An Open Source and Web Based Framework for Geographic and Multidimensional Processing Joel da Silva
Valeria C. Times ´
Ana Carolina Salgado
Center for Informatics Federal University of Pernambuco P. O. Box 7851 Cidade Universitaria, Recife - PE, ´ Brazil, 50.732-970
Center for Informatics Federal University of Pernambuco P. O. Box 7851 Cidade Universitaria, Recife - PE, ´ Brazil, 50.732-970
Center for Informatics Federal University of Pernambuco P. O. Box 7851 Cidade Universitaria, Recife - PE, ´ Brazil, 50.732-970
[email protected]
[email protected]
[email protected]
ABSTRACT The development of Business Intelligence (BI) systems has been the destination of high investments made by several enterprises. The motivation is because an efficient decision support environment brings them several business world advantages, mainly if it provides integrated functionalities for the geographical and/or multidimensional processing. The intended goal is to provide users with a system capable of processing both geographic and multidimensional data in a seamless way, by abstracting the complexity of separately querying and analyzing these data in a decision making process. However, this integration may not be fully achieved yet or may be built using proprietary technologies. This paper presents an open source and web based framework for geographic and multidimensional decision support. Our approach uses a geographical data warehouse, a metadata source, a query language and a geographical and multidimensional engine for processing queries sent by a web based client application.
Keywords Geographical Data Warehouse (GDW), Geographic and Multidimensional Processing, OLAP and GIS Integration
1.
INTRODUCTION
For the last years, several researchers from the Information Technology community have thoroughly investigated the problem of integrating the analytic and geographic processing [33, 14, 1]. However, much of this work does no more than proposing operations for the system interface. Moreover, this is an arduous task and deserves some special attention. The main idea of our proposal is to develop an open and extensible system with the analysis capabilities available in these two technologies. Thus, a geographic processing system could take the advantage of the facilities implemented by an analytic tool while the latter would receive a considerable gain in aggregating some spatial treatment to the geographic dimension.
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To achieve this, GOLAPA (Geographic Online Analytic Processing Architecture)Project [26], proposes a way of providing users with an abstraction of the complexity involved in querying both analytic and geographic data for decision support.
2.
GEOGRAPHIC AND MULTIDIMENSIONAL PROCESSING
Nowadays, to assist the market demand, the major Database Management Systems (DBMS) suppliers have been trying to adapt their solutions to provide the definition, storage, manipulation and the recovery of geographical data. In this section, we describe some relevant commercial and open source software infrastructure for spatial and multidimensional processing.
2.1
Commercial Solutions for Spatial and Analytical Decision Support
Oracle [8] DBMS is a post-relational database and in its last version, the Oracle offers the ORACLE OLAP package, which makes available several components for the creation of analytical systems, including a robust DBMS, ETL (Extraction, Transform, Loading) tool, DW and OLAP building functionalities, data mining services and tools for building charts and reports generation. Many of these functionalities are provided by the Oracle Warehouse Builder. For the geographical data processing, definition, storage and querying, the Oracle DBMS makes available the spatial extension named Oracle Spatial. With this extension, each geometry is stored as an spatial object named SDO GEOMETRY. This spatial extension is based on the ISO SQL/MM [16] and OGC Simple Feature Specification for SQL [3]. Microsoft Business Intelligence and Data Warehousing[7] is a commercial software package that provides support for BI, which is included in the last version of the Microsoft SQL Server DBMS. The SQL Server 2005 solution brings several components for the development of decision support environments including an ETL tool, a relational database, a component for analytical and data mining processing and tools for report management (SQL Server 2005 Reporting Services). For querying multidimensional data from a DW, the Analysis Services component uses the MDX (MultidimensionalExpressions) [27] query language. With regards to the spatial processing, the SQL Server 2005 does not offer a native functionality. However, geographical processing can be made available by using, for example, the Mapinfo SpatialWare [5], which is based on the SQL/Multimedia [35] and the OGC (Open Geospatial Consortium) [3] specifications.
