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Guatemala, 19 de Noviembre 2012Guatemala, 19 de Noviembre 2012
DR. MARIO MELGARDR. MARIO MELGAR
““Tendencias del Análisis Tendencias del Análisis de Datos a Nivel de Datos a Nivel
Mundial”Mundial”
Conferencia presentada en el Curso de:Conferencia presentada en el Curso de: Métodos de Investigación Cuantitativa Métodos de Investigación Cuantitativa Doctorado en Ciencias Agrícolas y Doctorado en Ciencias Agrícolas y Ambientales Facultad de AgronomíaAmbientales Facultad de Agronomía Universidad de San Carlos de GuatemalaUniversidad de San Carlos de Guatemala
Conferencia presentada en el Curso de:Conferencia presentada en el Curso de: Métodos de Investigación Cuantitativa Métodos de Investigación Cuantitativa Doctorado en Ciencias Agrícolas y Doctorado en Ciencias Agrícolas y Ambientales Facultad de AgronomíaAmbientales Facultad de Agronomía Universidad de San Carlos de GuatemalaUniversidad de San Carlos de Guatemala
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ContenidoContenido
BIG DATA ENFOQUES DATA SCIENCE MINERÍA DE DATOS VISUALIZACIÓN
BIG DATA ENFOQUES DATA SCIENCE MINERÍA DE DATOS VISUALIZACIÓN
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The next five years will produce more research data than has been produced in all of previous human history, presenting researchers with daunting discovery challenges.The data deluge was highlighted and deepened by monumental big bang and astronomy projects such as the Large Hadron Collider and the planned Square Kilometre Array, said Ross Wilkinson, executive director of the Monash University-based Australian National Data Service.
The European Union's collider would produce a petabyte of data each month, while the data generation of the SKA was so mind-boggling that the term exobyte had been coined to describe its data output.
"An exabyte is 1000 petabytes; a petabyte is 1000 terabytes; a terabyte is 1000 gigabytes and a gigabyte is 1000 megabytes," Dr. Wilkinson said."You can read about 2GB of text, or about as much text that can fit on two CDs, in a lifetime, so it's really scary numbers."
SOURCE: THE AUSTRALIAN, APRIL 29, 2009
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NOMBRE TAMAÑO
BYTE 1
KBYTE 1000
MEGABYTE 1 000 000
GYGABYTE 1 000 000 000
TERABYTE 1 000 000 000 000
PETABYTE 1 000 000 000 000 000
EXABYTE 1 000 000 000 000 000 000
ZETTABYTE 1 000 000000000000000000
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UNIDAD VALOR/EJEMPLO
Byte Es la unidad elemental de información que puede guardar un carácter: letra, número o signo
2 kilobytes Una página
5 megabytes Obras completas de Shakespare; 30 segundos de video.
100 megabytes Radiografia Digital.
500 megabytes CD
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1 gigabyte (GB) Sinfonía en sonido de alta fidelidad.
2 gigabytes 20 metros de estantería de libros, lo que una persona puede leer en toda su vida
20 gigabytes Archivos de audio de la obra de Beethoven
Terabyte (TB) 1000 GB
1 terabyte Todas las películas radiográficas de un hospital de alta tecnología.50000 árboles transformados en papel e impresos.
10 terabytes Colección impresa de la biblioteca del Congreso de EE.UU.
Petabytes (PB) 1000 TB
2 petabytes Todas las bibliotecas de investigación académica de EE.UU.
Exabyte (EB) 10000 PB
5 exabytes Todas las palabras dichas alguna vez por los seres humanos.
ZettabyteFuente: Ambrosi, H. 2008. La Verdad de las Estadisticas. Ediciones Lumiere.
1000 Exabytes
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1. Internet2. Celulares3. NASA4. Astronomía5. Universo6. Genómica7. Física8. Imágenes Médicas9. AT&T, WALMART, etc.
