EFFECTIVE DECISION SUPPORT IN THE BIG DATA ERA

This study conducts a review and synthesis of the Business Intelligence and Analytics (BI&A) evolution, applications, frameworks and emerging trends with the aim to provide a summary of core concepts, a succinct but valuable description of main applications and frameworks, and an account of main recommendations for addressing the Big Data challenges and opportunities. It develops an integrated and organized view on the BI&A evolution process and presents an integrated BI&A application framework to help organizations adopt or develop the appropriate BI&A solutions to derive the desired impact in the Big Data era. This paper also elicits a set of practical recommendations to executives and leaders in organizations worldwide for interpreting the BI&A literature and applying the rich body of knowledge for IT practitioners. It traces the BI&A evolution to data-driven discovery and highly proactive and creative decision-making utilizing advanced analytical techniques with unstructured and massive data sources to cope with a highly dynamic global business environment in the Big Data era.

DSS-OSM: An Integrated Decision Support System for Offshore Oil Spill Management

The marine ecosystem, human health and social economy are always severely impacted once an offshore oil spill event has occurred. Thus, the management of oil spills is of importance but is difficult due to constraints from a number of dynamic and interactive processes under uncertain conditions. An integrated decision support system is significantly helpful for offshore oil spill management, but it is yet to be developed. Therefore, this study aims at developing an integrated decision support system for supporting offshore oil spill management (DSS-OSM). The DSS-OSM was developed with the integration of a Monte Carlo simulation, artificial neural network and simulation-optimization coupling approach to provide timely and effective decision support to offshore oil spill vulnerability analysis, response technology screening and response devices/equipment allocation. In addition, the uncertainties and their interactions were also analyzed throughout the modeling of the DSS-OSM. Finally, an offshore oil spill management case study was conducted on the south coast of Newfoundland, Canada, demonstrating the feasibility of the developed DSS-OSM.

Transformation of oil and gas fields based on information technologies

Currently, there is an opportunity to ensure digital transformation in the leading oil and gas companies in Russia. The main task of the transformation is to reduce capital and operating costs and increase production efficiency. The objects of transformation are processes, information, and people. Considering the existing technological and geological constraints for the Arctic fields, it is advisable to ensure the initial implementation of the principles of a digital intelligent field when creating control systems for wells and control production complexes. An important component is the development of an effective decision support system as a tool for calculating forecast tasks that provides strategic and tactical planning when modeling geological and technological processes online. The materials provide the structure of remote management of geographically distributed facilities of PJSC Gazprom, as well as solutions already implemented and confirmed the effectiveness of management for the Bovanenkovo oil and gas condensate field located in the Arctic on the Yamal Peninsula.

Designing Personalized Treatment Plans for Breast Cancer

Breast cancer remains the leading cause of cancer deaths among women around the world. Contemporary treatment for breast cancer is complex and involves highly specialized medical professionals collaborating in a series of information-intensive processes. This poses significant challenges to optimization of treatment plans for individual patients. We propose a novel framework that enables personalization and customization of treatment plans for early stage breast cancer patients undergoing radiotherapy. Using a series of simulation experiments benchmarked with real-world clinical data, we demonstrate that the treatment plans generated from our proposed framework consistently outperform those from the existing practices in balancing the risk of local tumor recurrence and radiation-induced adverse effects. Our research sheds new light on how to combine domain knowledge and patient data in developing effective decision-support tools for clinical use. Although our research is specifically geared toward radiotherapy planning for breast cancer, the design principles of our framework can be applied to the personalization of treatment plans for patients with other chronic diseases that typically involve complications and comorbidities.

