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- Classification (2)
- Clustering (2)
- Data mining (2)
- Access control (1)
- Adaptive Resonance Theory (ART) (1)
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- Association rule mining (1)
- Brain-computer interface (1)
- Client-server architecture (1)
- Clip-based similarity (1)
- Constrained frequent episode (1)
- Constraint learning (1)
- Content-based Image Retrieval (1)
- Content-based retrieval (1)
- Data structures (1)
- Digital libraries (1)
- E-auction (1)
- E-procurement (1)
- Edge computing (1)
- Edge servers (1)
- Education (1)
- Episode mining (1)
- Episode prefix tree (1)
- Geography (1)
- Hierarchical video retrieval (1)
- I/O traffic patterns (1)
- Image Retrieval (1)
- Image classification (1)
- Image retrieval (1)
- Index structure (1)
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Articles 1 - 30 of 38
Full-Text Articles in Databases and Information Systems
Design Lessons On Access Features In Paper, Yin-Leng Theng, Dion Hoe-Lian Goh, Ming Yin, Eng-Kai Suen, Ee Peng Lim
Design Lessons On Access Features In Paper, Yin-Leng Theng, Dion Hoe-Lian Goh, Ming Yin, Eng-Kai Suen, Ee Peng Lim
Research Collection School Of Computing and Information Systems
Using Nielsen's Heuristic Evaluation, this paper reports a user study with six usability-trained subjects to evaluate PAPER's access features in assisting users to retrieve information efficiently, part of an on-going design partnership with stakeholders and designers/developers. PAPER (Personalised Adaptive Pathways for Exam Resources) is an improved version evolving from an earlier implementation of GeogDL built upon G-Portal, a geospatial digital library infrastructure. After two initial evaluations with student and teacher design partners, PAPER has evolved into a system containing a new bundle of personalized, interactive services with four modules: mock exam; personal coach (practice and review); trend analysis and performance …
Supporting Field Study With Personalized Project Spaces In A Geographical Digital Library, Ee Peng Lim, Aixin Sun, Zehua Liu, John Hedberg, Chew-Hung Chang, Tiong-Sa Teh, Dion Hoe-Lian Goh, Yin-Leng Theng
Supporting Field Study With Personalized Project Spaces In A Geographical Digital Library, Ee Peng Lim, Aixin Sun, Zehua Liu, John Hedberg, Chew-Hung Chang, Tiong-Sa Teh, Dion Hoe-Lian Goh, Yin-Leng Theng
Research Collection School Of Computing and Information Systems
Digital libraries have been rather successful in supporting learning activities by providing learners with access to information and knowledge. However, this level of support is passive to learners and interactive and collaborative learning cannot be easily achieved. In this paper, we study how digital libraries could be extended to serve a more active role in collaborative learning activities. We focus on developing new services to support a common type of learning activity, field study, in a geospatial context. We propose the concept of personal project space that allows individuals to work in their personalized environment with a mix of private …
Method For Identifying Individuals, Manoj Thulasidas
Method For Identifying Individuals, Manoj Thulasidas
Research Collection School Of Computing and Information Systems
A method and system for identifying a subject comprises obtaining a digitised recording of an electrocardiogram measurement of the subject to be identified, the digitised recording being a cyclic waveform having a peak amplitude. The digitised recording is normalised to reduce variations due to physiological effects, and the normalised recording is processed to determine a feature vector in the frequency domain. The distance between the determined feature vector and a predetermined feature vector is measured to identify the subject.
