A clausal resolution method for branching-time logic ECTL+

We expand the applicability of the clausal resolution technique to the branching-time temporal logic ECTL_. ECTL_ is strictly more expressive than the basic computation tree logic CTL and its extension, ECTL, as it allows Boolean combinations of fairness and single temporal operators. We show that any ECTL_ formula can be translated to a normal form the structure of which was initially defined for CTL and then applied to ECTL. This enables us to apply to ECTL_ a resolution technique defined over the set of clauses. Our correctness argument also bridges the gap in the correctness proof for ECTL: we show that the transformation procedure for ECTL preserves unsatisfiability

Search strategies for resolution in CTL-type logics: extension and complexity

A clausal resolution approach originally developed for the branching logic CTL has recently been extended to the logics ECTL and ECTL+. In the application of the resolution rules searching for a loop is essential. In this paper we define a Depth-First technique to complement the existing Breadth-First Search and provide the complexity analysis of the developed methods. Additionally, it contains a correction in our previous presentation of loops

WEIGHTBIT: An advancement in wearable technology

Wearable devices are becoming an important interface between users and fitness activities. Their capabilities are improving exponentially, and new strategies are being developed to track sports using sensors that are widely used in robotics. These wearable gadgets are normally created in conjunction with smartphone applications enabling the user to visualise the data and share it through social networks, or compete with other users. The technology behind these devices is often simple using sensors that can be found in a smartphone, such as GPS, accelerometer and gyroscope. However, there are currently no devices capable of measuring the gym activity of weight lifting. In this paper, we present WeightBit: a system consisting of technologically enhanced gym gloves, comprised of the aforementioned sensor’s as well as an additional weight sensor to detect weight and arm movements. Using this data in combination with a smartphone application, it will be possible to monitor a new series of sports activities with specific focus on weight training. Furthermore, the data collected by the application will enable broader research by medical researchers or institutions. The goal is to keep users focused and keen to live a healthy life, providing them a great tool to track their progress, and to develop a system that will allow medical institutions access to this data for further study

Discovering Business Processes in CRM Systems by leveraging unstructured text data

Recent research has proven the feasibility of using Process Mining algorithms to discover business processes from event logs of structured data. However, many IT systems also store a considerable amount of unstructured data. Customer Relationship Management (CRM) Systems typically store information about interactions with customers, such as emails, phone calls, meetings, etc. These activities are characteristically made up of unstructured data, such as a free text subject and description of the interaction, but only limited structured data is available to classify them. This poses a problem to the traditional Process Mining approach that relies on an event log made up of clearly categorised activities. This paper proposes an original framework to mine processes from CRM data, by leveraging the unstructured part of the data. This method uses Latent Dirichlet Allocation (LDA), an unsupervised machine learning technique, to automatically detect and assign labels to activities. This framework does not require any human intervention. A case study with real-world CRM data validates the feasibility of our approach

Modeling and Optimizing Patient Flows

constructing a consistent process model and its simulation can be instrumental to be used in healthcare issues such as Consistent patient flow modeling. Current process modeling techniques used in healthcare are intuitive and imprecise such as flowcharts, unified modeling language activity diagram (UML AD) and business process modeling notation (BPMN). These techniques are vague in process description and cannot fully capture the complexities of the types of activities and types of temporal constraints between them. Additionally, to schedule patient flows; current modeling techniques does not offer any mechanism so healthcare relies on critical path method(CPM) and program evaluation review technique (PERT) that also have limitations i.e. finish-start barrier. It is imperative that temporal constraints between the start and/or end of a process needs to be specified, e.g., the start of A precedes the start (or end) of B, etc., however, these approaches failed to provide us with a mechanism for handling these temporal situations. This paper proposes a framework that provides enumeration of core concepts to describe a general knowledge base for Business and Healthcare domains. Algorithms are provided to represent the semantics of concepts i.e. based on their ontology. Furthermore, this logical basis is supported by Point graph (PG); a graphical tool, which has a formal translation to a point interval temporal logic (PITL) is used to simulate Patient flows for enhanced reasoning and correct representation. We will briefly evaluate an illustrative discharge patient flow example initially modeled using Unified Modeling Language Activity Diagram (UML AD) with the intention to compare with the technique presented here for its potential use to model patient flows

