Prioritization of Interconnected Processes

Deciding which business processes to improve is a challenge for all organizations. The literature on business process management (BPM) offers several approaches that support process prioritization. As many approaches share the individual process as unit of analysis, they determine the processes’ need for improvement mostly based on performance indicators, but neglect how processes are interconnected. So far, the interconnections of processes are only captured for descriptive purposes in process model repositories or business process architectures (BPAs). Prioritizing processes without catering for their interconnectedness, however, biases prioritization decisions and causes a misallocation of corporate funds. What is missing are process prioritization approaches that consider the processes’ individual need for improvement and their interconnectedness. To address this research problem, the authors propose the ProcessPageRank (PPR) as their main contribution. The PPR prioritizes processes of a given BPA by ranking them according to their network-adjusted need for improvement. The PPR builds on knowledge from process performance management, BPAs, and network analysis – particularly the Google PageRank. As for evaluation, the authors validated the PPR’s design specification against empirically validated and theory-backed design propositions. They also instantiated the PPR’s design specification as a software prototype and applied the prototype to a real-world BPA

SPARCS: Stream-processing architecture applied in real-time cyber-physical security

In this paper, we showcase a complete, end-To-end, fault tolerant, bandwidth and latency optimized architecture for real time utilization of data from multiple sources that allows the collection, transport, storage, processing, and display of both raw data and analytics. This architecture can be applied for a wide variety of applications ranging from automation/control to monitoring and security. We propose a practical, hierarchical design that allows easy addition and reconfiguration of software and hardware components, while utilizing local processing of data at sensor or field site ('fog computing') level to reduce latency and upstream bandwidth requirements. The system supports multiple fail-safe mechanisms to guarantee the delivery of sensor data. We describe the application of this architecture to cyber-physical security (CPS) by supporting security monitoring of an electric distribution grid, through the collection and analysis of distribution-grid level phasor measurement unit (PMU) data, as well as Supervisory Control And Data Acquisition (SCADA) communication in the control area network

Risk and Business Goal Based Security Requirement and Countermeasure Prioritization

Companies are under pressure to be in control of their assets but at the same time they must operate as efficiently as possible. This means that they aim to implement “good-enough security” but need to be able to justify their security investment plans. Currently companies achieve this by means of checklist-based security assessments, but these methods are a way to achieve consensus without being able to provide justifications of countermeasures in terms of business goals. But such justifications are needed to operate securely and effectively in networked businesses. In this paper, we first compare a Risk-Based Requirements Prioritization method (RiskREP) with some requirements engineering and risk assessment methods based on their requirements elicitation and prioritization properties. RiskREP extends misuse case-based requirements engineering methods with IT architecture-based risk assessment and countermeasure definition and prioritization. Then, we present how RiskREP prioritizes countermeasures by linking business goals to countermeasure specification. Prioritizing countermeasures based on business goals is especially important to provide the stakeholders with structured arguments for choosing a set of countermeasures to implement. We illustrate RiskREP and how it prioritizes the countermeasures it elicits by an application to an action case

Economic MPC with periodic terminal constraints of nonlinear differential-algebraic-equation systems: Application to drinking water networks

© 2026 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksIn this paper, an Economic Model Predictive Control (EMPC) strategy with periodic terminal constraints is addressed for nonlinear differential-algebraic-equation systems with an application to Drinking Water Networks (DWNs). DWNs have some periodic behaviours because of the daily seasonality of water demands and electrical energy price. The periodic terminal constraint and economic terminal cost are implemented in the EMPC controller design for the purpose of achieving convergence. The feasibility of the proposed EMPC strategy when disturbances are considered is guaranteed by means of soft constraints implemented by using slack variables. Finally, the comparison results in a case study of the D-Town water network is provided by applying the EMPC strategy with or without periodic terminal constraints.Accepted versio

Review of the Learning Alliance for Adaptation in Smallholder Agriculture

The Learning Alliance for Adaptation in Smallholder Agriculture is a knowledge platform which leverages the strengths, opportunities and diverse audiences of the International Fund for Agriculture (IFAD) and the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS). The objective of the Learning Alliance is to produce and disseminate evidence for informed policy and implementation of climate-smart agriculture (CSA) interventions by capturing, analyzing and communicating lessons emerging from the IFAD supported global Adaptation in Smallholder Agriculture Programme (ASAP). The Learning Alliance strives to enable agricultural development policy-makers and practitioners make science-based decisions in the context of climate change. The underlying assumption of the Learning Alliance is that the “provision of demand-driven research outputs to policy-makers and practitioners is a key mechanism for improving the effectiveness of adaptation actions among ultimate beneficiaries, in this case smallholder farmers”. The review aims to identify areas for improvement to achieve the planned outcomes of the Learning Alliance more effectively. It provides recommendations to inform a further phase, based on the experience of those closely involved in the knowledge production and implementation of the Alliance

Search algorithms for regression test case prioritization

Regression testing is an expensive, but important, process. Unfortunately, there may be insufficient resources to allow for the re-execution of all test cases during regression testing. In this situation, test case prioritisation techniques aim to improve the effectiveness of regression testing, by ordering the test cases so that the most beneficial are executed first. Previous work on regression test case prioritisation has focused on Greedy Algorithms. However, it is known that these algorithms may produce sub-optimal results, because they may construct results that denote only local minima within the search space. By contrast, meta-heuristic and evolutionary search algorithms aim to avoid such problems. This paper presents results from an empirical study of the application of several greedy, meta-heuristic and evolutionary search algorithms to six programs, ranging from 374 to 11,148 lines of code for 3 choices of fitness metric. The paper addresses the problems of choice of fitness metric, characterisation of landscape modality and determination of the most suitable search technique to apply. The empirical results replicate previous results concerning Greedy Algorithms. They shed light on the nature of the regression testing search space, indicating that it is multi-modal. The results also show that Genetic Algorithms perform well, although Greedy approaches are surprisingly effective, given the multi-modal nature of the landscape