High Performance P300 Speller For Brain-Computer Interface, Cuntai Guan, Manoj Thulasidas, Jiankang Wu
High Performance P300 Speller For Brain-Computer Interface, Cuntai Guan, Manoj Thulasidas, Jiankang Wu
Research Collection School Of Computing and Information Systems
P300 speller is a communication tool with which one can input texts or commands to a computer by thought. The amplitude of the P300 evoked potential is inversely proportional to the probability of infrequent or task-related stimulus. In existing P300 spellers, rows and columns of a matrix are intensified successively and randomly, resulting in a stimulus frequency of 1/N (N is the number of rows or columns of the matrix). We propose a new paradigm to display each single character randomly and individually (therefore reducing the stimulus frequency to 1/(N*N)). On-line experiments showed that this new speller significantly improved the …
Accommodating Instance Heterogeneities In Database Integration, Ee Peng Lim, Roger Hsiang-Li Chiang
Accommodating Instance Heterogeneities In Database Integration, Ee Peng Lim, Roger Hsiang-Li Chiang
Research Collection School Of Computing and Information Systems
A complete data integration solution can be viewed as an iterative process that consists of three phases, namely analysis, derivation and evolution. The entire process is similar to a software development process with the target application being the derivation rules for the integrated databases. In many cases, data integration requires several iterations of refining the local-to-global database mapping rules before a stable set of rules can be obtained. In particular, the mapping rules, as well as the data model and query model for the integrated databases have to cope with poor data quality in local databases, ongoing local database updates …
Finding Constrained Frequent Episodes Using Minimal Occurrences, Xi Ma, Hwee Hwa Pang, Kian-Lee Tan
Finding Constrained Frequent Episodes Using Minimal Occurrences, Xi Ma, Hwee Hwa Pang, Kian-Lee Tan
Research Collection School Of Computing and Information Systems
Recurrent combinations of events within an event sequence, known as episodes, often reveal useful information. Most of the proposed episode mining algorithms adopt an apriori-like approach that generates candidates and then calculates their support levels. Obviously, such an approach is computationally expensive. Moreover, those algorithms are capable of handling only a limited range of constraints. In this paper, we introduce two mining algorithms - episode prefix tree (EPT) and position pairs set (PPS) - based on a prefix-growth approach to overcome the above limitations. Both algorithms push constraints systematically into the mining process. Performance study shows that the proposed algorithms …
The D-Tree: An Index Structure For Planar Point Queries Location-Based Wireless Services, Jianliang Xu, Baihua Zheng, Wang-Chien Lee, Dik Lun Lee
The D-Tree: An Index Structure For Planar Point Queries Location-Based Wireless Services, Jianliang Xu, Baihua Zheng, Wang-Chien Lee, Dik Lun Lee
Research Collection School Of Computing and Information Systems
Location-based services (LBSs), considered as a killer application in the wireless data market, provide information based on locations specified in the queries. In this paper, we examine the indexing issue for querying location-dependent data in wireless LBSs; in particular, we focus on an important class of queries, planar point queries. To address the issues of responsiveness, energy consumption, and bandwidth contention in wireless communications, an index has to minimize the search time and maintain a small storage overhead. It is shown that the traditional point-location algorithms and spatial index structures fail to achieve either objective or both. This paper proposes …
On Semantic Caching And Query Scheduling For Mobile Nearest-Neighbor Search, Baihua Zheng, Wang-Chien Lee, Dik Lun Lee
On Semantic Caching And Query Scheduling For Mobile Nearest-Neighbor Search, Baihua Zheng, Wang-Chien Lee, Dik Lun Lee
Research Collection School Of Computing and Information Systems
Location-based services have received increasing attention in recent years. In this paper, we address the performance issues of mobile nearest-neighbor search, in which the mobile user issues a query to retrieve stationary service objects nearest to him/her. An index based on Voronoi Diagram is used in the server to support such a search, while a semantic cache is proposed to enhance the access efficiency of the service. Cache replacement policies tailored for the proposed semantic cache are examined. Moreover, several query scheduling policies are proposed to address the inter-cell roaming issues in multi-cell environments. Simulations are conducted to evaluate the …
Spatial Queries In Wireless Broadcast Systems, Baihua Zheng, Wang-Chien Lee, Dik Lun Lee
Spatial Queries In Wireless Broadcast Systems, Baihua Zheng, Wang-Chien Lee, Dik Lun Lee
Research Collection School Of Computing and Information Systems
