A New Design Method for Managing Spatial Vagueness in Classical Relational Spatial OLAP Architectures

The design of Spatial OLAP (SOLAP) applications consists of (i) Spatial Data Warehouse (SDW) model design and (ii) SOLAP visualization definition because a specific set of understandable and readable cartographic visualizations corresponds to a particular type of SOLAP query. Unfortunately few works investigate geovisualization issues in SOLAP systems and propose new methodologies to visualize spatio-temporal data, and no works investigate tools for readable SOLAP cartographic displays. Moreover, some works propose ad-hoc methodologies for DWs and SDWs exclusively based on data and user analysis requirements. Therefore, we present in this paper (i) a new geovisualization methodology for SOLAP queries that yields readable maps and (ii) a new prototyping design methodology for SOLAP applications that accounts for geovisualization requirements.

Spatial OLAP (SOLAP) systems allow to explore and analyze huge volume of georeferenced data by means of pivot tables, graphical displays and maps. In the last years, SOLAP and Spatial Data Warehouses has received a lot of attention from the academic and industrial communities, who mainly focus on logical and physical data models to improve SOLAP queries performance. Although, geovisualization is essential for effective SOLAP analysis, few works propose advanced methods for the visualization of SOLAP queries results using cartographic displays. In this work, we present some geovisualization methods to reach an effective visualization of pivot table data over maps. We implement these proposals in a web-based SOLAP system, and we use a real case study concerning the analysis of the agrobiodiversity data for the demo scenario.

With a huge amount of data stored in spatial databases and the introduction of spatial components to many relational or object-relational databases, it is important to study the methods for spatial data warehousing and OLAP of spatial data. In this paper, we study methods for spatial OLAP, by integrating nonspatial OLAP methods with spatial database implementation techniques. A spatial data warehouse model, which consists of both spatial and nonspatial dimensions and measures, is proposed. Methods for the computation of spatial data cubes and analytical processing on such spatial data cubes are studied, with several strategies being proposed, including approximation and selective materialization of the spatial objects resulting from spatial OLAP operations. The focus of our study is on a method for spatial cube construction, called object-based selective materialization, which is different from cuboid-based selective materialization (proposed in previous studies of nonspatial data cube construction). Rather than using a cuboid as an atomic structure during the selective materialization, we explore granularity on a much finer level: that of a single cell of a cuboid. Several algorithms are proposed for object-based selective materialization of spatial data cubes, and a performance study has demonstrated the effectiveness of these techniques.

Large volumes of spatial data and multidimensional data are being published on the Semantic Web, which has led to new opportunities for advanced analysis, such as Spatial Online Analytical Processing (SOLAP). The RDF Data Cube (QB) and QB4OLAP vocabularies have been widely used for annotating and publishing statistical and multidimensional RDF data. Although such statistical data sets might have spatial information, such as coordinates, the lack of spatial semantics and spatial multidimensional concepts in QB4OLAP and QB prevents users from employing SOLAP queries over spatial data using SPARQL. The QB4SOLAP vocabulary, on the other hand, fully supports annotating spatial and multidimensional data on the Semantic Web and enables users to query endpoints with SOLAP operators in SPARQL. To bridge the gap between QB/QB4OLAP and QB4SOLAP, we propose an RDF2SOLAP enrichment model that automatically annotates spatial multidimensional concepts with QB4SOLAP and in doing so enables SOLAP on existing QB and QB4OLAP data on the Semantic Web. Furthermore, we present and evaluate a wide range of enrichment algorithms and apply them on a non-trivial real-world use case involving governmental open data with complex geometry types.

With the increasing development of the application of location services, massive check-in data is produced by social media applications on the mobile appliances, which includes characteristics of spatio-temporal information, user-emotion information, and etc. Traditional analysis techniques cannot handle check-in data well because of the complexity of spatio-temporal information. Spatial data warehouse provided a good architecture for spatial data’s storage and analysis. In this research, we designed a spatial data warehouse to store and manage the check-in data, used OLAP analysis technology to analyze it, and found many interesting results. It showed spatial data warehouse and OLAP provided a good frame to analyze check-in data.

