Geospatial Web Service Sub-chain Ranking and Recommendation

Wang, Xiaolei; Cheng, Zehui; Zhou, Zhangbing; Ning, Ke; Zhang, Liang Jie

doi:10.1109/scc.2014.21

Cited by 4 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zhou et al introduced an approach to identifying and recommending scientific workflows for reuse and repurposing with a layer hierarchy, which adopted a graph skeleton-based clustering technique to cluster the core workflows [26]. In [27], the researcher also retrieved and recommended subchains of possible service invocations by leveraging the semantic similarity between the name and textual description of parameters, where a network model was constructed to represent possible invocations between operations; the results proved that the researcher's approach could solve the problem of a geospatial Web service. An automated filtering mechanism capable of categorizing members within a group based on their response patterns was proposed in [28] by Thampi; the mechanism clustered user posts into groups based on stylistic, thematic, and emotional aspects.…”

Section: Location-based Recommendationmentioning

confidence: 99%

Enabling Smart Anonymity Scheme for Security Collaborative Enhancement in Location-Based Services

Zhang

2019

IEEE Access

View full text Add to dashboard Cite

Security enhancement is and always will be a prime concern for the deployment of pointof-interest (POI) recommendation services in mobile sensing environment. Recent tamper-proof technical protection such as strong encryption has undoubtedly become a major safeguard against threats to privacy in location-based services. Although the disclosure of location information could increase recommendation accuracy, the publication of trajectory data to untrusted entities could reveal sensitive details, e.g., daily routes, destinations, and favorite restaurants. In this paper, we propose a smart scheme named BUSA to approach the above problem by reconciling the tension between privacy protection and recommendation accuracy in location-based recommendation services. This scheme uses anonymizer agents positioned between the service-requesting users and location service providers; these agents operate by dividing the query information and using k-anonymity to enhance privacy protection. The scheme also utilizes clustering techniques to group users into clusters by learning their trajectory data and selects the spatial center cells as a cluster core and a benchmark for calculating recommendations via trust computing. An anonymizer coordination strategy is proposed to replace a low-performing anonymizer with one that provides stronger privacy protection for a recommendation service. The BUSA scheme adopts k-anonymity and clustering to protect privacy, and the calculated recommendation will be suitable for the cluster core that represents the entirety of users' location preferences. The security analysis reveals that the BUSA scheme can effectively protect privacy against fraudulent query requestors and the simulation results also indicate that it provides stronger privacy protection than its counterparts from the perspective of recommendation hit rate and the extent of disclosure.

show abstract

Section: Location-based Recommendationmentioning

confidence: 99%

Enabling Smart Anonymity Scheme for Security Collaborative Enhancement in Location-Based Services

Zhang

2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The preliminary result of this research has been reported in our previous work (X. Wang, Cheng et al, 2014). This paper gives a more comprehensive presentation and discussion about sub-chain retrieving, ranking, and recommendation techniques we have developed, including the details of definitions, tractable algorithms, and evaluation of algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Sub-Chain Ranking and Recommendation Mechanism for Facilitating Geospatial Web Service Composition

Zhou

Cheng

Ning³

et al. 2014

International Journal of Web Services Research

Self Cite

View full text Add to dashboard Cite

With a huge volume of geospatial information being collected and a huge number of domain-specific functions being developed for processing these geospatial information, an increasing number of Open Geospatial Consortium Web services (OWSs) are built and being available on the Web for the accessibility and processing of these information. Given the specific requirement specified by a certain user, normally, a composition (or chain) of OWSs, rather than a single OWS, can fulfill this requirement. Consequently, retrieving and recommending sub-chains of possible service invocations is an important research challenge. Leveraging the semantic similarity between the name and text description of parameters, a degree that represents the invocation possibility between operations in OWSs is calculated. Thereafter, a service network model is constructed for capturing possible invocations between operations. Given a user's requirement which is represented in terms of a pair of initial and ending operations, possible sub-chains of operations are retrieved, ranked and recommended. Based on which the user can select the most appropriate sub-chain with respect to her specific requirement. The result of evaluation leveraging a real OWSs set indicates that our technique is applicable in real applications from both the functional and performance perspectives.

show abstract