Towards a web coverage service for efficient multidimensional information retrieval E-Book

Anup Deshmukh

• Herausgeber: GRIN Verlag
• Sprache: Englisch

Veröffentlichungsjahr: 2008

Beschreibung

Master's Thesis from the year 2007 in the subject Geography / Earth Science - Miscellaneous, grade: 1,3, University of Bonn (Geographisches Institut), language: English, abstract: 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 BSINESS 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 COMPUTERBASED INFORMATION SYSTEM THAT ENABLES CAPTURE, MODELING, MANIPULATION, RETRIEVAL, AND PRESENTATION OF GEOGRAPHICALLY REFERENCED DATA. GIS provides functionalities like

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Table of Content

data. The abstract model of geography developed by the OGC is used as base to create GML.

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TH E   F O L L O W I N G  MA S T E R  THESIS  WAS  PREPARED  ON  MY  OWN  WITHOUT  ANY  ADDITIONAL  HELP 

O T H E R   T H A N   T H E   M E N T I ON E D   SO U R CE S  A N D   T OO L S.AL L   U S E D   S OU R C E S   O F   L I TE R AT U R E   AR E   L I S TE D   T   T H E   E N D   O F   T H E   T H E S I S. A

BO N N,FR I D A Y,OC T O B E R  26,  2007                                                               

AN U PDE S H M U K H

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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 webbased 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 

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

2Is 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.

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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 

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LIST OF ABBREVIATIONS 

BI - Business Intelligence CEN - European Committee for Standardization DPA - Deutsche Presse Agentur DSS - Decision Support System GBIS - Geographic Business Intelligence Solution GIS - Geographic Information System GML - Geographic Markup Language ISO - International Organization for Standardization IT - Information Technology KVP - Key-Value-Pair MDB - Multidimensional database OGC - Open Geospatial Consortium OLAP - OnLine Analytical Processing OLTP - OnLine Transactional Processing OWS - OGC Web Services RDBMS - Relational database Management System SDI - Spatial Data Infrastructure SOLAP - Spatial OLAP WFS - Web Feature Service WMS - Web Map Service WCS - Web Coverage Service XLink - XML Linking Language XML - Extended Markup Language XMLA - XML for Analysis W3C - World Wide Web Consortium WS-I - Web Services Interoperability

v 

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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 itsrichness,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 

COMPUTER­BASED INFORMATION SYSTEM THAT ENABLES CAPTURE, MODELING, MANIPULATION, RETRIEVAL,  AND PRESENTATION OF GEOGRAPHICALLY REFERENCED DATA.GIS provides functionalities like 1) spatial dataacquisitionandinput,2) spatial datastorageandmanagement,3) spatial datapresentationandoutput,and 4) spatial datamanipulationandanalysis

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

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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 GISsper seare 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

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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)informationandknowledge discovery,2)decision supportandintelligent 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. THIS INCLUDES THE ABILITY TO DISCERN NEW 

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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 theanalysis gapbetween 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  SPATIO­TEMPORAL  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.

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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 amultidimensional 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  MULTI­SCALE 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

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proprietary nature of an application is a deterrent to the interoperability3between 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

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.

3The 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.

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1.2 RESEARCH PROBLEM 

The research motivation forms the backbone of the research problem addressed. To summarize, the specific challenges identified above and encompassed in the research problem are: A. The current available solutions are predominantly proprietary in nature: closed, monolithic applications impeding interoperability and not easily extensible. B. Desktop-based solutions prevent easy and flexible transfer of data between organizations across various disciplines and industries. Also, the internal data format is a restricting factor for information exchange.

C. OLAP- and GIS-centric solutions offer only a subset of the possible functionalities and do not fully harness the analytic powers that are there at their disposal. D. The introduction of a new spatial multidimensional query language only adds to the complexity of the solution and is a deterrent to a rapid, collaborative development of a solution.

E. The introduction of spatial data in a multidimensional data model raises conceptual problems (Bimonte, et al.). These issues are established in Chapter 3.

Thus, the research scope of this thesis can be considered as:AN  INTEROPERABLE WEB­BASED HYBRID SOLAP SOLUTION SUPPORTING SPATIAL MULTIDIMENSIONAL INFORMATION EXPLORATION BY ABSTRACTING THE  COMPLEXITY  OF  SEPARATELY  QUERYING  THE  DATAas a possible solution to overcome the aforementioned challenges.

