Multi-Agent Planning for Automatic Geospatial Web Service Composition in Geoportals

Farnaghi, Mahdi; Mansourian, Ali

doi:10.3390/ijgi7100404

Cited by 6 publications

(3 citation statements)

References 38 publications

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“…Yue et al [18] used ontology and artificial intelligence planning methods to assist users in dynamically creating executable service chains for earth science applications, which they then applied to wildfire prediction. Farnaghi [19] proposed a multi-agent artificial intelligence (AI) planning solution, which works within the geoportal architecture and enables the geoportal to compose semantically annotated Open Geospatial Consortium (OGC) Web Services based on users' requirements, which they then applied to shelter site selection. These examples generate the service chain models based on expert knowledge or composition algorithms under certain constraints, using a relatively fixed process.…”

Section: Geospatial Service Compositionmentioning

confidence: 99%

An Augmented Geospatial Service Web Based on QoS Constraints and Geospatial Service Semantic Relationships

Jin

Liang

et al. 2022

IJGI

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The service network is capable of addressing large-scale service composition. However, existing service network works still have several limitations. Prior knowledge, such as expert-defined service chains, is not incorporated into the service network. QoS constraints are less considered in the service network, and thus the generated service chain does not always satisfy the optimal QoS constraints. Additionally, some basic services also require outputs to be used directly as inputs, which the service network cannot provide. To address these limitations, this paper proposes a geospatial service web (GSW) model named SR-QoS-GSW that incorporates service semantic relationships and QoS information. The SR-QoS-GSW model consists of atomic services and composite services that consider QoS, processing services, data services, and relationships among them. A SR-QoS-GSW prototype was developed using 570 atomic services and 27 composite services and evaluated using two case studies—a river network extraction and an urban housing selection. Then, the information entropy and time complexity between SR-QoS-GSW and the existing service network were compared. The results show that geospatial service chains can be created more efficiently by incorporating existing service chains as composite services. Integrating QoS information into the GSW would allow service composition algorithms to generate service chains that satisfy optimal QoS constraints. The outputs of services used as new inputs with additional self-matching relationships also give the service network greater flexibility. Finally, the analysis of the information entropy and time complexity verified the increased diversity and decreased the search space of the SR-QoS-GSW.

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Section: Geospatial Service Compositionmentioning

confidence: 99%

An Augmented Geospatial Service Web Based on QoS Constraints and Geospatial Service Semantic Relationships

Jin

Liang

et al. 2022

IJGI

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“…There are two types of GDSW: one is based on the open geospatial consortium (Inc., OGC), that is, an open-standard data website [14], and the other one is non-openstandard [15], which does not depend on OGC standard when publishing geospatial data. According to the published GDSW based on the OGC standard, whether the website is a geospatial data website can be evaluated by sending a specific request (see [16] for details).…”

Section: Introductionmentioning

confidence: 99%

A Method for Identifying Geospatial Data Sharing Websites by Combining Multi-Source Semantic Information and Machine Learning