Owing to the advent of wireless networking and personal digital devices, information systems in the era of mobile computing are expected to be able to handle a tremendous amount of traffic and service requests from the users. Wireless data broadcast, thanks to its high scalability, is particularly suitable for meeting such a challenge. Indexing techniques have been developed for wireless data broadcast systems in order to conserve the scarce power resources in mobile clients. However, most of the previous studies do not take into account the impact of location information of users. In this paper, we address the issues of …
A Novel Log-Based Relevance Feedback Technique In Content-Based Image Retrieval, Steven C. H. Hoi, Michael R. Lyu
A Novel Log-Based Relevance Feedback Technique In Content-Based Image Retrieval, Steven C. H. Hoi, Michael R. Lyu
Research Collection School Of Computing and Information Systems
Relevance feedback has been proposed as an important technique to boost the retrieval performance in content-based image retrieval (CBIR). However, since there exists a semantic gap between low-level features and high-level semantic concepts in CBIR, typical relevance feedback techniques need to perform a lot of rounds of feedback for achieving satisfactory results. These procedures are time-consuming and may make the users bored in the retrieval tasks. For a long-term study purpose in CBIR, we notice that the users' feedback logs can be available and employed for helping the retrieval tasks in CBIR systems. In this paper, we propose a novel …
Improving Transliteration With Precise Alignment Of Phoneme Chunks And Using Contextual Features, Wei Gao, Kam-Fai Wong, Wai Lam
Improving Transliteration With Precise Alignment Of Phoneme Chunks And Using Contextual Features, Wei Gao, Kam-Fai Wong, Wai Lam
Research Collection School Of Computing and Information Systems
Automatic transliteration of foreign names is basically regarded as a diminutive clone of the machine translation (MT) problem. It thus follows IBM’s conventional MT models under the sourcechannel framework. Nonetheless, some parameters of this model dealing with zero-fertility words in the target sequences, can negatively impact transliteration effectiveness because of the inevitable inverted conditional probability estimation. Instead of source-channel, this paper presents a direct probabilistic transliteration model using contextual features of phonemes with a tailored alignment scheme for phoneme chunks. Experiments demonstrate superior performance over the source-channel for the task of English-Chinese transliteration.
Clip-Based Similarity Measure For Hierarchical Video Retrieval, Yuxin Peng, Chong-Wah Ngo
Clip-Based Similarity Measure For Hierarchical Video Retrieval, Yuxin Peng, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
This paper proposes a new approach and algorithm for the similarity measure of video clips. The similarity is mainly based on two bipartite graph matching algorithms: maximum matching (MM) and optimal matching (OM). MM is able to rapidly filter irrelevant video clips, while OM is capable of ranking the similarity of clips according to the visual and granularity factors. Based on MM and OM, a hierarchical video retrieval framework is constructed for the approximate matching of video clips. To allow the matching between a query and a long video, an online clip segmentation algorithm is also proposed to rapidly locate …
Blocking Reduction Strategies In Hierarchical Text Classification, Ee Peng Lim, Aixin Sun, Wee-Keong Ng, Jaideep Srivastava
Blocking Reduction Strategies In Hierarchical Text Classification, Ee Peng Lim, Aixin Sun, Wee-Keong Ng, Jaideep Srivastava
Research Collection School Of Computing and Information Systems
One common approach in hierarchical text classification involves associating classifiers with nodes in the category tree and classifying text documents in a top-down manner. Classification methods using this top-down approach can scale well and cope with changes to the category trees. However, all these methods suffer from blocking which refers to documents wrongly rejected by the classifiers at higher-levels and cannot be passed to the classifiers at lower-levels. We propose a classifier-centric performance measure known as blocking factor to determine the extent of the blocking. Three methods are proposed to address the blocking problem, namely, threshold reduction, restricted voting, and …
A Spectroscopy Of Texts For Effective Clustering, Wenyuan Li, Wee-Keong Ng, Kok-Leong Ong, Ee Peng Lim
A Spectroscopy Of Texts For Effective Clustering, Wenyuan Li, Wee-Keong Ng, Kok-Leong Ong, Ee Peng Lim
Research Collection School Of Computing and Information Systems
For many clustering algorithms, such as k-means, EM, and CLOPE, there is usually a requirement to set some parameters. Often, these parameters directly or indirectly control the number of clusters to return. In the presence of different data characteristics and analysis contexts, it is often difficult for the user to estimate the number of clusters in the data set. This is especially true in text collections such as Web documents, images or biological data. The fundamental question this paper addresses is: ldquoHow can we effectively estimate the natural number of clusters in a given text collection?rdquo. We propose to use …
Sclope: An Algorithm For Clustering Data Streams Of Categorical Attributes, Kok-Leong Ong, Wenyuan Li, Wee-Keong Ng, Ee Peng Lim
Sclope: An Algorithm For Clustering Data Streams Of Categorical Attributes, Kok-Leong Ong, Wenyuan Li, Wee-Keong Ng, Ee Peng Lim