This article describes how Spatial On-Line Analytical Processing, such a decisional technology, offers the possibilities of spatial and multidimensional analysis of data stored in multidimensional structure namely spatial data warehouse. However, this technology is limited in the quality aspect of the decision related to the multicriteria consideration. In the current article, the objective is to propose a Spatial Decision Support System namely “Silvicultura” for facilitating decision making in complex situations. This approach is based on integrating multicriteria analysis with SOLAP in order to enrich the spatial and multidimensional analysis with the contribution of MCA tools for mitigating conflict situations. The authors have based their proposal modeling on Unified Modeling language, since it is a well-known standard modeling language and can be easily extended for multidimensional modeling. Finally, in order to validate their proposal, the authors present a case study to show how to use it in the agroforestry management.

Maritime shipping is a complex process because it requires a continuous analysis of huge amounts of information. Much of this information has variable spatial and temporal properties. In addition, the analysis must be effective to help in making decisions in a timely manner. Generally the analysis is applied with Spatial On-Line Analytical Processing (SOLAP) basing on spatial data warehouses SDW. Data warehousing by current technologies with relational database management systems (RDBMS) cannot be applied effectively to manage the increasing complexity and volume of geo-referenced data. In order to increase the performance and availability of services, NoSQL databases technologies have appeared. This paper presents the modeling and the realization of a spatial decision support system (SDSS) for maritime navigation namely "Maritima", basing on the integration of the ECDIS and OLAP systems. In fact, the spatio-multidimensional analysis of the stored data can benefit from the expressive power of the electronic navigation chart offered by the ECDIS system. In order to benefit from the NoSQL model advantages, we try to apply transformations for the conceptual model to create document NoSQL DW for maritime data. In addition to that, the multidimensional analysis at different dimensions and aggregation levels can generate beneficial indicators for the decision-aid and can be very useful in addition of other steering systems for secure maritime navigation. Finally, "Maritima" is tested with a set of spatial data related to two different harbors "Arzew" and "Oran" in western of Algeria.

Although many real world phenomena are vague and characterized by having uncertain location or vague shape, existing spatial data warehouse models do not support spatial vagueness and then cannot properly represent these phenomena. In this paper, we propose the VSCube conceptual model to represent and manipulate shape vagueness in spatial data warehouses, allowing the analysis of business scores related to vague spatial data, and therefore improving the decision-making process. Our VSCube conceptual model is based on the cube metaphor and supports geometric shapes and the corresponding membership values, thus providing more expressiveness to represent vague spatial data. We also define vague spatial aggregation functions (e.g. vague spatial union) and vague spatial predicates to enable vague SOLAP queries (e.g. intersection range queries). Finally, we introduce the concept of vague SOLAP and its operations (e.g. drill-down and roll-up). We demonstrate the applicability of our model by describing an application concerning pest control in agriculture and by discussing the reuse of existing models in the VSCube conceptual model.

Spatial Data warehouses and Spatial OLAP (SOLAP) systems allow analyzing huge volumes of georeferenced datasets. SOLAP has been successfully applied in several domains such as marketing, health, urbanism, etc. Few of SOLAP applications in the agricultural context have been proposed. The analysis of work reveals specific issues related to spatio-multidimensional analysis of agri-environmental data. Therefore, in this paper, we present our theoretical and implementation framework for agricultural SOLAP applications.

In this paper we present a Spatial Data Warehouse system that we use for aggregation and analysis of huge amounts of spatial data. The data is generated by utilities meters communicating via radio. In order to provide sufficient efficiency for our system we propose data and workload distribution as well as advanced indexing techniques. The system is based on a cascaded star model, which is a spatial development of a standard star schema and contains interconnected and often nested star schemas. The cascaded star allows efficient storage and analysis of spatial data, whose range extends from meter measurement values to weather information. The indexing tree structure and operation is tightly integrated with the spatial character of the data. Thanks to an available memory evaluating mechanism the system is very flexible in the field of aggregates accuracy. We also implemented indexing structure updating mechanism. The system is written in Java; for the data base we use Oracle 9i. Basing on the wide variety of tests results, we prove that a distributed system significantly surpasses the centralized version in terms of efficiency. We also show that a selective materialization of indexing structure fragments strongly increases system efficiency.

