A First Framework for Mutually Enhancing Chorem

and Spatial OLAP Systems

Sandro Bimonte

, François Johany

and Sylvie Lardon

Irstea, TSCF, Aubiere, France

INRA, UMR Métafort, Aubiere, France

Keywords: Spatial OLAP, Geovisualization, Chorems, Spatial Data Warehouse.

Abstract: Spatial OLAP systems aim to interactively analyze huge volumes of geo-referenced data. They allow

decision-makers to on-line explore and visualize warehoused spatial using pivot tables, graphical displays

and interactive maps. On the other hand, it has been recently shown that chorem maps represent an excellent

geovisualzation technique to summarize spatial phenomena. Therefore, in this paper we introduce a

framework being capable to merge the interactive analysis capability of SOLAP systems and the potentiality

of a chorem-based visual notation in terms of visual summary. We also propose a general architecture based

on standards to automatically extract and visualize chorems from SDWs according to our framework.

1 INTRODUCTION

Spatial On-Line Analytical Processing (SOLAP)

systems allow on-line analyzing huge volume of

spatial data to provide numerical indicators

according to some analysis axes (Bédard et al.,

2006). SOLAP has been sucessufylly applied in

several application domains such as health,

agriculture, etc. SOLAP systems integrate

Geographic Information Systems (GIS)

functionalities with OLAP systems to provide a

cartographic visualization of these indicators

(Bédard et al., 2006). Decision-makers trigger

SOLAP operators by the simple interaction with

visual components of SOLAP clients (pivot tables,

graphical and cartographic displays). Therefore, they

can easily and interactively explore geo-referenced

data set looking for unknown and/or unexpected

patterns and/or confirm their decisional hypothesis

on some spatial phenomena. The success of SOLAP

rests on the visual analytic paradigm “Analyze First

- Show the Important - Zoom, Filter, Analyze

Further - Details on Demand” (Keim et al., 2006),

and its adaptation to geographic information, the so

called Geovisualization. Geovisualization integrates

the techniques of scientific visualization,

cartography, image analysis, and data mining to

provide a theory of methods and tools for the

representation and discovery of spatial knowledge

(MacEachren et al., 2004). Geovisualization

analytics tasks are performed using SOLAP

operators (Slice and Dice, Roll-up and Drill-down)

whose results are displayed in interactive thematic

maps. However, a part from thematic maps, SOLAP

systems lack of advanced Geovisualization

techniques as described in (Bimonte, 2014). In

particular, summarizing information (i.e. Zoom

visual analytic task) is reduced to aggregation of

measures values using SQL aggregation functions of

Roll-Up operator (e.g. SUM, MIN, MAX), but no

additional visual summary is provided.

Consequently, sometimes SOLAP cartographic

displays are not well adapted to complex spatial

phenomena, which need several or temporal

indicators leading to useless and/or clutterd maps

(Silva et al., 2012).

Per contra, recent results have demonstrated that

chorems can be used to both catch a thematic global

view of a territory and its phenomena (De Chiara et

al., 2011) (Del Fatto et al., 2008), and investigate

complex spatial phenomena by accessing data

characterizing them. A chorem is a schematized

spatial representation, which eliminates any detail

unnecessary to the map comprehension (Brunet,

1986). The main limitation of these approaches is

that chorem map extraction cannot be done on-

demand according to spatial decision-makers needs.

This limits the potentiality of the spatial decision-

making process, since, as stated in (MacEachren et

al., 2004), high interactivity exploration and analysis

Bimonte S., Johany F. and Lardon S..

A First Framework for Mutually Enhancing Chorem and Spatial OLAP Systems.

DOI: 10.5220/0005515200380048

In Proceedings of 4th International Conference on Data Management Technologies and Applications (DATA-2015), pages 38-48

ISBN: 978-989-758-103-8

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

are mandatory when dealing with complex and

unknown datasets.

In this paper we carry out our aim of supporting

users’ tasks through advanced solutions and systems,

and describe how chorems can be used to perform

on-line analysis through a simple and immediate

approach that exploits analysis capabilities of

SOLAP systems. On this basis, in this paper we

propose a unique framework, called ChoremOLAP,

that, starting from warehoused spatial datasets, is

able to on-line produce and visualize chorem maps

exploiting the functionalities of SOLAP systems.