Research Collection School Of Computing and Information Systems
Clustering is a difficult problem especially when we consider the task in the context of a data stream of categorical attributes. In this paper, we propose SCLOPE, a novel algorithm based on CLOPE's intuitive observation about cluster histograms. Unlike CLOPE however, our algorithm is very fast and operates within the constraints of a data stream environment. In particular, we designed SCLOPE according to the recent CluStream framework. Our evaluation of SCLOPE shows very promising results. It consistently outperforms CLOPE in speed and scalability tests on our data sets while maintaining high cluster purity; it also supports cluster analysis that other …
Towards Personalised Web Intelligence, Ah-Hwee Tan, Hwee-Leng Ong, Hong Pan, Jamie Ng, Qiu-Xiang Li
Towards Personalised Web Intelligence, Ah-Hwee Tan, Hwee-Leng Ong, Hong Pan, Jamie Ng, Qiu-Xiang Li
Research Collection School Of Computing and Information Systems
The Flexible Organizer for Competitive Intelligence (FOCI) is a personalised web intelligence system that provides an integrated platform for gathering, organising, tracking, and disseminating competitive information on the web. FOCI builds personalised information portfolios through a novel method called User-Configurable Clustering, which allows a user to personalise his/her portfolios in terms of the content as well as the organisational structure. This paper outlines the key challenges we face in personalised information management and gives a detailed account of FOCI’s underlying personalisation mechanism. For a quantitative evaluation of the system’s performance, we propose a set of performance indices based on information …
Shared-Storage Auction Ensures Data Availability, Hady W. Lauw, Siu-Cheung Hui, Edmund M. K. Lai
Shared-Storage Auction Ensures Data Availability, Hady W. Lauw, Siu-Cheung Hui, Edmund M. K. Lai
Research Collection School Of Computing and Information Systems
Most current e-auction systems are based on the client-server architecture. Such centralized systems provide a single point of failure and control. In contrast, peer-to-peer systems permit distributed control and minimize individual node and link failures' impact on the system. The shared-storage-based auction model described decentralizes services among peers to share the required processing load and aggregates peers' resources for common use. The model is based on the principles of local computation at each peer, direct inter-peer communication, and a shared storage space.
Robust Classification Of Event-Related Potential For Brain-Computer Interface, Manoj Thulasidas
Robust Classification Of Event-Related Potential For Brain-Computer Interface, Manoj Thulasidas
Research Collection School Of Computing and Information Systems
We report the implementation of a text input application (speller) based on the P300 event related potential. We obtain high accuracies by using an SVM classifier and a novel feature. These techniques enable us to maintain fast performance without sacrificing the accuracy, thus making the speller usable in an online mode. In order to further improve the usability, we perform various studies on the data with a view to minimizing the training time required. We present data collected from nine healthy subjects, along with the high accuracies (of the order of 95% or more) measured online. We show that the …
Group-Based Relevance Feedback With Support Vector Machine Ensembles, Steven C. H. Hoi, Michael R. Lyu
Group-Based Relevance Feedback With Support Vector Machine Ensembles, Steven C. H. Hoi, Michael R. Lyu
Research Collection School Of Computing and Information Systems
Support vector machines (SVMs) have become one of the most promising techniques for relevance feedback in content-based image retrieval (CBIR). Typical SVM-based relevance feedback techniquessimply apply the strict binary classifications: positive (relevant) class and negative (irrelevant) class. However, in a real-world relevance feedback task, it is more reasonable and practical to assume the data come from multiple positive classes and one negative class. In order to formulate an effective relevance feedback algorithm, we propose a novel group-based relevance feedback scheme constructed with the SVM ensembles technique. Experiments are conducted to evaluate the performance of our proposed scheme and the traditional …
Gesture Tracking And Recognition For Lecture Video Editing, Feng Wang, Chong-Wah Ngo, Ting-Chuen Pong
Gesture Tracking And Recognition For Lecture Video Editing, Feng Wang, Chong-Wah Ngo, Ting-Chuen Pong
Research Collection School Of Computing and Information Systems
This paper presents a gesture based driven approach for video editing. Given a lecture video, we adopt novel approaches to automatically detect and synchronize its content with electronic slides. The gestures in each synchronized topic (or shot) are then tracked and recognized continuously. By registering shots and slides and recovering their transformation, the regions where the gestures take place can be known. Based on the recognized gestures and their registered positions, the information in slides can be seamlessly extracted, not only to assist video editing, but also to enhance the quality of original lecture video.
