Towards a web coverage service for efficient multidimensional information retrieval

INDEX

ACKNOWLEDGEMENT

ABSTRACT

ZUSAMMENFASSUNG

LIST OF ABBREVIATIONS

1. INTRODUCTION
1.1 MOTIVATION
1.2 RESEARCH PROBLEM
1.3 RESEARCH APPROACH

2. APPLICATION SCENARIOS
2.1 SCENARIO 1 - T HE B USINESS M AN
2.2 SCENARIO 2 - K NOWLEDGE IS THE K EY
2.3 SCENARIO 3 - D EUTSCHE P RESSE A GENTUR
2.4 SUMMARY

3. GENERAL CONCEPTS
3.1 WEB SERVICES - AN INTRODUCTION
3.1.1 EXTENSIBLE MARKUP LANGUAGE
3.1.2 GEOGRAPHY MARKUP LANGUAGE
3.1.3 SIMPLE OBJECT ACCESS PROTOCOL
3.1.4 UNIVERSAL DESCRIPTION, DISCOVERY AND INTEGRATION
3.1.5 WEB SERVICES DESCRIPTION LANGUAGE
3.2 ONLINE ANALYTICAL PROCESSING - AN OVERVIEW
3.2.1 OLAP CONCEPTS
3.2.2 OLAP OPERATIONS AND ARCHITECTURE
3.2.3 OLAP QUERYING CAPABILITIES
3.3 OGC WEB SERVICES - INTEROPERABLE GEOPROCESSING
3.3.1 WEB MAP SERVICE
3.3.2 WEB FEATURE SERVICE
3.3.3 WEB COVERAGE SERVICE
3.3.4 SUMMARY
3.4 RESEARCH APPROACH - REVISITED

4. RELATED WORK
4.1 INTRODUCTION
4.2 SPATIAL OLAP SOLUTIONS - AN OVERVIEW
4.3 SUMMARY

5. CASE STUDY: A STRATEGY FOR WEB COVERAGE SERVICE AND ONLINE ANALYTICAL PROCESSING INTEROPERABILITY
5.1 INTRODUCTION
5.2 PLATFORM AND SOFTWARE REQUIREMENTS
5.2.1 GIS DOMAIN
5.2.2 OLAP DOMAIN
5.3 THE DATA BASIS
5.3.1 DEUTSCHE PRESSE AGENTUR DATASET
5.3.2 GERMAN CITIES AND STATE GEOGRAPHIC DATA
5.4 MEDIATION PROCESS - CONCEPTUAL MAPPING
5.4.1 MAPPING A COVERAGE TO A CUBE
5.4.2 SERVICE CONFIGURATION
5.4.3 MAPPING AND WRAPPING - GML TO XMLA
5.5 PROOF OF CONCEPT
5.6 SUMMARY

6. CONCLUSION AND FUTURE WORK
6.1 CONCLUSION
6.2 RECOMMENDATIONS AND FUTURE WORK

7. REFERENCES

8. APPENDIX A - GLOSSARY

9. APPENDIX B - SAMPLE DEEGREE CONFIGURATION FILES

10. APPENDIX C - EXAMPLE REQUESTS, RESPONSES

ACKNOWLEDGEMENT

Wernher Von Braun once said BASIC RESEARCH IS WHAT I AM DOIN WHEN I DON’T KNOW WHAT I AM DOING. This has certainly been the case on many occasions when I didn’t know what I was doing and what I had to do. I would like to take this opportunity to extend my sincere gratitude to individuals who showed me the path and made me see light at the end of the tunnel.

First of all, there are of course people who were directly involved in the research project. Dr. Angelika Voss - she has been inspirational. Her never ending enthusiasm for the work, reviewing new pieces I wrote almost overnight, and her insights have left a lasting impression. Vera Hernandez - this thesis is her brainchild. Thank you for all the support and advice. Without your initiative and belief in the work this thesis would not have been possible. To the people working at the Spatial Decision Support department at IAIS Fraunhofer - Thank You. It has been a pleasure.

