The Experts below are selected from a list of 57159 Experts worldwide ranked by ideXlab platform
Luyi Bai - One of the best experts on this subject based on the ideXlab platform.
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Querying multi-source heterogeneous fuzzy Spatiotemporal Data
Journal of Intelligent & Fuzzy Systems, 2021Co-Authors: Luyi Bai, Lishuang Liu, Xuesong HaoAbstract:With the rapid development of the environmental, meteorological and marine Data management, fuzzy Spatiotemporal Data has received considerable attention. Even though some achievements in querying aspect have been made, there are still some unsolved problems. Semantic and structural heterogeneity may exist among different Data sources, which will lead to incomplete results. In addition, there are ambiguous query intentions and conditions when the user queries the Data. This paper proposes a fuzzy Spatiotemporal Data semantic model. Based on this model, the RDF local semantic models are converted into a RDF global semantic model after mapping relational Data and XML Data to RDF local semantic models. The existing methods mainly convert relational Data to RDF Schema directly. But our approach converts relational Data to XML Schema and then converts it to RDF, which utilizes the semi-structured feature of XML schema to solve the structural heterogeneity between different Data sources. The integration process enables us to perform global queries against different Data sources. In the proposed query algorithms, the query conditions inputted are converted into exact queries before the results are returned. Finally, this paper has carried out extensive experiments, calculated the recall, precision and F-Score of the experimental results, and compared with other state-of-the-art query methods. It shows the importance of the Data integration method and the effectiveness of the query method proposed in this paper.
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An integration approach of multi-source heterogeneous fuzzy Spatiotemporal Data based on RDF
Journal of Intelligent & Fuzzy Systems, 2021Co-Authors: Luyi Bai, Huilei BaiAbstract:With the growing importance of the fuzzy Spatiotemporal Data in information application, there is an increasing need for researching on the integration method of multi-source heterogeneous fuzzy Spatiotemporal Data. In this paper, we first propose a fuzzy Spatiotemporal RDF graph model based on RDF (Resource Description Framework) that proposed by the World Wide Web Consortium (W3C) to represent Data in triples (subject, predicate, object). Secondly, we analyze and classify the related heterogeneous problems of multi-source heterogeneous fuzzy Spatiotemporal Data, and use the fuzzy Spatiotemporal RDF graph model to define the corresponding rules to solve these heterogeneous problems. In addition, based on the characteristics of RDF triples, we analyze the heterogeneous problem of multi-source heterogeneous fuzzy Spatiotemporal Data integration in RDF triples, and provide the integration methods FRDFG in this paper. Finally, we report our experiments results to validate our approach and show its significant superiority.
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Adaptive query relaxation and result categorization of fuzzy Spatiotemporal Data based on XML
Expert Systems with Applications, 2021Co-Authors: Luyi Bai, Lin Zhu, Minghao Liu, Yizong XingAbstract:Abstract With the rapid development of Spatiotemporal information and its applications, querying Spatiotemporal Data has received considerable attention. Meanwhile, the imprecision and uncertainty of information cannot be ignored in many practical applications. Querying fuzzy Spatiotemporal Data have become one of the most important topics in academia and industry. Although there have been some achievements in querying aspect, the study about fuzzy Spatiotemporal query relaxation is still few. In fact, query relaxation is necessary when the amount of query results is small or even empty, especially in the process of querying fuzzy Spatiotemporal Data. In this paper, we propose an adaptive query relaxation and result categorization approach for fuzzy Spatiotemporal Data based on XML, which is compatible with Spatiotemporal features when Spatiotemporal related queries are performed. The approach does not depend on any specific domain or user, it can adaptively relax the initial query requirements, and classify the results by user context preferences and Data distribution after query relaxation. In order to locate the Spatiotemporal Data quickly and show the corresponding fuzziness apparently, we adopted XML to construct the fuzzy Spatiotemporal model for query relaxation because XML Data can be represented as a tree model which can flexibly arrange the Spatiotemporal nodes at the specified position and mark the fuzziness of nodes on the path. In addition, after query relaxation, we present the results categorization algorithm to address the problem of information overload, and then return a navigation tree to the user. Finally, we launch a comprehensive set of experiments to demonstrate the effectiveness and efficiency of our proposed approach. Results of experiments demonstrate that our adaptive query relaxation and result categorization approach based on XML has higher recall and precision in Spatiotemporal related query, and can capture the user’s needs and preferences effectively as well.
