Spatial OLAP Research Articles

Spatial data warehouses and spatial OLAP come towards the cloud: design and performance

Cloud computing systems handle large volumes of data by using almost unlimited computational resources, while spatial data warehouses (SDWs) are multidimensional databases that store huge volumes of both spatial data and conventional data. Cloud computing environments have been considered adequate to host voluminous databases, process analytical workloads and deliver database as a service, while spatial online analytical processing (spatial OLAP) queries issued over SDWs are intrinsically analytical. However, hosting a SDW in the cloud and processing spatial OLAP queries over such database impose novel obstacles. In this article, we introduce novel concepts as cloud SDW and spatial OLAP as a service, and afterwards detail the design of novel schemas for cloud SDW and spatial OLAP query processing over cloud SDW. Furthermore, we evaluate the performance to process spatial OLAP queries in cloud SDWs using our own query processor aided by a cloud spatial index. Moreover, we describe the cloud spatial bitmap index to improve the performance to process spatial OLAP queries in cloud SDWs, and assess it through an experimental evaluation. Results derived from our experiments revealed that such index was capable to reduce the query response time from 58.20 up to 98.89 %.

Spatial Online Analytical Processing for Hotspots Distribution Based on Socio-economic Factors in Riau Province Indonesia

Hotspot data analysis will generate more useful information if the data are combined with socio-economics condition. The relation between hotspots and socio-economic conditions is important to be explored in order to identify the human factors that may cause forest fires. In the previous research, a spatial data warehouse and Spatial Online Analytical Processing (SOLAP) system for hotspots distribution were developed. However, the system does not provide other useful information such as socio-economy to analyse the cause of forest fires. The purpose of this study is to create socio-economy dimensions in the spatial data warehouse system.

A generic geovisualization model for spatial OLAP and its implementation in a standards-based architecture

A generic geovisualization model for spatial OLAP and its implementation in a standards-based architecture

Spatial OLAP integrity constraints: From UML-based specification to automatic implementation: Application to energetic data in agriculture

Spatial OnLine Analytical Processing systems (SOLAP) are Business Intelligence technologies allowing efficient and interactive analysis of large spatial data cubes. In this type of systems, the correctness of analysis depends on the warehoused data quality, how aggregations are performed and how data cubes are explored. In this paper, we study quality control techniques (based on integrity constraints) related to exploration of spatial data cubes. We extend our Unified Modeling Language (UML) framework previously proposed with a UML profile allowing the conceptual design of several classes of exploration integrity constraints. We also propose a tool for their automatic implementation. We validate our proposal in the context of a real case study concerning the analysis of energetic farm indicators.

A Generic Spatial OLAP Model for Evaluating Natural Hazards in a Volunteered Geographic Information Context

Spatial data warehouses (SDW) and spatial OLAP (SOLAP) systems are well-known business intelligence technologies that aim to support a multidimensional and online analysis for a large volume of geo-referenced datasets. SOLAP systems are already used in the context of natural hazards for analyzing sensor data and experts' measurements. Recently, new data gathering tools coined as volunteered geographic information systems (VGI) have been adopted especially by non-expert users. Hence, (spatial) application development is facing a new challenge, which is the integration of expert-oriented data with citizen-provided data. In this paper, we propose a new generic spatio-multidimensional model based on the question/answer risk evaluation model that allows the integration of VGI data with classical SDW and SOLAP systems for the online analysis of natural hazards monitored by volunteers.

A new Spatial OLAP approach for the analysis of Volunteered Geographic Information

Volunteered Geographic Information (VGI) has great potential for enhancing analysts’ capabilities, assuming that VGI data quality issues, such as credibility and precision, are properly addressed. In this paper, we study the integration of VGI in Spatial OLAP (SOLAP) systems, which allow the integration and analysis of large volumes of good quality data. Using a real-world scenario, we highlight some similarities and differences among these two types of systems. We define a conceptual quality-oriented framework for warehousing and OLAPing VGI data. In particular, to address precision and credibility problems related to VGI data, we propose two new ETL operators: aggregation based on the VGI credibility and a filter based on the historical precision. We also define a new spatio-multidimensional model that provides decision makers with a global description of the quality of the aggregated data. To validate our proposal, we extend the classical relational SOLAP architecture using a standard VGI system.

