4,381 research outputs found
Business process modeling and simulation
The textbook provides the essentials of the Business Process (BP) Modeling and Simulation (M&S) from the verbal BP description to the formulation of the mathematical scheme of the model and the simulation program.
Both the analytical modeling and the simulation approaches to BP M&S are considered. Special attention is given to the theoretical and practical aspects of the BP M&S. The text covers the following topics: fundamentals of the BP M&S, conceptual modeling using IDEF3 standard, cost metrics and the activity based costing, analytical modeling (queuing networks, linear and dynamic programming), simulation with GPSS, timed Petri Nets, and Crystal Ball toolkits. Case studies include BP simulations with BPwin and GPSS.
The intended readers are senior graduate students and junior postgraduate students of computer science and industrial management
Final report on the evaluation of RRM/CRRM algorithms
Deliverable public del projecte EVERESTThis deliverable provides a definition and a complete evaluation of the RRM/CRRM algorithms selected in D11 and D15, and evolved and refined on an iterative process. The evaluation will be carried out by means of simulations using the simulators provided at D07, and D14.Preprin
Probabilistic Rateless Multiple Access for Machine-to-Machine Communication
Future machine to machine (M2M) communications need to support a massive
number of devices communicating with each other with little or no human
intervention. Random access techniques were originally proposed to enable M2M
multiple access, but suffer from severe congestion and access delay in an M2M
system with a large number of devices. In this paper, we propose a novel
multiple access scheme for M2M communications based on the capacity-approaching
analog fountain code to efficiently minimize the access delay and satisfy the
delay requirement for each device. This is achieved by allowing M2M devices to
transmit at the same time on the same channel in an optimal probabilistic
manner based on their individual delay requirements. Simulation results show
that the proposed scheme achieves a near optimal rate performance and at the
same time guarantees the delay requirements of the devices. We further propose
a simple random access strategy and characterized the required overhead.
Simulation results show the proposed approach significantly outperforms the
existing random access schemes currently used in long term evolution advanced
(LTE-A) standard in terms of the access delay.Comment: Accepted to Publish in IEEE Transactions on Wireless Communication
Optimal Resource Allocation for U-Shaped Parallel Split Learning
Split learning (SL) has emerged as a promising approach for model training
without revealing the raw data samples from the data owners. However,
traditional SL inevitably leaks label privacy as the tail model (with the last
layers) should be placed on the server. To overcome this limitation, one
promising solution is to utilize U-shaped architecture to leave both early
layers and last layers on the user side. In this paper, we develop a novel
parallel U-shaped split learning and devise the optimal resource optimization
scheme to improve the performance of edge networks. In the proposed framework,
multiple users communicate with an edge server for SL. We analyze the
end-to-end delay of each client during the training process and design an
efficient resource allocation algorithm, called LSCRA, which finds the optimal
computing resource allocation and split layers. Our experimental results show
the effectiveness of LSCRA and that U-shaped PSL can achieve a similar
performance with other SL baselines while preserving label privacy. Index
Terms: U-shaped network, split learning, label privacy, resource allocation,
5G/6G edge networks.Comment: 6 pages, 6 figure
Computer architecture for efficient algorithmic executions in real-time systems: New technology for avionics systems and advanced space vehicles
Improvements and advances in the development of computer architecture now provide innovative technology for the recasting of traditional sequential solutions into high-performance, low-cost, parallel system to increase system performance. Research conducted in development of specialized computer architecture for the algorithmic execution of an avionics system, guidance and control problem in real time is described. A comprehensive treatment of both the hardware and software structures of a customized computer which performs real-time computation of guidance commands with updated estimates of target motion and time-to-go is presented. An optimal, real-time allocation algorithm was developed which maps the algorithmic tasks onto the processing elements. This allocation is based on the critical path analysis. The final stage is the design and development of the hardware structures suitable for the efficient execution of the allocated task graph. The processing element is designed for rapid execution of the allocated tasks. Fault tolerance is a key feature of the overall architecture. Parallel numerical integration techniques, tasks definitions, and allocation algorithms are discussed. The parallel implementation is analytically verified and the experimental results are presented. The design of the data-driven computer architecture, customized for the execution of the particular algorithm, is discussed
EVEREST IST - 2002 - 00185 : D23 : final report
Deliverable pĂşblic del projecte europeu EVERESTThis deliverable constitutes the final report of the project IST-2002-001858 EVEREST. After its successful completion, the project presents this document that firstly summarizes the context, goal and the approach objective of the project. Then it presents a concise summary of the major goals and results, as well as highlights the most valuable lessons derived form the project work. A list of deliverables and publications is included in the annex.Postprint (published version
Flexible runtime support of business processes under rolling planning horizons
This work has been motivated by the needs we discovered when analyzing real-world processes from the
healthcare domain that have revealed high flexibility demands and complex temporal constraints. When trying
to model these processes with existing languages, we learned that none of the latter was able to fully address
these needs. This motivated us to design TConDec-R, a declarative process modeling language enabling the
specification of complex temporal constraints. Enacting business processes based on declarative process models,
however, introduces a high complexity due to the required optimization of objective functions, the handling of
various temporal constraints, the concurrent execution of multiple process instances, the management of crossinstance
constraints, and complex resource allocations. Consequently, advanced user support through optimized
schedules is required when executing the instances of such models. In previous work, we suggested a method for
generating an optimized enactment plan for a given set of process instances created from a TConDec-R model.
However, this approach was not applicable to scenarios with uncertain demands in which the enactment of
newly created process instances starts continuously over time, as in the considered healthcare scenarios. Here,
the process instances to be planned within a specific timeframe cannot be considered in isolation from the ones
planned for future timeframes. To be able to support such scenarios, this article significantly extends our previous
work by generating optimized enactment plans under a rolling planning horizon. We evaluate the approach
by applying it to a particularly challenging healthcare process scenario, i.e., the diagnostic procedures required
for treating patients with ovarian carcinoma in a Woman Hospital. The application of the approach to this sophisticated
scenario allows avoiding constraint violations and effectively managing shared resources, which
contributes to reduce the length of patient stays in the hospital.Ministerio de EconomĂa y Competitividad TIN2016-76956-C3-2-RMinisterio de Ciencia e InnovaciĂłn PID2019-105455 GB-C3
Emerging policies and partnerships under CAADP: Implications for long-term growth, food security, and poverty reduction
The Comprehensive Africa Agriculture Development Programme (CAADP) is one of the main components of the New Partnership for Africa's Development (NEPAD). CAADP is an initiative launched by the African Union Commission (AUC) in 2002 to serve as a continent-wide framework to facilitate faster agricultural growth and progress toward poverty reduction and food and nutrition security in Africa. CAADP seeks to promote policies and partnerships and raise investments in Africa's agricultural sector and achieve better development outcomes. It is an unprecedented, comprehensive effort to rally governments and other stakeholders around a set of key values and principles; create partnership mechanisms at continental, regional, and country levels; promote evidence-based and outcome-driven policy design and implementation; and establish inclusive dialogue and review processes to increase the effectiveness of the development process among African countries. This paper examines the new policy and investment planning and the review, dialogue, and partnership modalities and evaluates their likely impact on future growth and poverty-reduction outcomes.Agriculture, CAADP, Growth, NEPAD, Nutrition, partnership, Poverty,
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