My family has been a bastion of great support; Annegret and Dr. Dilip Gadkari - no matter where I am, I will always remember you. Shikha Dalmia, your understanding and unconditional love has helped me through some dark phases. Mom, Dad - I dedicate this thesis to you. You made it possible.

Prof. Dr. Klaus Greve - this acknowledgment cannot be complete without appreciating your support over the past two years. You have been phenomenal.

Bonn, Friday, October 26, 2007

Anup Deshmukh

ABSTRACT

Historically, Business Intelligence (BI) and Geographic Information Services (GIS) have followed separate development and implementation paths. Customer requests for a complete operational picture and the ability to be more proactive has led to the demand for a synergistic power that can be exploited by integrating BI and GIS. An integrated geographic business intelligence solution (GBIS), a term coined by (ESRI, 2005), enables users to both visualize and manage spatial information and empower decision makers, at different levels, with the location-based intelligence they need to assess, plan and deliver services, present information and deal with ad hoc business queries. The integrated solution improves decision-making and responsiveness while extending the reach of GIS to address a wider range of business solutions. The investigations for a GBIS solution led to the introduction of a new sub-category of spatial decision-making solutions: Spatial Online Analytical Processing (SOLAP) or Spatial OLAP. This study contributes to the development of the SOLAP domain by presenting an interoperable web- based open and extensible prototype solution with the analysis capabilities available in the two technologies. The prototypical solution is an integration based on the Web Coverage Service (WCS¹ ), as defined by the Open Geospatial Consortium (OGC² ), and an OLAP (OnLine Analytical Processing) server. The author has extended an existing WCS implementation by supporting additional coverage types, as defined by the Geography Mark-up Language (GML) specification, and the ability to serve multidimensional data retrieved from an OLAP server. The distinctive feature of this solution being the proficiency to explore the two domains based on a single querying mechanism. The results of the augmented solution, investigated based on scenarios conceptualized by using the Deutsche Presse Agentur (dpa) dataset, have been positive and offer a solid base for further research work in this multidisciplinary domain.

Keywords: BI; DPA; GIS; GIS Web Services; GML; OGC; OLAP; SOLAP; WCS

ZUSAMMENFASSUNG

Historisch sind Business Intelligence (BI) und Geografische Informationssysteme (GIS) getrennten Entwicklungs- und Implementierungspfaden gefolgt. Kundenanfragen nach einem kompletten betrieblichen Bild, und der Bedarf nach mehr pro-aktivität hat zu einer synergistischen Macht geführt, die mit der Integration von BI und GIS ausgenutzt werden kann. Eine integrierte Geographic Business Intelligence Solution (GBIS), ein von (ESRI, 2005) geprägter Begriff, ermöglicht Benutzern, räumliche Information zu visualisieren und verwalten. Entscheidungsträger können mit standortbezogener Intelligenz auf verschiedenen Ebenen bewerten, planen und Dienste leisten, Information präsentieren und ad hoc Geschäftsfragen beantworten. Eine integrierte Lösung verbessert die Entscheidungsfindung und Ansprechbarkeit, indem sie die Eignung von GIS auf eine breitere Reihe von Geschäftslösungen ausdehnt. Die Suche nach einer GBIS-Lösung führte zur Einführung einer neuen Unterkategorie von raumbezogene Entscheidungfähige Lösungen: Spatial Online Analytical Processing (SOLAP) oder Spatial OLAP. Diese Studie trägt zur Entwicklung der SOLAP Thematik bei, indem sie eine interoperable, web-basierte, offene und erweiterbare prototypische Lösung mit den Analyse- Fähigkeiten der beiden Technologien präsentiert. Der Prototyp beruht auf dem Web Coverage Service (WCS) nach Definition des Open Geospatial Konsortium (OGC), kombiniert mit einen OLAP Server. Der Autor hat eine vorhandene WCS Implementierung erweitert, um noch nicht vorhandene Coverage-Typen und die Fähigkeit mehrdimensionale Daten von einem OLAP Server anzufordern und zu verarbeiten. Die Besonderheit dieser Lösung besteht darin, beide Aspekte auf der Grundlage einer einzigen Abfragemechanismus abzufragen. Szenarien, konzipiert für Datensätze der Deutsche Presse Agentur (dpa) bilden die Grundlage zur Evaluation der erweiterten Lösung. Die Ergebnisse sind positiv und bieten eine solide Basis für die weitere Forschungsarbeit in diesem mehrdisziplinarischen Gebiet.

