Spatial Data Warehouse – A Prototype

2003

Progress on data integration has come to the point that we must deal with the need of semantic integration. In this work we present a prototype of an integration tool of information sources (Semantic Integration Tool for Spatial Data). It focuses on schema integration of spatial databases. This tool is able to recognize the similarities and the differences between entities to be integrated. It is based on a Federated Architecture and, therefore, it is a leveled architecture. We obtain the XMI models from metadata of spatial sources that will be integrated. A parser takes the entities and attributes from XMI models by means of a semantic match process and we assess the similarities and differences between the objects. A domain-dependent ontology is created from the FGDC standard (Federal Geographic Data Committee) and domain-independent ontologies (Cyc and Wordnet). This ontology is represented in OWL. We make use of a ratio model (ontology nodes distance) for the assessment of the similarities and differences between terms. The prototype takes advantage of standardization in spatial data tools. For instance, ESRI or others that incorporate a lot of standards into their applications. We also make use for this work of the latest technology: XML, XMI, GML, OWL, and others.

Metadata represents the information about data to be stored in Data Warehouses. It is a mandatory element of Data Warehouse to build an efficient Data Warehouse. Metadata helps in data integration, lineage, data quality and populating transformed data into data warehouse. Spatial data warehouses are based on spatial data mostly collected from Geographical Information Systems (GIS) and the transactional systems that are specific to an application or enterprise. Metadata design and deployment is the most critical phase in building of data warehouse where it is mandatory to bring the spatial information and data modeling together. In this paper, we present a holistic metadata framework that drives metadata creation for spatial data warehouse. Theoretically, the proposed metadata framework improves the efficiency of accessing of data in response to frequent queries on SDWs. In other words, the proposed framework decreases the response time of the query and accurate information is fetched from Data Warehouse including the spatial information.

2011

In recent years, there has been a large increase in the amount of spatial data obtained from remote sensing, GPS receivers, communication terminals and other domains. Data warehouses help in modeling and mining large amounts of data from heterogeneous sources over an extended period of time. However incorporating spatial data into data warehouses leads to several challenges in data modeling, management and the mining of spatial information.

2014

This paper presents the use of Service Oriented Architecture (SOA) for integrating multi source heterogeneous geospatial data in order to facilitate geospatial data warehouse. In this study, Real Based ETL (RETL) concept is adapted in order to extract, transform and load data from a variety of heterogeneous data sources. ETL will transform data to schematic format and loading data into the Geo spatial data warehouse.By using a rule-based technique, the distribution of parallel ETL pipeline will enhance and perform more efficient in large scale of data and overcome data bottleneck and performance overhead. This can ease the disaster management and enables planners to monitor disaster emergency response in an efficient manner.

International Journal of Geographical Information Science, 1998

This paper investigates the problems that arise when application requirements command that autonomous spatial databases be integrated into a federated one. The paper focuses on the most critical issues raised by the integration of databases of different scales. A short presentation of approaches to interoperability and of the main steps composing the integration process is given first. Next, a general format is proposed for precisely defining correspondences between objects of two databases. The format can deal with a wide range of discrepancies in GIS data. Last, a solution is presented for aggregation conflicts which arise when one object of one database corresponds to a set of objects in the other database, a very frequent case when the databases are of different scales. The method is applied to excerpts of real cartographic databases.

Lecture Notes in Computer Science, 2009

Data aggregation in Geographic Information Systems (GIS) is a desirable feature, spatial data are integrated in OLAP engines for this purpose. However, the development and operation of those systems is still a complex task due to methodologies followed. There are some ad hoc solutions that deal only with isolated aspects and do not provide developer and analyst with an intuitive, integrated and standard framework for designing all relevant parts. To overcome these problems, we have defined a model driven approach to accomplish Geographic Data Warehouse (GDW) development. Then, we have defined a data model required to implement and query spatial data. Its modeling is defined and implemented by using an extension of UML metamodel and it is also formalized by using OCL language. In addition, the proposal has been verified against a example scenario with sample data sets. For this purpose, we have accomplished a developing tool based on Eclipse platform and MDA standard. The great advantage of this solution is that developers can directly include spatial data at conceptual level, while decision makers can also conceptually make geographic queries without being aware of logical details.

International Journal of Computer Applications, 2015

With the expeditious development in Geographic Information System (GIS) and their applications, leads to the increment in the geographical databases that have been developed by different vendors. However, the amalgam of data and accessing is still havoc for the development of GIS. Spatial or geometric data types provide basic abstraction for modeling the geometric structure of objects in space and building the relationships among objects, properties and their operations. Operations on spatial data types include spatial operations on the area of a region, topological relationships checking the relative position of spatial objects to each other like overlap, meet, disjoint, or inside, and cardinal direction relationships like north or southeast.

Proceedings of the 28th Annual International Computer Software and Applications Conference, 2004. COMPSAC 2004.

This paper presents a study on Geography Markup Language (GML), the issues that arise from using GML for spatial databases and solutions that have been proposed. We concentrate on three aspects of GML, including storage, parsing, and querying. GML is an XML encoding for storing geographic data. It has been developed by the OpenGIS as a medium for uniform geographic data storage and exchange among diverse applications. The underlying concepts of XML therefore can also be applied to GML data. This results in both advantages and disadvantages, which are discussed in the paper. GML is a comparatively new language in the field of geographic information systems and still in its developmental stage. Most of the data processing techniques need to be further developed in order for GML to be an efficient medium for geographic data storage and processing.

Lecture Notes in Computer Science, 2010

Since the data warehouse integrates the information provided by data sources, it is crucial to reconcile these sources with the information requirements of decision makers. It is specially true when novel types of data and metadata are stored in the data sources, e.g. spatial issues. In this way, spatial requirements have to be conformed with the available spatial metadata in order to obtain a data warehouse that, at the same time, satisfies decision maker spatial needs and do not attempt against the available metadata stored in the data sources. Therefore, in this paper, we have based on multidimensional forms and some spatial and geometric considerations to define a set of Query/View/Transformation (QVT) relations to formally define a set of rules that help designers in this tedious and prone-to-fail task. The novelty of our approach is to consider an hybrid viewpoint to develop spatial data warehouses (SDW), i.e., we firstly obtain the conceptual schema of the SDW from user requirements and then we verify its correctness against spatial data sources by using automatic transformations. Finally, the designer could take decisions to overcome the absence or incompactibility of certain spatial data by using our Eclipse CASE tool.

2011

Data warehouses and OLAP systems help to analyze complex multidimensional data and provide decision support. With the availability of large amounts of spatial data in recent years, several new models have been proposed to enable the integration of spatial data in data warehouses and to help analyze such data.