Spatial Data Objects Research Articles

Adaptive Indexing of Objects with Spatial Extent

Can we quickly explore large multidimensional data in main memory? Adaptive indexing responds to this need by building an index incrementally, in response to queries; in its default form, it indexes a single attribute or, in the presence of several attributes, one attribute per index level. Unfortunately, this approach falters when indexing spatial data objects, encountered in data exploration tasks involving multidimensional range queries. In this paper, we introduce the Adaptive Incremental R-tree (AIR-tree): the first method for the adaptive indexing of non-point spatial objects; the AIR-tree incrementally and progressively constructs an in-memory spatial index over a static array, in response to incoming queries, using a suite of heuristics for creating and splitting nodes. Our thorough experimental study on synthetic and real data and workloads shows that the AIR-tree consistently outperforms prior adaptive indexing methods focusing on multidimensional points and a pre-built static R-tree in cumulative time over at least the first thousand queries.

Study on Intelligent Analysis and Processing Technology of Computer Big Data Based on Clustering Algorithm

Aim: Clustering belongs to unsupervised learning, which divides the data objects into the data set into multiple clusters or classes, so that the objects in the same cluster have high similarity Background: The clustering of spatial data objects can be solved by optimization based on the clustering objective function. Objective: Study on intelligent analysis and processing technology of computer big data based on clustering algorithm. Methods: First, a new dynamic self-organizing feature mapping model is proposed, and the training algorithm of the model is given. Then, the spectral clustering technology and related concepts are introduced. The spectral clustering algorithm is studied and analyzed, and a spectral clustering algorithm that automatically determines the number of clusters is proposed. Furthermore, an algorithm for constructing a discrete Morse function to find the optimal solution is proposed, proving that the constructed function is the optimal discrete Morse function. At the same time, two optimization models based on the discrete Morse theory are constructed. Finally, the optimization model based on discrete Morse theory is applied to cluster analysis, and a density clustering algorithm based on the discrete Morse optimization model is proposed. Results: This study is focused on designing and implementing a partitional-based clustering algorithm based on big data, that is suitable for clustering huge datasets to meet low computational requirements. The experiments are conducted in terms of time and space complexity and it is observed that the measure of clustering quality and the run time is capable of running in very less time without negotiating the quality of clustering. The results show that the experiments are carried out on the artificial data set and the UCI data set. Conclusion: The efficiency and superiority of the new model, are verified by comparing it with the clustering results of the DBSCAN algorithm.

Direction-based Spatial Skyline for Retrieving Arbitrary-Shaped Surrounding Objects

Abstract Retrieval of arbitrary-shaped surrounding data objects has many potential applications in spatial databases including nearby arbitrary-shaped object-of-interests retrieval surrounding a user. In this paper, we propose directional zone concept to determine directional similarity among spatial data objects. Then, we propose a novel query, called direction-based spatial skyline (DSS), which retrieves non-dominated arbitrary-shaped surrounding data objects in spatial databases for a user. The proposed DSS query is rotationally invariant as well as fair. We develop efficient algorithms for processing DSS queries in spatial databases by designing novel data pruning techniques using R-Tree data indexing scheme. Finally, we demonstrate the effectiveness and efficiency of our approach by conducting extensive experiments with real datasets.

An efficient multiple-user location-based query authentication approach for social networking

With the increasing mobile use of social network services and the proliferation of location-aware applications, it is of interest to consider a Multiple-User Location-based Query (MULQ), which returns a group of users with k POIs based on their locations and individual preferences. Further, when the query processing is outsourced to a service provider capable of handling voluminous spatial data objects, the query authentication becomes desirable. In this paper, we index the multi-criteria POIs with MRS-tree, based on which, we propose an efficient solution to query processing and authentication. We also propose a bitmap-based dominance relationship comparison algorithm to improve the efficiency of MULQ processing and authentication. A theoretical cost analysis and experimental evaluations show that our solutions are scalable and efficient under various parameter settings.

