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					Approximation and Visualization
  of Interactive Decision Maps
       Short course of lectures

         Alexander V. Lotov

  Dorodnicyn Computing Center of Russian
         Academy of Sciences and
    Lomonosov Moscow State University
 Lecture 8. Reasonable Goals Method for
supporting the finite multi-attribute choice
           (database screening)

 Plan of the lecture
 1. The problem of database screening
 2. A simple graphic description of the method
 3. Software demonstration (comparison of RGM
   and FGNL)
 4. Screening procedure
 5. Several applications of the RGM/IDM
          The problem of database screening
A database of alternatives in the form of a decision matrix is
considered, i.e., table of N decision alternatives (rows) given
by a finite number of attributes (columns), a part of which is
  used as the criteria in database screening: one or several
    preferable rows must be selected from the database.
       Features of the problem
The criteria, which used for selecting a small number of
  alternatives, are assumed to be real values. Thus, an
  alternative is associated with a criterion point. The
  method is based on visualization of the Pareto frontier
  of the “cloud” of criterion points.
The decision maker has to identify the goal on the
  Pareto frontier of the “cloud”. Such information of
  the DM’s preferences helps to select a small number
  of «good» alternatives. This study can be considered
  as a special form of data mining.
   Possible sources of the alternatives
The alternatives can be found in multiple large lists in Web
     describing the selection options: real estate on sale, second-
     hand cars, hotels, universities, etc.
On the other hand, alternative points can be results of scientific
     experiments or collecting research data.
Lists of financial projects can be developed described by such
     attributes as discounted investment, discounted income, term
     of complete investment return, reliability, etc.;
Alternative variants of portfolio or of assets allocation can exist
     described by such attributes as dividend, income, variability,
Alternative variants of business location given in a Geographical
     Information System can be found;
Large by finite number of Pareto-optimal strategies for solving of
     environmental or technical problems can be developed by
     using Feasible Goals technique, etc.
Example: real estate on sale
A simple graphic description of the
      For illustrative purposes, let m=2
(criterion points are displayed in the plane).
Non-dominated points are given by crosses.
Enveloping the criterion points
Approximating the Edgeworth-Pareto hull
of the convex hull (the so-called CEPH)
 Pareto frontier is analyzed by user and a
preferred combination of criterion values
      (reasonable goal) is identified
The alternatives that are close to the
         goal are selected
  General case (m from 3 to 8)

