17,822 research outputs found
A Process Modelling Framework Based on Point Interval Temporal Logic with an Application to Modelling Patient Flows
This thesis considers an application of a temporal theory to describe and model the patient journey in the hospital accident and emergency (A&E) department. The aim is to introduce a generic but dynamic method applied to any setting, including healthcare. Constructing a consistent process model can be instrumental in streamlining healthcare issues. Current process modelling techniques used in healthcare such as flowcharts, unified modelling language activity diagram (UML AD), and business process modelling notation (BPMN) are intuitive and imprecise. They cannot fully capture the complexities of the types of activities and the full extent of temporal constraints to an extent where one could reason about the flows. Formal approaches such as Petri have also been reviewed to investigate their applicability to the healthcare domain to model processes.
Additionally, to schedule patient flows, current modelling standards do not offer any formal 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 to specify the temporal constraints between the start and/or end of a process, e.g., the beginning of a process A precedes the start (or end) of a process B. However, these approaches failed to provide us with a mechanism for handling these temporal situations. If provided, a formal representation can assist in effective knowledge representation and quality enhancement concerning a process. Also, it would help in uncovering complexities of a system and assist in modelling it in a consistent way which is not possible with the existing modelling techniques.
The above issues are addressed in this thesis by proposing a framework that would provide a knowledge base to model patient flows for accurate representation based on point interval temporal logic (PITL) that treats point and interval as primitives. These objects would constitute the knowledge base for the formal description of a system. With the aid of the inference mechanism of the temporal theory presented here, exhaustive temporal constraints derived from the proposed axiomatic system’ components serves as a knowledge base.
The proposed methodological framework would adopt a model-theoretic approach in which a theory is developed and considered as a model while the corresponding instance is considered as its application. Using this approach would assist in identifying core components of the system and their precise operation representing a real-life domain deemed suitable to the process modelling issues specified in this thesis. Thus, I have evaluated the modelling standards for their most-used terminologies and constructs to identify their key components. It will also assist in the generalisation of the critical terms (of process modelling standards) based on their ontology. A set of generalised terms proposed would serve as an enumeration of the theory and subsume the core modelling elements of the process modelling standards. The catalogue presents a knowledge base for the business and healthcare domains, and its components are formally defined (semantics). Furthermore, a resolution theorem-proof is used to show the structural features of the theory (model) to establish it is sound and complete.
After establishing that the theory is sound and complete, the next step is to provide the instantiation of the theory. This is achieved by mapping the core components of the theory to their corresponding instances. Additionally, a formal graphical tool termed as point graph (PG) is used to visualise the cases of the proposed axiomatic system. PG facilitates in modelling, and scheduling patient flows and enables analysing existing models for possible inaccuracies and inconsistencies supported by a reasoning mechanism based on PITL. Following that, a transformation is developed to map the core modelling components of the standards into the extended PG (PG*) based on the semantics presented by the axiomatic system.
A real-life case (from the King’s College hospital accident and emergency (A&E) department’s trauma patient pathway) is considered to validate the framework. It is divided into three patient flows to depict the journey of a patient with significant trauma, arriving at A&E, undergoing a procedure and subsequently discharged. Their staff relied upon the UML-AD and BPMN to model the patient flows. An evaluation of their representation is presented to show the shortfalls of the modelling standards to model patient flows. The last step is to model these patient flows using the developed approach, which is supported by enhanced reasoning and scheduling
An Analytical Solution for Probabilistic Guarantees of Reservation Based Soft Real-Time Systems
We show a methodology for the computation of the probability of deadline miss
for a periodic real-time task scheduled by a resource reservation algorithm. We
propose a modelling technique for the system that reduces the computation of
such a probability to that of the steady state probability of an infinite state
Discrete Time Markov Chain with a periodic structure. This structure is
exploited to develop an efficient numeric solution where different
accuracy/computation time trade-offs can be obtained by operating on the
granularity of the model. More importantly we offer a closed form conservative
bound for the probability of a deadline miss. Our experiments reveal that the
bound remains reasonably close to the experimental probability in one real-time
application of practical interest. When this bound is used for the optimisation
of the overall Quality of Service for a set of tasks sharing the CPU, it
produces a good sub-optimal solution in a small amount of time.Comment: IEEE Transactions on Parallel and Distributed Systems, Volume:27,
Issue: 3, March 201
A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing
Data Grids have been adopted as the platform for scientific communities that
need to share, access, transport, process and manage large data collections
distributed worldwide. They combine high-end computing technologies with
high-performance networking and wide-area storage management techniques. In
this paper, we discuss the key concepts behind Data Grids and compare them with
other data sharing and distribution paradigms such as content delivery
networks, peer-to-peer networks and distributed databases. We then provide
comprehensive taxonomies that cover various aspects of architecture, data
transportation, data replication and resource allocation and scheduling.