The paper is organized as follows. Section 2

recalls some basic notions about SOLAP systems

and chorems, detailing related work. Section 3

present a real case study. The ChoremOLAP system

is described in Section 4. Section 5 presents a

discussion of our proposal. Conclusions and future

work are drawn in Section 6.

2 RELATED WORK

Data warehouses (DW) and OLAP systems are

Business Intelligence technologies aim to support

on-line analysis of huge amounts of data.

Warehoused data are structured according the so-

called multidimensional model, which represents

data according to different analysis axes

(dimensions) and facts (Inmon, 1992). Dimensions

are composed of hierarchies, which define groups

for data (members) used as analysis axes. Facts

represent the subjects of analysis, and they are

described by numerical measures, which are

analysed at different granularities associated to the

levels of hierarchies. Decision-makers can aggregate

measures at coarser hierarchy levels using classical

SQL aggregation functions (AVG, SUM, MIN,

MAX, COUNT). OLAP operators are defined to

explore warehoused data. Classical OLAP operator

are: Slice which selects of a part of the data

warehouse, Dice which projects a dimension,

RollUp which aggregates measures climbing on a

dimension hierarchy, and DrillDown, which is the

reverse of RollUp.

Since OLAP systems do not allow to integrate

spatial data into the analysis and exploration

process, Spatial OLAP (SOLAP) systems have been

introduced (Bédard et al., 2006). SOLAP systems

integrate OLAP and Geographic Information System

functionalities in a unique framework to take

advantage from the analysis capabilities associated

to spatial data. SOLAP redefines main OLAP

concepts. In particular, the integration of spatial data

in OLAP dimensions brings to the definition of

spatial dimension: non geometric dimension, spatial

geometric dimension (i.e. members with a

cartographic representation) or mixed spatial

dimension (i.e. combine cartographic and textual

members). When the studied subject of the decision

process is the spatial information itself, then the

concept of spatial measures are introduced. A

SOLAP system allows visualizing results of SOLAP

queries using interactive tabular and map displays.

Few works investigate using advanced

geovisualization methods (such as Space-Time

cubes, geobrowser) for SOLAP (a survey can be

found in (Bimonte, 2014)). Indeed, existing SOLAP

tools are limited to classical GIS visualization maps

such as chloropleth and thematic maps.

In the last few years, much work has been done

on the chorem concept and on its exploitation as an

appealing visual notation to convey information

about phenomena occurring in specific application

domains, such as land use and territorial

management. The term chorem derives from the Old

Greek word χώρα (read chora). According to the

definition of the French geographer Roger Brunet

(Brunet, 1986), a chorem is a schematized spatial

representation, which eliminates any unnecessary

details to the map comprehension. A chorem is a

synthetic global view of data of interest which

emphasizes salient aspects. Figure 1 shows an

example of a chorem map, which contains chorems

referring to the environmental dynamics of the area

around the city of Poitiers, France.

This map results from a participatory process

with agents of DREAL Poitou-Charentes (regional

office of the environment) (Lebourg M-N, et al.,

2014). They handy draw all the information needed

using a Geographic Information System. Then, they

simplify the geometries of the map using the GIS

functionalities according an adapted semiotic. This

chorem map shows the predominance of transport

corridors in the organization of the territory, the

urban continuity and the expansion of the cities.

Moreover, chorems show the interaction between

these dynamics and the environmental issues of this

area.

The evolution of chorems both in terms of

applications and semantics is extensively discussed

in (Del Fatto, 2009) where the author provides a

review about the history of chorem, from its

definition (Brunet, 1986) to recent applications

(Kilppel et al., 2005).

A more recent application of the chorem concept

has been illustrated in (Del Fatto et al., 2008) where

the authors show how chorems can be used to

AFirstFrameworkforMutuallyEnhancingChoremandSpatialOLAPSystems

Figure 1: Chorems example.

visually summarize database content. To this aim

they provide for a definition and a classification of

chorems meant both to homogenize chorem

construction and usage, along with a usable

framework for computer systems. In particular, this

work emphasizes the role that chorems may play in

supporting decision-makers when analyzing

scenarios, by acquiring syntactic information (what,

where and when), as well as semantic aspects (why

and what if), useful to human activity of modelling,

interpreting and analyzing the reality of interest. A

prototype is also described, targeted to generate

chorems from a spatial dataset through a uniform

approach that takes into account both their structure

and meaning.

