ETSI GR CIM 049 V1.1.1 (2024-11)

Context Information Management (CIM); Usage of geo-information

DGR/CIM-0049

General Information

• Status: Not Published
• Technical Committee: CIM - cross-cutting Context Information Management
• Current Stage: 12 - Citation in the OJ (auto-insert)
• Due Date: 12-Nov-2024
• Completion Date: 07-Nov-2024

Standards Content (Sample)

GROUP REPORT
Context Information Management (CIM);
Usage of geo-information
Disclaimer
The present document has been produced and approved by the cross-cutting Context Information Management (CIM) ETSI
Industry Specification Group (ISG) and represents the views of those members who participated in this ISG.
It does not necessarily represent the views of the entire ETSI membership.

2 ETSI GR CIM 049 V1.1.1 (2024-11)

Reference
DGR/CIM-0049
Keywords
API, IoT, NGSI-LD
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3 ETSI GR CIM 049 V1.1.1 (2024-11)
Contents
Intellectual Property Rights . 5
Foreword . 5
Modal verbs terminology . 5
1 Scope . 6
2 References . 6
2.1 Normative references . 6
2.2 Informative references . 6
3 Definition of terms, symbols and abbreviations . 6
3.1 Terms . 6
3.2 Symbols . 7
3.3 Abbreviations . 7
4 Problem Statement . 7
5 Methodology . 9
5.1 Information required. 9
5.2 Information gathering process . 9
5.3 Interviews . 9
6 Results of interviews - Use cases . 10
6.1 General . 10
6.2 Real Estate and urban infrastructure . 10
6.2.1 Overview . 10
6.2.2 The 10-minute city . 11
6.2.3 Underground utilities networks . 11
6.3 Dealing with disasters . 11
6.4 Mobility . 12
6.5 Other use cases . 12
7 Challenges related to geospatial data for cities . 13
7.1 Challenge of defining regions of interest . 13
7.2 The need for context to observations . 13
7.3 The need to work at different scales . 13
7.4 Batch integration and near real time integration . 13
7.5 Local Digital Twins . 14
7.6 The challenge of data models . 14
7.6.1 Complexity . 14
7.6.2 The need of subject experts to develop the data models . 14
7.6.3 The need of a consistent approach . 15
7.7 Identifiers . 15
7.8 Issues with software . 15
7.8.1 GIS Software . 15
7.8.2 Game engines . 16
7.8.3 Mapping software . 16
8 Examples of good practice . 17
8.1 Introduction . 17
8.2 Minimal Interoperability Mechanisms . 17
8.3 Civitas Connect . 17
8.4 Valencia . 19
8.5 Other cities and projects . 19
9 Overall priorities . 20
9.1 Introduction . 20
9.2 The use of NGSI, NGSI v2 and LD . 20
9.3 Aligning data sets . 20
9.4 Domain Driven Design . 20
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9.5 Addressing the issue of defining regions of interest . 21
9.6 Minimal Interoperability Mechanisms . 21
10 Conclusions . 21
Annex A: Interview Questions . 22
History . 23

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Intellectual Property Rights
Essential patents
IPRs essential or potentially essential to normative deliverables may have been declared to ETSI. The declarations
pertaining to these essential IPRs, if any, are publicly available for ETSI members and non-members, and can be
found in ETSI SR 000 314: "Intellectual Property Rights (IPRs); Essential, or potentially Essential, IPRs notified to
ETSI in respect of ETSI standards", which is available from the ETSI Secretariat. Latest updates are available on the
ETSI IPR online database.
Pursuant to the ETSI Directives including the ETSI IPR Policy, no investigation regarding the essentiality of IPRs,
including IPR searches, has been carried out by ETSI. No guarantee can be given as to the existence of other IPRs not
referenced in ETSI SR 000 314 (or the updates on the ETSI Web server) which are, or may be, or may become,
essential to the present document.
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Foreword
This Group Report (GR) has been produced by ETSI Industry Specification Group (ISG) cross-cutting Context
Information Management (CIM).
Modal verbs terminology
In the present document "should", "should not", "may", "need not", "will", "will not", "can" and "cannot" are to be
interpreted as described in clause 3.2 of the ETSI Drafting Rules (Verbal forms for the expression of provisions).
"must" and "must not" are NOT allowed in ETSI deliverables except when used in direct citation.