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Determining Topological Relationship of Fuzzy Spatiotemporal Data in XML
Studies in Computational Intelligence, 2020Co-Authors: Luyi Bai, Li YanAbstract:How to determine topological relationship is one of the most important operations on fuzzy Spatiotemporal Data. The proposed strategies impose strict restrictions on structure and Data types of fuzzy Spatiotemporal Data, and fall short in their abilities to handle fuzzy attributes extension and fuzzy time extension. To overcome these limitations, we propose strategies of transforming two general fuzzy Spatiotemporal Data trees into one binary fuzzy Spatiotemporal Data tree. In succession, an effective algorithm to match the desired twigs is proposed after extending the region coding scheme to compatible with fuzzy Spatiotemporal Data. Our approach adopts XML twig pattern technique to determine topological relationship continuously so that it can reduce unnecessary execution time of querying the desired nodes. More importantly, pointer array is used to eliminate unnecessary execution time of twig matching. Finally, the experimental results demonstrate the performance advantages of our approach.
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Fuzzy Spatiotemporal Data Semantics
Studies in Computational Intelligence, 2020Co-Authors: Luyi Bai, Li YanAbstract:A considerable amount of Spatiotemporal Data emerges in various Spatiotemporal applications. Since much Spatiotemporal Data is usually fuzzy in the real-world applications because their values are subjective to real applications, the requirement of managing fuzzy Spatiotemporal Data has attracted much attention both from academia and industry. The preliminary core issue on managing fuzzy Spatiotemporal Data is fuzzy Spatiotemporal Data semantics. In this chapter, we introduce representations of fuzzy temporal Data, fuzzy spatial Data, and fuzzy Spatiotemporal Data. On the basis of them, different types of relations between fuzzy temporal Data, fuzzy spatial Data, and fuzzy Spatiotemporal Data are studied.
Li Yan - One of the best experts on this subject based on the ideXlab platform.
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Fuzzy Spatiotemporal Data Semantics
Studies in Computational Intelligence, 2020Co-Authors: Luyi Bai, Li YanAbstract:A considerable amount of Spatiotemporal Data emerges in various Spatiotemporal applications. Since much Spatiotemporal Data is usually fuzzy in the real-world applications because their values are subjective to real applications, the requirement of managing fuzzy Spatiotemporal Data has attracted much attention both from academia and industry. The preliminary core issue on managing fuzzy Spatiotemporal Data is fuzzy Spatiotemporal Data semantics. In this chapter, we introduce representations of fuzzy temporal Data, fuzzy spatial Data, and fuzzy Spatiotemporal Data. On the basis of them, different types of relations between fuzzy temporal Data, fuzzy spatial Data, and fuzzy Spatiotemporal Data are studied.
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Transformation of Fuzzy Spatiotemporal Data Between Relational Databases and XML
Studies in Computational Intelligence, 2020Co-Authors: Luyi Bai, Li YanAbstract:Since XML could benefit greatly from Database support and more specifically from relational Database systems, we study the methodology of modeling fuzzy Spatiotemporal Data in XML and relational Database, respectively. Furthermore, the approaches of transforming fuzzy Spatiotemporal Data between XML and relational Databases have been proposed as well. To accomplish this, fuzzy Spatiotemporal Data models are devised in XML and in relational Database to capture the semantics of fuzzy Spatiotemporal features, respectively. To allow for better and platform independent sharing of fuzzy Spatiotemporal Data stored in a relational format, a temporal edge approach is proposed to accomplish the transformation of fuzzy Spatiotemporal Data between XML and relational Databases. The unique feature of our approach is that no schema information is required for transformation of fuzzy Spatiotemporal Data. Moreover, temporal, spatial, and fuzzy features of fuzzy Spatiotemporal Data in XML documents are taken into consideration. Such approach of transformation could provide a significant consolidation of the interoperability of fuzzy Spatiotemporal Data between XML and relational Databases.