Conceptual model for spatial data cubes: A UML profile and its automatic implementation

The increasing availability of geo-referenced data has increased the need to enrich OLAP with spatial analysis, leading to the concept of Spatial OLAP (SOLAP). The conceptual modelling of spatial data cubes requires the definition of two kinds of metadata: (i) warehouse metadata that model data structures that maintain integrated data from multiple data sources and (ii) aggregation11In this paper, the term “aggregation” does not refer to UML aggregation associations. In the text of our article, “aggregation” is used in the sense of calculating a result (as in the field of databases) — the terms “aggregation level” and “aggregating relationship” refer to OLAP aggregations. metadata that specify how the warehoused data should be aggregated to meet the analysis goals of decision makers.In this paper we provide a review of existing conceptual spatial data cube models. We highlight some limits of these models concerning the aggregation model design, and their implementation in existing CASE tools and SOLAP architectures.Firstly, we propose a new UML (Unified Modeling Language) profile for modelling complex Spatial Data Warehouses and aggregations. Our profile is implemented in the MagicDraw CASE tool.Secondly, we propose a tool for the automatic implementation of conceptual spatial data cube models, designed using our profile, in a SOLAP architecture. In particular, our solution allows: (i) generating different logical representations of the SDW (Spatial Data Warehouse) model (star schema and snow-flake schema) and (ii) implementing complex SOLAP analysis indicators using MDX (MultiDimensional eXpressions language).

A Map-Reduce-enabled SOLAP cube for large-scale remotely sensed data aggregation

Spatial On-Line Analytical Processing (SOLAP) is a powerful decision support systems tool for exploring the multidimensional perspective of spatial data. In recent years, remotely sensed data have been integrated into SOLAP cubes, and this improvement has advantages in spatio-temporal analysis for environment monitoring. However, the performance of aggregations in SOLAP still faces a considerable challenge from the large-scale dataset generated by Earth observation. From the perspective of data parallelism, a tile-based SOLAP cube model, the so-called Tile Cube, is presented in this paper. The novel model implements Roll-Up/Drill-Across operations in the SOLAP environment based on Map-Reduce, a popular data-intensive computing paradigm, and improves the throughput and scalability of raster aggregation. Therefore, the long time-series, wide-range and multi-view analysis of remotely sensed data can be processed in a short time. The Tile Cube prototype was built on Hadoop/Hbase, and drought monitoring is used as an example to illustrate the aggregations in the model. The performance testing indicated the model can be scaled along with both the data growth and node growth. It is applicable and natural to integrate the SOLAP cube with Map-Reduce. Factors that influence the performance are also discussed, and the balance of them will be considered in future works to make full use of data locality for model optimisation.

OLAPing Field Data: a Theoretical and Implementation Framework

Integration of spatial data into multidimensional models leads to the concept of Spatial OLAP SOLAP. Usually, SOLAP models exploit discrete spatial data. Few works integrate continuous field data into dimensions and measures. In this paper, we provide a formal multidimensional model that supports measures and dimension as continuous field data, independently of their implementation. We provide also a proposal for a logical model allowing aggregation of field measures in a feasible ROLAP architecture.

A hybrid risk-aware design method for spatial datacubes handling spatial vague data: implementation and validation

Spatial data warehouses SDWs and spatial OLAP SOLAP are well-known business intelligence BI technologies that aim to support multidimensional and online analysis of huge volumes of data with spatial reference. Spatial vagueness is one of the most neglected imperfections of spatial data. Although several works propose new ad-hoc models for handling spatial vagueness, their implementation in spatial database management systems DBMS and SDW is still in an embryonic state. In this paper, we present a new design method for SOLAP datacubes that allows handling vague spatial data analysis issues. This method relies on a risk management method applied to the potential risks of data misinterpretation and decision makers' tolerance levels to those risks. We also present a tool implementing our method and a validation of the method is done based on the designed datacubes schemas testing.

User-centric spatial data warehousing: a survey of requirements and approaches

The increased availability of spatial data in recent years has lead to new challenges in the analysis of large multidimensional datasets. One solution is to integrate GIS with OLAP and relational databases. Another strategy has been to leverage existing spatial capabilities of databases to perform spatial OLAP. In this article, we review existing modelling strategies for spatial data warehousing at all three levels: conceptual, logical and implementation. We gather the most essential requirements for handling spatial data and use insights from spatial databases and GIS systems to design a meta-framework that would enable a user-centric modelling of complex data. Our strategy is to keep the user as the focal point in the analysis process and lay the foundation for clear data abstraction at different levels using multidimensional abstract data types and operations and thus support complex spatial data in data warehouses.

