Related papers: Decision-Analytic Approaches to Operational Decisi…
Data-driven optimization uses contextual information and machine learning algorithms to find solutions to decision problems with uncertain parameters. While a vast body of work is dedicated to interpreting machine learning models in the…
Online Analytical Processing (OLAP) for relational databases is a business decision support application. The application receives queries about the business database, usually requesting to summarize many database records, and produces few…
Decision making under uncertainty is at the heart of any autonomous system acting with imperfect information. The cost of solving the decision making problem is exponential in the action and observation spaces, thus rendering it unfeasible…
Many research questions can be answered quickly and efficiently using data already collected for previous research. This practice is called secondary data analysis (SDA), and has gained popularity due to lower costs and improved research…
Advancement in information and communication technology (ICT) has given rise to explosion of data in every field of operations. Working with the enormous volume of data (or Big Data, as it is popularly known as) for extraction of useful…
In the era of the fourth industrial revolution, it is essential to automate fault detection and diagnosis of machineries so that a warning system can be developed that will help to take an appropriate action before any catastrophic damage.…
Prescriptive Analytics (PSA), an emerging business analytics field suggesting concrete options for solving business problems, has seen an increasing amount of interest after more than a decade of multidisciplinary research. This paper is a…
This paper describes the architecture of R&D Analyst, a commercial intelligent decision system for evaluating corporate research and development projects and portfolios. In analyzing projects, R&D Analyst interactively guides a user in…
In today's uncertain and competitive market, where enterprises are subjected to increasingly shortened product life-cycles and frequent volume changes, reconfigurable manufacturing systems (RMS) applications play a significant role in the…
Predicting the next activity in an ongoing process is one of the most common classification tasks in the business process management (BPM) domain. It allows businesses to optimize resource allocation, enhance operational efficiency, and…
In the last decades, data have become a cornerstone component in many business decisions, and copious resources are being poured into production and acquisition of the high-quality data. This emerging market possesses unique features, and…
The proliferation of SQL for data processing has often occurred without the rigor of traditional software development, leading to siloed efforts, logic replication, and increased risk. This ad-hoc approach hampers data governance and makes…
In order to protect the environment and address fossil fuel scarcity, renewable energy is increasingly used for power generation. However, due to the uncertainties it brings to electricity production, deterministic optimization is no longer…
A sound performance of health care organizations depends on vigorous financing system, skilled and well paying personnel, trustworthy information, well sustained health services and reliable technologies they used. The management and…
We describe the current state and future plans for a set of tools for scientific data management (SDM) designed to support scientific transparency and reproducible research. SDM has been in active use at our MRI Center for more than two…
Business statistics play a crucial role in implementing a data-driven strategic plan at the enterprise level to employ various analytics where the outcomes of such a plan enable an enterprise to enhance the decision-making process or to…
The objective-based forecasting considers the asymmetric and non-linear impacts of forecasting errors on decision objectives, thus improving the effectiveness of its downstream decision-making process. However, existing objective-based…
Traditionally, data scientists use exploratory data analysis techniques such as correlation analysis, summary statistics, and regression analysis for identifying the most product enhancements and roadmap planning. However, these…
Analytic functions represent the state-of-the-art way of performing complex data analysis within a single SQL statement. In particular, an important class of analytic functions that has been frequently used in commercial systems to support…
Managing the configurations of a database system poses significant challenges due to the multitude of configuration knobs that impact various system aspects.The lack of standardization, independence, and universality among these knobs…