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Determining Topological Relationship of Fuzzy Spatiotemporal Data in XML
Studies in Computational Intelligence, 2020Co-Authors: Luyi Bai, Li YanAbstract:How to determine topological relationship is one of the most important operations on fuzzy Spatiotemporal Data. The proposed strategies impose strict restrictions on structure and Data types of fuzzy Spatiotemporal Data, and fall short in their abilities to handle fuzzy attributes extension and fuzzy time extension. To overcome these limitations, we propose strategies of transforming two general fuzzy Spatiotemporal Data trees into one binary fuzzy Spatiotemporal Data tree. In succession, an effective algorithm to match the desired twigs is proposed after extending the region coding scheme to compatible with fuzzy Spatiotemporal Data. Our approach adopts XML twig pattern technique to determine topological relationship continuously so that it can reduce unnecessary execution time of querying the desired nodes. More importantly, pointer array is used to eliminate unnecessary execution time of twig matching. Finally, the experimental results demonstrate the performance advantages of our approach.
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Consistencies of fuzzy Spatiotemporal Data in XML documents
Fuzzy Sets and Systems, 2018Co-Authors: Luyi Bai, Yoshiharu Ishikawa, Li YanAbstract:Abstract Researches on Spatiotemporal Data based on XML has received increasing attention due to that XML has a lot of advantages such as extensibility and flexibility. Although XML has been employed to model and handle Spatiotemporal Data, relatively little work has been carried out to further investigate the consistencies of Spatiotemporal Data, especially fuzzy Spatiotemporal Data in XML documents. In this paper, we first propose a fuzzy Spatiotemporal Data model, and then present the structure of fuzzy Spatiotemporal Data in XML document. After studying consistency conditions for fuzzy Spatiotemporal Data in XML documents, we demonstrate how updating operations, inserting operations, and deleting operations effect on consistencies of fuzzy Spatiotemporal Data in XML documents. Furthermore, we propose algorithms for fixing these inconsistencies. After investigating several characteristics of the three primitive changing operations on the fuzzy Spatiotemporal Data model, the performances of inconsistency fixing time are evaluated.
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Interpolation and Prediction of Spatiotemporal Data Based on XML Integrated with Grey Dynamic Model
ISPRS International Journal of Geo-Information, 2017Co-Authors: Luyi Bai, Li YanAbstract:Interpolation and prediction of Spatiotemporal Data are integral components of many real-world applications. Thus, approaches of interpolating and predicting Spatiotemporal Data have been extensively investigated. Currently, the grey dynamic model has been used to enhance the performance of interpolating and predicting Spatiotemporal Data. Meanwhile, the Extensible Markup Language (XML) has unique characteristics of information representation and exchange. In this paper, we first couple the grey dynamic model with the Spatiotemporal XML model. Based on a definition of the position part of the Spatiotemporal XML model, we extract the corresponding position information of each time interval and propose an algorithm for constructing an AVL tree to store them. Then, we present the architecture of an interpolating and predicting process and investigate change operations in positions. On this basis, we present an algorithm for interpolation and prediction of Spatiotemporal Data based on XML integrated with the grey dynamic model. Experimental results demonstrate the performance advantages of the proposed approach.
Shashi Shekhar - One of the best experts on this subject based on the ideXlab platform.
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Spatiotemporal Data Mining: A Computational Perspective
ISPRS International Journal of Geo-Information, 2015Co-Authors: Shashi Shekhar, Zhe Jiang, Reem Y. Ali, Emre Eftelioglu, Xun Tang, Venkata M. V. Gunturi, Xun ZhouAbstract:Explosive growth in geospatial and temporal Data as well as the emergence of new technologies emphasize the need for automated discovery of Spatiotemporal knowledge. Spatiotemporal Data mining studies the process of discovering interesting and previously unknown, but potentially useful patterns from large Spatiotemporal Databases. It has broad application domains including ecology and environmental management, public safety, transportation, earth science, epidemiology, and climatology. The complexity of Spatiotemporal Data and intrinsic relationships limits the usefulness of conventional Data science techniques for extracting Spatiotemporal patterns. In this survey, we review recent computational techniques and tools in Spatiotemporal Data mining, focusing on several major pattern families: Spatiotemporal outlier, Spatiotemporal coupling and tele-coupling, Spatiotemporal prediction, Spatiotemporal partitioning and summarization, Spatiotemporal hotspots, and change detection. Compared with other surveys in the literature, this paper emphasizes the statistical foundations of Spatiotemporal Data mining and provides comprehensive coverage of computational approaches for various pattern families. ISPRS Int. J. Geo-Inf. 2015, 4 2307 We also list popular software tools for Spatiotemporal Data analysis. The survey concludes with a look at future research needs.