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1 Scope
The present document contains the key learning gained from 13 interviews with a variety of key city stakeholders.
2 References
2.1 Normative references
Normative references are not applicable in the present document.
2.2 Informative references
References are either specific (identified by date of publication and/or edition number or version number) or
non-specific. For specific references, only the cited version applies. For non-specific references, the latest version of the
referenced document (including any amendments) applies.
NOTE: While any hyperlinks included in this clause were valid at the time of publication, ETSI cannot guarantee
their long term validity.
The following referenced documents are not necessary for the application of the present document but they assist the
user with regard to a particular subject area.
[i.1] ETSI GS CIM 050: "Context Information Management (CIM); Aligning with geo-information".
[i.2] The Smart City Strategy of the City of Brussel.
[i.3] Local Digital Twins: Forging the Cities of Tomorrow.
[i.4] A proof of concept to show the ingestion of data into a FROST-Server using the Orion Context
Broker API.
[i.5] Recommendation ITU-T Y.4505: "Minimal Interoperability Mechanisms for smart and sustainable
cities and communities".
[i.6] Open Geospatial Consortium (OGC): "Web Feature Service".
[i.7] Open Geospatial Consortium (OGC): "OGC APIs - Features".
[i.8] GeoJSON.
[i.9] Open Geospatial Consortium (OGC): "Geography Markup Language (GML)".
[i.10] OGC GeoPackage.
[i.11] Open Geospatial Consortium (OGC): "CityGML".
[i.12] ISO 16739-1:2024: "Industry Foundation Classes (IFC) for data sharing in the construction and
facility management industries - Part 1: Data schema".
[i.13] Directive 2007/2/EC of the European Parliament and of the Council of 14 March 2007 establishing
an Infrastructure for Spatial Information in the European Community (INSPIRE)
3 Definition of terms, symbols and abbreviations
3.1 Terms
Void
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3.2 Symbols
Void
3.3 Abbreviations
For the purposes of the present document, the following abbreviations apply:
AI Artificial Intelligence
API Application Programming Interface
BIM Building Information Modeling
CAD Computer Aided Design
DG Directorate General
DS4SSCC Data Space for Smart Communities
GIS Geographic Information System
GML Geography Markup Language
GS Group Specification
IEC International Electrotechnical Commission
IFC Industry Foundation Classes
IoT Internet of Things
IT Information Technology
JRC Joint Research Center
JTC Joint Technical Committee
LD Linked Data
LDT Local Digital Twin
MIM Minimal Interoperability Mechanism
MIM Minimal Interoperable Mechanism
NGSI Next Generation Service Interface
NGSI-LD Next Generation Service Interface Linked Data
OGC Open Geospatial Consortium
SAREF Smart Applications REFerence ontology
STA SensorThings API
SyC Systems Committee
WFS Web Feature Service
4 Problem Statement
The present document has been written to provide an accurate picture of how smart cities and territories are using
geo-information at the moment, what standards they are using for what purposes and what experiences there are in
using NGSI-LD as part of those use cases. The present document also covers the use of geo-information related to
environmental issues more generally, as covered by the INSPIRE directive [i.13].
The aim is to be to highlight the key challenges of using NGSI-LD to link to data managed using such standards such as
OGC WFS [i.6] and OGC API [i.7] as well as the INSPIRE directive's [i.13] requirements, or encoded using such
standards as GeoJSON [i.8], GML [i.9], GeoPackage [i.10], CityGML [i.11] and IFC [i.12]. These challenges would
then be addressed by ETSI GS CIM 050 [i.1]. The aim, along with the associated deliverable ETSI GS CIM 050 [i.1], is
to enable ETSI to further develop the work on NGSI-LD to:
• specify how to make geodata accessible as Linked Data, how to share spatial (and spatio-temporal) data, and
how to make them interoperable with, within, and between systems and territories;
• specify how to both establish and maintain the number of connections between NGSI-LD entities and their
geographical 2D/3D representations.
The three families of standards considered; NGSI-LD, Geospatial standards developed by the Open Geospatial
Consortium, and BIM and IFC standards developed by BuildingSmart International (see
https://www.buildingsmart.org), are all used by cities although often by different departments within those cities.
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All three sets of standards need to handle the same set of issues:
• Data about locations and movements.
• Data about urban infrastructure - buildings, roads, bridges.
• How to link different data sources together to provide insight.
However, because of their different focus, they each have their own strengths and weaknesses. At a very high-level:
• NGSI-LD is particularly good at enabling IoT data to be linked with valuable context data to show its
significance. It can handle geospatial and building data but only to a certain level.
• OGC standards allow geo-spatial data to be handled to a high degree of sophistication, but can only provide a
certain degree of context and building related information.
• BIM/IFC standards provide a rich and detailed way of describing buildings and urban infrastructure, but
struggle to indicate precise geographic location and wider context.
The interviews highlighted that smart cities and communities are increasingly recognizing that to tackle almost any
urban issues, they need to be able access detailed sets of specific information about location, urban infrastructure and
context taken from data collected using these different sets of standards.
They do not need all possible information - just the minimal but "good enough" sets of information relevant to guide
them in tackling specific challenges. They are therefore trying to identify practical solutions and "work arounds" that
will enable them to gather the information they need from data collected using the different standards. The present
document identifies a number of these "work arounds" that cities are using. ETSI, along with OGC and BuildingSmart
International, need to see how the standards they develop can be enhanced to support the use of these solutions.
There are many ways to improve ease of integration between different data structured according to different data
standards. For instance, it is helpful if the different data sets use common data models for all the key entities, or at least
ones that can easily be aligned. Similarly, the use of common identifiers will greatly add data integration. The use of
these solutions can greatly enhance the ease of integration between the different types of data.
"Plug & Play" interoperability
Common
Data from Data from
Identifiers agreed
standards standards
family A family B
Alignment of data
models
No commonalities identified. Requires
completely customised integration