   Visualization of the Pareto frontier
is based on approximation of the CEPH
    and application of the Interactive
     Decision Maps technique for the
interactive analysis of the frontiers of the
        Software demonstration
RGM/IDM-based software Visual Market – 2
Example of the screening procedure
      (maximization case)
Several applications of the
  IDM/RGM technique
          Selecting a location for rural
              health practice in Idaho
Many rural areas in the USA, compete for medical doctor, thus
  creating choice opportunities for those interested in practicing
  rural medicine. Yet, these efforts coupled with various US
  federal programs have had mixed results in attracting and
  retaining primary health care providers in rural localities. One
  possible cause for this is the lack of effective information tools
  that would aid the prospective medical doctors in screening
  practice locations options and learning about tradeoffs involved.
The approach based on the RGM/IDM technique allows the
  prospective doctors to select preferable locations based on
  their closeness to the goal specified by them without the
  difficulty of specifying criterion weights. Along with the
  RGM/IDM technique, the DSS applies the geographic data
  query and visualization module implemented in ArcViewTM 3.0.
Database of alternatives and the
        map of Idaho
            Description of the database
Data representing health-care, social, economic, and environmental information
   were aggregated by 47 Primary Care Service Area (PCSA) encompassing
   the entire state of Idaho. The attribute database describing the PCSA
   provided information for evaluation criteria. The criteria were grouped into
   professional and personal.
Professional criteria included:
• need for physicians denoted by DOCS, this is a derived index measure:
   the higher the DOCS value the higher the need. DOCS can also assume
   negative values representing low demand for physicians or lack of thereof,
• population in 1990 (POP90),
• percent of population receiving Medicare and Medicaid (MEDICARE),
• fertility rate (FERTILITY),
• loan repayment program (LOAN_REPA),
• number of hours per week on call (ON_CALL), etc.
Personal criteria included:
• percentage of unemployed population (UNEMPLOYED),
• percentage of population below poverty level (POVERTY),
• percentage of population with college degree (POP_DEGREE), etc.
                 DSS application
The modules supplement each other in providing decision
  support functions. The prospective physician can learn quickly
  about the location of places offering practice opportunities,
  their physical and socio-demographic characteristics,
  amenities offered by them, and relate this information to the
  surrounding physical environment by viewing and querying
  reference maps in ArcView. The information gained from
  spatial data query and visualization becomes useful in
  selecting a reasonable goal for the health practice location.
The goal selection, which is performed by the user, results in
  returning a list of few locations that are <<close>> to the
  selected goal in the sense described above. These locations
  can be in turn displayed and analyzed in ArcView. The
  process is interactive and iterative. Its intended outcome is a
  better-informed decision on the part of prospective health care
  professional about rural practice location selection.
In order to illustrate the application of the method,
  we use five attributes for the location selection
• need for physicians (DOCS) – to be maximized;
• population in 1990 (POP90) – to be maximized;
• weekly number of hours on call (ON_CALL) – to
  be minimized;
• fertility rate (FERTILITY) – to be maximized
• percentage below poverty level (POVERTY) – to
  be minimized.
An alternative matrix of decision maps
Actually, Nampa is the only location, which is close to the identified
   goal in the common sense of this word (note that the need for
   physicians is much higher in Nampa than it was set in the goal).
   Since the software does not know the preference trade-off of the
   user, it displays three different locations, which are not very near the
   goal, but may happen to be better locations from the user
   perspective than Nampa. Note that in Nampa the prospective
   physician has to spend 1.29 hours on call instead of one hour in the
   identified goal.
In St.Maries one has to spend only 0.92 hours on-call per week.
   Perhaps, the user would agree to sacrifice the population level for
   this advantage? Only the user can decide it. Weiser was selected
   since it is a little bit larger than St.Maries. Finally, Boise, the capital
   of Idaho, was chosen since Nampa does not meet the population
   level of the goal. Perhaps this is important for the user? The user has
   to decide whether any of the selected places is attractive enough for
   the location of health care practice. It is important to note that all
   other places in the database were further away from the identified
   goal, and so they were not selected.
  Application to local water quality
    planning in Kolomna region
  (“Revival of the Volga River” program)
• 390625 decision alternatives were formulated;
• decision alternatives were enveloped and
  provided in the form of decision maps;
• reasonable goal was identified by the user;
• related alternatives were found and displayed in
  decision maps.
390625 decision alternatives were
   considered and evaluated
Initial decision map
 How it is dangerous to use simple
      pollution minimization!

• Take into account the tradeoff rate related
  to minimization of the pollutant p3!
• About US$ 5,000,000 are needed to
  minimize the pollution while just the same
  value can be obtained with the cost of US$
Decision map with restricted cost
Decision map with the goal
Selected alternatives that are close to the goal

   Alberto Barrón Alcántara, PEMEX PEP SCTI
 David Alberto Salas de León, ICML de la UNAM
Glicinia Valentina Ortiz Zamora ICML de la UNAM
   Mardocheo Palma Muñoz ICML de la UNAM
         Wiliam Bandy ICML de la UNAM
         Carlos Mortera ICML de la UNAM
             Alexander Lótov CCARC
              Román Efrémov URJC
          Steps of the study

1.- Estructuración de geobase de datos
2.- Filtrado de datos
3.- Selección de variables
4.- Extracción de conocimiento
5.- Interpretación y evaluación

Number of decision alternatives was about 9000.
Decision alternatives
Matrix of decision maps
Selected decision map
Selected alternatives
  Aplicación de la Minería de Datos
        para la exploración
   óptima de reservas petroleras

                           PEMEX PEP
                    Ing. Alberto Barrón Alcántara

CCARC ( Centro de Computación de la Academia Rusa de Ciencias)
                       Dr. Alexander Lótov
                      Mr. Alexander Kistanov
                      Mr. Alexander Zaitzev
                       Dr. Roman Efremov



        Rocas Generadoras

                                               Rocas Sello

                                            Rocas Almacenadoras
             Criterios para determinar la
            ubicación de las instalaciones

             Potenciales                                   Tipo de            DISTANCIA A
Localizaci                    ad de        Inversión                                           VPN
             (MMbpce o                                  Hidrocarburo   Pg     INSTALACIO
      ón                    Equipo (      1000*MM$                                           1000*MM$
               MMb)                                     (Grados API)            NES (KM)

Aak-1             234,72      2021         10,3514884        10        0,13           7,55     30,70195

Aak-1           231,7224      2021         10,2147851        12        0,11           7,55     30,71549
Abaco-1          15,2861      2008         0,36560871        17        0,29           7,90     56,61074