Keywords: BI; DPA; GIS; GIS Web Services; GML; OGC; OLAP; SOLAP; WCS

LIST OF ABBREVIATIONS

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INDEX OF FIGURES

Figure 1: BI and GIS foundation for a SOLAP. After: (Bédard, 2005/02/10)

Figure 2: The three-tier architecture model of a web service. After: (BrainBell, 2006)

Figure 3: Web services architecture (roles and operations). After: (Champion, 2002a)

Figure 4: Sample XML document. After: (SysOnyx, 2002)

Figure 5: Simple example of a GML-encoded feature. After: (Brentjens, 2004)

Figure 6: HTML and GML System analogy. After: (Lake, 2006)

Figure 7: Sample SOAP request with header and body. After: (Twardoch, 2003)

Figure 8: Schematic view of the WSDL document syntax. After: (Twardoch, 2003)

Figure 9: Interaction of the web technologies used in web services. After: (Twardoch, 2003)

Figure 10: Categories of Business Intelligence. After: (BI, 2005)

Figure 11: OLAP Cube structure example. After: (Databeacon;, 2004)

Figure 12: Logical Multidimensional model. After: (Oracle, 2003)

Figure 13: Dimension examples: Left - thematic dimension, Right - Spatial dimension. After: (Rodolphe, 2004)

Figure 14: OLAP Star schema. After: (OracleDBAExpert, 2003)

Figure 15: Snowflake schema. After: (OracleDBAExpert, 2003)

Figure 16: Aggregation Hierarchies

Figure 17: OLAP data exploration. After: (Pederson, et al., 2001)

Figure 18: OLAP System architecture. After: (Baltzer, 2006)

Figure 19: OLAP storage options. After: (White, 2003)

Figure 20: OLAP Storage option for the prototypical solution

Figure 21: MDX syntax

Figure 22: XMLA concept and architecture. After: (Zhaohui, et al., 2005)

Figure 23: DISCOVER request syntax

Figure 24: XMLA sample EXECUTE request

Figure 25: The Open Web Service (OWS) Framework. After: (OpenGIS, 2003)

Figure 26: Summary - OGC data services. After: (Wojnarowska, et al., 2002)

Figure 27: Standard interoperable web mapping architecture. After: (Ding, et al., 2002)

Figure 28: WMS protocol diagram

Figure 29: WFS protocol diagram

Figure 30: Conceptual model of a coverage. After: (211/WG, 2004)

Figure 31: Conceptual (Graphic) Coverage model. After: (Baumann, 2006)

Figure 32: Feature subtypes. Based on: (211/WG, 2004)

Figure 33: List (partial) of supported Coverage types. Based on: (211/WG, 2004)

Figure 34: Coverage UML (partial) Schema. Based on: (211/WG, 2004)

Figure 35: Spatiotemporal Domain UML schema diagram. Based on: (211/WG, 2004)

Figure 36: RangeSet UML schema diagram

Figure 37: WCS protocol diagram

Figure 38: Conceptual model for the integrated solution

Figure 40: Overview of the SOLAP solutions domain

Figure 41: GMLA WS prototype. After: (Silva, et al., 2006)