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Hyperion Essbase[15] multidimensional database offers the Hyperion Essbase OLAP Server, that has been optimized for the development of analytical applications. In order to perform multidimensional data definition, manipulation and querying, the Essbase uses both the MaxL and MDX. For geographical processing in the Essbase, there is a need to use a library named ESRI MapObjects [10], which is a set of ActiveX components to manipulate spatial data. With the IBM DB2 Data Warehouse Enterprise Edition [4] users have the DBMS DB2, an integrated OLAP processing, many data mining functionalities, an ELT tool and a lot of improvements for building business intelligence environments. For the OLAP server, the IBM offers the DB2 OLAP Server, which is a packing of the Hyperion Essbase Olap Server, listed above. Moreover, the DB2 Spatial Extender offers support for spatial data processing in the DB2, including data definition, storage, recovery and analysis based on the OGC Simple Feature Specification for SQL [3]. IBM Informix [4] includes the IBM Red Brick Warehouse, that is a relational database optimized for analytical multidimensional processing. The Red Brick Warehouse offers two sets of analytical functions. The first one is the RISQL, which has specially been developed to be used in the Red Brick Warehouse. The second set of analytical functions is based on a traditional SQL extension for multidimensional data processing, which is based on the ISO SQL/OLAP [16] specification. The spatial processing component of the IBM Informix is the IBM Informix Spatial DataBlade, which allows the definition, storage, management and recovery of spatial data. Sybase Adaptive Server [36] is another solution for BI and analytical processing. The OLAP server included in this software package is the Sybase IQ Enterprise Edition, which has been fully optimized for decision support applications. By using the Spatial Query Server (SQS) [37], users can make use of functionalities provided for the development of many geographical applications. Teradata Warehouse [38] offers a lot of tools for the development of decision support applications, including a DBMS, an ETL tool and some analytical processing resources. The Teradata have extended the traditional SQL and created a new query language which has been optimized for OLAP processing, by providing, a lot of functions to analyze multidimensional data. Additionally, the Terradata can also be used for spatial processing by using the SQS [37], discussed in the last paragraph.
2.2
Open Source DBMS for Spatial and Analytical Decision Support
By using the Mondrian OLAP Server [22], users can implement multidimensional application using the PostgreSQL [28], which is an open source post-relational DBMS. For querying multidimensional data, the MDX [27] query language is used. The PostGis [30] is a spatial extension for users to develop geographical applications using the PostgreSQL DBMS. The PostGis offers geographical data types, spatial functions, spatial indexing techniques and an spatial query language. PostGis implementations are based on the OGC Simple Features Specification for SQL [3]. MySQL [24] DBMS has initially been designed to be used in OLTP applications. However, a commercial application named OLAP4ALL [34] has been provided to multidimensional processing over MySQL. The MySQL geographical processing can be achieved using its spatial extension, which is based on the OGC Simple Feature Specification For SQL [3] specification.
2.3
Some Considerations
Finally, we can conclude that there are several software packages
for the development of BI applications. However, none of them provide integrated functionalities for spatial and multidimensional processing, nor makes available a query language with a unique syntax for querying a GDW (Geographical Data Warehouse) using geographical and analytical operators simultaneously. Many times, commercial solutions can be very expensive. On the other hand, users can develop integrated environments for decision support with low cost by using open source technologies.
3.
LANGUAGES FOR SPATIAL AND MULTIDIMENSIONAL QUERYING
A very important component of a decision support application is the query language. Nowadays, dozens of approaches have been proposed to query geographical and multidimensional data. However, because of space limitation, in this section, we will list the most relevant ones only.
3.1
Multidimensional Query Languages
One of the most important approaches for querying multidimensional data is MDX (Multidimensional Expressions) [27]. By using MDX, users can perform many complex queries over a multidimensional data cube, making available configurable data view in different angle and aggregation levels by using analytical operators. Despite being similar to the traditional SQL, MDX is not an SQL extension. MDX is a special query language with a lot of analytical functions and has been optimized for querying multidimensional data. The MD-CAL Multidimensional Calculus [20] is another work that aims at providing a multidimensional query language. MDCAL performs calculus operations over a fact table in a multidimensional data source, supporting a high level analysis over analytical data and allowing the use of scalar and aggregated functions built-in in its expressions. Another approach for multidimensional querying is the Data Cube [18] operator, which provides support to perform multidimensional data grouping, sub-totals, crosstabulation, roll-up and drill-down operators. The last work outlined in this section is the SQL-M [9], which is an approach composed by a data model, a formal algebra and a multidimensional query language for analytical data analysis. Finally, one of the great advantages of this work is the capability of manipulating complex and irregular hierarchies.