MANAGEMENT OF LARGE INDOOR POINT CLOUDS: AN INITIAL EXPLORATION

Abstract. Indoor navigation and visualization become increasingly important nowadays. Meanwhile, the proliferation of new sensors as well as the advancement of data processing provide massive point clouds to model the indoor environment in high accuracy. However, current state-of-the-art solutions fail to manage such large datasets efficiently. File based solutions often require substantial development work while database solutions are still faced with issues such as inefficient data loading and indexing. In this research, through a case study which aims to solve the problem of intermittent rendering of massive points in the context of indoor navigation, we devised and implemented an algorithm to compute the continuous Level of Detail (cLoD) where geometric and classification information are considered. Benchmarks are developed and different approaches in Oracle are tested to learn the pros and cons. Surprisingly, the flat table approach could be very efficient compared with other schemes. The crucial point lies in how to address priority of different dimensions including cLoD, classification and spatial dimensions, and avoid unnecessary scanning of the table. Writing results either to the memory or the disk constitutes major part of the time cost when large output is concerned. Conventional solutions based on spatial data objects present poor performance due to cumbersome indexing structure, inaccurate selection and additional decoding process. Besides, approximate selection in the unit of physical object is proposed and the performance is satisfactory when large amount of data is requested. The knowledge acquired could prompt the development of a novel data management of high dimensional point clouds where the classification information is involved.

COMPARATIVE STUDY OF CYCLONE SEVERITY LEVEL PREDICTION (CSLP) FOR CLASSIFICATION IN SPATIAL TEMPORAL DATA

The field of Data Mining has grown exponentially in the last decade. Spatial Temporal mining is a sub task, which deals with large set of dynamically varying spatial temporal data objects. Prediction of cyclone intensity level is very essential to take defensivemethods in the neighboring spatial regions. Cyclones are characterized by inward spiralling winds that rotate about a zone of low pressure. It is caused by atmospheric disturbances around a low-pressure area and is usually accompanied by violent storms and severe weather conditions. Differences in wind speed and wind shear keep cyclones effectively tearing the storms apart, before they reach a certain size. The wind patterns reduce wind shear and enables cyclone to grow twice as intense. Comparative study does experiments on various classification algorithms suitable to spatial temporal cyclone data and proposes a solution which removes outliers and accurately classifies cyclone intensity levels.

Measuring Moran's I in a Cost-Efficient Manner to Describe a Land-Cover Change Pattern in Large-Scale Remote Sensing Imagery

Detection and analysis of a land-cover change pattern from remotely sensed imagery have gained increasing research interests in recent years. A number of spatial statistics and landscape pattern metrics have been explored for this purpose. Moran's index (Moran's I) of spatial autocorrelation is one such spatiostatistical measure, which has been proved to be useful in characterizing the land-cover change, especially in Landsat data. However, since the Moran's I estimation needs to deal with spatial weight between each pair of spatial data objects, it becomes almost unfeasible to apply Moran's I in the case of large-scale remote sensing data, containing several millions of pixels. This paper proposes a method for computing Moran's I in the Hadoop MapReduce framework and thereby helps in describing spatial patterns in large-scale remotely sensed data. The contributions of the work include: 1) the exhaustive description of the Mapper and Reducer implementation for cost-effective estimation of Moran's I, and 2) the computational complexity analysis of the respective algorithms. Furthermore, two case studies have been presented, considering both the rook case and the queen case of spatial contiguity. Case Study 1 demonstrates the computational efficiency of the proposed implementation, and Case Study 2 illustrates an application of Moran's I in describing the urban sprawling pattern in two large spatial zones in Kolkata, India.