Figure 42: IPTC Subject codes. Based on: IPTC/TEC 9701

Figure 43: Example of a report from the dpa dataset

Figure 44: Dimension Report Subject and levels

Figure 45: Dimension Report Location and levels

Figure 46: Dimension Report Time and levels

Figure 47: Measure(s) Priority and count

Figure 48: DPA OLAP cube model in star-schema

Figure 49: Multipoint coverage - XML schema. After: (211/WG, 2004)

Figure 50: MultiSurface coverage - XML schema. After:(211/WG, 2004)

Figure 51: Germany cities dataset representing only those cities in germany those are available in the dpa report set

Figure 52: Germany States dataset representing all the german states

Figure 53: deegree Coverage class diagram

Figure 54: RangeSubset UML diagram. Based on: (Whiteside, et al., 2006)

Figure 55: Cube UML structure diagram

Figure 56: Coverage to Cube Mapping

Figure 57: WCS service architecture and modules. After: (lat-lon, 2006)

Figure 58: deegree WCS configuration document

Figure 59: CoverageDescription OLAP extension

Figure 60: Coverage to Cube Mapping

Figure 61: deegree Datastore class diagram

Figure 62: deegree OLAP API package diagram

Figure 63: deegree request handling

Figure 64: DescribeCoverage request parameters. After: (Whiteside, et al., 2006)

Figure 65: DescribeCoverage response UML class diagram. After: (Whiteside, et al., 2006)

Figure 66: Mapping example - DescribeCoverage to Discover

Figure 67: DescribeCoverage to Discover mapping

Figure 68: DescribeCoverage xml request example

Figure 69: DescribeCoverage response (partial) document

Figure 70: GetCoverage request parameters. After: (Whiteside, et al., 2006)

Figure 71: Mapping GetCoverage to Execute

Figure 72: GetCoverage (partial) request

Figure 73: Mapped Execute request

Figure 74: GetCoverage (partial) response - domain set

Figure 75: GetCoverage (partial) response - range set

Figure 76: Scenario 1 - Proof of Concept

Figure 77: Scenario 1 - GetCoverage (domain set) response document

Figure 78: Scenario 1 - GetCoverage (partial range set) response document

Figure 79: Scenario 1 - GetCoverage (partial range set) response document

Figure 80: Scenario visualization using the CommonGIS tool

Figure 81: Scenario 2 - Proof of Concept

Figure 82: Scenario 2 - GetCoverage (partial range set) response document

Figure 83: Scenario 2 - GetCoverage (partial range set) response document

Figure 84: Scenario visualization using the CommonGIS tool ..

INDEX OF TABLES

Table 1: Summarized differences between OLTP and OLAP. After: (Navathe, 2004)

Table 2: Overview OLAP query interfaces

Table 3: XMLA DISCOVER schema rowsets

Table 4: XMLA supported properties

Table 5: Components of WCS operations

Table 6: OGC Web Services summary

Table 7: Research problem characteristics summary

Table 8: Related Works summary

Table 9: GIS Framework evaluation

Table 10: OLAP solution evaluation

Table 11: 'Mapping' WCS and XMLA request schemas

Table 12: Prototype solution evaluation

1. INTRODUCTION

Many organizations face the challenge of managing and presenting the sheer quantity of data being captured on a monthly, weekly, daily and hourly level. The introduction of business intelligence (BI) applications and technologies has helped organizations gather, provide access to, analyze, and present data and information easily to the decision makers. The applications utilize both relational and multidimensional technologies to form the overall BI infrastructure. From a historical perspective BI is a popularized umbrella term introduced by Howard Dresner of the Gartner Group in 1989 to describe a set of concepts and methods to improve business decision making by using fact-based support systems. BI is a broad category of applications and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions. BI solutions include the activities of decision support systems, query and reporting, online analytical processing (OLAP), statistical analysis, forecasting and data mining. Microsoft defines BI as: THE PROCESS OF EXTRACTING DATA FROM A DATABASE AND THEN ANALYZING THAT DATA FOR INFORMATION THAT YOU CAN USE TO MAKE INFORMED BUSINESS DECISIONS AND TAKE ACTION . However, data is not always used to its full potential and part of its richness, the spatial component, is simply left out. It has been estimated that about 80% of the data stored in corporate databases integrates spatial information that can be characterized by position, shape, orientation or size (Frankin, April 1992). It is obvious that this meaningful data is worth being integrated in the decision making process to provide a complete operational picture.

