A spatial data warehouse recommendation approach: conceptual framework and experimental evaluation

Aissi, Saida; Gouider, Mohamed Salah; Sboui, Tarek; Saïd, Lamjed Ben

doi:10.1186/s13673-015-0045-y

Cited by 11 publications

(8 citation statements)

References 30 publications

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“…Spatial data mining has become more and more important as spatial data collection increases due to technological developments, such as geographic information systems (GIS) and global positioning system (GPS) [5,6]. The four main techniques used in spatial data mining are spatial clustering [7], spatial classification [8], the spatial association rule [9], and spatial characterization [10].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Grid-Based K-Prototypes Algorithm for Sustainable Decision-Making on Spatial Objects

Jang

Kim

et al. 2018

Sustainability

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Data mining plays a critical role in sustainable decision-making. Although the k-prototypes algorithm is one of the best-known algorithms for clustering both numeric and categorical data, clustering a large number of spatial objects with mixed numeric and categorical attributes is still inefficient due to complexity. In this paper, we propose an efficient grid-based k-prototypes algorithm, GK-prototypes, which achieves high performance for clustering spatial objects. The first proposed algorithm utilizes both maximum and minimum distance between cluster centers and a cell, which can reduce unnecessary distance calculation. The second proposed algorithm as an extension of the first proposed algorithm, utilizes spatial dependence; spatial data tends to be similar to objects that are close. Each cell has a bitmap index which stores the categorical values of all objects within the same cell for each attribute. This bitmap index can improve performance if the categorical data is skewed. Experimental results show that the proposed algorithms can achieve better performance than the existing pruning techniques of the k-prototypes algorithm.

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Section: Introductionmentioning

confidence: 99%

An Efficient Grid-Based K-Prototypes Algorithm for Sustainable Decision-Making on Spatial Objects

Jang

Kim

et al. 2018

Sustainability

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“…In addition to the aforementioned recommendation techniques (cf. Section 2.1 ), data warehousing concepts have been utilized for generating recommendations and creating RSs in many applications such as movies [ 15 , 59 ], websites [ 60 ], books [ 61 ], tourism [ 62 ], and Geographical Information Systems (GIS) [ 63 ].…”

Section: Related Workmentioning

confidence: 99%

“…In [ 62 ], the authors proposed a DW-based recommendation system to help tourism managers and pilots in the soaring community make soaring decisions by providing accurate and timely information. Moreover, in [ 63 ], the authors proposed a Spatial OLAP (SOLAP) recommendation approach that assists users in exploiting spatial data warehouses and retrieving relevant information by recommending spatial MultiDimensional eXpressions (MDX) queries. The proposed approach detects the user’s preferences by comparing the current user’s preferences to the preferences of previous data warehouse users.…”

Section: Related Workmentioning

confidence: 99%

A Data Warehouse-Based System for Service Customization Recommendations in Product-Service Systems

Esheiba

Helal

Elgammal

et al. 2022

Sensors

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Nowadays, manufacturers are shifting from a traditional product-centric business paradigm to a service-centric one by offering products that are accompanied by services, which is known as Product-Service Systems (PSSs). PSS customization entails configuring products with varying degrees of differentiation to meet the needs of various customers. This is combined with service customization, in which configured products are expanded by customers to include smart IoT devices (e.g., sensors) to improve product usage and facilitate the transition to smart connected products. The concept of PSS customization is gaining significant interest; however, there are still numerous challenges that must be addressed when designing and offering customized PSSs, such as choosing the optimum types of sensors to install on products and their adequate locations during the service customization process. In this paper, we propose a data warehouse-based recommender system that collects and analyzes large volumes of product usage data from similar products to the product that the customer needs to customize by adding IoT smart devices. The analysis of these data helps in identifying the most critical parts with the highest number of incidents and the causes of those incidents. As a result, sensor types are determined and recommended to the customer based on the causes of these incidents. The utility and applicability of the proposed RS have been demonstrated through its application in a case study that considers the rotary spindle units of a CNC milling machine.

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“…Recently, spatial data mining has become more and more important as spatial data collection is increasing due to technological developments such as geographic information system (GIS) and global positioning system (GPS) [26] [27]. The main techniques of spatial data mining are spatial clustering [1], spatial classification [2], spatial association rule [3], and spatial characterization [4].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Grid-based K-prototypes Algorithm for Sustainable Decision Making Using Spatial Objects

Jang¹,

Kim²,

Kim³

et al. 2018

Preprint

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Data mining plays a critical role in the sustainable decision making. The k-prototypes algorithm is one of the best-known algorithm for clustering both numeric and categorical data. Despite this, however, clustering a large number of spatial object with mixed numeric and categorical attributes is still inefficient due to its high time complexity. In this paper, we propose an efficient grid-based k-prototypes algorithms, GK-prototypes, which achieves high performance for clustering spatial objects. The first proposed algorithm utilizes both maximum and minimum distance between cluster centers and a cell, which can remove unnecessary distance calculation. The second proposed algorithm as extensions of the first proposed algorithm utilizes spatial dependence that spatial data tend to be more similar as objects are closer. Each cell has a bitmap index which stores categorical values of all objects in the same cell for each attribute. This bitmap index can improve the performance in case that a categorical data is skewed. Our evaluation experiments showed that proposed algorithms can achieve better performance than the existing pruning technique in the k-prototypes algorithm.

show abstract

A spatial data warehouse recommendation approach: conceptual framework and experimental evaluation

Cited by 11 publications

References 30 publications

An Efficient Grid-Based K-Prototypes Algorithm for Sustainable Decision-Making on Spatial Objects

An Efficient Grid-Based K-Prototypes Algorithm for Sustainable Decision-Making on Spatial Objects

A Data Warehouse-Based System for Service Customization Recommendations in Product-Service Systems

An Efficient Grid-based K-prototypes Algorithm for Sustainable Decision Making Using Spatial Objects

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