3.2
Spatial Query Languages
With regard to the geographical query languages, some work has been developed in this research area as well. The Spatial SQL, proposed by Egenhofer [17], is based on an extension of the traditional SQL. Its language is composed by two modules: (i) a query language and (ii) a presentation language. The first module is based on the traditional SQL and preserves the SELECT-FROM-WHERE clause, while the presentation language, named GPL (Graphical Presentation Language), allows users to customize how spatial objects are presented. Another relevant work is the GeoSQL [11], which is used in an object-oriented GIS prototype named YH-GIS. In a query expression, the non spatial constraints are expressed using logical and comparison operators, similarly to the SQL WHERE clause. The spatial constraints are expressed using logical expressions and spatial predicates, which are based on the relationships found among the geographical features. SQL/SDA [21] extends the traditional SQL for spatial analysis and is based on the OGC Simple Feature ForSQL[3] specification, and thus, offers a lot of spatial functions for the management
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and querying of geographical features. In the same context, a visual query language for spatial databases was proposed in [23]. The chosen technique for this approach is the translation from the queries expressed in flow diagrams to an SQL based spatial extension. One of the most important work related to spatial query languages is the ISO SQL MM [16]. This specification is an effort to include spatial processing functionalities in the traditional SQL and is also based on the OGC Filter Encoding Specification[3], resulting in the provision of many functions for the management, storage, analysis and recovery of geographical features.
3.3
Some Considerations
As we can see, none of the previously outlined work provides a query language with capabilities that integrate analytical and spatial operators in a unique syntax. In this sense, the proposal presented in [29] has almost achieved this objective. In this work, the authors present an object-oriented geographical database, which has been extended to support links to analytical data stored in a multidimensional cube. However, a query language that fully integrates analytical and spatial operators is not described. Moreover, the geographical database and the multidimensional data sources have not been fully integrated because a geographical DW has not been used.
4.
THE PROPOSED FRAMEWORK
The proposed framework can be considered as an instance of the GOLAPA architecture [26], which is composed by three layers (I, II and III), which provide data, services and graphical user interface, respectively. The first layer (I) contains the Geographical Data Warehouse (GDW) which is based on the GeoDWFrame [25]. A GDW schema is similar to the traditional DW schemas (e.g. star schema) [32]. However, the geometries of the geographic data are stored in the GDW as well. GeoDWFrame, respectively: 1) does not apply spatial measures, 2) normalizes the geometrical data, 3) provides geographical data in any dimensional level and 4) stores the descriptive data of geographical features. In order to provide support for these issues, GeoDWFrame proposes two types of dimensions, namely: geographical and hybrid. The first one is classified into primitive and composed, while the second one is classified into micro, macro and joint. The primitive and composed dimensions have at all levels (or fields) just geographical data (e.g. customer addresses and its geo-references), while the others deal with geographical and conventional data (e.g. customer addresses and its geo-references plus ages and genders). A detailed description of the GeoDWFrame implementation can be found in [25]. The open source DBMS used for creating the GDW is the PostgreSQL with its spatial extension named PostGis, previously mentioned in this paper. For the extraction, transformation and loading of the geographical and multidimensional data, we have used the scripts based on the PostgreSQL PL/pgSQL language. The second layer (II) implements the Geographical Online Analytical Processing Engine (GOLAPE) component of the GOLAPA architecture. This component is responsible for receiving and processing geographical and/or multidimensional requests. In this layer, we have some software modules for query processing, query optimization and query management. This engine has been implemented by extending the Mondrian OLAP server [22] for adding support for spatial queries processing. Thus, three types of requests can be processed. These query types are based on the GMLA Request Schema [19]. The possible requests types are MD, GEO and GEOMD. A query of type MD just contains analytic parameters that allow the execution of a multidimensional query. A request of type