To gain better advantage of the spatial dimension in decision making the appropriate tools must be used. Geographic Information Systems (GIS) are the obvious potential candidate for such a task. (Worboys, 1995) provide this typical definition of a conventional GIS: A GIS IS A COMPUTER ­ BASED INFORMATION SYSTEM THAT ENABLES CAPTURE , MODELING , MANIPULATION , RETRIEVAL , AND PRESENTATION OF GEOGRAPHICALLY REFERENCED DATA . GIS provides functionalities like

1) spatial data acquisition and input,
2) spatial data storage and management,
3) spatial data presentation and output, and
4) spatial data manipulation and analysis

Spatial analysis identifies the subset of techniques that are applicable when, as a minimum, data can be referenced on a two-dimensional frame and relate to terrestrial activities. The results of spatial analysis will change if location or extent of the frame changes, or if objects are repositioned within it. Spatial analysis typically include, for example in a vector context, operations such as map overlay (combining of two or more map layers according to predefined rules), simple buffering (identifying regions on the map within a specified distance of one or more features, such as towns, roads or rivers) and similar basic operations. For raster-based GIS, widely used in the environmental sciences and remote sensing, this typically involves a range of actions applied to grid cells of one or more maps (or images) often involving filtering and/or algebraic operations (map algebra). Descriptive statistics, such as cell counts, mean value, variance, maxima, minima, cumulative values, frequencies and a number of other measures and distance computations are also often included in the generic term spatial analysis.

Since GIS was developed for the spatial domain it lacks the ready availability of analysis tools to help in decision-support beyond the domain. It is recognized that existing GISs per se are not adequate for decision-support applications when used alone and that alternative solutions must be used. (Bédard, 2002). Although a wide palette of analysis functionalities are available, this initial set should be enlarged to support a large variety of statistical techniques (descriptive, exploratory, and explanatory) that have been designed specifically for spatial and spatio-temporal data to take full advantage of the data.

BI tools on the other hand, though well-suited for knowledge discovery, are not adapted for the analysis of spatial data (Caron, 1998). In fact, business intelligence treats spatial data like any other data and spatial analysis is limited to predefined nominal locations (e.g. names of countries, states, regions, cities). Support for spatio-temporal analyses is limited (no spatial visualization, practically no spatial analysis, no map-based exploration of data, etc.) The union of spatial and non-spatial technologies, GIS and BI, is an interesting option to overcome the shortcomings of the two domains.

Historically, BI and GIS have followed separate development and implementation paths. Customer requests for a complete operational picture and the ability to be more proactive has led to the demand for a synergistic power that can be exploited by integrating business intelligence and geographic information systems. An integrated geographic business intelligence solution (GBIS), a term coined by (ESRI, 2005), enables users to both visualize and manage spatial information and empower decision makers, at different levels, with the location-based intelligence they need to assess, plan and deliver services, present information and deal with ad hoc business queries. The integrated solution improves decision-making and responsiveness while extending the reach of GIS to address a wider range of business solutions. This study contributes to the development of the geographic business intelligence research area by presenting an interoperable web-based open and extensible prototype solution with the analysis capabilities available in the two technologies.

