The Experts below are selected from a list of 207 Experts worldwide ranked by ideXlab platform
Yuguang Fang - One of the best experts on this subject based on the ideXlab platform.
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System error prediction for Business Support Systems in telecommunications networks
IEEE Transactions on Parallel and Distributed Systems, 2020Co-Authors: Enhau Yeh, Phone Lin, Xinxue Lin, Jeuyih Jeng, Yuguang FangAbstract:Reliability and stability have been treated as the major requirements for the Business Support System (BSS) in telecommunications networks. It is crucial and essential for service providers to maintain good operating state of the BSS. In this article, we aim at System error prediction for a BSS, i.e., we predict occurrences of the abnormal state or behavior of the BSS. Because the occurrences of System errors are rare events in the BSS (i.e., the dataset of System status is highly imbalanced), it is highly challenging to use machine learning or deep learning algorithms to predict System error for the BSS. To address this challenge, we propose a machine learning-based framework for the System error prediction and a Frequency-based Feature Creation (FFC) algorithm to create new features to improve prediction. By adding the time-series information created by the existing features, the proposed FFC can amplify the effects of important features. Our experimental results show that the FFC significantly improves the prediction performance for the Random Forest algorithm.
Enhau Yeh - One of the best experts on this subject based on the ideXlab platform.
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System error prediction for Business Support Systems in telecommunications networks
IEEE Transactions on Parallel and Distributed Systems, 2020Co-Authors: Enhau Yeh, Phone Lin, Xinxue Lin, Jeuyih Jeng, Yuguang FangAbstract:Reliability and stability have been treated as the major requirements for the Business Support System (BSS) in telecommunications networks. It is crucial and essential for service providers to maintain good operating state of the BSS. In this article, we aim at System error prediction for a BSS, i.e., we predict occurrences of the abnormal state or behavior of the BSS. Because the occurrences of System errors are rare events in the BSS (i.e., the dataset of System status is highly imbalanced), it is highly challenging to use machine learning or deep learning algorithms to predict System error for the BSS. To address this challenge, we propose a machine learning-based framework for the System error prediction and a Frequency-based Feature Creation (FFC) algorithm to create new features to improve prediction. By adding the time-series information created by the existing features, the proposed FFC can amplify the effects of important features. Our experimental results show that the FFC significantly improves the prediction performance for the Random Forest algorithm.
Hui Tan - One of the best experts on this subject based on the ideXlab platform.
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Research and Design on B2B E-Commerce Supply Chain Management System
Applied Mechanics and Materials, 2013Co-Authors: Hui TanAbstract:This paper introduces a mature and complete design idea and function structure of B2B E-commerce supply chain management System based on intermediary, wherein the System is mainly based on the front end E-commerce trading System and the back end Business Support System, and can be simultaneously integrated with such Systems of external partners as logistics distribution System, online payment System, SCM System, and ERP System. The System has solved the problem of information disjunction between the upper and downstream enterprises in conventional supply chain management, and realized efficient, reliable and flexible integrated application of every module within the entire e-commerce supply chain management System.
Johan Silvander - One of the best experts on this subject based on the ideXlab platform.
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KES - Introducing intents to the OODA-loop
Procedia Computer Science, 2019Co-Authors: Johan Silvander, Lars AngelinAbstract:Together with Ericsson AB, we are using the design science framework when investigating how to create an intent-driven System for their Business Support System and its Business studio. The aim is t ...
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BMSD - Business Process Optimization with Reinforcement Learning
Lecture Notes in Business Information Processing, 2019Co-Authors: Johan SilvanderAbstract:We investigate the use of deep reinforcement learning to optimize Business processes in a Business Support System. The focus of this paper is to investigate how a reinforcement learning algorithm named Q-Learning, using deep learning, can be configured in order to Support optimization of Business processes in an environment which includes some degree of uncertainty. We make the investigation possible by implementing a software agent with the help of a deep learning tool set. The study shows that reinforcement learning is a useful technique for Business process optimization but more guidance regarding parameter setting is needed in this area.
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Supporting Continuous Changes to Business Intents
International Journal of Software Engineering and Knowledge Engineering, 2017Co-Authors: Johan Silvander, Magnus Wilson, Krzysztof Wnuk, Mikael SvahnbergAbstract:Software Supporting an enterprise’s Business, also known as a Business Support System, needs to Support the correlation of activities between actors as well as influence the activities based on knowledge about the value networks in which the enterprise acts. This requires the use of policies and rules to guide or enforce the execution of strategies or tactics within an enterprise as well as in collaborations between enterprises. With the help of policies and rules, an enterprise is able to capture an actor’s intent in its Business Support System, and act according to this intent on behalf of the actor. Since the value networks an enterprise is part of will change over time the Business intents’ life cycle states might change. Achieving the changes in an effective and efficient way requires knowledge about the affected intents and the correlation between intents. The aim of the study is to identify how a Business Support System can Support continuous changes to Business intents. The first step is to find a theoretical model which serves as a foundation for intent-driven Systems. We conducted a case study using a focus group approach with employees from Ericsson. This case study was influenced by the spiral case study process. The study resulted in a model Supporting continuous definition and execution of an enterprise. The model is divided into three layers; Define, Execute, and a common governance view layer. This makes it possible to Support continuous definition and execution of Business intents and to identify the actors needed to Support the Business intents’ life cycles. This model is Supported by a meta-model for capturing information into viewpoints. The research question is addressed by suggesting a solution Supporting continuous definition and execution of an enterprise as a model of value architecture components and Business functions. The results will affect how Ericsson will build the Business studio for their next generation Business Support Systems.
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Towards Intent-Driven Systems
2017Co-Authors: Johan SilvanderAbstract:Context: Software Supporting an enterprise’s Business, also known as a Business Support System, needs to Support the correlation of activities between actors as well as influence the activities bas ...
Yoshifumi Tsuge - One of the best experts on this subject based on the ideXlab platform.
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Construction of Business Support System for supply chain management at re-roll maker
Tetsu To Hagane-journal of The Iron and Steel Institute of Japan, 2008Co-Authors: Naomi Yabuta, Takaya Hada, Naoki Kimura, Yoshifumi TsugeAbstract:With the dramatic increase in demand for steel materials by many foreign countries, especially by China, the supply for steel materials have become more limited than ever before, and this trend is expected to continue for sometime. Therefore many steel companies try to introduce SCM (Supply Chain Management) to respond timely and precisely to market movements.The objective of SCM is to maximize the operation efficiency covering the entire process of a supply chain. There are two necessary conditions for applying SCM between several companies. One is that the basic idea of SCM is owned jointly by each company, The other is to create a data base containing records for evaluating SCM. Recently, information technology has developed enough to establish such an information System.Toyokohan has promoted SCM since 2002. Until now, we have established a consistent production and material procurement System. Both Systems have performed very well so far.As for the next step of promoting SCM, we have developed a new Business Support System for sales, purchasing and production control departments. This System can provide important and necessary information for SCM and Support activities of these departments.