Figure 1: Examples of solutions to improve data integration
It is interesting that OGC have also recognized the need to find alignment with smart data models and linked data and
the present document learns from their proof of concept. So, there is an opportunity to work at this from both sides.
The interviews indicated that the key issue is not one or two specific use cases where smart cities and communities are
using geospatial standards, but rather that they are increasingly realizing that to tackle almost every challenge they face,
they need to be able to bring together information from geospatial data and services and context data from NGSI v2
along with NGSI-LD and other formats using linked data.
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5 Methodology
5.1 Information required
The work was aimed at using interviews to identify common use cases where smart cities and territories need to use
geospatial data as a key part of the solution and specifically, any information about any role that NGSI might play in
helping to link context information to the geospatial data.
In addition, the interviews were used to identify any challenges in linking NGSI with geospatial data and any solutions
developed by those interviewed so that this could feed into the work of deliverable D4 within this project.
5.2 Information gathering process
The work commenced with informal interviews with the European Commission DG JRC team managing the INSPIRE
initiative, Forum Virium Helsinki, Civitas-Connect and Porto Digital to gain a general overview of the key issues.
Using these interviews, and internal discussions, a set of eleven questions was agreed to act as the basis for a further set
of interviews, to ensure that all key issues were covered. The questions are provided in Annex A.
The interviews were transcribed using the help of an AI application and many of them were also recorded.
All the participants acknowledged the value of the work that ETSI has originated and are open to further interviews and
questions, which will be a useful input to the work on deliverable ETSI GS CIM 050 [i.1].
5.3 Interviews
13 interviews were conducted, each lasting on average around one hour:
• European Commission DG JRC/INSPIRE on the general issues of cities and communities linking their
geospatial data with other data.
• Overview of Swedish municipalities use of geospatial data by Tobbe Lahrin - a public sector geospatial expert
since the 80 s and who has been working for the last few years on supporting Swedish municipalities around
the use of IoT.
• Valencia that uses NGSI extensively and has done a lot of work on a platform that can provide two-way
alignment with geospatial data.
• Porto digital (2 x) the innovation agency of the city of Porto, that is actively exploring how to integrate their
NGSI-data with their GIS data.
• Civitas-Connect a German not for profit made up of 7 cities and regions and 6 municipal companies, all
exclusively in the public sector that is developing a core data sharing platform on behalf of the members,
which specifically aims to address aligning NGSI and OGC standards.
• Rennes - a French city that is investigating the potential role of NGSI in its data platform, with the link with
geospatial data as one of the key issues.
• Forum Virium Helsinki - a city with a strong geospatial background, that is looking at how to link geospatial
with other types of data to provide added insights, specifically around the use of local digital twins.
• Riga.
• Eindhoven.
• City of Brussels.
• Catalonia/Norway consortium preparing proposal for a bid for DS4SSCC pilot focusing on linking different ®
types of data and with a strong FIWARE and OGC background.
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• A Consortium that has won a proposal starting in September 2024 3DXVERSE on developing a platform to
link LDTs.
These interviews provided a great deal of information about key use cases related to geospatial data and some helpful
information about the challenges that this will involve.
6 Results of interviews - Use cases
6.1 General
Based on recent research commissioned by the European Commission DG JRC, the team interviewed confirmed that all
cities heavily use geospatial data for many issues. This is because cities are required to maintain much of this
information by government regulations and also because they need it to manage all the assets that they own and tackle
key issues such as managing traffic and the environment, for which geospatial data is vital.
However, the research showed that this city data tends to be siloed off and not readily available for re-use. The
interviews conducted for the present document highlighted the fact that in many cities the Geospatial department tends
to manage its data separately from the data collected by other departments.
This provides the opportunity for significant gains from leveraging geospatial data more effectively and linking it with
other data to help the city tackle the key issues it faces. In this clause 6, some of the main use cases within smart cities
and communities are covered for linking geospatial data with other data to gain greater insight.
The interviews made it clear that cities and communities need to use geospatial data alongside other data in order to
address a number of different use cases and some of the most important of these are covered in this clause.