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Spatiotemporal Data mining in the era of big spatial Data algorithms and applications
International Workshop on Analytics for Big Geospatial Data, 2012Co-Authors: Ranga Raju Vatsavai, Varun Chandola, Auroop R. Ganguly, Anthony Stefanidis, Scott Klasky, Shashi ShekharAbstract:Spatial Data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from the spatial and Spatiotemporal Data. However, explosive growth in the spatial and Spatiotemporal Data, and the emergence of social media and location sensing technologies emphasize the need for developing new and computationally efficient methods tailored for analyzing big Data. In this paper, we review major spatial Data mining algorithms by closely looking at the computational and I/O requirements and allude to few applications dealing with big spatial Data.
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BigSpatial@SIGSPATIAL - Spatiotemporal Data mining in the era of big spatial Data: algorithms and applications
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data - BigSpatial '12, 2012Co-Authors: Ranga Raju Vatsavai, Varun Chandola, Auroop R. Ganguly, Anthony Stefanidis, Scott Klasky, Shashi ShekharAbstract:Spatial Data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from the spatial and Spatiotemporal Data. However, explosive growth in the spatial and Spatiotemporal Data, and the emergence of social media and location sensing technologies emphasize the need for developing new and computationally efficient methods tailored for analyzing big Data. In this paper, we review major spatial Data mining algorithms by closely looking at the computational and I/O requirements and allude to few applications dealing with big spatial Data.
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Spatial and Spatiotemporal Data mining: Recent advances
Data Mining: Next …, 2008Co-Authors: Shashi Shekhar, Ranga Raju Varsavai, Mete CelikAbstract:Explosive growth in geospatial Data and the emergence of new spatial technologies emphasize the need for automated discovery of spatial knowledge. Spatial Data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial Databases. The complexity of spatial Data and intrinsic spatial relationships limits the usefulness of conventional Data mining techniques for extracting spatial patterns. In this chapter we explore the emerging field of spatial Data mining, focusing on four major topics: prediction and classification, outlier detection, co-location mining, and clustering. Spatiotemporal Data mining is also briefly discussed.
Lin Zhu - One of the best experts on this subject based on the ideXlab platform.
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Adaptive query relaxation and result categorization of fuzzy Spatiotemporal Data based on XML
Expert Systems with Applications, 2021Co-Authors: Luyi Bai, Lin Zhu, Minghao Liu, Yizong XingAbstract:Abstract With the rapid development of Spatiotemporal information and its applications, querying Spatiotemporal Data has received considerable attention. Meanwhile, the imprecision and uncertainty of information cannot be ignored in many practical applications. Querying fuzzy Spatiotemporal Data have become one of the most important topics in academia and industry. Although there have been some achievements in querying aspect, the study about fuzzy Spatiotemporal query relaxation is still few. In fact, query relaxation is necessary when the amount of query results is small or even empty, especially in the process of querying fuzzy Spatiotemporal Data. In this paper, we propose an adaptive query relaxation and result categorization approach for fuzzy Spatiotemporal Data based on XML, which is compatible with Spatiotemporal features when Spatiotemporal related queries are performed. The approach does not depend on any specific domain or user, it can adaptively relax the initial query requirements, and classify the results by user context preferences and Data distribution after query relaxation. In order to locate the Spatiotemporal Data quickly and show the corresponding fuzziness apparently, we adopted XML to construct the fuzzy Spatiotemporal model for query relaxation because XML Data can be represented as a tree model which can flexibly arrange the Spatiotemporal nodes at the specified position and mark the fuzziness of nodes on the path. In addition, after query relaxation, we present the results categorization algorithm to address the problem of information overload, and then return a navigation tree to the user. Finally, we launch a comprehensive set of experiments to demonstrate the effectiveness and efficiency of our proposed approach. Results of experiments demonstrate that our adaptive query relaxation and result categorization approach based on XML has higher recall and precision in Spatiotemporal related query, and can capture the user’s needs and preferences effectively as well.