High Accuracy Classification Of Eeg Signal, Wenjie Xu, Cuitai Guan, Chng Eng Siong, S. Ranganatha, Manoj Thulasidas, Jiankang Wu
High Accuracy Classification Of Eeg Signal, Wenjie Xu, Cuitai Guan, Chng Eng Siong, S. Ranganatha, Manoj Thulasidas, Jiankang Wu
Research Collection School Of Computing and Information Systems
Improving classification accuracy is a key issue to advancing brain computer interface (BCI) research from laboratory to real world applications. This article presents a high accuracy EEC signal classification method using single trial EEC signal to detect left and right finger movement. We apply an optimal temporal filter to remove irrelevant signal and subsequently extract key features from spatial patterns of EEG signal to perform classification. Specifically, the proposed method transforms the original EEG signal into a spatial pattern and applies the RBF feature selection method to generate robust feature. Classification is performed by the SVM and our experimental result …
Ltam: A Location-Temporal Authorization Model, Hai Yu, Ee Peng Lim
Ltam: A Location-Temporal Authorization Model, Hai Yu, Ee Peng Lim
Research Collection School Of Computing and Information Systems
This paper describes an authorization model for specifying access privileges of users who make requests to access a set of locations in a building or more generally a physical or virtual infrastructure. In the model, primitive locations can be grouped into composite locations and the connectivities among locations are represented in a multilevel location graph. Authorizations are defined with temporal constraints on the time to enter and leave a location and constraints on the number of times users can access a location. Access control enforcement is conducted by monitoring user movement and checking access requests against an authorization database. The …
A Support-Ordered Trie For Fast Frequent Itemset Discovery, Ee Peng Lim, Yew-Kwong Woon, Wee-Keong Ng
A Support-Ordered Trie For Fast Frequent Itemset Discovery, Ee Peng Lim, Yew-Kwong Woon, Wee-Keong Ng
Research Collection School Of Computing and Information Systems
The importance of data mining is apparent with the advent of powerful data collection and storage tools; raw data is so abundant that manual analysis is no longer possible. Unfortunately, data mining problems are difficult to solve and this prompted the introduction of several novel data structures to improve mining efficiency. Here, we critically examine existing preprocessing data structures used in association rule mining for enhancing performance in an attempt to understand their strengths and weaknesses. Our analyses culminate in a practical structure called the SOTrielT (support-ordered trie itemset) and two synergistic algorithms to accompany it for the fast discovery …
An Interactive Learning Environment For A Dynamic Educational Digital Library, Ee Peng Lim, Dion Hoe-Lian Goh, Yin-Leng Theng, Eng-Kai Suen
An Interactive Learning Environment For A Dynamic Educational Digital Library, Ee Peng Lim, Dion Hoe-Lian Goh, Yin-Leng Theng, Eng-Kai Suen
Research Collection School Of Computing and Information Systems
GeogDL is a digital library of geography examination resources designed to assist students in preparing for a national geography examination in Singapore. We describe an interactive learning environment built into GeogDL that consists of four major components. The practice and review module allows students to attempt individual examination questions, the mock exam provides a simulation of the actual geography examination, the trends analysis tool provides an overview of the types of questions asked in previous examinations, while the contributions module allows students and teachers to create and share knowledge within the digital library.