1.1 MOTIVATION

Since BI and GIS are designed to accommodate and serve different purposes they are separate and distinct. The problem of integrating these two environments is multi-faceted. It includes consideration for technological as well as strategic issues. Traditionally BI and GIS applications are closed, isolated and incompatible with each other. Their integration to create GBIS solutions is a nightmare, due to poor documentation, obscure semantics of data, diversity of datasets, heterogeneity of existing systems in terms of data modeling concepts, data encoding techniques, storage structures, access functionality, etc (Bimonte, et al.).

Much of the research investigating the problem of integrating analytic and geographic processing has been carried out by the Information Technology (IT) community (Shekhar S., 2000). GBIS solutions allow an amalgamation of spatial solutions with the different categories of BI solutions. The three main categories being:

1) information and knowledge discovery,
2) decision support and intelligent systems, and
3) visualization

In this context we restrict ourselves to the information and knowledge discovery category of BI solutions. The concept of information and knowledge discovery is very broad and can take different forms. They are applications and subsystems that help people make decisions based on data that is culled from a wide range of sources. Information and knowledge discovery is an agglomeration of many parts (see Chapter 3) with OnLine Analytical Processing or OLAP being a prominent component. The wide acceptance of the new solution because of the advantages OLAP brings (see Chapter 3) has led to the concentration on OLAP solutions for decision support. OLAP has been first defined as: … THE NAME GIVEN TO THE DYNAMIC ENTERPRISE ANALYSIS REQUIRED : TO CREATE , MANIPULATE , ANIMATE AND SYNTHESIZE INFORMATION FROM EXEGETICAL , CONTEMPLATIVE AND FORMULAIC DATA ANALYSIS MODELS . T HIS INCLUDES THE ABILITY TO DISCERN NEW OR UNANTICIPATED RELATIONSHIPS BETWEEN VARIABLES , THE ABILITY TO IDENTIFY THE PARAMETERS NECESSARY TO HANDLE LARGE AMOUNTS OF DATA , TO CREATE AN UNLIMITED NUMBER OF DIMENSIONS AND EXPRESSIONS . (Codd, et al., 1993) OLAP solutions were introduced to solve some limitations of the traditional transactional systems (i.e. OLTP - OnLine Transaction Processing - such as Relational Database Management System - RDBMS), to support aggregated information, rapid comparisons in space, time and other dimensions, trends and knowledge discovery, quick response to unforeseen queries and other complex operations needed during tactic and strategic decision-making processes.

The investigations for a GBIS solution led to the introduction of a new sub-category of spatial decision-making solutions: Spatial Online Analytical Processing (SOLAP) or Spatial OLAP (Rivest, 2001). SOLAP relies on the multidimensional paradigm and on an enriched interactive data exploration processing, thus filling the analysis gap between spatial data and geographic knowledge discovery. In spite of its short history, SOLAP already has reached a first level of maturity with its own concepts, technologies and applications. The multidimensional paradigm makes SOLAP an interesting option to be studied in detail for the scope of this study. SOLAP can be defined as (Bédard, et al., October, 2004): A VISUAL PLATFORM BUILT ESPECIALLY TO SUPPORT RAPID AND EASY SPATIO ­ TEMPORAL ANALYSIS AND EXPLORATION OF DATA FOLLOWING A MULTIDIMENSIONAL APPROACH COMPRISED OF AGGREGATION LEVELS AVAILABLE IN CARTOGRAPHIC DISPLAYS AS WELL AS IN TABULAR AND DIAGRAM DISPLAYS . These solutions add a spatial component to the traditional OLAP tool.

FIGURE 1: BI AND GIS FOUNDATION FOR A SOLAP. AFTER: (BÉDARD, 2005/02/10)

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Figure 1 illustrates the two important components, geospatial and non-geospatial, of a SOLAP solution. The geospatial and the non-geospatial components can be divided into the sub-divisions - aggregated and not-aggregated. The SOLAP solution can be illustrated as a solution supporting aggregated geospatial data in a decision making process.