Review and Comparative Study of Decision Support Tools for the Mitigation of Urban Heat Stress

Over the last few decades, Urban Heat Stress (UHS) has become a crucial concern of scientists and policy-makers. Many projects have been implemented to mitigate Urban Heat Island (UHI) effects using nature-based solutions. However, decision-making and selecting an adequate framework are difficult because of complex interactions between natural, social, economic and built environments. This paper contributes to the UHI issue by: (i) identifying the most important key factors of a Decision Support Tool (DST) used for urban heat mitigation, (ii) presenting multi-criteria methods applied to urban heat resilience, (iii) reviewing existing spatial and non-spatial DSTs, (iv) and analyzing, classifying and ranking DSTs. It aims to help decision-makers through an overview of the pros and cons of existing DSTs and indicate which tool is providing maximum support for choosing and planning heat resilience measures from the designing phase to the heat mitigation phase. This review shows that Multi-Criteria Decision Analysis (MCDA) can be used for any pilot site and the criteria can be adapted to the given location accordingly. It also highlights that GIS-based spatial tools have an effective decision support system (DSS) because they offer a quick assessment of interventions and predict long-term effects of urban heat. Through a comparative study using specific chosen criteria, we conclude that the DSS tool is well suited and fulfils many prerequisites to support new policies and interventions to mitigate UHS.

A data value metric for quantifying information content and utility

Data-driven innovation is propelled by recent scientific advances, rapid technological progress, substantial reductions of manufacturing costs, and significant demands for effective decision support systems. This has led to efforts to collect massive amounts of heterogeneous and multisource data, however, not all data is of equal quality or equally informative. Previous methods to capture and quantify the utility of data include value of information (VoI), quality of information (QoI), and mutual information (MI). This manuscript introduces a new measure to quantify whether larger volumes of increasingly more complex data enhance, degrade, or alter their information content and utility with respect to specific tasks. We present a new information-theoretic measure, called Data Value Metric (DVM), that quantifies the useful information content (energy) of large and heterogeneous datasets. The DVM formulation is based on a regularized model balancing data analytical value (utility) and model complexity. DVM can be used to determine if appending, expanding, or augmenting a dataset may be beneficial in specific application domains. Subject to the choices of data analytic, inferential, or forecasting techniques employed to interrogate the data, DVM quantifies the information boost, or degradation, associated with increasing the data size or expanding the richness of its features. DVM is defined as a mixture of a fidelity and a regularization terms. The fidelity captures the usefulness of the sample data specifically in the context of the inferential task. The regularization term represents the computational complexity of the corresponding inferential method. Inspired by the concept of information bottleneck in deep learning, the fidelity term depends on the performance of the corresponding supervised or unsupervised model. We tested the DVM method for several alternative supervised and unsupervised regression, classification, clustering, and dimensionality reduction tasks. Both real and simulated datasets with weak and strong signal information are used in the experimental validation. Our findings suggest that DVM captures effectively the balance between analytical-value and algorithmic-complexity. Changes in the DVM expose the tradeoffs between algorithmic complexity and data analytical value in terms of the sample-size and the feature-richness of a dataset. DVM values may be used to determine the size and characteristics of the data to optimize the relative utility of various supervised or unsupervised algorithms.

Trustworthy Predictive Algorithms for Complex Forest System Decision-Making

Advances in predictive algorithms are revolutionizing how we understand and design effective decision support systems in many sectors. The expanding role of predictive algorithms is part of a broader movement toward using data-driven machine learning (ML) for modalities including images, natural language, speech. This article reviews whether and to what extent predictive algorithms can assist decision-making in forest conservation and management. Although state-of-the-art ML algorithms provide new opportunities, adoption has been slow in forest decision-making. This review shows how domain-specific characteristics, such as system complexity, impose limits on using predictive algorithms in forest conservation and management. We conclude with possible directions for developing new predictive tools and approaches to support meaningful forest decisions through easily interpretable and explainable recommendations.

Identifying Organizational Factors for Successful Business Intelligence Implementation

Business intelligence (BI) has proliferated due to its growing application for business decision support. Research on organizational factors may offer significant use in BI implementation. However, a limited number of studies focus on organizational factors for revealing adverse impacts on effective decision support. The aim of this theoretical study is to conduct a literature analysis to identify organizational factors relevant to BI implementation. Through a systematic literature review, a qualitative content analysis on 49 relevant sample articles for generating themes inductively is adopted to reveal organizational factors. Findings suggest two contexts: information management that integrates factors such as technological capability and personnel capability and organizational context that integrates factors such as organizational capability, managerial decision, and organizational culture for facilitating embedding information management capability for BI implementation in businesses. It is hoped that these contextual understanding can be useful for further BI implementations.