J. Liu

Publications
Influence

BestConfig: tapping the performance potential of systems via automatic configuration tuning

Y. Zhu, J. Liu, +5 authors Yingchun Yang
Computer Science
SoCC
24 September 2017

We present Best Config, a system for automatically finding a best configuration setting within a resource limit for a deployed system under a given application workload. Expand

Location-Aware Collaborative Filtering for QoS-Based Service Recommendation

Mingdong Tang, Yechun Jiang, J. Liu, X. Liu
Computer Science
IEEE 19th International Conference on Web…
24 June 2012

We propose a method of location-aware collaborative filtering to recommend Web services to users by incorporating locations of both users and services in predicting QoS values. Expand

An Effective Web Service Recommendation Method Based on Personalized Collaborative Filtering

Yechun Jiang, J. Liu, Mingdong Tang, X. Liu
Computer Science
IEEE International Conference on Web Services
4 July 2011

We present an effective personalized collaborative filtering method for Web service recommendation that takes into account the personalized influence of users and services when computing similarity measurement of Web services. Expand

Web Service Selection for Resolving Conflicting Service Requests

Guosheng Kang, J. Liu, Mingdong Tang, X. Liu, K. K. Fletcher
Computer Science
IEEE International Conference on Web Services
4 July 2011

This paper aims at solving these conflicts and developing a global optimal service selection method for multiple related service requesters, thereby optimizing service resources and improving performance of the system. Expand

A case study of an inter-enterprise workflow-supported supply chain management system

J. Liu, Shensheng Zhang, Y. Wang, J. Cao
Computer Science
Oper. Res.
2002

This paper presents how the system is designed and implemented in detail as well as the experiences and lessons gained. Expand

ClubCF: A Clustering-Based Collaborative Filtering Approach for Big Data Application

Rong Hu, W. Dou, J. Liu
Computer Science
IEEE Transactions on Emerging Topics in Computing
11 March 2014

A clustering-based collaborative filtering approach is proposed in this paper, which aims at recruiting similar services in the same clusters to recommend services collaboratively. Expand

Integrated Content and Network-Based Service Clustering and Web APIs Recommendation for Mashup Development

Buqing Cao, X. Liu, M. Rahman, Bing Li, J. Liu, Mingdong Tang
Computer Science
IEEE Transactions on Services Computing
1 January 2020

We propose an integrated content and network-based service clustering and Web APIs recommendation method for Mashup development. Expand

A Constraint-driven Execution Plan for Maximizing Concurrency in Product Development

Jinmin Hu, J. Liu, Brian Prasad
Computer Science
Concurr. Eng. Res. Appl.
1 December 2003

This paper discusses a constraint-driven execution plan to handle the concurrency during scheduling of various dependent tasks. Expand

A Probabilistic Topic Model for Mashup Tag Recommendation

Min Shi, J. Liu, Dong Zhou, Mingdong Tang, F. Xie, Tingting Zhang
Computer Science
IEEE International Conference on Web Services…
1 June 2016

We propose a novel Mashup-API-Tag model for automatic mashup tag recommendation that incorporates composition relationships between mashups and APIs as well as annotation relationships between APIs and tags to discover the latent topics. Expand

Towards a trust evaluation middleware for cloud service selection

Mingdong Tang, Xiaoling Dai, J. Liu, J. Chen
Computer Science
Future Gener. Comput. Syst.
1 September 2017

With the advent of cloud computing, employing various cloud services to build highly reliable cloud applications has become increasingly popular. The trustworthiness of cloud services is a critical… Expand

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