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Algebraic Operations on Spatiotemporal Data Based on RDF
ISPRS International Journal of Geo-Information, 2020Co-Authors: Lin Zhu, Luyi BaiAbstract:In the context of the Semantic Web, the Resource Description Framework (RDF), a language proposed by W3C, has been used for conceptual description, Data modeling, and Data querying. The algebraic approach has been proven to be an effective way to process queries, and algebraic operations in RDF have been investigated extensively. However, the study of Spatiotemporal RDF algebra has just started and still needs further attention. This paper aims to explore an algebraic operational framework to represent the content of Spatiotemporal Data and support RDF graphs. To accomplish our study, we defined a Spatiotemporal Data model based on RDF. On this basis, the Spatiotemporal semantics and the Spatiotemporal algebraic operations were investigated. We defined five types of graph algebras, and, in particular, the filter operation can filter the Spatiotemporal graphs using a graph pattern. Besides this, we put forward a Spatiotemporal RDF syntax specification to help users browse, query, and reason with Spatiotemporal RDF graphs. The syntax specification illustrates the filter rules, which contribute to capturing the Spatiotemporal RDF semantics and provide a number of advanced functions for building Data queries.
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Determining topological relations of uncertain Spatiotemporal Data based on counter-clock-wisely directed triangle
Applied Intelligence, 2017Co-Authors: Luyi Bai, Lin Zhu, Weijia JiaAbstract:Determining topological relations has proved to be one of the most important operations on Spatiotemporal Data, which still merits further attention. In this paper, we propose a valid and efficient topological relationship mechanism that allows identification of topological relations of uncertain Spatiotemporal Data over time. Our approach adopts polygon approximation and triangulation to represent uncertain Spatiotemporal Data. The unique feature is that our approach not only considers the polygon approximation of a Spatiotemporal region but also takes number of the salient points into account. Moreover, determining topological relations of uncertain Spatiotemporal Data is detailed investigated based on counter-clock-wisely directed triangle. Finally, we apply our approach to meteorological events and experiments are run to validate our approach and show its performance advantages.
Weijia Jia - One of the best experts on this subject based on the ideXlab platform.
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Determining topological relations of uncertain Spatiotemporal Data based on counter-clock-wisely directed triangle
Applied Intelligence, 2017Co-Authors: Luyi Bai, Lin Zhu, Weijia JiaAbstract:Determining topological relations has proved to be one of the most important operations on Spatiotemporal Data, which still merits further attention. In this paper, we propose a valid and efficient topological relationship mechanism that allows identification of topological relations of uncertain Spatiotemporal Data over time. Our approach adopts polygon approximation and triangulation to represent uncertain Spatiotemporal Data. The unique feature is that our approach not only considers the polygon approximation of a Spatiotemporal region but also takes number of the salient points into account. Moreover, determining topological relations of uncertain Spatiotemporal Data is detailed investigated based on counter-clock-wisely directed triangle. Finally, we apply our approach to meteorological events and experiments are run to validate our approach and show its performance advantages.
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Uncertain Spatiotemporal Data modeling and algebraic operations based on XML
Earth Science Informatics, 2017Co-Authors: Luyi Bai, Xingru Cao, Weijia JiaAbstract:The problem of modeling and operating Spatiotemporal Data has received a great deal of interest, due to its various applications in the real world such as GIS and sensor Database. A wide range of work covering spatial Data, temporal Data and Spatiotemporal Data assumes that the Data is known, accurate and complete. But in reality, information is often imprecise and imperfect. In addition, traditional Data models which are investigating in the context of traditional Database suffer from some inadequacy of necessary semantics such as inability to handle imprecise and uncertain information. Consequently, the advent of XML, which has the advantages of simplicity, readability and extensibility, seems to provide an opportunity for modeling and operating uncertain Spatiotemporal Data. Hence, the new problem that emerges is how to model and operate uncertain Spatiotemporal Data in XML. Therefore, in this paper, we establish an uncertain Spatiotemporal Data model based on XML. Then, on the basis of the model we provide a set of algebraic operations for capturing and handling uncertain Spatiotemporal Data. By employing algebraic operations, we demonstrate how to translate queries expressed in XQuery to our algebra. A translation example shows that our algebraic operations are full of expressive power and illustrates that our algebra can be applied to general Data. Apart from this, we also propose a set of equivalence rules to optimize the process of query and give an example to show how the optimization approach works.