Elastic Spatial Query Processing in OpenStack Cloud Computing Environment for Time-Constraint Data Analysis

Geospatial big data analysis (GBDA) is extremely significant for time-constraint applications such as disaster response. However, the time-constraint analysis is not yet a trivial task in the cloud computing environment. Spatial query processing (SQP) is typical computation-intensive and indispensable for GBDA, and the spatial range query, join query, and the nearest neighbor query algorithms are not scalable without using MapReduce-liked frameworks. Parallel SQP algorithms (PSQPAs) are trapped in screw-processing, which is a known issue in Geoscience. To satisfy time-constrained GBDA, we propose an elastic SQP approach in this paper. First, Spark is used to implement PSQPAs. Second, Kubernetes-managed Core Operation System (CoreOS) clusters provide self-healing Docker containers for running Spark clusters in the cloud. Spark-based PSQPAs are submitted to Docker containers, where Spark master instances reside. Finally, the horizontal pod auto-scaler (HPA) would scale-out and scale-in Docker containers for supporting on-demand computing resources. Combined with an auto-scaling group of virtual instances, HPA helps to find each of the five nearest neighbors for 46,139,532 query objects from 834,158 spatial data objects in less than 300 s. The experiments conducted on an OpenStack cloud demonstrate that auto-scaling containers can satisfy time-constraint GBDA in clouds.

Mining Spatial Association Rules to Automatic Grouping of Spatial Data Objects Using Multiple Kernel-Based Probabilistic Clustering

AbstractWith the extensive application of spatial databases to various fields ranging from remote sensing to geographical information systems, computer cartography, environmental assessment, and planning, discovery of interesting and hidden knowledge in the spatial databases is a considerable chore for classifying and using the spatial data and knowledge bases. The literature presents different spatial data mining methods to mine knowledge from spatial databases. In this paper, spatial association rules are mined to automatic grouping of spatial data objects using a candidate generation process with three constraint measures, such as support, confidence, and lift. Then, the proposed multiple kernel-based probabilistic clustering is applied to the associate vector to further group the spatial data objects. Here, membership probability based on probabilistic distance is used with multiple kernels, where exponential and tangential kernel functions are utilized. The performance of the proposed method is analyzed with three data sets of three different geometry types using the number of rules and clustering accuracy. From the experimentation, the results proved that the proposed multi-kernel probabilistic clustering algorithm achieved better accuracy as compared with the existing probabilistic clustering.

Wavelet-Based Immune Fuzzy C-Means Algorithm

Fuzzy clustering can express the ambiguity ofsample category, and better reflect the actual needs of datamining. By introducing wavelet transform and artificial immunealgorithm to fuzzy clustering, Wavelet-based Immune Fuzzy C-means Algorithm (WIFCM) is proposed for overcoming theimperfections of fuzzy clustering, such as falling easily into localoptimal solution, slower convergence speed and initialization-dependence of clustering centers. Innovations of WIFCM arethe elite extraction operator and the descent reproductive mode.Using the locality and multi-resolution of wavelet transform, theelite extraction operator explores the distribution and densityinformation of spatial data objects in multi-dimensional spaceto guide the search of cluster centers. Taking advantage ofthe relationship between the relative positions of elite centersand inferior centers, the descent reproductive mode obtains theapproximate fastest descent direction of objective function values,and assures fast convergence of algorithm. Compared to theclassic fuzzy C-means algorithm, experiments on 3 UCI data setsshow that WIFCM has obvious advantages in average numberof iterations and accuracy.

A multiple window-based co-location pattern mining approach for various types of spatial data

Co-location pattern analysis represents the subsets of spatial events whose instances are found in close geographic proximity. Given a collection of Boolean spatial features, the co-location pattern discovery process finds the subsets of features frequently located together. Key challenges in co-location pattern analysis are modelling of neighbourhood in spatial domain, minimum prevalent threshold to generate collocation patterns and analysing extended spatial objects. We discuss the above key challenges using event centric approach and N-most prevalent co-location patterns approach. We propose a window-based model to find the neighbourhood for point spatial datasets and the multiple window model for extended spatial data objects. We also use N-most prevalent co-location patterns approach to filter the number of co-location pattern generation. We propose a more generic and efficient window-based model algorithm to find colocation patterns. Towards the end, we have done a comparative study of the existing approaches with our proposed approach.