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The Conversation Prism Infographichttp://jess3.com/the-conversation-prism-v3/
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2008 MySQL Conference & Expo Jacek Becla, SLAC
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212008 MySQL Conference & Expo Jacek Becla, SLAC
Science & Petabytes
4 PB in 2005 (images)
NASA: Earth Observing System
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222008 MySQL Conference & Expo Jacek Becla, SLAC
Science & Petabytes
Huge telescopes Multi-gigapixel cameras Getting ready for…
– Trillions of observations– 50+ PB of images– 20+ PB database
Astronomy
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232008 MySQL Conference & Expo Jacek Becla, SLAC
Untangling the Universe
Overlapping Moving Disappearing Highly correlated
Astronomy: It’s All About “Astronomical Objects”
Needle in haystack Spatial correlations Time series
Needle in haystack Spatial correlations Time series
Needle in haystack Spatial correlations Time series
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242008 MySQL Conference & Expo Jacek Becla, SLAC
Science & Petabytes
Trying to put together database of all known DNA sequences
Multi-petabytes
Genomics
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How does the human genome stack up?
Organism Genome Size (Bases) Estimated Genes
Amoeba dubia (ameba) 670 billion ?
Porocentrum micans (protista) 245 billion 92,000
Pez leopardo (Propterus aethiopicus) 130 billion ?
Caña de azúcar (S. officinarum) 7.4 billion 35,000
Human (Homo sapiens) 3 billion 25,000
Laboratory mouse (M. musculus) 2.6 billion 30,000
Mustard weed (A. thaliana) 100 million 25,000
Roundworm (C. elegans) 97 million 19,000
Fruit fly (D. melanogaster) 137 million 13,000
Yeast (S. cerevisiae) 12.1 million 6,000
Bacterium (E. coli) 4.6 million 3,200
Human immunodeficiency virus (HIV) 9700 9
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Examples of Large Data Sets:Genomics
• 25,000 genes in human genome
• 3 billion bases
• 3 Gigabytes of genetic data
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272008 MySQL Conference & Expo Jacek Becla, SLAC
Understanding Dynamics of Biological Processes
Needle in haystack Correlations Time series
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422008 MySQL Conference & Expo Jacek Becla, SLAC
Science & Petabytes
½ PB/sec– Small fraction saved
Trillions of collisions 15 PB/year
– Starting later this year
High Energy Physics: LHC
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Examples of Large Data Sets:Credit card transactions
• 142 billion transactions in 2004 in US alone
• 115 Terabytes of data transmitted to processing center in 2004
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Examples of Large Data Sets:Phone call billing records
• 250M calls/day
• 60G calls/year
• 40 bytes/call
• 2.5 Terabytes/year
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462008 MySQL Conference & Expo Jacek Becla, SLAC
0
50
100
150
2000 2005 2010 2015 2020 2025
year
PB
Science, Industry & Petabytes
?GoogleYahoo!
Microsoft
AT&TWalmart
EBayFacebookfew others
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Enfoques
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Competitive Edger or Colossal Migraine?
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InfoVis 51
Big Challenge
• How do we make sense of it?• How do we harness this data in
decision-making processes?
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Enfoques para el Manejo de los Datos
53
1. Empresas Especializadas en Gerencia de Datos
2. Data Science3. Minería de Datos4. Visualización
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Grandes Empresas
IBMMicrosoftOracleSAPIMBSASEtc. …Están surgiendo cientos…
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Enfoques para el Manejo de los Datos
81
1. Empresas Especializadas en Gerencia de Datos
2. Data Science3. Minería de Datos4. Visualización
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Data Science: An Introduction/A History of Data Science
Chapter SummaryData Science is a composite of a number of pre-existing disciplines. It is a young professional and academic discipline. The term was first coined in 2001. Its popularity has exploded since 2010, pushed by the need for teams of people to analyze the big data that corporations and governments are collecting. The Google search engine is a classic example of the power of data science.
DiscussionData science is a discipline that incorporates varying degrees of Data Engineering, Scientific Method, Math, Statistics, Advanced Computing, Visualization, Hacker mindset, and Domain Expertise. A practitioner of Data Science is called a Data Scientist. Data Scientists solve complex data analysis problems.
OriginsThe term "Data Science" was coined at the beginning of the 21st Century. It is attributed to William S. Cleveland[1] who, in 2001, wrote "Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics."[2] About a year later, the International Council for Science: Committee on Data for Science and Technology[3] started publishing the CODATA Data Science Journal beginning April 2002.[4] Shortly thereafter, in January of 2003, Columbia University began publishing The Journal of Data Science .