Querying Cardinal Directions between Complex Objects in Data Warehouses

Data warehouses help to store and analyze large multidimensional datasets and provide enterprise decision support. With an increased availability of spatial data in recent years, several new strategies have been proposed to enable their integration into data warehouses and and perform complex OLAP analysis. Cardinal directions have turned out to be very important qualitative spatial relations due to their numerous applications in spatial wayfinding, GIS, qualitative spatial reasoning and in domains such as cognitive sciences, AI and robotics. They are frequently used as selection and restriction criteria in spatial queries. In data warehouses, cardinal directions can be used to perform spatial OLAP and feature navigation operations. In this article, we introduce and develop the Objects Interaction Graticule OIG approach to query the cardinal direction relations among spatio-temporal objects in data warehouses. First, we apply a tiling strategy that determines the zones belonging to the nine cardinal directions of each spatial object at a particular time and intersects them. This leads to a collection of grids over time called the Objects Interaction Graticule OIG. For each grid cell, the information about the spatial objects that intersect it is stored in a Objects Interaction Matrix. In the second phase, an interpretation method is applied to these matrices to determine the cardinal direction between the moving objects. The results obtained for each valid instant over the objects' lifetime describe the variation in the objects movement over time. This is integrated as a spatio-temporal OLAP operation in a novel moving objects data warehouse MODW that provides an extensible framework for supporting complex structured objects. Finally, we define new directional predicates that extend MDX querying and leverage OLAP between moving objects.

Special section on “Spatial data warehouses and SOLAP”

Data Warehouses (DW) and OnLine Analytical Processing (OLAP) tools are used in Business Intelligence (BI) applications in order to support decision-making processes. DWs are a specific type of database used to integrate, accumulate and analyze data from various sources. OLAP tools provide means to query and to analyze the warehoused information and produce online statistical summaries (indicators) at different levels of details. These indicators are computed using aggregate functions (e.g. Sum, Avg, Min, Max, etc.). Users can explore DWs by performing OLAP operations (e.g., Roll-up, Drill-down, etc.). Data mining algorithms can also be used in DWs, aiming towards improving data analysis; these techniques help to discern automatically the correlations and causal links between data. The increasing availability of geo-referenced data has made necessary the need to enrich OLAP with spatial analysis tools. OLAP systems were successfully adapted to solve problems in different areas of application and new kinds of decision support systems, named Spatial Data Warehouses (SDW) and Spatial OLAP (SOLAP) tools have been made available. SDWs are a collection of geographical information supporting spatial analysis. SOLAP tools allow users to perform spatio-temporal exploration of data: these tools combine OLAP analysis with cartographic visualization capabilities of GIS systems. The relevance of SOLAP technology Geoinformatica (2014) 18:269–272 DOI 10.1007/s10707-013-0196-9

A New Sensor-Based Spatial OLAP Architecture Centered on an Agricultural Farm Energy-Use Diagnosis Tool

Agricultural energy consumption is an important environmental and social issue. Several diagnosis tools have been proposed to define indicators for analyzing the large-scale energy consumption of agricultural farm activities (year, farm, production activity, etc.). In Bimonte, Boulil, Chanet and Pradel (2012), the authors define (i) new appropriate indicators to analyze agricultural farm energy-use performance on a detailed scale and (ii) show how Spatial Data Warehouse (SDW) and Spatial OnLine Analytical Processing (SOLAP) GeoBusiness Intelligence (GeoBI) technologies can be used to represent, store, and analyze these indicators by simultaneously producing graphical and cartographic reports. These GeoBI technologies allow for the analysis of huge volumes of georeferenced data by providing aggregated numerical values visualized by means of interactive tabular, graphical, and cartographic displays. However, existing data collection systems based on sensors are not well adapted for agricultural data. In this paper, the authors show the global architecture of our GeoBI solution and highlight the data collection process based on agricultural ad hoc sensor networks, the associated transformation and cleaning operations performed by means of Spatial Extract Transform Load (ETL) tools, and a new implementation of the system using a web-services-based loosely coupled SOLAP architecture to provide interoperability and reusability of the complex multi-tier GeoBI architecture. Moreover, the authors detail how the energy-use diagnosis tool proposed in Bimonte, Boulil, Chanet and Pradel (2012) theoretically fits with the sensor data and the SOLAP approach.

GCUBE Indexing

Spatial Online Analytical Processing System involves the non-categorical attribute information also whereas standard Online Analytical Processing System deals with only categorical attributes. Providing spatial information to the data warehouse (DW); two major challenges faced are;1.Defining and Aggregation of Spatial/Continues values and 2.Representation, indexing, updating and efficient query processing. In this paper, we present GCUBE(Geographical Cube) storage and indexing procedure to aggregate the spatial information/Continuous values. We employed the proposed approach storing and indexing using synthetic and real data sets and evaluated its build, update and Query time. It is observed that the proposed procedure offers significant performance advantage.