Clustering spatial data with a geographic constraint: exploring local search

Spatial data objects that possess attributes in the optimization domain and the geographic domain are now widely available. For example, sensor data are one kind of spatial data objects. The location of a sensor is an attribute in the geographic domain, while its reading is an attribute in the optimization domain. Previous studies discuss dual clustering problems that attempt to partition spatial data objects into several groups, such that objects in the same group have similar values in their optimization attributes and form a compact region in the geographic domain. However, previous studies do not clearly define compact regions. Therefore, this paper formulates a connective dual clustering problem with an explicit connected constraint given. Objects with a geographic distance smaller than or equal to the connected constraint are connected. The goal of the connective dual clustering problem is to derive clusters that contain objects with similar values in the optimization domain and are connected in the geographic domain. This study further proposes an algorithm CLS (Clustering with Local Search) to efficiently derive clusters. This algorithm consists of two phases: the ConGraph (standing for Connective Graph) transformation phase and the clustering phase. In the ConGraph transformation phase, CLS first transforms the data objects into a ConGraph that captures geographic constraints among data objects and selects initial seeds for clustering. Then, the initial seeds selected nearby data objects and formed coarse clusters by exploring local search in the clustering phase. Moreover, coarse clusters are merged and finely turned. Experiments show that CLS algorithm is more efficient and scalable than existing methods.

Managing and Retrieving Spatial Information in Architectural Floor Plan Databases

This research investigates spatial information retrieval in architecture focused on constructing efficient metadata that is crucial for data retrieval. Generally speaking, metadata is 'structured data about data' to describe resources especially in digital realm. In this research, metadata is a sort of data object useful in searching spatial information, to describe raw spatial data objects with not only attribute but also content consisted of structure and semantics. There are two issues that motivate this research; 1) how to represent intangible space as a form of data object, and 2) how to search and browse these data objects in a huge database, an issue of content-based information retrieval. We analyze spatial information of a floor plan, especially focused on the apartment unit floor plan common in Korea. Then we extract the metadata items in a structured manner. To manage the items efficiently, we develop a data model for spatial information according to the concept of "Structured Floor Plan." For exploiting the metadata, this research shows several possibilities of query operations to present a set of sample queries about L-D-K(Living room - Dining room - Kitchen). Implementation of the prototype system is divided into three parts: 1) a modeling module, Vitruvius; 2) an indexing module, SpaceCore; and 3) a browsing module, SpaceScope.

Oversize shelves: a storage management technique for large spatial data objects

In this paper we present a new technique to improve the performance of spatial access methods by minimizing redundancy: the oversize shelf. Oversize shelves are additional disk pages that are attached to the interior nodes of a treebased spatial access method (such as the R-tree or the cell tree). These pages are used to accommodate very large data objects in order to avoid their excessive fragmentation. Whenever inserting a new object into the tree, one now has to decide whether to store it on an oversize shelf or insert it into the corresponding subtrees. For this purpose, we developed an analytic model for the behaviour of dynamic spatial access methods under insertion and deletions. The model yields a threshold value for the size of an object, such that it is more favourable to put it on the oversize shelf if and only if its size is greater than the threshold value. Otherwise the insertion into the corresponding subtrees is preferable. Practical experiments indicate that this policy results in a faster computation of search queries, and that the cell tree with oversize shelves is one of the most efficient spatial access methods currently available.

Data‐Base Management System Technology and Geographic Information Systems

Current operational data-base management system technology is restrictive in its capacity to clearly model and efficiently manage complex data objects, in general, and spatial data objects, such as those handled by geographic information system (GIS) software, in particular. This has led to the prevalence of hybrid GIS software architectures, in which the lexical and geometric components of spatial data are managed in a disintegrated fashion. However, recent developments in object-oriented DBMS technology suggest approaches for the unified management of spatial data. The need for distributed management of spatial data poses further technological challenges. Structure transfer protocols must be further developed to support real-time interchanges of subsets of spatial database on a query-by-query basis. Distributed crash recovery, concurrency control, and multiple copy updated capabilities as developed for distributed relational data bases must also be provided for distributed object-oriented data bases. Also, the technology to support physical and semantic integration needs to be further developed to allow distributed GISs to be built from pre-existing single-site components.