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El “Ecosistema” de los datosKNOWLEDGE
WHAT WHYHOW
DATA COLLECTOR
DATA CUSTODIAN
DATA CONSUMER
ROLE 1 ROLE 2 ROLE 3
DATA COLLECTION
DATA STORAGE AND
MAINTENANCE
DATA UTILIZATION
PROCESS 1 PROCESS 2 PROCESS 3
DATA QUALITY DIMENSIONS
FIDELITYCOMPLETENESS
COMPLETENESSACCESIBILITYTEMPORALITY
RELEVANCE
KNOWLEDGE
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Data Mining: Confluence of Multiple Disciplines
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ANÁLISIS UNIVARIADO ASOCIACIÓN ENTRE VARIABLES
Análisis multivariado
Variables Cualitativas
Variables Cuantitativas
Cualitativas con
cualitativas
Cualitativas con cuantitativas
Cuantitativas con cuantitativas
FrecuenciasProporciones Gráficas de barrasGráficas de pastelPictogramasGráficas de puntosPirámides
Distr. De frecuenciasHistogramasGráficas de tallos y hojasGráficas de cajasGráficas de normalidadMedidas de: - Tendencias central - Dispersión - Percentiles
Tablas de ContingenciaGráficas de barras
Tablas de clasificaciónGráficas de medias
Diagrama de dispersión3 D
Gráficas de HistogramasCaras de ChernoffEstrellasFlechasGlifosMatriz de correlaciones
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Temas tratados en el libro digital. http://www.statsoft.com/textbook
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Temas tratados en el libro digital. http://www.statsoft.com/textbook
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VARIABLES CUANTITATIVAS VARIABLES CUALITATIVAS
POBLACIONES Contínuas Discretas Binominal Multinomial
Una t Wilcoxon Binomial, Z X²
Dos independientes
t Mann-Whitney Irwin-FisherExacta FisherX², Z
X²
Dos dependientes t Wilcoxon McNemar Stuart
Tres o más independientes
F ANDEVA D.C.A.Una vía
Contrastes
Kruskal-Wallis
Contrastes
X²Contrastes
X²Contrastes
Tres o más dependientes
F ANDEVAD.C.A.Dos vías
Contrastes
Friedman
Contrastes
Cochran
Contrastes
Friedman (Ordinal)
Dependencia
Relación
Regresión
Pearson
Wilcoxon
SpearmanHipergeométrica0
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Análisis Multivariado• Conjunto de técnicas para el análisis estadístico de datos,
obtenidas a través de la medición de varias variables sobre cada individuo o unidad estudiada.
• Esas variables están correlacionadas.
UNIDAD Variables a explicar o dependientes
Variables Explicativas o independientes
Y1, Y2, . . . . . . . . . . . .Yɋ X1, X2, . . . . . .. . . . .Xƿ
U1
U2
.
.
.Un
ESTRUCTURA DE LOS DATOS PROVENIENTES DE UN
ESTUDIO
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Variables dependientes
Variables independientesCualitativa Cuantitativa
1 variable >1 variable 1 variable >1 variable
Ninguna Binomial Chi-cuadradoMedidas de asociación
T Matriz de correlacionesComponentesPrincipalesAnálisis de factoresAnálisis Cluster
Cualitativa1
>1
Chi-cuadradoExactaFischer
Log-LinearModelos
Log-LinearModelosRegresiónLogística
Log-Linearmodelos
RegresiónLogística
Análisis discriminante
RegresiónLogística
Análisis discriminante
Cuantitativa1
>1
TAnálisis de varianza
T² HotellingAnálisis de varianzaMultivariado
Análisis de varianza
Análisis de varianzaMultivariado
Regresión linealRegresión no linealCorrelación
Regresión multivariadaCorrelación canónica
Regresión múltiple
Regresión multivariadaCorrelación canónicaPath Analysisestructurales
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SAS
SQL
XL Miner
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Visualización
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InfoVis 119
Example
Example courtesyof Chris North
Which state has the highest income?Is there a relationship between income and education?Are there any outliers?
Questions:
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InfoVis 120
Visualize the Data
Per Capita Income
Colle
ge D
eg
ree %
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InfoVis 121
Atlanta Flight Traffic
AJC
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InfoVis 122
London Subway
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• Tabla periodica
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1252008 MySQL Conference & Expo Jacek Becla, SLAC
Summary Data avalanche Need scalable,
sophisticated tools
You are facing it too
Credit: ncids.org
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HANS
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BienvenidosBienvenidosCENGICAÑACENGICAÑA
Visión:Ser líderes en generar cambios tecnológicos para
incrementar la competitividad de la Agroindustria Azucarera en la región.