Modeling and Querying Continuous Fields with OLAP Cubes

The notion of SOLAP (Spatial On-Line Analytical Processing) is aimed at exploring spatial data in the same way as OLAP operates over tables. SOLAP, however, only accounts for discrete spatial data. Current decision support systems are increasingly being needed for handling more complex types of data, like continuous fields, which describe physical phenomena that change continuously in time and/or space (e.g., temperature). Although many models have been proposed for adding spatial (continuous and discrete) information to OLAP tools, no one is general enough to allow users to just perceive data as a cube, and analyze any type of spatial data together with typical alphanumerical discrete OLAP data, using only the classic OLAP operators (e.g., Roll-up, Drill-down). In this paper the authors propose a model and an algebra supporting it, that allow operating over data cubes, independently of the underlying data types and physical data representation. That means, in this approach, the final user only sees the typical OLAP operators at the query level, whereas at lower abstraction levels the authors provide discrete and continuous spatial data support as well as different ways of partitioning the space. As far as the authors are aware of, this is the first proposal, which provides such a general framework for spatiotemporal data analysis.

Spatial Online Analytical Processing and Its Application in Multidimensional Analysis of Forest Information

Spatial Online Analytical Processing and Its Application in Multidimensional Analysis of Forest Information

A probabilistic data model and algebra for location-based data warehouses and their implementation

This paper proposes a novel, probabilistic data model and algebra that improves the modeling and querying of uncertain data in spatial OLAP (SOLAP) to support location-based services. Data warehouses that support location-based services need to combine complex hierarchies, such as road networks or transportation infrastructures, with static and dynamic content, e.g., speed limits and vehicle positions, respectively. Both the hierarchies and the content are often uncertain in real-world applications. Our model supports the use of probability distributions within both facts and dimensions. We give an algebra that correctly aggregates uncertain data over uncertain hierarchies. This paper also describes an implementation of the model and algebra, gives a complexity analysis of the algebra, and reports on an empirical, experimental evaluation of the implementation. The work is motivated with a real-world case study, based on our collaboration with a leading Danish vendor of location-based services.

Using geospatial business intelligence paradigm to design a multidimensional conceptual model for efficient coastal erosion risk assessment

One of the main challenges in Coastal Erosion Risk Assessment (CERA) is integrating and analysis of conflicting data in various time periods and spatial scales through dissimilar environmental, social, and economic criteria. Currently, Geographical Information Systems (GIS) are widely used in risk assessment despite their drawbacks and limitations as transactional systems for multi-scales, multi-epochs, and multi-themes analysis. Hence, an analytical conceptual framework is proposed in this paper based on geospatial business intelligence paradigm to develop a Spatial Multidimensional Conceptual Model (SMCM) to assess coastal erosion risk. The model is designed based on Spatial On-Line Analytical Processing (SOLAP) platform, on the top of both analytical and transactional paradigms, to allow fast synthesis of cross-tabulated data and easy comparisons over space, scales, epochs, and themes. This objective is achieved through a comprehensive integration of multiple environmental, social, and economic criteria as well as their interactions at various scales. It also takes into account multiple elements at risk such as people, infrastructure, and built environment as different dimensions of analysis. Using this solution allows decision makers to benefit from on-demand, interactive, and comprehensive information in a way that is not possible using GIS alone. The developed model can easily be adapted for any other coastal region through the proposed framework to perform risk assessment. The advantages and drawbacks of the proposed framework are also discussed and new research perspectives are presented.

Spatial data warehouse design and spatial OLAP implementation for decision making of geospatial data update

Many authorities and local governments collect and maintain immense geospatial databases composed of various types of vector and raster data. These geospatial data need to be updated and monitored on a regular basis to provide accurate information. As the mapping area and the variety of data (GPS, DEM, LIDAR, aerial photography, satellite imagery, 3D building models, texture images, videos, etc.) collected for these areas increase, the maintenance costs also increases. Therefore, it is crucial to make effective costsaving decisions regarding the time, place, and type of geospatial data to be updated. In this study, geospatial data updating criteria are discussed and a multi-dimensional database model is proposed to aid the decision-making process in updating geospatial data. Based on this model, a star schema SDW (Spatial Data Warehouse) is designed and a SOLAP (Spatial OLAP) is implemented to aid in making optimal decisions regarding geospatial database updating.