In looking at these use cases, it is important not just to focus on the technical issues of interoperability, but also to look
more widely at how data can support the procurement of solutions and the wider business case for the investment
needed in gathering and using the data. It is not enough to develop solutions that work if they cannot be implemented
because of the lack of a clear business case.
So, when looking for solutions, it is useful to look at each use case, not only from the perspective of what data is needed
to make it work, but also to identify what data is needed to demonstrate value for money. Most of the data needed will
be common, but there is likely to be some additional requirements, the fulfilment of which would help to enrich the
business case for using geospatial and other data to tackle that use case.
For instance, it is important to show that the solution is described in a way that demonstrates how it opens up the data to
be used in other ways by providing some hooks that will allow solutions to other use cases to be built on more easily in
the future.
6.2 Real Estate and urban infrastructure
6.2.1 Overview
In looking at use cases, the obvious place to start is real estate within the city - the buildings and their inhabitants, the
roads, along with all of the urban infrastructure - as this is the first reason why cities needed to collect geospatial data.
The city needs to be able to identify where all the buildings in the city are, who owns them and who uses them. It needs
to be able to identify the boundaries of parcels of land and who has rights over the use of that land.
There are many specific use cases. For instance, when procuring work to resurface a road, it is important to know the
surface area of that road so as to quantify how much tarmac will be needed to cover it, and to be able to define exactly
the precise length of road that needs to be resurfaced. Defining the surface area of a road is also important when it
comes to putting salt on the roads during icy weather, or using snow ploughs to clear the roads, or even cleaning up
roads after a major storm from mud and debris.
Then there are all the requirements of city planning to make long term plans for the city and decide where new housing
can be built, where industry should be located, and where there should be commercial offices.
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6.2.2 The 10-minute city
A specific and current example of the importance of geospatial data in city planning is the move towards redesigning
the city to make it a 10-minute, or 15-minute city.
The aim is to increase the quality of life of the inhabitants and to become more sustainable.by making sure that all basic
amenities are located within a maximum of 10 (or 15) minutes on foot or by bicycle for the city's inhabitants and users.
These might include:
• Places where citizens can interactive: schools, nurseries, sports facilities, cultural venues, health institutions,
senior citizens' residences, social services, etc.
• Services of the residential economy: specialized or general food stores, non-food shops (pharmacies, post
offices, cash machines, etc.).
• Places that improve the environment and the living environment: publicly accessible green spaces,
playgrounds, communal gardens and allotments, recycling centres, glass recycling points, etc.
• Mobility infrastructures: railway stations, public transport stations, car or bicycle sharing stations, bicycle
parking, etc.
The city's public infrastructure has therefore to be spread across the region in an efficient and well-thought-out manner
(see [i.2]).
To make this work, it is not enough to identify some kind of arbitrary straight-line distance away from each service
access point, but rather it is necessary to map actual routes to make sure that the services can be reached within
10 minutes, and this requires sophisticated use of geospatial data and local digital twins.
6.2.3 Underground utilities networks
Cities also need accurate records of underground pipes, cables and ducting. In Sweden the Swedish Postal
Telecommunication Agency maintains accurate and detailed information about everything that is in the ground. That
information is not accessible for everyone because of national security and commercial confidentiality. But this means
that before any digging takes place in the city, the Agency can say where the dig can take place and where it cannot.
Elsewhere, this might be the responsibility of the city or regional administration.
More generally, this is an important requirement to ensure proper maintenance. It is vital that accurate information can
be maintained about the age and condition of sewers, water and gas pipes, electrical and telecommunications cabling
and ducting, along with information about when they were last inspected and who is responsible to maintain them.
The challenge is that these different types of infrastructure are often managed by different agencies, some regional or
national in character. But it is important that this information is all brought together so that the city can have proper
oversight of the essential utilities provided to its citizens.
6.3 Dealing with disasters
Cities and communities need to be prepared to deal with disasters and this requires the gathering and use of extensive
geospatial data.
A good example of the importance of this is shown by a major forest fire in Sweden that resulted in billions of Swedish
