The article presents an analysis of drive workloads from enterprise storage systems. The drive workloads are obtained from field return units from a cross-section of enterprise storage system vendors and thus provides a view of the workload characteristics over a wide spectrum of end-user applications. The workload parameters that have been characterized include transfer lengths, access patterns, throughput, and utilization. The study shows that reads are the dominant workload accounting for 80% of the accesses to the drive. Writes are dominated by short block random accesses while reads range from random to highly sequential. A trend analysis over the period 2010–2014 shows that the workload has remained fairly constant even as the capacities of the drives shipped has steadily increased. The study shows that the data stored on disk drives is relatively cold—on average less than 4% of the drive capacity is accessed in a given 2h interval.

Data incompleteness and inconsistency are common issues in data-driven decision models. To some extend, they can be considered as two opposite circumstances, since the former occurs due to lack of information and the latter can be regarded as an excess of heterogeneous information. Although these issues often contribute to a decrease in the accuracy of the model, most modeling approaches lack of mechanisms to address them. This research focuses on an advanced belief rule-based decision model and proposes a dynamic rule activation (DRA) method to address both issues simultaneously. DRA is based on “smart” rule activation, where the actived rules are selected in a dynamic way to search for a balance between the incompleteness and inconsistency in the rule-base generated from sample data to achive a better performance. A series of case studies demonstrate how the use of DRA improves the accuracy of this advanced rule-based decision model, without compromising its efficiency, especially when dealing with multi-class classification datasets. DRA has been proved to be beneficial to select the most suitable rules or data instances instead of aggregating an entire rule-base. Beside the work performed in rule-based systems, DRA alone can be regarded as a generic dynamic similarity measurement that can be applied in different domains.

Real-world decision problems are usually associated with a certain geographical area, and therefore can and should be geographically referenced in most of cases. While traditional Decision Support Systems (DSSs) ignore the spatial dimension of the problem, most Geographic Information System (GIS)-based Spatial Decision Support Systems (SDSSs) focus mainly on the spatial analysis of the problem, avoiding other relevant factors like uncertainty and incompleteness of data sets. This research is based on a recently developed intelligent belief rule-based DSS, called RIMER+, which is shown to be capable of capturing vagueness, incompleteness, uncertainty, and nonlinear causal relationships in an integrated way. The main contribution of this research is to explore the possibilities of achieving a higher degree of integration of DSSs in a GIS environment, i.e., integration of RIMER+ within GIS system by using an embedded approach, which not only enhances further the capability and applicability of the RIMER+ by integrating the spatial component of the problem into the decision making process, but also takes advantage of the GIS software capabilities in terms of spatial analysis and visualization. Finally, this research employs a comparative case study to demonstrate performance of the proposed Spatial RIMER+ methodology against the well-known Geographically Weighted Regression (GWR) methodology.

Taking into account the need of handling hybrid information with uncertainty in human decision making, a new belief rule-base inference methodology (RIMER) has been recently proposed. RIMER approach and its relevant extensions have proved to be highly positive solving decision problems. However, for an end user it is difficult to implement the methods and algorithms from the raw equations in order to solve a specific problem. This paper presents a decision support tool based on the RIMER approach that facilitates its implementation and use to end-users. The overall structure and main functionalities of the tool are outlined, followed by an example to illustrate the use of this tool for applications.