et al. 2021

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Geospatial data sharing is an inevitable requirement for scientific and technological innovation and economic and social development decisions in the era of big data. With the development of modern information technology, especially Web 2.0, a large number of geospatial data sharing websites (GDSW) have been developed on the Internet. GDSW is a point of access to geospatial data, which is able to provide a geospatial data inventory. How to precisely identify these data websites is the foundation and prerequisite of sharing and utilizing web geospatial data and is also the main challenge of data sharing at this stage. GDSW identification can be regarded as a binary website classification problem, which can be solved by the current popular machine learning method. However, the websites obtained from the Internet contain a large number of blogs, companies, institutions, etc. If GDSW is directly used as the sample data of machine learning, it will greatly affect the classification precision. For this reason, this paper proposes a method to precisely identify GDSW by combining multi-source semantic information and machine learning. Firstly, based on the keyword set, we used the Baidu search engine to find the websites that may be related to geospatial data in the open web environment. Then, we used the multi-source semantic information of geospatial data content, morphology, sources, and shared websites to filter out a large number of websites that contained geospatial keywords but were not related to geospatial data in the search results through the calculation of comprehensive similarity. Finally, the filtered geospatial data websites were used as the sample data of machine learning, and the GDSWs were identified and evaluated. In this paper, training sets are extracted from the original search data and the data filtered by multi-source semantics, the two datasets are trained by machine learning classification algorithms (KNN, LR, RF, and SVM), and the same test datasets are predicted. The results show that: (1) compared with the four classification algorithms, the classification precision of RF and SVM on the original data is higher than that of the other two algorithms. (2) Taking the data filtered by multi-source semantic information as the sample data for machine learning, the precision of all classification algorithms has been greatly improved. The SVM algorithm has the highest precision among the four classification algorithms. (3) In order to verify the robustness of this method, different initial sample data mentioned above are selected for classification using the same method. The results show that, among the four classification algorithms, the classification precision of SVM is still the highest, which shows that the proposed method is robust and scalable. Therefore, taking the data filtered by multi-source semantic information as the sample data to train through machine learning can effectively improve the classification precision of GDSW, and comparing the four classification algorithms, SVM has the best classification effect. In addition, this method has good robustness, which is of great significance to promote and facilitate the sharing and utilization of open geospatial data.

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“…Then web service composition becomes a planning problem. To solve this planning problem, AI planning algorithms, for instance, the hierarchical task network (HTN), can be used to find a sequence of actions (i.e., a plan) to change the initial state satisfying the pre-defined goal state (i.e., desired input data for geographic models) [97][98][99][100][101][102]. As a result, modelers could use an AI planner together with an ontology inference engine to create plans and translate them to executable service chains for preparing input data for geographic models.…”

mentioning

confidence: 99%

From Manual to Intelligent: A Review of Input Data Preparation Methods for Geographic Modeling

Hou

Qin

Zhu

et al. 2019

IJGI

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One of the key concerns in geographic modeling is the preparation of input data that are sufficient and appropriate for models. This requires considerable time, effort, and expertise since geographic models and their application contexts are complex and diverse. Moreover, both data and data pre-processing tools are multi-source, heterogeneous, and sometimes unavailable for a specific application context. The traditional method of manually preparing input data cannot effectively support geographic modeling, especially for complex integrated models and non-expert users. Therefore, effective methods are urgently needed that are not only able to prepare appropriate input data for models but are also easy to use. In this review paper, we first analyze the factors that influence data preparation and discuss the three corresponding key tasks that should be accomplished when developing input data preparation methods for geographic models. Then, existing input data preparation methods for geographic models are discussed through classifying into three categories: manual, (semi-)automatic, and intelligent (i.e., not only (semi-)automatic but also adaptive to application context) methods. Supported by the adoption of knowledge representation and reasoning techniques, the state-of-the-art methods in this field point to intelligent input data preparation for geographic models, which includes knowledge-supported discovery and chaining of data pre-processing functionalities, knowledge-driven (semi-)automatic workflow building (or service composition in the context of geographic web services) of data preprocessing, and artificial intelligent planning-based service composition as well as their parameter-settings. Lastly, we discuss the challenges and future research directions from the following aspects: Sharing and reusing of model data and workflows, integration of data discovery and processing functionalities, task-oriented input data preparation methods, and construction of knowledge bases for geographic modeling, all assisting with the development of an easy-to-use geographic modeling environment with intelligent input data preparation.

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Multi-Agent Planning for Automatic Geospatial Web Service Composition in Geoportals

Cited by 6 publications

References 38 publications

An Augmented Geospatial Service Web Based on QoS Constraints and Geospatial Service Semantic Relationships

An Augmented Geospatial Service Web Based on QoS Constraints and Geospatial Service Semantic Relationships

A Method for Identifying Geospatial Data Sharing Websites by Combining Multi-Source Semantic Information and Machine Learning

From Manual to Intelligent: A Review of Input Data Preparation Methods for Geographic Modeling

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