Visión:Ser líderes en generar cambios tecnológicos para
incrementar la competitividad de la Agroindustria Azucarera en la región.
02/03/2012
Dr. Mario MelgarDr. Mario Melgar
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Evolución de la Evolución de la productividad Guatemalaproductividad Guatemala
60 65 70 75 80 85 90 95 00 05 100
1
2
3
4
5
6
7
8
9
10
11
TAH
Año
Quinquenio TCH % Sac TAH
1959/60* 53 9.70 5.20
1960/65 57 9.34 5.34
1965/70 62 9.24 5.76
1970/75 74 8.83 6.58
1975/80 77 8.49 6.54
1980/85 76 9.10 6.58
1985/90 71 9.66 6.90
1990/95 82 10.10 8.32
1995/00 85 10.42 8.87
2000/05 90 11.33 10.17
2005/10 94 10.75 10.05
Rendimiento de Azúcar/TAH 1960-2010
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Evolución de análisis Evolución de análisis de productividad de de productividad de
la Agroindustria la Agroindustria Azucarera Azucarera
GuatemaltecaGuatemalteca
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Factores relacionados con el Factores relacionados con el rendimiento de un cultivorendimiento de un cultivo
y = f (A, G, M)y = f (A, G, M)
Y = RendimientoY = RendimientoA = AmbienteA = AmbienteG = GenéticaG = GenéticaM = ManejoM = Manejo
Fuente: Altieri, M. 1987. Agroecology. Westview Press. 227 p.
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TCHTCHTAHTAH$/H$/H
AmbientaleAmbientaless
GenéticosGenéticos
ManejoManejo
Zona agroecológica (1-44)Zona agroecológica (1-44)Finca (1 – nFinca (1 – n11))Lote (1 – nLote (1 – n22) (total 14,000)) (total 14,000)
Variedad (67)Variedad (67)No. de corteNo. de corteMes de cosechaMes de cosecha
Ingenio (1-8)Ingenio (1-8)
FertilizaciónFertilización
Riegos (1-4)Riegos (1-4)
Madurantes (1-6)Madurantes (1-6)
Edad de cosechaEdad de cosecha
Balance Balance hídricohídricoGrupos de Grupos de suelosuelo
N (1-N (1-7)7)P (1-4)P (1-4)K (1-4)K (1-4)S (1-3)S (1-3)
VARIABLVARIABLES ES
RESPUESRESPUESTATA
FACTORESFACTORES
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AnálisiAnálisiss
Sistemas Sistemas de de InformacióInformación n GeográficaGeográfica
Bases de Bases de datosdatos
Análisis Análisis detalladdetallado de o de variedadvariedadeses
MapasMapas
Estadística Estadística descriptiva descriptiva (Gráficos, cuadros)(Gráficos, cuadros)
Estadística Estadística inferencialinferencialMinería de datosMinería de datos
ZAE, finca, lote, ZAE, finca, lote, Mes de cosechaMes de cosechaFactores de Factores de manejomanejo
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BASE DE DATOS DE EXCEL PARA COMPARTIR
Menú Principal Formularios de Comparación
Menú Principal Gráficos
Productividad
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Fuente: LMC Sugar Technical Performance - Executive Summary-Sma605 September 2008Fuente: LMC Sugar Technical Performance - Executive Summary-Sma605 September 2008
Indicadores de Competitividad
Al 02/05/2010
USA
Thailand
swazilandia
Sudan
Sudáfrica
México
India
Guatemala
Colombia
China
Brasil (N.E.)
Brasil (C.S.)