The solutions are based on coupling OLAP functionalities, used to provide multidimensional support, and GIS functionalities, used to store and visualize spatial information (Kouba Z., 2000), (Tchounikine A., 2005). Depending on the functionalities that are prioritized, the solution is termed as (LGS Group, 2000):

1) GIS-centric - the dominant tool - GIS offers its full functionality, but gets minimum capabilities from the OLAP tool;
2) OLAP-centric - the dominant tool - OLAP offers its complete functionality, and GIS offers minimum capabilities;
3) Hybrid - tightly coupled functionality, both the GIS and OLAP domain functionalities are equally represented.

These solutions, some OLAP-dominant and others GIS-dominant, offer a more or less elaborated subset of the desirable functionalities. Although much research has been done on this topic reflected by the continued success and maturing of the field, much needs to be done across many different areas of SOLAP solutions. In particular, the following challenges have been recognized by (Bimonte, et al.) that need to be addressed:

1. The stringent definition of a SOLAP solution supporting spatial data in a multidimensional model is also known as a tightly coupled hybrid solution. The introduction of spatial data in a multidimensional model raises major problems from the implementation and theoretical point of view. SOLAP implies a real rethinking of OLAP concepts, for example, storing and modeling the spatial dimension, and extending the spatial algebra. (Bédard, et al., 2001) offers a slightly tempered version of the definition for the non-expert, where SOLAP is defined as: A NEW TYPE OF USER INTERFACE FOR MULTI ­ SCALE GIS APPLICATIONS AND WEB MAPPING. This definition makes it possible to define a loosely coupled hybrid SOLAP solution where the GIS is used as a visual tool for OLAP operations. Loosely coupled hybrid solutions are the most widely available solutions in the market today.

2. The solutions available are proprietary and inflexible in nature, catering to specific needs and rarely providing an extensible interface resulting in high development cost. The proprietary nature of an application is a deterrent to the interoperability³ between applications. There is a need for standardized and open solutions to move away from the current status quo in which the solutions are tightly coupled to their internal data models and structures.

3. The solutions do not take full advantage of global communications network, such as the Internet, to perpetuate the broader and free exchange of information and knowledge discovery results. This is due to the tight coupling of the data structure and application logic of the solution itself (a legacy of proprietary solutions).

4. A GIS-centric SOLAP solution exposes the lack of powerful analytic capabilities to deal with problems. [(Burrough, 1990);(Jannsen, et al., 1990); (Carver, 1991)] state the following ones:

- In most GIS solutions spatial analytical functionalities lie mainly in the ability to perform deterministic overlay and buffer operations which are of limited use when multiple and conflicting criteria are concerned.
- Current GIS solutions do not permit the assessment and comparison of different scenarios. They identify only solutions satisfying all criteria simultaneously.
- Analytic functionalities found in most GIS solutions are oriented towards management and visualization of data but not effective analysis of them.

5. Closely related to the previous point, an OLAP-centric solution offers limited GIS functionality to view the spatial distribution and correlations of phenomena, and limited or no spatial operators to navigate through aggregated spatial data, the analysis would be counter-productive and incomplete even leading to false conclusions in some cases.

6. The querying capabilities of both GIS and OLAP domains are not adequate to explore spatial multidimensional data. SOLAP solutions require new spatial multidimensional exploration query languages adding to their complexity.

The conceptual idea of this research is to offer an adequate integrated platform prototype with the endeavor to overcome the aforementioned challenges availing the solutions offering SOLAP analysis capabilities.

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¹ Is an OGC standard web service exchanging geospatial data (coverage)

² Is a non-profit, international, voluntary consensus standards organization that is leading the development of standards for geospatial and location based services. It defines a palette of open geospatial web interfaces.

³ The capability to communicate, execute programs, or transfer data among various functional units in a manner that requires the user to have little or no knowledge of the unique characteristics of those units.