This paper proposes a scheduling strategy which divides OLAM (Online Analytical Mining) tasks into query tasks, mining tasks and updating tasks in order to improve the efficiency of Bl (Business Intelligence) systems....
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This paper proposes a scheduling strategy which divides OLAM (Online Analytical Mining) tasks into query tasks, mining tasks and updating tasks in order to improve the efficiency of Bl (Business Intelligence) systems. OLAM mechanism is considered as the seamless integration of OLAP (Online Analytical processing) and data mining on data cubes, according to which the scheduling strategy focuses on. This paper emphasizes the interaction of OLAP, data mining and data cube updating, illustrates the details of the OLAM scheduling strategy and demonstrates its efficiency by performing it in a financial Bl system.
This paper proposes, for the first time, a new issue called utility paradox which can not be solved effectively by the existing methods in evidence theory. A utility analysis method considering the subjective preferen...
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This paper proposes, for the first time, a new issue called utility paradox which can not be solved effectively by the existing methods in evidence theory. A utility analysis method considering the subjective preference of decision maker is presented to solve the utility paradox problem. First, the degrees of belief given by experts are transformed into the utility beliefs of decision makers by a utility-belief function. The utility beliefs determine decision makers' action. Second, the rule of combination in evidence theory is employed to combine the utility beliefs. This paper gives the properties the utility-belief function should have, then a specific function is given and its validation is proved. Experiment shows the method is valid to solve the utility paradox problem.
This paper considers the problem of uniform parallel machine scheduling with unequal release dates so as to minimize makespan. This problem is proved to an NP-hard problem. Heuristics in existence for the problem are ...
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ISBN:
(纸本)9781424425020
This paper considers the problem of uniform parallel machine scheduling with unequal release dates so as to minimize makespan. This problem is proved to an NP-hard problem. Heuristics in existence for the problem are analyzed, and then we present an improved algorithm. The performance of the algorithms by experiment is also analyzed. The heuristic is further extended by applying the method of variable neighborhood search, which is used for improving the quality of the solutions obtained by the original heuristics.
This paper considers how to design coordination strategies to achieve the coordination in decentralized supply chain, considering pricing, inventory and transportation cost simultaneously under the price sensitive env...
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ISBN:
(纸本)9781424425020
This paper considers how to design coordination strategies to achieve the coordination in decentralized supply chain, considering pricing, inventory and transportation cost simultaneously under the price sensitive environment. Based on a supplier-Stackelberg game structure, we analyze the non-cooperation decision model and design the algorithm. Then we propose the cooperation decision model and design the algorithm. Revenue sharing and price discount strategies are used to coordinate the decentralized supply chain. The experimental results show that both revenue sharing and price discount strategy can efficiently make the coordination of the supplier and the retailer.
This paper studies the reverse logistics vehicle routing problem of simultaneous distribution of commodities and collection of reusable ones the same size as the initial state with a single depot and a homogeneous fle...
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This paper studies the reverse logistics vehicle routing problem of simultaneous distribution of commodities and collection of reusable ones the same size as the initial state with a single depot and a homogeneous fleet of vehicles with limited capacities and maximum distance, and constructs a mixed integer programming model. To solve this problem, an Ant Colony System (ACS) approach combining with the pheromone updating strategy of ASRank (Rank-based Version of Ant System) and MMAS (MAX-MIN Ant System) is proposed. A new heuristic factor is designed to improve the vehicle loading ability as well as the vehicle distance, and the initial vehicle load is designed to be a random value correlated to the delivery and pick-up demand of the rest customers on the path. The experimental study indicates that the approach could improve the vehicle load rate and get rid of the additional total distance caused by the fluctuating vehicle load and the limited capacity. It could obtain the satisfied solution with high convergence speed in the acceptable time.
This article investigates the reverse logistics vehicle routing problem with a single depot, simultaneous distribution and collection of the goods by a homogeneous fleet of vehicles under the restrictions of maximum c...
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This article investigates the reverse logistics vehicle routing problem with a single depot, simultaneous distribution and collection of the goods by a homogeneous fleet of vehicles under the restrictions of maximum capacities and maximum distance. A mixed integer programming model is established. To solve the model, an Ant Colony System (ACS) approach combined with the pheromone updating strategy of ASRank and MMAS ant algorithm is proposed. In such approach, the vehicle residual loading capacity is introduced into the heuristic function considering the complex feature of fluctuating vehicle load. Moreover, the initial load is designed to be a random value correlated to the delivery and pick-up demands of the rest clients. The experimental study indicates that the proposed approach could improve the vehicle load rate and avoid the added total distance caused by the fluctuating load and the maximum capacity constraint. It could reach the satisfied solutions with high convergence speed in an acceptable computational time.
Music recommender systems play a critical role in music streaming platforms by providing users with music that they are likely to enjoy. Recent studies have shown that user emotions can influence users’ preferences f...
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Music recommender systems play a critical role in music streaming platforms by providing users with music that they are likely to enjoy. Recent studies have shown that user emotions can influence users’ preferences for music moods. However, existing emotion-aware music recommender systems (EMRSs) explicitly or implicitly assume that users’ actual emotional states expressed through identical emotional words are homogeneous. They also assume that users’ music mood preferences are homogeneous under the same emotional state. In this article, we propose four types of heterogeneity that an EMRS should account for: emotion heterogeneity across users, emotion heterogeneity within a user, music mood preference heterogeneity across users, and music mood preference heterogeneity within a user. We further propose a Heterogeneity-aware Deep Bayesian Network (HDBN) to model these assumptions. The HDBN mimics a user’s decision process of choosing music with four components: personalized prior user emotion distribution modeling, posterior user emotion distribution modeling, user grouping, and Bayesian neural network-based music mood preference prediction. We constructed two datasets, called EmoMusicLJ and EmoMusicLJ-small, to validate our method. Extensive experiments demonstrate that our method significantly outperforms baseline approaches on metrics of HR, Precision, NDCG, and MRR. Ablation studies and case studies further validate the effectiveness of our HDBN. The source code and datasets are available at https://***/jingrk/HDBN.
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