Abadeji-1         29,124      2012         1,13433301        39        0,24           1,45     34,76395
              4907 alternativas…
  En total,5,3664 2008
Aban-1                  0,19704057 10                                  0,24          22,81     36,87619
      Criterios para determinar la
     ubicación de las instalaciones
      Nombre de la Columna                                        Descripción

Localización                        Nombre del Pozo que se propone a Perforar

VPN (MM$)                           Valor Presente Neto en miles de millones de pesos

Tipo de Hidrocarburo (Grados API)   Determina la Calidad del Petróleo

Disponibilidad del Equipo de        Indica el año en que se tendrá disponible el equipo para perforar el pozo
    Perforación (año)

Pg                                  Probabilidad Geológica Determina el grado de probabilidad de éxito de
                                        obtener el volumen de hidrocarburo estimado

Inversión (MM$)                     Inversión estimada para perforar y poner en operación el pozo

Distancia a Instalaciones (KMs)     La distancia del pozo que se propone perforar a la instalación más
                                        cercana disponible
Soluciones factibles: otro algoritmo



                                        3      y1
   RGM for non-linear models

• First, the Pareto frontier for a non-linear
  model is approximated by a large number
  of non-dominated objective points (FGNL
• Then, the RGM/IDM technique is used for
  exploration of the Pareto frontier of the
  convex hull and several non-dominated
  points for the non-linear model.
    Software demonstration
    Comparison of RGM/IDM
technique with FGNL visualization
      (Pareto Front Viewer)
• Real-life. Exploration of marginal pollution
  abatement cost in the electricity sector of
  Israel (Ministry of National Infrastructures).
  The software system was used at the Ministry
  for about five years.
• Methodological. Optimization of River Basin
  Management Plan (Italy)
    Exploration of pollution abatement cost
in the Electricity Sector – Israeli case study
    (jointly with D. Soloveitchik and other
                specialists from
 Ministry of National Infrastructures, Israel)
Several hundreds of pollution reduction
 alternatives for the Israel electricity sector
 were developed for the period 2003 – 2013
 by application of a complicated non-linear
 mathematical model (FGNL does not exist
 that time). Then, the IDM-based screening of
 alternatives was applied.
       Five selection criteria were used:

•   percent of CO2 reduction (CO2_%);
•   percent of NOx reduction (NOx_%);
•   additional total cost (NPV_D);
•   marginal abatement cost (NPV_DC($));
•   percent of growing average cost of
    electricity (AV.C_%).
    Decision map displays trade-off between CO2_% and
NPV_D for several levels of NOx reduction as well as given
 levels of marginal abatement cost and percent of growing
                 average cost of electricity
Selected alternatives
                    L. Galbiati1, F.J. Elorza2, A. Udias3, F. Bouraoui1
          (1) JRC, Institute for Environment and sustainability. Rural, Water and
                              Ecosystem Resources Unit (Italy).
      (2) D.M.A.M.I. (E.T.S.I. de Minas). Universidad Politécnica de Madrid. email:
      (3) D.E.I.O. (E.S. de C.C. Experimentales y Tecnología). U.R.J.C. Mostoles,
                                       Madrid (Spain).
Diffuse sources of pollution coming from agricultural practices are among the biggest
   problem to tackle when dealing with water management. Often the crop
   management is oriented to maximize production without considering the
   contamination caused to the ecosystem. This study is focusing on the identification
   of an optimize crop rotation, applying a MOEA technique coupled with a
   comprehensive hydrological model (ISSm) capable to take account all the
   components of the water cycle as well as the contamination occurring during
   agricultural practices and benefits from crop production. This tool has been applied
   to Burana-Po di Volano basin (Italy), demonstrating the effectiveness to provide
   tradeoff solutions that simultaneously minimize the leaching of pollutants to the
   groundwater and maximizes the exploitation benefits only choosing the adequate
   crop sequences. This methodology is an important support for policy makers and
   water managers, providing information about the cost-effectiveness of alternative
   agricultural practices.
The map of Burana-Po di Volano basin
Sub-basins in the watershed
 Criteria and decision variables
• Benefit that is equal to the income minus
  fertilizer cost and irrigation cost;
• the average annual phosphorous yield;
• the average annual nitrogen yield.
Decision variables: allocation of crops
  between all sub-sub-regions for each year
  After approximating the Pareto frontier by using a multi-
objective evolutionary optimization algorithm, the RGM/IDM
                     technique was used
Application of RGM/IDM in Web.
Reasonable Goals for DataBases
Concept of the Web RGDB application
Data input
Example of the RGDB display
Selected alternatives

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