Finally, we map the proposed taxonomy to various Data Grid systems not only to
validate the taxonomy but also to identify areas for future exploration.
Through this taxonomy, we aim to categorise existing systems to better
understand their goals and their methodology. This would help evaluate their
applicability for solving similar problems. This taxonomy also provides a "gap
analysis" of this area through which researchers can potentially identify new
issues for investigation. Finally, we hope that the proposed taxonomy and
mapping also helps to provide an easy way for new practitioners to understand
this complex area of research.Comment: 46 pages, 16 figures, Technical Repor
Autonomous grid scheduling using probabilistic job runtime scheduling
Computational Grids are evolving into a global, service-oriented architecture –
a universal platform for delivering future computational services to a range of
applications of varying complexity and resource requirements. The thesis focuses
on developing a new scheduling model for general-purpose, utility clusters
based on the concept of user requested job completion deadlines. In such a
system, a user would be able to request each job to finish by a certain deadline,
and possibly to a certain monetary cost. Implementing deadline scheduling is
dependent on the ability to predict the execution time of each queued job, and
on an adaptive scheduling algorithm able to use those predictions to maximise
deadline adherence. The thesis proposes novel solutions to these two problems
and documents their implementation in a largely autonomous and self-managing
way.
The starting point of the work is an extensive analysis of a representative
Grid workload revealing consistent workflow patterns, usage cycles and correlations between the execution times of jobs and its properties commonly collected
by the Grid middleware for accounting purposes. An automated approach is
proposed to identify these dependencies and use them to partition the highly
variable workload into subsets of more consistent and predictable behaviour.
A range of time-series forecasting models, applied in this context for the first
time, were used to model the job execution times as a function of their historical
behaviour and associated properties. Based on the resulting predictions of job
runtimes a novel scheduling algorithm is able to estimate the latest job start
time necessary to meet the requested deadline and sort the queue accordingly to
minimise the amount of deadline overrun.
The testing of the proposed approach was done using the actual job trace
collected from a production Grid facility. The best performing execution time
predictor (the auto-regressive moving average method) coupled to workload
partitioning based on three simultaneous job properties returned the median
absolute percentage error centroid of only 4.75%. This level of prediction
accuracy enabled the proposed deadline scheduling method to reduce the average deadline overrun time ten-fold compared to the benchmark batch scheduler.
Overall, the thesis demonstrates that deadline scheduling of computational
jobs on the Grid is achievable using statistical forecasting of job execution times
based on historical information. The proposed approach is easily implementable,
substantially self-managing and better matched to the human workflow making
it well suited for implementation in the utility Grids of the future
Design and Analysis of a Task-based Parallelization over a Runtime System of an Explicit Finite-Volume CFD Code with Adaptive Time Stepping
FLUSEPA (Registered trademark in France No. 134009261) is an advanced
simulation tool which performs a large panel of aerodynamic studies. It is the
unstructured finite-volume solver developed by Airbus Safran Launchers company
to calculate compressible, multidimensional, unsteady, viscous and reactive
flows around bodies in relative motion. The time integration in FLUSEPA is done
using an explicit temporal adaptive method. The current production version of
the code is based on MPI and OpenMP. This implementation leads to important
synchronizations that must be reduced. To tackle this problem, we present the
study of a task-based parallelization of the aerodynamic solver of FLUSEPA
using the runtime system StarPU and combining up to three levels of
parallelism. We validate our solution by the simulation (using a finite-volume
mesh with 80 million cells) of a take-off blast wave propagation for Ariane 5
launcher.Comment: Accepted manuscript of a paper in Journal of Computational Scienc
An overview of recent research results and future research avenues using simulation studies in project management
This paper gives an overview of three simulation studies in dynamic project scheduling integrating baseline scheduling with risk analysis and project control. This integration is known in the literature as dynamic scheduling. An integrated project control method is presented using a project control simulation approach that combines the three topics into a single decision support system. The method makes use of Monte Carlo simulations and connects schedule risk analysis (SRA) with earned value management (EVM). A corrective action mechanism is added to the simulation model to measure the efficiency of two alternative project control methods. At the end of the paper, a summary of recent and state-of-the-art results is given, and directions for future research based on a new research study are presented
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