Finally, in (De Chiara et al., 2011) the authors

enhance the role that a chorem map may play in

geographic domains, by extending the semantics

associated with it through a more expressive visual

notation. In particular, by adopting the revisited

Shneiderman’s mantra, namely “Overview, zoom

and filter, details on demand” (Keim et al., 2006),

they allow users to acquire information about a

single phenomenon by accessing data characterizing

it from the underlying database. Each task of

interaction assumes a context-sensitive meaning and

invokes a proper function among the ones specified

in agreement with the mantra. As an example, when

a zoom / filter combination is applied on a chorem,

users are provided with data from spatial dataset

which initially contributed to its definition.

The main limitation of previously described

approaches is that chorem map extraction cannot be

done on-demand according to spatial decision-

makers needs. This limits the analysis of decision-

making process since as stated in (MacEachren et

al., 2004), a high interactivity exploration and

analysis is mandatory when dealing with complex

and unknown datasets.

To be useful for decision-makers, some authors

define classes of chorems in order to help decision-

makers to choice the right visual representation for a

particular phenomenon. Therefore, from the initial

chorematic grid of Brunet (1986), JP Deffontaines et

al. (1990) have formalized spatial models to

represent agricultural phenomena. S Lardon and P-L

Osty (2000) have shown an application of spatial

modelling on bushes expansion in farming lands.

Lardon and Piveteau (2005) have revised and

adapted the chorem grid for territory management

purposes. They distinguished structures (considered

space objects) and dynamics (spatial processes in

which these objects are identified). However,

decision-makers have hand-extract and hand-draw

their chorems from data sources.

Thus, the framework presented in the paper

enhances chorems systems since it allows the online

extraction and visualization of chorems using

SOLAP operators. At the same time, adding chorems

visualization to SOLAP improves its

geovisualization analysis capabilities (c.f. Section

5).

3 FAO CASE STUDY

An example of a Spatial DW (SDW), which will be

used all along the paper to describe our proposal, is

depicted in Figure 2 using the UML profile

presented in (Boulil et al., 2015). Here, several

stereotypes have been defined, one for each element

of the SDW. For example a spatial dimension

presents the <<SpatialDimension>> stereotype, a

spatial level is identified with <<SpatialAggLevel>>

stereotype. The <<Fact>> stereotype designs facts

and numerical and spatial measures have the

stereotypes <<NumericalMeasure>> and

<<SpatialMeasure>>.

The SDW is loaded using open-data of FAO

(FAO2015). It allows analysis of agricultural

cultivated surface and production per year, country

and crop. It presents a spatial hierarchy grouping

countries in areas, and years by decade. Using this

SDW it is possible to answer queries like: “What is

the total surface and production of wheat per country

and year?”. More complex analysis could be

performed using this SDW. In particular to evaluate

national agricultural policies, it is possible to

compare agricultural production and surface over

time, for example using the query: “What are

differences of total surface and production per

DATA2015-4thInternationalConferenceonDataManagementTechnologiesandApplications

Figure 2: FAO Spatial Data Warehouse.

country on the last 5 years. In our case study, we

work using national wheat production and national

wheat area harvested of European countries between

1991 and 2011. These data allow us to analysing not

only the variations of production and acreage but

also variation of crop yields and productivity. The

range of the period studied lets us to do several

analysis in different temporal scales.

4 CHOREMOLAP

In this section, we present the theoretical framework

for integration of SOLAP and chorems (Sec 4.2),

and its implementation in a SOLAP tool (Sec 4.1).

4.1 Architecture

ChoremOLAP architecture is described in Figure 3.

It is based on a Relational SOLAP architecture

composed of three tiers: SDW, SOLAP server and

SOLAP client.

Figure 3: ChoremOLAP architecture.

The Spatial Data Warehouse tier is implemented

using the Spatial DBMS PostGIS (PostGIS, 2015).

PostGIS is an extension of Postgres providing a

native support for spatial data and spatial analysis

functions. This tier is used for storing alphanumeric

and spatial multidimensional data. Warehoused

spatial data is stored using the star-schema (Kimball,

1996), where levels of the denormalized spatial

dimension present geometric attributes.

