Crisp Clustering Algorithm for 3D Geospatial Vector Data Quantization

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

Anton

et al. 2016

ABSTRACT:Locating and analysing the location of new stores or outlets is one of the common issues facing retailers and franchisers. This is due to assure that new opening stores are at their strategic location to attract the highest possible number of customers. Spatial information is used to manage, maintain and analyse these store locations. However, since the business of franchising and chain stores in urban areas runs within high rise multi-level buildings, a three-dimensional (3D) method is prominently required in order to locate and identify the surrounding information such as at which level of the franchise unit will be located or is the franchise unit located is at the best level for visibility purposes. One of the common used analyses used for retrieving the surrounding information is Nearest Neighbour (NN) analysis. It uses a point location and identifies the surrounding neighbours. However, with the immense number of urban datasets, the retrieval and analysis of nearest neighbour information and their efficiency will become more complex and crucial. In this paper, we present a technique to retrieve nearest neighbour information in 3D space using a clustered hierarchical tree structure. Based on our findings, the proposed approach substantially showed an improvement of response time analysis compared to existing approaches of spatial access methods in databases. The query performance was tested using a dataset consisting of 500,000 point locations building and franchising unit. The results are presented in this paper. Another advantage of this structure is that it also offers a minimal overlap and coverage among nodes which can reduce repetitive data entry.

Section: Discussionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

3d Nearest Neighbour Search Using a Clustered Hierarchical Tree Structure

Suhaibah

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

Anton

et al. 2016

“…However, the transition of R-Tree to 3D had increase the overlap among node and requires more storage. This would lead to repetitive data and multipath query among node (Azri et al, 2015). Although an effort has been made to improve the structure of 3D R-Tree, application using large amount of data still faces low data retrieval efficiency.…”

Section: Existing Data Structure For Big Data Handling In the Databasementioning

confidence: 99%

Dendrogram Clustering for 3d Data Analytics in Smart City

Azri

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

Rahman

2018

<p><strong>Abstract.</strong> Smart city is a connection of physical and social infrastructure together with the information technology to leverage the collective intelligence of the city. Cities will build huge data centres. These data are collected from sensors, social media, and legacy data sources. In order to be smart, cities needs data analysis to identify infrastructure that needs to be improved, city planning and predictive analysis for citizen safety and security. However, no matter how much smart city focus on the updated technology, data do not organize themselves in a database. Such tasks require a sophisticated database structure to produce informative data output. Furthermore, increasing number of smart cities and generated data from smart cities contributes to current phenomenon called big data. These large and complex data collections would be difficult to process using regular database management tools or traditional data processing applications. There are multiple challenges for big data, including visualization, mining, analysis, capture, storage, search, and sharing. Efficient data analysis mechanisms are necessary to search and extract valuable patterns and knowledge through the big data of smart cities. In this paper, we present a technique of three-dimensional data analytics using dendrogram clustering approach. Data will be organized using this technique and several output and analyses are carried out to proof the efficiency of the structure for three – dimensional data analytics in smart city.</p>

“…In our previous work (Azri et al, 2015;Azri et al, 2014) clustered hierarchical structure is developed based on partitionbased clustering as a clustering algorithm to group the objects. Partition-based clustering is a method of clustering that requires a pre-set number of clusters from the user.…”

Section: Clustering Algorithmmentioning

confidence: 99%

“…This structure is constructed based on group clustering and transformed into a hierarchical structure. Based on our previous work (Azri et al, 2015;Azri et al, 2014), the structure produced a minimal coverage and overlap percentage among nodes to avoid repetitive data entry. Thus, in this paper, we proposed the clustered hierarchical structure in order to retrieve nearest neighbour information.…”

Section: Introductionmentioning

confidence: 99%

Improving Nearest Neighbour Search in 3d Spatial Access Method

Suhaibaha

Rahman

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

et al. 2016

Nearest Neighbour (NN) is one of the important queries and analyses for spatial application. In normal practice, spatial access method structure is used during the Nearest Neighbour query execution to retrieve information from the database. However, most of the spatial access method structures are still facing with unresolved issues such as overlapping among nodes and repetitive data entry. This situation will perform an excessive Input/Output (IO) operation which is inefficient for data retrieval. The situation will become more crucial while dealing with 3D data. The size of 3D data is usually large due to its detail geometry and other attached information. In this research, a clustered 3D hierarchical structure is introduced as a 3D spatial access method structure. The structure is expected to improve the retrieval of Nearest Neighbour information for 3D objects. Several tests are performed in answering Single Nearest Neighbour search and k Nearest Neighbour (kNN) search. The tests indicate that clustered hierarchical structure is efficient in handling Nearest Neighbour query compared to its competitor. From the results, clustered hierarchical structure reduced the repetitive data entry and the accessed page. The proposed structure also produced minimal Input/Output operation. The query response time is also outperformed compared to the other competitor. For future outlook of this research several possible applications are discussed and summarized.