GEO just contains geographic parameters for performing an spatial query. In addition, a GEOMD request can be further classified as follows: 1) Mapping GEOMD, where an analytic request is sent to the GMLA WS and data with geographic correspondence are displayed on a map, and 2) Integration GEOMD, where analytic and spatial restrictions are specified and used in the request processing. A Geographical and Multidimensional Query Language, namely GeoMDQL, has been developed to express the queries mentioned above. It is based on an extension of the MDX and the OGC Simple Feature Specification for SQL [3] allowing the utilization of spatial and analytical operators in a unique syntax. We have designed GeoMDQL based on the MDX because it is the market standard for multidimensional data querying [31]. With the MDX syntax, complex analytical queries can be formulated in an intuitive form, allowing the manipulation of many dimensions in a query. On the other hand, by inheriting the spatial, positional and many other types of operators for geographical features handling, which are implemented in the PostGis, GeoMDQL became a novel query language to be used in a GDW. In the third (III) layer, the graphical user interface is given. This component has been implemented extending the JPivot client application, which is responsible for submitting geographic and/or multidimensional requests to the GOLAPE engine. After obtaining the response document, the query result viewer module has been designed for graphically displaying the results in charts, tables and/or maps using the HTML language and the SVG (Scalable Vector Graphics) [39] technology as well. The metadata source (METADATA) plays an important role in this work. The integration metadata are accessed by the GOLAPE whenever a GEOMD request is received. Thus, the GOLAPE component can find out if the analytic data have some geographic correspondences. A geographic correspondence is the information representing the spatial object geometry. The metadata source implementation has been based on the CWM OLAP, MOF (Meta Object Facility) [13] and on the metamodels GAM (Geographical and Analytical Metamodel) and GeoMDM (Geographical Multidimensional Metamodel) presented in [26]. In addition, the MDR [2] is used as a metadata repository. The CWM OLAP, GAM and GeoMD metadata are stored in this repository and accessed by the GOLAPE engine whenever a geographical and multidimensional request is received. We also highlight that an exclusively geographical request or an exclusively multidimensional request usually does not need to access the GOLAPA metadata while a geographical and multidimensional request always needs to access them. As we can see, the framework presented in this section is based on open and extensible standards. Thus, it is suitable for the development of environments for decision support with low cost and according to current market standards. In the next section, we will describe a case study to validate the proposed ideas.
5.
A CASE STUDY ON PUBLIC HEALTH SYSTEM
In order to validate our previous ideas, we have implemented a Geographical Data Warehouse (GDW) whose data was obtained from the Brazilian public health system. This GDW structure is responsible for managing significant amounts of historical data that include geo-referenced location and enables us to explore the capabilities of both systems by improving the manipulation and evaluation of the considered data. The used data have annual information about infantile mortality, woman’s health, control of illnesses and buccal health; making pos-
65
sible an analysis about health and life conditions of the population through the display of tables, charts and maps to detect geographic and secular variations. Hence, this GDW may help authorities in the processes of planning, management and evaluation of public politics in the health care assistance, as well as being an efficient mean of elaborating goals to improve the existing public health system services offered to the Brazilian population. GDW schema was designed according to the GeoDWFrame recommendations [25]. Some query examples exploiting the schema designed include: 1) Which municipalities have tax of infantile mortality less than 0,05 for the last year? 2) Which State has the rates of vaccine covering in the year 2000? and 3) Which municipalities, whose distance from Recife is less than or equal to 100Km, have at least two medical consultations per habitant. According to the GMLA Request Schema [19], queries 1 and 2 are of type Mapping GEOMD while the third one is of type Integration GEOMD. When an Integration GEOMD query is handed over to the system, the application program has to estimate the execution plans of sub-queries and decide how best performance rates can be achieved. After this, the engine redirects sub-queries to the extended Mondrian OLAP Server; which collects the partial results to integrate them and send information to the client. The graphical user interface (GUI) uses JPivot to provide the infrastructure needed to visualize the query results through charts, tables and maps. Then, the result set is showed on a web browser at the client side. As an example, in Figure 1, we present a query result, which contains the multidimensional view (Figure 1A) , the geographical view (Figure 1B) and the corresponding chart in the (Figure 1C).