AFirstFrameworkforMutuallyEnhancingChoremandSpatialOLAPSystems

Example. The logical model of our case study is

shown on Figure 4, where a table for each dimension

is defined, and one table for the fact.

Figure 4: Star-schema of the FAO SDW.

The spatial dimension presents a geometry

column for each spatial level (Geom_country and

Geom_area). We also note that two additional

geometries representing the centroid of the countries

and the areas have been defined since, as detailed in

the next sections, they are used for the chorems

visualization.

The SOLAP server used is GeoMondrian

(GeoMondrian, 2015). GeoMondrian is an open-

source SOLAP Server supporting GeoMDX.

GeoMondrian represents dimensions and measures

using an XML file, which defines a mapping on the

logical schema. GeoMondrian supports SOLAP

queries on the top of Postgis.

The SOLAP client is a web-based client

composed of the OLAP client JPivot and the GIS

client OpenLayers. In particular, JPivot is an open-

source web-based OLAP client implementing all

OLAP operators by the simple interaction with the

pivot table. JPivot supports MDX. Cartographic

visualization of SOLAP queries is provided by the

cartographic web client OpenLayers. Openlayers is

an open source JavaScript library for displaying map

data in web browsers.

The architecture presented in (Bimonte, 2014)

has been extended in two ways to support chorems

extraction and visualization as described in Section

4.3. We have used this SOLAP system since it

provides an open and customizable visual interface.

Indeed, the main idea for implementing

customizable cartographic visual displays in the

SOLAP client is the usage of SLD and GML

standards, which are used by standard mapping web

services (e.g., WMS). A Styled Layer Descriptor

(SLD) is an XML schema specified by the Open

Geospatial Consortium (OGC) for describing the

appearance of map layers. Moreover, the Geography

Markup Language (GML), defined by the Open

Geospatial Consortium, allows expressing

geographical features. We use GML to represent

spatial data and the SLD for its appearance.

The original geovisualization proposed method

in (Bimonte, 2014) consists of chloropleth maps

(e.g. coloured geometries) implemented using GML

and SLD. Here, we have added the visualization of

icons representing chorems as described in the Sec

4.2.

In the SOLAP server, we have implemented a

component that translates a chorem query in a

classical SOLAP GeoMDX query. In this way,

chorem queries are transparently handled by any

SOLAP server. The proposed extensions are detailed

in the rest of this section.

4.2 Principles

Our geovisualization methods are based on two main

groups:

 Chorem-based geovisualization methods, which

are based on chorems, and

 Non chorem-based geovisualization methods,

which are classical geovisualization methods.

4.2.1 Chorem-based Geovisualization

Methods Principles

The chorems used in our approach are a subset of

chorems identified by S Lardon and P-L Osty (2000)

that can be extracted from spatial warehoused data,

as shown on Figure 5. Here, 5 main groups of

chorems are described.

Figure 5: Chorems and extraction mapping of the

ChoremOLAP framework.

DATA2015-4thInternationalConferenceonDataManagementTechnologiesandApplications

In particular, Grid represents how the territory is

divided by actors (for example, municipalities).

Network designs the presence of network structures

such as roads, rivers, but also informational

networks drained and supplied the territory.

Hierarchy specifies the different entities and how

they organize the territory.

The dynamic chorems result from the temporal

evolution of these structures. The Territorial

Dynamic transforms differentiated spaces, even by

continuous expansion or by discrete allocation.

Example. In Figure 6 an example of instance for

each group of chorems is also presented using our

case study.

Let us now describe what elements of the spatio-

multidimensional model are used to extract chorems

(Figure 5).

Grid chorems group concerns only the

geometries of spatial levels.

Example. In our case study the chorem “Area

limits” is simply defined using the spatial level

“Area” of the SDW.

In the same way, Network chorems group is also

only associated to spatial levels defined as spatial

network levels in (Bimonte et al., 2013).

Hierarchy chorems group refers to spatial

members and their numerical properties, which can

change along non-spatial dimensions.

Example. In our example, “Production increase”

chorem is defined using the spatial level “Country”

and the measure “Production”.