Modeling Patient Flows: A Temporal Logic Approach

Constructing a consistent process model can be instrumental in streamlining healthcare issues. Current process modeling techniques used in healthcare, such as flowcharts, unified modeling language activity diagram (UML AD), and business process modeling notation (BPMN) are intuitive and imprecise. These techniques are vague in process description and cannot fully capture the complexities of the types of activities and full extent of temporal constraints between them. Additionally, to schedule patient flows, current modeling techniques do not offer any mechanism, so healthcare relies on critical path method (CPM) and program evaluation review technique (PERT), that also have limitations i.e. finish-start barrier. It is imperative that temporal constraints between the start and/or end of a process needs to be specified, e.g., the start of A precedes the start (or end) of B, etc., however, these approaches failed to provide us with a mechanism for handling these temporal situations. This paper proposes a framework that provides enumeration of core terms/concepts to describe a general knowledge basis for Business and Healthcare domains. Definitions are provided to present the semantics of concepts i.e. based on their ontology. Furthermore, this logical basis is supported by Point graph (PG) notation; a graphical tool, which has a formal translation to a point interval temporal logic (PITL), and is used to model Patient flows suitable for enhanced reasoning and correct representation. We will evaluate an illustrative discharge patient flow example initially modeled using Unified Modeling Language Activity Diagram (UML AD) with the intention to compare with the technique presented here for its potential use to model patient flows

An Exploration of Ethical Decision Making with Intelligence Augmentation

In recent years, the use of Artificial Intelligence agents to augment and enhance the operational decision making of human agents has increased. This has delivered real benefits in terms of improved service quality, delivery of more personalised services, reduction in processing time, and more efficient allocation of resources, amongst others. However, it has also raised issues which have real-world ethical implications such as recommending different credit outcomes for individuals who have an identical financial profile but different characteristics (e.g., gender, race). The popular press has highlighted several high-profile cases of algorithmic discrimination and the issue has gained traction. While both the fields of ethical decision making and Explainable AI (XAI) have been extensively researched, as yet we are not aware of any studies which have examined the process of ethical decision making with Intelligence augmentation (IA). We aim to address that gap with this study. We amalgamate the literature in both fields of research and propose, but not attempt to validate empirically, propositions and belief statements based on the synthesis of the existing literature, observation, logic, and empirical analogy. We aim to test these propositions in future studies

Comparative analysis of clustering-based remaining-time predictive process monitoring approaches

Predictive process monitoring aims to accurately predict a variable of interest (e.g. remaining time) or the future state of the process instance (e.g. outcome or next step). Various studies have been explored to develop models with greater predictive power. However, comparing the various studies is difficult as different datasets, parameters and evaluation measures have been used. This paper seeks to address this problem with a focus on studies that adopt a clustering-based approach to predict the remaining time to the end of the process instance. A systematic literature review is undertaken to identify existing studies that adopt a clustering-based remaining-time predictive process monitoring approach and performs a comparative analysis to compare and benchmark the output of the identified studies using five real-life event logs

Investigating Social Contextual Factors in Remaining-Time Predictive Process Monitoring—A Survival Analysis Approach

Predictive process monitoring aims to accurately predict a variable of interest (e.g., remaining time) or the future state of the process instance (e.g., outcome or next step). The quest for models with higher predictive power has led to the development of a variety of novel approaches. However, though social contextual factors are widely acknowledged to impact the way cases are handled, as yet there have been no studies which have investigated the impact of social contextual features in the predictive process monitoring framework. These factors encompass the way humans and automated agents interact within a particular organisation to execute process-related activities. This paper seeks to address this problem by investigating the impact of social contextual features in the predictive process monitoring framework utilising a survival analysis approach. We propose an approach to censor an event log and build a survival function utilising the Weibull model, which enables us to explore the impact of social contextual factors as covariates. Moreover, we propose an approach to predict the remaining time of an in-flight process instance by using the survival function to estimate the throughput time for each trace, which is then used with the elapsed time to predict the remaining time for the trace. The proposed approach is benchmarked against existing approaches using five real-life event logs and it outperforms these approaches

Investigating the Diffusion of Workload-Induced Stress—A Simulation Approach

Work-induced stress is widely acknowledged as harming physical and psychosocial health and has been linked with adverse outcomes such as a decrease in productivity. Recently, workplace stressors have increased due to the COVID-19 pandemic. This study aims to contribute to the literature base in a couple of areas. First, it extends the current knowledge base by utilising generative additive modelling (GAMs) to uncover the nature of the relationship between workload (a key workplace stressor) and productivity based on real-world event logs. Additionally, it uses recursive partitioning modelling to shed light on the factors that drive the relationship between these variables. Secondly, it utilises a simulation-based approach to investigate the diffusion of workload-induced stress in the workplace. Simulation is a valuable tool for exploring the effect of changes in a risk-free manner as it provides the ability to run multiple scenarios in a safe and virtual environment with a view to making recommendations to stakeholders. However, there are several recognised issues with traditional simulation approaches, such as inadequate resource modelling and the limited use of simulations for operational decision making. In this study, we propose an approach which extracts the required parameters from an event log and subsequently utilises them to initialise a workload-induced stress diffusion simulation model accurately. We also explore the effects of varying the parameters to control the spread of workload-induced stress within the network. With suitable amendments, this approach can be extended to model the spread of disease (e.g., COVID-19), diffusion of ideas, among other things, in the workplace