Figure 1: A Mapping GEOMD query result The query result presented in Figure 1 corresponds to a Mapping GEOMD query. This query recovers the Infantile Mortality ratings for all the Brazilian regions in the year 2000. Here, a multidimensional query is sent to the GOLAPE engine, which recover the multidimensional data and their geographical correspondences by accessing the METADATA source. As a result, the Infantile Mortality ratings for each Brazilian geographical region are given (as shown in the Figure 1A). Also, the geometries of each geographical features are rendered in a map, as we can see in Figure 1B. Optionally, the graphical interface can show a chart with the mea-
sures displayed in the Figure 1A. Additionally, by clicking on the ’+’ symbol (column Region in Figure 1A), we can obtain more detailed ratings by applying a drill down operation over the selected region. This consists in automatically formulating a new query and sending it to the GOLAPE engine. After applying a drill down operation, we can revert the result by applying a roll up operation. The chart area is fully configurable, making available several options for displaying the analytical data. Finally, the classical geographical operations can be applied over the map area as well. As a result, the prototype developed consists of a number of open source interacting components: from database that stores data up to user interface that allows browsing and information analysis.
6.
CONCLUSION AND FUTURE WORK
The integration between analytic and geographic processing as a single tool provides a wider context for decision support. This is so because it aggregates: the capability of quickly analyzing a large volume of data and the power of visualizing data on maps and running spatial queries. The work presented in this paper is part of the GOLAPA project, which is concerned with the development of an open and extensible architecture for multidimensional and/or geographical processing. The presented framework is composed by some models for guiding the development of a GDW, a metadata source and makes available an engine for geographicmultidimensional processing. Moreover, the GeoMDQL is a novel query language for managing and recovering data from a GDW. We have extended MDX because it can be considered as the market standard for multidimensional querying, and because traditional SQL is not suitable for multidimensional querying [20, 6, 12]. PostgreSQL with its spatial extension named PostGis is the open source DBMS used for creating the GDW, which was populated with multidimensional and geographical data, using scripts based on the PostgreSQL PL/pgSQL language. Regarding to the GOLAPE engine implementation, we have extended the Mondrian OLAP Server for supporting geographical processing. Similarly, the web client application JPivot has been extended to support the handling of geographic data by incorporating a software module for the dynamically rendering of maps using SVG, in a web browser. The metadata repository used is MDR, and a software package was developed for the metadata insertion and querying. The programming languages used in all these implementation extensions were the Java, HTML and JSP. Because the framework presented in this paper is based on open and extensible standards, it is suitable for the development of environments for decision support with low cost and according to some market standards. Thus, this framework may be applied to any other projects aiming at integrating analytic and geographic processing. In order to validate the proposed ideas, we have presented a case study where a GDW was implemented to makes available information from a national health department. Thus, a lot of queries can be sent to the GOLAPE engine, involving analytical and geographical operators for obtaining a detailed analysis over the GDW. Some planned approaches to future work are given as follows. In the layer II, some implementations will be accomplished for the query optimization module. Moreover, in the layer I, for the graphical user interface, some improvements will be added for a better user interaction with charts, tables and maps.
7.
ADDITIONAL AUTHORS
Vivianne N. Medeiros,
[email protected], Center for Informatics, Federal University of Pernambuco, P. O. Box 7851 Cidade
66
Universit´aria, Recife - PE, Brazil, 50.732-970, Federal Service of Data Processing (SERPRO); and Robson Fidalgo,
[email protected], Center for Informatics, Federal University of Pernambuco, P. O. Box 7851 Cidade Universit´aria, Recife - PE, Brazil, 50.732-970.
[19]
8.
[20]
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