1.3 RESEARCH APPROACH 

The research approach has been divided into three sections. The first two sections deal with the theoretical aspect whereas the third section is based on a use-case scenario to create a prototype to test the integration between the two technologies, OLAP and GIS.

First,based on application scenarios, the author has formulated the requirements an integrated solution must satisfy. The scenarios outline the use and importance of an integrated business and geographic domain application.

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Second,the clear trend to a broader use of location information throughout the organization has led to a perpetuation of SOLAP solutions. The author critically investigates the current commercial, academic and open solutions available and highlights the niche the current research aims to occupy.

Thethirdpart of the thesis concentrates on the development of a SOLAP prototype. The research focuses on finding a solution to overcome the above stated challenges. The author proposes the following approach to handle the abovementioned challenges: A. Introducing an interoperable solution compliant with (geospatial) standards and specifications.

B. A web service solution complying with web interfacing standards and general software engineering requirements offering a scalable and extensible interface. C. A tight hybrid integration of a GIS and an OLAP tool. The integration must comply with the industry standards and target specific requirements.

D. The solution must offer a single querying interface to abstract the querying complexity associated with exploring two different data formats with different interfaces. E. Pending the resolving of the issues involved the integration of spatial data in a multidimensional data model must be avoided. (Miquel, et al., 2002) highlights the problems facing such an undertaking.

The aforementioned salient features are a direct reference to the challenges discussed in Section 1.2.

The prototypical solution is an integration based on the Web Coverage Service (WCS4), as defined by the Open Geospatial Consortium (OGC5), and an OLAP server6. The use of OGC web services as the communication interface offers an open and interoperable platform independent implementation for spatial data exploration.

As far as known, at this point of time no OLAP integrated WCS has been realized. Therefore the developed application in the case study can be used to get some first practical experience with the integration application domain and to study the strengths and weaknesses of such a solution.

4Is an OGC standard web service exchanging geospatial data (coverage)

5Is 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.

6MS SQL Server

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DESCRIPTION OF THE REMAINING CHAPTERS 

Our starting point is inapplication scenarios,introducing the scenarios where the integrated solution is a much needed tool (Chapter 2)

Chapter 3,general conceptsstarts with introducing the web service concepts, continues on to describe in detail the important concepts of online analytical processing before introducing the OpenGIS web services with special emphasis on the Web Coverage Service.

In Chapter 4,related worksare introduced, including a short summarization of the strengths and weaknesses of the individual solutions.

Acase-studybased on the Deutsche Presse Agentur dataset for the period between 2000 and 2005 is presented to outline and discuss the architecture and methodology of the integration process. (Chapter 5)

Conclusions and future worksummarizes the study and presents an outlook to the direction in

which the technology would be heading (Chapter 6).

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2. APPLICATION SCENARIOS 

A ubiquitous scenario in many businesses and organizations today is to rely upon reporting from business intelligence systems for summarized historical data to manage critical operations. For example, distance or drive time to the nearest outlet is a key consideration when planning whom to target for retail networks, which makes geo-mapping indicators an essential ingredient in customer and prospect databases in these sectors. Questions like: 1) Where should the next store be located; 2) Where to dispatch emergency crews and which is the fastest route, as well as questions predictive in nature: 1) What if a hurricane occurs in this location; 2) What if a competitor builds a store here, can be answered with the help of a GBIS solution. The integration of GIS and BI systems has many advantages and challenges, and is increasingly finding application in the real world.

The following fictitious, but realistic scenario summaries illustrate this. The scenarios are solution-neutral. The first scenario portrays a use-case for the retail market. An investment sector use-case characterizes the advantages and disadvantages in the second scenario. The third scenario handles the Deutsche Presse Agentur (dpa) news stories. The dpa scenario will be dealt with in greater detail in Chapter 5.

2.1 SCENARIO 1 - THE BUSINESS MAN 

BACKGROUND OF THE SCENARIO 

With the advent of globalization and the multinational setup of the large business organizations, it is of interest to make available the knowledge gained to a larger audience. In today’s volatile economy, effective and successful business models are those that are capable of eliminating time and geographic barriers to reach international markets from ones’ desktop. An understanding of global expectations, regionalism, nationalism, laws, work hours, and language differences are crucial in order to penetrate and compete in global markets, product brands, and operations. While accepting the challenge of the 21stcentury it is required to establish an appropriate and cost effective technological framework that is capable of integrating and managing such business intelligence.

10| APPLICATIONScenarios