Spatial OLAP and Multicriteria Integrated Approach for Decision Support System

Spatial Data Warehouses (SDW) and Spatial OLAP (SOLAP) systems are well-known Business Intelligence technologies that aim to support multidimensional and online analysis of huge volumes of datasets 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 in information systems, the implementation of those models in Spatial DBMS and SDW is still in an embryonic state. Thus, 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 system implementing our method.

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.

Geographic information systems (GIS) have shown their power when dealing with information that is tied to places. However their analytical functionalities are oriented towards the management of data not towards their effective analysis. On the other hand OLAP technology on which decision support systems (DSS) are based, provide powerful, user-friendly tools that help in the decision making process. The coupling GIS and OLAP has lead to the concept of SOLAP (spatial OLAP) where GIS provides the cartographic representation and OLAP provides multidimensional perspective of data and. In this paper, we present a review of literature of SOLAP, some of the open research issues which are under investigation. We also present some trends in SOLAP such as continuous SOLAP, near real time SOLAP applications, Web based interfaces, and so on.

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.

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.

Introducing spatial data into multidimensional models leads to the concept of Spatial OLAP (SOLAP). Existing SOLAP models do not completely integrate the semantic component of geographic information (alphanumeric attributes and relationships) or the flexibility of spatial analysis into multidimensional analysis. In this paper, the authors propose the GeoCube model and its associated operators to overcome these limitations. GeoCube enriches the SOLAP concepts of spatial measure and spatial dimension and take into account the semantic component of geographic information. The authors define geographic measures and dimensions as geographic and/or complex objects belonging to hierarchy schemas. GeoCube’s algebra extends SOLAP operators with five new operators, i.e., Classify, Specialize, Permute, OLAP-Buffer and OLAP-Overlay. In addition to classical drill-and-slice OLAP operators, GeoCube provides two operators for navigating the hierarchy of the measures, and two spatial analysis operators that dynamically modify the structure of the geographic hypercube. Finally, to exploit the symmetrical representation of dimensions and measures, GeoCube provides an operator capable of permuting dimension and measure. In this paper, GeoCube is presented using environmental data on the pollution of the Venetian Lagoon.

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.

Unified Modeling Language (UML) has emerged as the software industry’s dominant modeling language. It is the de facto modeling language standard for specifying, visualizing, constructing, and documenting the components of software systems. Despite its prominence and status as the standard modeling language, UML has its critics. Opponents argue that it is complex and difficult to learn. Some question the rationale of having nine diagramming techniques in UML and the raison d’être of those nine techniques in UML. Others point out that UML lacks a comprehensive methodology to guide its users, which makes the language even more convoluted. A few studies on UML can be found in the literature. However, no study exists to provide a quantitative measure of UML complexity or to compare UML with other object-oriented techniques. In this research, we evaluate the complexity of UML using complexity metrics. The objective is to provide a reliable and accurate quantitative measure of UML complexity. A comparison of the complexity metrical values of UML with other object-oriented techniques was also carried out. Our findings suggest that each diagram in UML is not distinctly more complex than techniques in other modeling methods. But as a whole, UML is very complex–2-11 times more complex than other modeling methods.

Unified Modeling Language (UML) has emerged as the software industry’s dominant modeling language. It is the de facto modeling language standard for specifying, visualizing, constructing, and documenting the components of software systems. Despite its prominence and status as the standard modeling language, UML has its critics. Opponents argue that it is complex and difficult to learn. Some question the rationale of having nine diagramming techniques in UML and the raison d’être of those nine techniques in UML. Others point out that UML lacks a comprehensive methodology to guide its users, which makes the language even more convoluted. A few studies on UML can be found in the literature. However, no study exists to provide a quantitative measure of UML complexity or to compare UML with other object-oriented techniques. In this research, we evaluate the complexity of UML using complexity metrics. The objective is to provide a reliable and accurate quantitative measure of UML complexity. A comparison of the complexity metrical values of UML with other object-oriented techniques was also carried out. Our findings suggest that each diagram in UML is not distinctly more complex than techniques in other modeling methods. But as a whole, UML is very complex–2-11 times more complex than other modeling methods.

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.

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.

The main purpose of this paper is to present knowledge-based Enterprise model (EM) sufficiency as data repository for Unified Modelling Language (UML) models generation. UML models are one of the most usable modelling languages in system lifecycle design stage, despite the problem domain of the system. UML models can be generated from Enterprise Model by using particular transformation algorithms presented in previous researches. Generation process from Enterprise model is represented by certain Hospital Information Management process example. Generated UML dynamic Use Case, Activity, Sequence and State models of different perspectives of Hospital Information Management process prove sufficiency of stored information in Enterprise model.

This tutorial presents the modeling concepts of the standard object modeling language, the Unified Modeling Language (UML). Particular attention is paid to the use of the UML for modeling system/software requirements and designs. A system development process and an example of its application is presented. The materials will be based on the recently released UML 1.4 standard. The tutorial also presents some of the major issues and challenges facing the evolution of the UML.

Software product line (SPL) is a software engineering paradigm for software development. A software product within a product line often has specific functionalities that are not common to all other products within the product line. Those specific functionalities are termed “variant features” in a product line. SPL paradigm involves the modeling of variant features. However, little work in SPL investigates and addresses the modeling of variant features specific to user interface (UI). Unified Modeling Language (UML) is the de facto modeling language for object-oriented software systems. It is known that UML needs better support in modeling UIs. Thus, much research developed UML extensions to improve UML support in modeling UIs. Yet little of this work is related to developing such extensions for modeling UIs for SPLs in which variant features specific to UI modeling must be addressed. This research develops a UML extension -Web User Interface Modeling Language (WUIML) to address these problems. WUIML defines elements for modeling variant features specific to user interfaces for Web-based SPLs. The model elements in WUIML extend from the metaclass and BasicActivity of the UML2.0 metamodel. WUIML integrates the modeling of variant features specific to user interfaces to UML. For example, in a Web-based patient registration software product line, member products targeting British users may use British date format in the user interface, while member products targeting United States users may use United States date format in the user interface. Thus, this is a variant feature for this product line. WUIML defines a model element, XOR, to represent such exclusive or conditions in a product line user interface model. WUIML would reduce SPL engineers’ efforts needed in UI development. To validate the WUIML research outcome, a case study was conducted. The results of this empirical study indicate that modeling UIs for Web-based SPLs using WUIML is more effective and efficient than using standard UML.

In this chapter, we provide the essential theory and practical details of employing the principles of Object Orientation (OO) in system architecture modeling. We do not attempt to provide an exhaustive OO tutorial, since there exists a very large literature on this subject (see the references to this chapter). Our goal, instead, is to give the reader the level of understanding needed to master the MBSAP methodology and apply it to practical system architecting challenges. First, we present the rationale for OO and define the essential concepts and terminology, stressing the benefits it delivers in dealing with complex entities. Although we use the Systems Modeling Language (SysML) as the basis for this book, we begin by discussing and illustrating the primary diagrams of the Unified Modeling Language (UML), which is the root language standard for OO. This is then followed by a description of the changes to UML introduced by the SysML, which is defined as a Profile of UML. The reality is that a large part of the SysML is taken directly from the UML language specification. We believe understanding of this material is enhanced by first considering the parent UML and then explicitly showing how UML modifies and extends the language. We include generic UML and SysML diagrams that illustrate important principles and syntax and that can serve as format samples in developing real architecture models. Appendices A and B are concise references to UML and SysML, respectively, intended to give readers a quick and easy way to look up the details. Chapter objective: the reader will be able to create and read UML and SysML diagrams and to describe the role of each.