Biased Support Vector Machine For Relevance Feedback In Image Retrieval, Steven Hoi, Chi-Hang Chan, Kaizhu Huang, Michael R. Lyu, Irwin King
Biased Support Vector Machine For Relevance Feedback In Image Retrieval, Steven Hoi, Chi-Hang Chan, Kaizhu Huang, Michael R. Lyu, Irwin King
Research Collection School Of Computing and Information Systems
Recently, support vector machines (SVMs) have been engaged on relevance feedback tasks in content-based image retrieval. Typical approaches by SVMs treat the relevance feedback as a strict binary classification problem. However, these approaches do not consider an important issue of relevance feedback, i.e. the unbalanced dataset problem, in which the negative instances largely outnumber the positive instances. For solving this problem, we propose a novel technique to formulate the relevance feedback based on a modified SVM called biased support vector machine (Biased SVM or BSVM). Mathematical formulation and explanations are provided for showing the advantages. Experiments are conducted to evaluate …
Proprietary And Open Systems Adoption In E-Procurement: A Risk-Augmented Transactions Cost Perspective, Robert J. Kauffman, Hamid Mohtadi
Proprietary And Open Systems Adoption In E-Procurement: A Risk-Augmented Transactions Cost Perspective, Robert J. Kauffman, Hamid Mohtadi
Research Collection School Of Computing and Information Systems
We present an economic model that enables the study of incentives for business-to-business (B2B) e-procurement systems investments that permit inventory coordination and improved operational control. We focus on the information technology adoption behavior of firms in the presence of transaction costs, agency costs and information uncertainty. We conclude that it is appropriate to rethink the prior theory and develop an extended transaction-cost theory perspective that incorporates the possibility of shocks. We distinguish among three kinds of B2B e-procurement systems platforms. Proprietary platform procurement systems involve traditional electronic data interchange (EDI) technologies. Open platform procurement systems are associated with e-market Web …
Steganographic Schemes For File System And B-Tree, Hwee Hwa Pang, Kian-Lee Tan, Xuan Zhou
Steganographic Schemes For File System And B-Tree, Hwee Hwa Pang, Kian-Lee Tan, Xuan Zhou
Research Collection School Of Computing and Information Systems
While user access control and encryption can protect valuable data from passive observers, these techniques leave visible ciphertexts that are likely to alert an active adversary to the existence of the data. We introduce StegFD, a steganographic file driver that securely hides user-selected files in a file system so that, without the corresponding access keys, an attacker would not be able to deduce their existence. Unlike other steganographic schemes proposed previously, our construction satisfies the prerequisites of a practical file system in ensuring the integrity of the files and maintaining efficient space utilization. We also propose two schemes for implementing …
Web Image Learning For Searching Semantic Concepts In Image Databases, Steven Hoi, Michael R. Lyu
Web Image Learning For Searching Semantic Concepts In Image Databases, Steven Hoi, Michael R. Lyu
Research Collection School Of Computing and Information Systems
Without textual descriptions or label information of images, searching semantic concepts in image databases is still a very challenging task. While automatic annotation techniques are yet a long way off, we can seek other alternative techniques to solve this difficult issue. In this paper, we propose to learn Web images for searching the semantic concepts in large image databases. To formulate effective algorithms, we suggest to engage the support vector machines for attacking the problem. We evaluate our algorithm in a large image database and demonstrate the preliminary yet promising results.
Modified Art 2a Growing Network Capable Of Generating A Fixed Number Of Nodes, Ji He, Ah-Hwee Tan, Chew-Lim Tan
Modified Art 2a Growing Network Capable Of Generating A Fixed Number Of Nodes, Ji He, Ah-Hwee Tan, Chew-Lim Tan
Research Collection School Of Computing and Information Systems
This paper introduces the Adaptive Resonance Theory under Constraint (ART-C 2A) learning paradigm based on ART 2A, which is capable of generating a user-defined number of recognition nodes through online estimation of an appropriate vigilance threshold. Empirical experiments compare the cluster validity and the learning efficiency of ART-C 2A with those of ART 2A, as well as three closely related clustering methods, namely online K-Means, batch K-Means, and SOM, in a quantitative manner. Besides retaining the online cluster creation capability of ART 2A, ART-C 2A gives the alternative clustering solution, which allows a direct control on the number of output …
Spatial Queries In The Presence Of Obstacles, Jun Zhang, Dimitris Papadias, Kyriakos Mouratidis, Manli Zhu
Spatial Queries In The Presence Of Obstacles, Jun Zhang, Dimitris Papadias, Kyriakos Mouratidis, Manli Zhu
Research Collection School Of Computing and Information Systems
Despite the existence of obstacles in many database applications, traditional spatial query processing utilizes the Euclidean distance metric assuming that points in space are directly reachable. In this paper, we study spatial queries in the presence of obstacles, where the obstructed distance between two points is defined as the length of the shortest path that connects them without crossing any obstacles. We propose efficient algorithms for the most important query types, namely, range search, nearest neighbors, e-distance joins and closest pairs, considering that both data objects and obstacles are indexed by R-trees. The effectiveness of the proposed solutions is verified …