Kroner worth of damage. A key reason for this was the fact that those responsible for managing it did not have the
information they needed about the roads, the key sites, where people were living that need to be evacuated, and so on.
The information needed in a disaster is not what is needed in normal life. It is not important whether a road has been
designed for cars or as a route for bicycles. In a disaster what is important is whether it is wide enough and robust
enough to allow fire trucks or ambulances to pass.
For potential flooding from heavy rainfall or high tides, information is needed about which areas would be flooded and
how quickly the water would reach them.
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To better prepare for the impact of the climate crisis the Copernicus data sets are very useful, for instance to identify.
the heat spots where the temperature is higher than the surrounding areas so that the causes can be identified in order to
mitigate the problems as the average temperature increases over the next few years, and in order to learn how to better
design urban areas in the future.
There are many other use cases that could be covered here. For instance, where the city has a database of all the trees in
the city and these can be filtered in different ways, then should there be an infestation of, for instance, the processionary
caterpillar that lives on oak trees, it would be easy to find out where the oak trees are located in the city to quickly
respond and deal with the problem.
6.4 Mobility
Clearly mobility is one of the key use cases for cities and communities.
Here data interoperability is an important focus point because mobility is inherently in an interdisciplinary setting.
There is the mobility side, that traditionally has had its own data formats and systems, and there is urban planning
where there is a different set of conventions. The challenge is how to describe the urban environment so that it can be
used for both simulation, which is a well-established process in mobility, and also for urban planning, which
traditionally has been more about the urban geometry.
There are some steps towards this, for instance in CityGML, where the latest versions aim to address both aspects, but
this is still coming very much from the geospatial side.
Then there is traffic planning; to know how traffic could be re-routed if there is an accident or if roads need to be closed
for repair.
Simulations could be used for bus travel to understand how to improve the routes and timings. Similarly, simulations
could be used to identify where effective cycling and walking routes could be developed.
Linking live information to online maps can support the use of city bikes and e-scooters by showing how many bikes or
scooters each parking station has so that people can know where they can find a bike or e-scooter and where they can
park it at the end of their journey. It can also show where there are vacant parking spaces for cars and where the nearest
taxi is parked.
Geospatial data can also be used to support, for instance, visually impaired people, in navigating around the city. It can
provide the blind or visually impaired person with information about everything that is of interest for them. It could
warn them about obstacles in their way such as a bicycle park or an outdoor restaurant, or something that is normally
not there, such as roadworks cutting across the walking route or glass on the street. Such tools can give them confidence
to go out on their own and even use buses and subways.
6.5 Other use cases
Many other use cases were mentioned by the cities interviewed including the following:
• Displaying information about current and planned events at key points of interest on the online map of the city.
• Developing maps to visualize data and help citizens and decision makers understand the key issues more
quickly and easily.
• Asset management.
• Environmental issues such as dust particles, noise levels, and so on.
• Supporting responses to crime.
• Tax management of building related taxes.
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7 Challenges related to geospatial data for cities
7.1 Challenge of defining regions of interest
One of the challenges mentioned was those cases where it is difficult to define the regions of interest for the different
datasets that need to be linked, since the detail levels can be very different. For instance, there may be a dataset from,
citizen participation which includes very detailed points of interest that that people have tagged on maps to show that
this particular point feels uncomfortable or that they like this particular feature. So that could be precise point data.
Then there might be aspects which are really much cruder in in terms of spatial resolution. For instance, some of the
demographics data for example might be linked to postal code zones or something else that cannot be pinpointed to an
individual coordinate point.
Bringing those together in a meaningful way so as not to create false observations is challenging. That has some
parallels to what people have done in cartography, historically, where different scales and probabilities exist when there
are fuzzy boundaries and things like that. So, there is some good experience to draw on, but the work has to be done.
7.2 The need for context to observations
