Trusted Service Scheduling and Optimization Strategy Design of Service Recommendation

Xia, Xiaona; Yu, Jiguo; Zhang, Shanshan; Wu, Shu-Pao

doi:10.1155/2017/9192084

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…(1) input: -the original trace (2) deps -map of sv to its dependence relations (3) output: scheduleSeq -sequence of thread schedule (4) begin (5) for ← 1 to | | − 1 do (6) [ ] ← the thread identifier of the event [ ]; (7) ← the latest event whose thread identifier is [ ] ; (8) ← the location of in the new trace; (9) if [ ].getDep() = null then (10) insert [ ] immediately after ; (11) else (12) ← [ ].getDep(); (13) ← the location of in the new trace; (14) if < then (15) insert [ ] immediately after ; (16) end if (17) end if (18) end for (19) end…”

Section: Experimental Configurationsmentioning

confidence: 99%

“…Testing-as-a-Service (TaaS) based on cloud platform provides automated software testing services, saving capacity and reducing expense [1,2]. With the increasing popularity of service computing, a vast amount of services-related business applications has emerged, such as service composition [3,4], service recommendation [5][6][7][8], service evaluation [9][10][11], and service optimization [12][13][14][15]. As an important guarantee to the QoS (Quality of Service), such as test effectiveness and efficiency, service optimization has attracted much attention of researchers in software engineering.…”

Section: Introductionmentioning

confidence: 99%

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A Constraint-Aware Optimization Method for Concurrency Bug Diagnosis Service in a Distributed Cloud Environment

Jiang

2018

Security and Communication Networks

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The advent of cloud computation and big data applications has enabled data access concurrency to be prevalent in the distributed cloud environment. In the meantime, security issue becomes a critical problem for researchers to consider. Concurrency bug diagnosis service is to analyze concurrent software and then reason about concurrency bugs in them. However, frequent context switches in concurrent program execution traces will inevitably impact the service performance. To optimize the service performance, this paper presents a static constraint-aware method to simplify concurrent program buggy traces. First, taking the original buggy trace as the operation object, we calculate the maximal sound dependence relations based on the constraint models. Then, we iteratively check the dependent constraints and move forward current event to extend thread execution intervals. Finally, we obtain the simplified trace that is equivalent to the original buggy trace. To evaluate our approach, we conduct a set of experiments on 12 widely used Java projects. Experimental results show that our approach outperforms other state-of-the-art approaches in terms of execution time.

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Section: Experimental Configurationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Constraint-Aware Optimization Method for Concurrency Bug Diagnosis Service in a Distributed Cloud Environment

Jiang

2018

Security and Communication Networks

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A recommendation of pension service based on user preferences and trust relationships

Zhang

Chu

2020

IOP Conf. Ser.: Mater. Sci. Eng.

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Recommendation technology have been widely used in social networks and many e-commerce websites, but there are few recommendations of pension service and those recommendation results are not accurate enough. For more accurately recommending personalized pension services to users, this paper proposes a recommendation method of pension service based on the user preferences and the trust relationships between users. Firstly, by mining user socialization relationship information, the degree of trust relationships between users is calculated, and the Pearson correlation coefficient is used to obtain the user similarity, then their results are combined and used to find the neighbour user set of the target user. Secondly, a timeliness model of user ratings based on the user evaluation time are established, which is used to determine the user’s preference for the service, and finally to predict the target user’s rating value for the project. The experiment takes the Douban review data and the users social data as the case to verify the results. The experimental results show that compared with the traditional recommendation method, the recommendation method based on user preferences and trust relationships improves the accuracy of recommendation.

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POI neural-rec model via graph embedding representation

Yang

Zhu

Guo

2021

Tinshhua Sci. Technol.

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With the booming of the Internet of Things (IoT) and the speedy advancement of Location-Based Social Networks (LBSNs), Point-Of-Interest (POI) recommendation has become a vital strategy for supporting people's ability to mine their POIs. However, classical recommendation models, such as collaborative filtering, are not effective for structuring POI recommendations due to the sparseness of user check-ins. Furthermore, LBSN recommendations are distinct from other recommendation scenarios. With respect to user data, a user's check-in record sequence requires rich social and geographic information. In this paper, we propose two different neural-network models, structural deep network Graph embedding Neural-network Recommendation system (SG-NeuRec) and Deepwalk on Graph Neural-network Recommendation system (DG-NeuRec) to improve POI recommendation. combined with embedding representation from social and geographical graph information (called SG-NeuRec and DG-NeuRec).Our model naturally combines the embedding representations of social and geographical graph information with user-POI interaction representation and captures the potential user-POI interactions under the framework of the neural network. Finally, we compare the performances of these two models and analyze the reasons for their differences. Results from comprehensive experiments on two real LBSNs datasets indicate the effective performance of our model.

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Trusted Service Scheduling and Optimization Strategy Design of Service Recommendation

Cited by 3 publications

References 18 publications

A Constraint-Aware Optimization Method for Concurrency Bug Diagnosis Service in a Distributed Cloud Environment

A Constraint-Aware Optimization Method for Concurrency Bug Diagnosis Service in a Distributed Cloud Environment

A recommendation of pension service based on user preferences and trust relationships

POI neural-rec model via graph embedding representation

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