Finally, Territorial Dynamics chorems group is

similar to Hierarchy, but here the properties are

strictly related to the geometries of the spatial level

(spatial measures).

Example. The “surface” measure is a spatial

measure, and so the “Surface Reduction” chorem is

calculated using the spatial level “City” and

“surface” measure.

4.2.2 Non Chorem-based Geovisualization

Methods Principles

The system proposed in (Bimonte, 2014) allows to

displays results of SOLAP queries using chloropeth

maps. Here we extended them by using the

simplified geometries of the Grid chorems group.

Moreover, it allows visualizing nominal measu-

res using an iconic representation. For example, in

order to visualize production evolution, we define

three icons:

if there is an augmentation, if

there is a diminution, and

otherwise.

Figure 6: Chorems of the FAO SDW.

4.3 Extraction and Visualization

In this section, we detail how chorems of Figure 5

are extracted (Section 4.3.1) and visualized (Section

4.3.2) on the top of the SOLAP architecture of

Figure 3

4.3.1 Extraction

In order to extract chorems on the top of a classical

SOLAP server, we use MDX. MDX is de-facto

standard query language of OLAP servers. MDX

allows defining calculated measures (i.e. measures

calculated using measures values stored in the SDW

tier).

The template MDX formula for the Territorial

Dynamics chorem is presented in Figure 7. The

chorem is represented by the calculated measure

[Measures].[ChoremE]that assumes the

values:

 “-1” the phenomenon reduces

 “0”: the phenomenon does not change

 “+1” the phenomenon expands

where:

 Phenomena is the measure used for the chorem

definition. For example Phenomena =

“Surface” allows the extraction of Surface

Reduction, Surface Stagnation and Surface

Expansion chorems respectively (Figure 7);

 TimeRange represents the interval between two

dates (Date) (for example TimeRange =5 allows

comparing Phenomena values of each year with

5 years ago values).

AFirstFrameworkforMutuallyEnhancingChoremandSpatialOLAPSystems

Figure 7: Territorial Dynamics chorem MDX template.

4.3.2 Visualization

The visualization of the Grid chorems group is

simply achieved by the visualization of simplified

geometries of the spatial levels members stored in

the SDW tier (Figure 5).

Example. An example of Grid visualization is

shown on Figure 8.

Figure 8: Visualization of the Grid chorem.

The visualization of the Territorial Dynamics

chorems group is implemented using a simple SLD

template (Figure 9).

Figure 9: Territorial Dynamics chorem SLD template.

An SLD template is generated for each

combination of non spatial members

(NonSpatialDimensionsMembersChorem) present in

the pivot table result (for example “2000”). It also

defines a rule for each chorem value ChoremValue

corresponding to the calculated measure

[Measures].[ChoremE] (-1, 0, +1). For each

rule, an image associate to the chorem value is

visualized (ChoremImage). The location where this

image is displayed is represented using GML. We

use the centroid of the spatial member, when it is a

polygon stored in the spatial dimension table (Figure

4).

Example. Figure 10 shows an example of the

chorems Surface Stagnation, Surface Reduction, and

Surface Expansion for each country in 2000.

Decision-makers rapidly “see” that Italy presents a

surface reduction, while France has a surface

expansion.

Figure 10: Surface Stagnation, Surface Reduction, and

Surface Expasion.

The Hierarchy chorems group is implemented in

the same way, but here we present only an example

of visualization.

Example. An example of Hierarchy chorem

visualization of the production on 2000 is shown on

Figure 11 (Italy and Switzerland are in the same

category, while France production is higher).

Figure 11: Production value.

An example of non-chorem based

geovisualization (Sec. 4.2.2) showing a chloropleth

map for the value of the surface on 2000 and the

production evolution per country is shown on Figure

12.

DATA2015-4thInternationalConferenceonDataManagementTechnologiesandApplications

Figure 12: Non chorem-based geovisualization.

5 DISCUSSION

In this section, we analyse how our approach

mutually improves SOLAP and chorems systems.

5.1 Chorems Improved by SOLAP

As we state in the previous section, ChoremOLAP

allows to interactively creating chorems. This is

achieved by simply triggering SOLAP queries, as

described in the following.