Spatial OLAP (SOLAP) systems are powerful GeoBusiness Intelligence tools for analysing massive volumes of geo-referenced datasets. Therefore, these technologies are receiving considerable attention in the research community and in the database industry as well. Applications of these technologies are current in several domains such as ad marketing, healthcare, and urban development, to name a few. Contrary to other application domains, in the context of agri-environmental data and analysis, SOLAP systems have been underexploited. Therefore, in this paper, the author makes an exhaustive survey of most of the published studies in the domain of the SOLAP analysis of agri-environmental data with an emphasis on the reasons why only few recent works investigate the use of SOLAP systems in the agri-environmental context. In particular, the author focuses on the complexity of the spatio-multidimensional model and its implementation. Moreover, based on surveying the state of the art in this domain, this paper identifies some general guidelines that must be considered by the scientific community to design and implement efficient SOLAP approaches to the analysis of geo-referenced agri-environmental datasets. Finally, open issues about warehousing and OLAPing agri-environmental data are also shown in the paper.

A spatial data warehouse (SDW) is constructed to support the spatial data analysis for decision support purposes. Selectively materializing spatial views to rewrite input queries and thus reduce query response time is a challenging issue for spatial query evaluations. In this paper, we first investigate the issue of using spatial metadata to construct a spatial view dependency framework, which implies an order to materialize views. We then propose a cost model to evaluate the cost for processing spatial queries, which measures the online computation vs. space cost for spatial queries. A greedy algorithm is introduced to materialize a set of views based on the view dependence framework with associated cost value at each dependence level, which shows the local cost optimality of designing an SDW.

Data warehouses and OLAP systems help to interactively analyze huge volume of data. This data, extracted from transactional databases, frequently contains spatial information which is useful for decision-making process. Integration of spatial data in multidimensional models leads to the concept of SOLAP (Spatial OLAP). Using a spatial measure as a geographical object, i.e. with geometric and descriptive attributes, raises problems regarding the aggregation operation in its semantic and implementation aspects. This paper shows the requirements for a multidimensional spatial data model and presents a multidimensional data model which is able to support complex objects as measures, inter-dependent attributes for measures and aggregation functions, use of ad-hoc aggregation functions and n to n relations between fact and dimension, in order to handle geographical data, according to its particular nature in an OLAP context.

Investigation shows that a huge number of spatial data exists in current business databases. Traditional data warehousing and OLAP, however, could not exploit the spatial information to get deep insight into the business data in decision making. In this paper, we propose a novel approach to enabling spatial OLAP by aggregating on the spatial hierarchy. A spatial index mechanism is employed to derive the spatial hierarchy for pre-aggregation and materialization, which in turn are leveraged by the OLAP system to efficiently answer spatial OLAP queries. Our prototype system shows that the proposed approach could be integrated easily into the existing data warehouse and OLAP systems to support spatial analysis. Preliminary experiment results are also presented.

Prevention and control of influenza epidemics are major challenges for public health care services. Early identification of flu outbreak is an important step towards implementing effective disease interventions for reducing mortality and morbidity in human populations. Indeed, health officials need a real geo-making tool for monitoring and prediction. The aim of the current study is to discuss a novel spatiotemporal tool for monitoring and predicting the phenomenon of the seasonal influenza epidemic spread in the human population using multiple regression analysis. The suggested tool is mainly based on three sub-systems. It allows generating simulation data by the use of a simulation system, integrating data sources in a data warehouse (DW) system and performing a specific online analysis Spatial On-Line Analytical Processing (SOLAP). Our proposal enables also to illustrate evolution of disease through visualizations in time and space. It can examine social network effects to better understand the topological structure of social contact and the impact of its properties. A regression analysis is performed on the influenza epidemic to examine the main factors influencing flu incidence number and therefore to predict and track influenza epidemic.