Australia
6
11
16
21
26
31
36
6 7 8 9 10 11 12 13 14 15 16
Rendimiento azúcar (TAH)
Azú
car
prod
ucid
a po
r to
nela
da d
e ca
paci
tdad
de
mol
iend
a (ti
b az
úcar
/ton
ca
paci
dad)
11.5
9.4
6.5
9.5
14.6
12.2
7.9
9.3
6.6
10.8
13.9
7.3
9.1
13.15
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Gráficos por países, períodos por quinquénios: Gráficos por países, períodos por quinquénios: 19881988
País TAH
Utilización de la
Capacidad
Área Sembrada
Australia 9.80 12.60 352,023.00Brasil (C.S.) 8.60 15.00 2,585,063.00Brasil (N.E.) 5.80 15.40 1,287,453.00China 4.80 9.40 881,000.00Colombia 11.10 24.40 140,297.00Guatemala 6.99 8.80 84,333.00India 7.10 12.50 3,072,052.00México 8.00 10.00 535,884.00South Africa 5.70 19.90 408,743.00Swaziland 23.90 36,014.00Thailand 5.30 7.80 559,638.00USA 9.30 10.60 344,436.00
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Gráficos por países, períodos por quinquénios: Gráficos por países, períodos por quinquénios: 19981998
País TAH
Utilización de la
Capacidad
Área Sembrada
Australia 11.60 17.40 420,572.00Brasil (C.S.) 9.90 18.30 3,250,190.00Brasil (N.E.) 6.30 12.00 1,105,856.00China 6.80 9.50 915,878.00Colombia 12.50 27.90 178,687.00Guatemala 10.30 12.50 151,540.00India 8.00 13.60 3,836,021.00México 9.10 12.70 570,322.00South Africa 4.90 15.70 397,026.00Swaziland 13.60 22.00 38,182.00Thailand 6.40 8.40 963,256.00USA 8.80 10.60 391,873.00
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Gráficos por países, períodos por quinquénios: Gráficos por países, períodos por quinquénios: 20082008
País TAH
Utilización de la
Capacidad
Área Sembrada
Australia 11.50 16.70 453,316.00Brasil (C.S.) 10.40 22.70 5,035,284.00Brasil (N.E.) 7.40 17.00 1,120,375.00China 9.50 13.20 1,128,841.00Colombia 14.60 31.80 199,910.00Guatemala 12.20 15.90 197,600.00India 7.90 12.20 4,294,400.00México 9.30 14.80 686,855.00South Africa 6.60 23.50 426,738.00Swaziland 13.90 24.30 50,720.00Thailand 7.30 8.60 1,070,630.00USA 9.10 11.40 393,744.00
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CONCLUSIONES1. A nivel mundial esta ocurriendo literalmente una explosión de datos, tanto a
nivel social (redes sociales, comunicación digital, etc.), a nivel comercial (internet, tarjetas de crédito, etc.) a nivel científico (astronomía, física, genómica, medicina, etc.). A este fenómeno se le a denominado “BIG DATA”. Se menciona ya no solo gigabytes sino Tera, peta y exabytes y hasta zettabytes.
2. Se están desarrollando constantemente tecnologías para la recolección (sensores, imágenes, etc.) Almacenamiento (datawarehouse, nube, etc), análisis y visualización de los datos. Los datos deben convertirse en información y estos en conocimiento.
3. Todos los países, sectores, empresas o personas, que quieran obtener un valor agregado de este diluvio de datos, deben de prepararse para utilizar la tecnologías apropiadas.
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4. El Big Data Análisis que es la aplicación de técnicas avanzadas de análisis para conjuntos de grandes volúmenes de datos, esta generando el surgimiento de cientos de empresas asociadas: Microsoft, Oracle, SAP, Tableau, Teradata, SAS, Cloudera, MySQL, Hadoop, Cassandra, Data Miner , Cubenube, etc.
5. Esta surgiendo una nueva ciencia denominada “La Ciencia de los Datos”, que reúne disciplinas como: Ingeniería de datos, método científico, matemáticas, estadística, computación avanzada, visualización y experiencia en áreas especificas para resolver problemas de análisis de datos. Data Science requiere trabajo multidisciplinario.
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CONCLUSIONES ESPECIFICASDE LA AGROINDUSTRIA AZUCARERA
GUATEMALTECA
1. En cada uno de los eslabones de la cadena de valor de la agroindustria (campo, fabrica, transporte y comercialización) esta creciendo el volumen de datos que se generan, algunos ingenios han contratado empresas especificas para el manejo de los datos: Automatización, manejo integral de toda la información: Pantaleón (SAP), La Unión (BIOSALC), Magdalena(ORACLE), etc.
2. Para el manejo de información tecnológica de las áreas de trabajo de CENGICAÑA, se esta desarrollando la base de datos institucional con aportes principalmente del área de análisis de productividad, sistemas de información para agricultura de precisión (SIAP), Sistema de información meteorológica (SIM) y CENGIDOC. Para el desarrollo de la base de datos institucional cada área deberá aportar la información respectiva y actualizarse en el uso de las tecnologías de información.
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Muchas graciasMuchas gracias
Foto: Paulo StupielloFoto: Paulo Stupiello