SOLAP operators allow to explore the warehoused

data on-line aggregating measures values, and in our

tool also chorem values. For example, decision-

makers can move from the “Area” spatial level to

the “Country” spatial level by the simply interaction

with the pivot table of our web-client (i.e.

DrillDown operation on the spatial dimension)

(Figure 13). As shown on Figure 13a, the surface

chorem is visualized at the “Area” spatial level.

When the decision-maker DrillDowns to the

“Country” spatial level, the chorem map is

instantaneously re-calculated for each country

(Figure 13b).

In the same way, the decision-maker can

dynamically change other dimensions. For example

starting from the chorem map of figure 13a, he

change the year, for example moving from 2000 to

1995, and the chorem map is online calculated.

Thus, we can conclude that SOLAP system allows

the online creation and visualization of chorem

maps.

5.2 SOLAP Improved by Chorems

Let us now describe how SOLAP maps are

improved by chorems visualization.

In order to evaluate the new analysis capabilities

offered by our framework from a visualization point

of view, we performed a comparative study of

ChoremOLAP against one of the most advanced

commercial SOLAP clients. We compare our

proposal to classical SOLAP visualization methods

and we analyze the ability to represent different kind

of SOLAP queries.

In table 1 we present what and how many measures

can be visualized with govisualization methods for

SOLAP including our chorem maps.

(a)

Figure 13: Drill Down for automatic chorem extraction and visualization.

AFirstFrameworkforMutuallyEnhancingChoremandSpatialOLAPSystems

(b)

Figure 13: Drill Down for automatic chorem extraction and visualization (cont.).

Table 1: Geovisualization methods for SOLAP.

In table 2 we evaluate these geovisualization

methods on 6 SOLAP queries. These queries

represent all possible combinations of possible

measures involved in a SOLAP query.

Query Q1 is a classical SOLAP query, therefore

there is no need to use chorems.

Query Q3 concerns one chorem (surface

evolution). Here using chorem visualization is very

satisfying for the decision-maker since to each

nominal value of the chorem (stagnation, etc.) a

particular icon is used. Chloropleth map can be also

used, but decision-maker is forced to mentally

associate a colour to a surface evolution.

Thus, chorem maps should be preferable to

chloropleth maps for nominal measures.

Table 2: Evaluation of geovisualization methods.

For the query Q2 chorem maps and chloropleth

maps have the same expression power.

For the Queries Q4 and Q5, our framework

presents the same advantage of Query 3.

Query Q6 concerns 2 chorems (surface and

production evolution). Therefore, chloropleth

multimaps (one chloropleth map per measure) can

be used, but the main limitation is that the decision-

maker has mentally to overlay the maps to compare

DATA2015-4thInternationalConferenceonDataManagementTechnologiesandApplications

the two measures country by country. Thus our

approach seems perform chloropleth multimaps.

To conclude, our geovisualization methods based

on chorems do not always replace classical

geovisualization methods of SOLAP tools, but they

appear useful when dealing with phenomena that can

be represented as chorems.

However, usability test should be provided to

quantify the advantage of using chorem maps

instead of SOLAP maps. They represent our future

work.

6 CONCLUSIONS AND FUTURE

WORK

SOLAP systems allow decision-makers to on-line

explore warehoused spatial data by means of

SOLAP operators, which aggregate numerical

indicators, to produce reports composed of pivot

tables, graphical displays and thematic maps.

However, when the analysed spatial phenomena are

complex, advanced geovisualization techniques are

need. On the other hand, it has been recently shown

that chorem maps represent an excellent

geovisualzation technique to summarize and reveal

hidden spatial phenomena. However, chorem

systems are based on pre-defined maps, which limit

potentiality of spatial decision-making process.

Thus, the goal of this paper is to introduce a

framework being capable to merge the interactive

analysis capability of SOLAP systems and the

potentiality of a chorem-based visual notation in

terms of visual summary.

In detail, we propose a set of methods to on-line

extract and visualize chorems on the top of a SDW.

We also propose an implementation of our

framework using a general architecture based on

standards.

As future work, we plan to investigate other

chorems as defined in (Lardon et al., 2005). We also

plan to define a usability study to evaluate in a

quantitative way the pro and cons of the usage of

chorems instead of classical SOLAP

geovisualization methods from a visualization point

of view.

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