The example of urban traffic counters was mentioned, which provide the city with a system that counts vehicles or
pedestrians passing a certain point. But if this is to help with traffic simulation, then the point observations need to be
linked to the traffic network to help understand the implications of any. For instance, to understand whether a blockage
on one piece of road cuts off a very important branch that would have significant impacts across the network.
So it is important to have more context when measuring traffic in a particular point about which parts of the network
does this point influence. At that particular point where the traffic flows are being measured, it is important to know
where the people are coming from and going to so that alternatives routes could be identified to enable them to get to
where they want to go. Without having that information, it is difficult to work out the implications of any anomalies in
the measurements that are being collected.
7.3 The need to work at different scales
When for instance, mapping real estate or describing it in geospatial terms it is important to take into consideration that
the real estate does not simply have one geometry connected to it. When zooming right out to say a city scale, it may be
right to use a single point to represent say a building. Then, when zooming in, it might be appropriate to show a
generalized border to give the approximate shape of the building or plot of land. Zooming further in, there might be a
more detailed polygon or area. And at some stage, there might be a 3D model of the real estate that can describe slopes
and different levels. Zooming in even further it might be necessary to define individual rooms or features.
So, when one wants to have an identity for a spatial object and connect data to it, web feature services are needed using
vectors. The problem with using vectors is that there needs to be different generalization on different zoom levels. For
instance, if roads are kept consistently to scale they would not be visible when zooming out. So, in most maps covering
a large area, the roads are not shown in the scale that is realistic but are widened to make them visible. A road that
might be 10 metres wide in reality might be shown as 100 metres or 200 metres in the zoomed-out map.
7.4 Batch integration and near real time integration
For some use cases, it is good enough to align the geospatial and other data sets on a periodic basis, say once a day. In
other cases, near-real time alignment is required. For instance, changes in ownership of buildings may only need to be
updated once a day, whereas indicating vacant parking spaces requires near real time updates to a map.
Near real time alignment is clearly mainly focused on information coming from sensors, but in other situations such as
disasters, other information may also need to be updated in near real time. This brings the added challenge that
potentially some information may normally be updated using batch processing, but might need to be updated in near
real time during emergencies.
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7.5 Local Digital Twins
Local Digital Twins [i.3] clearly have a strong geospatial dimension and the move towards the implementation of LDTs
is a key driver for cities to see how they can better use geospatial data. However, particularly in the context of [i.3], an
important aspect to consider is the social dimensions of LDTs and the need to blend in wider sets of data.
For instance, when considering topics such as vulnerability to climate change, this is a theme where there are physical
phenomena that can be simulated to identify the direct impacts of climate change in the urban environment. But then
there are equally important issues, such as the demographics or the vulnerability of people, in the urban setting that need
to be part of such simulations. One aspect is issues such as flooding, storms, temperature and the impact on the city. On
the other side it is about the demographics and how, for instance, these might be more challenging for older people to
navigate through.
Given that the city has limited resources and so cannot deal with every problem, judgements have to be made regarding
issues of equality and justice, and what will bring the greatest cost benefit. For instance, it is generally more important
to know which is the hottest kindergarten in the city than which is the hottest warehouse.
Where there is a challenge like adapting to, or responding to climate change, geospatial data needs to be brought
together with lots of other data, some of which is quantitative and some qualitative. This makes interoperability more
difficult compared to a situation where, for instance, there are two different types of sensor networks that need to be
integrated, but both of them are just numbers.
And the geospatial data can only have practical applications if it can properly be linked with these other types of data, to
feed into models that make sense and take into account of all of the different issues.
7.6 The challenge of data models
7.6.1 Complexity
Commonly there has been an idea that one data model could be created that would cover everything related to a
particular object or entity and then a data model is developed containing maybe at most 15 tables.
However, there are many instances where this does not work. Typically, to provide information
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