Making

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Qinrong Feng ◽  
Xiao Guo
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Comparison Analysis ◽  
Fuzzy Soft Set ◽  
Soft Sets ◽  
Novel Approach ◽  
Decision Making Problem ◽  
Fuzzy Soft Sets ◽  
Effective Group

There are many uncertain problems in practical life which need decision-making with soft sets and fuzzy soft sets. The purpose of this paper is to develop an approach to effectively solve the group decision-making problem based on fuzzy soft sets. Firstly, we present an adjustable approach to solve the decision-making problems based on fuzzy soft sets. Then, we introduce knowledge measure and divergence degree based on α-similarity relation to determine the experts’ weights. Further, we develop an effective group decision-making approach with unknown experts’ weights. Finally, sensitivity analysis about the parameters and comparison analysis with other existing methods are given.

SmartWoW – constructing a tool for knowledge work performance analysis

2015 ◽  
Vol 64 (4) ◽  
pp. 479-498 ◽  
Author(s):  
Miikka Palvalin ◽  
Maiju Vuolle ◽  
Aki Jääskeläinen ◽  
Harri Laihonen ◽  
Antti Lönnqvist
Keyword(s):  
Work Performance ◽  
Knowledge Work ◽  
Well Being ◽  
Management Tool ◽  
Research Approach ◽  
Work Practices ◽  
Content Type ◽  
Individual Level ◽  
New Ways Of Working ◽  
Novel Approach

Purpose – New Ways of Working (NewWoW) refers to a novel approach for improving the performance of knowledge work. The purpose of this paper is to seek innovative solutions concerning facilities, information technology tools and work practices in order to be able to “work smarter, not harder.” In order to develop work practices toward the NewWoW mode there is a need for an analytical management tool that would help assess the status of the organization’s current work practices and demonstrate the impacts of development initiatives. This paper introduces such a tool. Design/methodology/approach – Constructive research approach was chosen to guide the development of the Smart ways of working (SmartWoW) tool. The tool was designed on the basis of previous knowledge work performance literature as well as on interviews in two knowledge-intensive organizations. The usefulness of the tool was verified by applying it in four organizations. Findings – SmartWoW is a compact questionnaire tool for analyzing and measuring knowledge work at the individual level. The questionnaire consists of four areas: work environment, personal work practices, well-being at work and productivity. As SmartWoW is a standardized tool its results are comparable between organizations. Research limitations/implications – SmartWoW was designed a pragmatic managerial tool. It is considered possible that it can be valuable as a research instrument as well but the current limited amount of collected data does not yet facilitate determining its usefulness from that perspective. Originality/value – This paper makes a contribution to the existing literature on knowledge work measurement and management by introducing an analytical tool which takes into account the NewWoW perspective.

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