SlideShare una empresa de Scribd logo
1 de 12
Descargar para leer sin conexión
   
Geophone Data Mining
Ricardo Aguirre
   
Università degli Studi di 
Padova
   
What does Data Mining is?
● The process for:
– select
– explore
– model big data volumes.
● For discover periodicity and not knowing
relations.
● It search useful and clear results
● Is many useful for the data base proprietary.
   
Knowledge Data Discovering
   
The Data mining process
● 1. Defining analysis goals.
● 2. Select, organize and prepare data.
● 3. Data exploration analysis and eventual
transformation.
● 4. Establish statistical methods for
elaboration phase:
– Exploratory methods
– Descriptive methods
– Forecast methods
– Local methods
   
The Data mining process (will continue)
● 5. Data elaboration, using the previously selected
methods.
● 6. Evaluation and validation of statistical methods,
select a final analysis model.
● 7. Model interpretation and future appliances on
decision processes.
   
1. Defining analysis goals.
● shows citizen behaviors like:
– Who lives/works in certain area
– Which are their “working days”
– Who like certain things (such as assist to the
soccer stadium each two weeks)
– Who buy in well known supermarkets.
– Who has little childs, because go to kinder
garden each day in the open/close hours.
– Who is using train service, because he follow
the rail lines.
– Who use car daily, because follow the freeway
route.
   
2. Select, organize and prepare data.
● Create Metadata Database
● Populate it
– Delimitate problem just for a city
– Make an database extraction just
for considering that city.
– Research entire city services and its
addresses
– Transform each addresses in
geopositions
– Create Relations between “Service
Places” and “base stations”
   
3. Data exploration analysis and
eventual transformation
● Transaction Data with Missing and Incomplete
Fields
– CELL_TO_LOCATION_TRACE()
– lookUnlocalizedCells()
● Content changes along the time
   
4. Establish statistical methods for
elaboration phase
● We decide to use a Business Rule-Engine
– The underlying idea of a rule engine is
to externalize the business or application logic
   
Data Mining Differs from
Typical Operational Business
   
next steps?
● Finish Geophone Data Mining
– Continue working with the Rule-Engine
– Making Decision Trees
– Link analysis
– Cluster Analysis
● Create Real-time Embedded System,
– this software piece will replace Mobile Application
– will be installed on Base-Stations
– will avoid all cell management problems and many
of current data acquisition problems.
● Get ready for Anthropological Approach

Más contenido relacionado

Similar a Geophone -- Data Mining Presentation

Similar a Geophone -- Data Mining Presentation (20)

EFFICIENT DATA EXTRACTION USING ARTIFICIAL INTELLIGENCE
EFFICIENT DATA EXTRACTION USING  ARTIFICIAL INTELLIGENCEEFFICIENT DATA EXTRACTION USING  ARTIFICIAL INTELLIGENCE
EFFICIENT DATA EXTRACTION USING ARTIFICIAL INTELLIGENCE
 
Knowledge discovery process
Knowledge discovery process Knowledge discovery process
Knowledge discovery process
 
Methodology - Conceptual Database Design Transparencies .pptx
Methodology -  Conceptual Database Design Transparencies .pptxMethodology -  Conceptual Database Design Transparencies .pptx
Methodology - Conceptual Database Design Transparencies .pptx
 
Internship Presentation.pdf
Internship Presentation.pdfInternship Presentation.pdf
Internship Presentation.pdf
 
Machine learning and big data
Machine learning and big dataMachine learning and big data
Machine learning and big data
 
A Space X Industry Day Briefing 7 Jul08 Jgm R4
A Space X Industry Day Briefing 7 Jul08 Jgm R4A Space X Industry Day Briefing 7 Jul08 Jgm R4
A Space X Industry Day Briefing 7 Jul08 Jgm R4
 
EMOS 2018 Big Data methods and techniques
EMOS 2018 Big Data methods and techniquesEMOS 2018 Big Data methods and techniques
EMOS 2018 Big Data methods and techniques
 
Advanced Use Cases for Analytics Breakout Session
Advanced Use Cases for Analytics Breakout SessionAdvanced Use Cases for Analytics Breakout Session
Advanced Use Cases for Analytics Breakout Session
 
Data management planning: the what, the why, the who, the how
Data management planning: the what, the why, the who, the howData management planning: the what, the why, the who, the how
Data management planning: the what, the why, the who, the how
 
Datagrinch product experience
Datagrinch product experienceDatagrinch product experience
Datagrinch product experience
 
Big Data Infrastructure and Analytics Solution on FITAT2013
Big Data Infrastructure and Analytics Solution on FITAT2013Big Data Infrastructure and Analytics Solution on FITAT2013
Big Data Infrastructure and Analytics Solution on FITAT2013
 
Business analytics and data visualisation
Business analytics and data visualisationBusiness analytics and data visualisation
Business analytics and data visualisation
 
Unit 1 DSS
Unit 1 DSSUnit 1 DSS
Unit 1 DSS
 
Data Analytics and Big Data on IoT
Data Analytics and Big Data on IoTData Analytics and Big Data on IoT
Data Analytics and Big Data on IoT
 
IT Architectures for Handling Big Data in Official Statistics: the Case of Sc...
IT Architectures for Handling Big Data in Official Statistics: the Case of Sc...IT Architectures for Handling Big Data in Official Statistics: the Case of Sc...
IT Architectures for Handling Big Data in Official Statistics: the Case of Sc...
 
Management Information system
Management Information systemManagement Information system
Management Information system
 
Strategies for the seamless deployment of travel diary collection systems to ...
Strategies for the seamless deployment of travel diary collection systems to ...Strategies for the seamless deployment of travel diary collection systems to ...
Strategies for the seamless deployment of travel diary collection systems to ...
 
Webinar: Realizing the Promise of Machine to Machine (M2M) with MongoDB
Webinar: Realizing the Promise of Machine to Machine (M2M) with MongoDBWebinar: Realizing the Promise of Machine to Machine (M2M) with MongoDB
Webinar: Realizing the Promise of Machine to Machine (M2M) with MongoDB
 
Data mining basic concept and Data warehousing
Data mining basic concept and Data warehousingData mining basic concept and Data warehousing
Data mining basic concept and Data warehousing
 
WebSite Visit Forecasting Using Data Mining Techniques
WebSite Visit Forecasting Using Data Mining  TechniquesWebSite Visit Forecasting Using Data Mining  Techniques
WebSite Visit Forecasting Using Data Mining Techniques
 

Último

+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
?#DUbAI#??##{{(☎️+971_581248768%)**%*]'#abortion pills for sale in dubai@
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
panagenda
 

Último (20)

FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024
 
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingRepurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
 
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
 
Artificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : UncertaintyArtificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : Uncertainty
 
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
 
Apidays Singapore 2024 - Scalable LLM APIs for AI and Generative AI Applicati...
Apidays Singapore 2024 - Scalable LLM APIs for AI and Generative AI Applicati...Apidays Singapore 2024 - Scalable LLM APIs for AI and Generative AI Applicati...
Apidays Singapore 2024 - Scalable LLM APIs for AI and Generative AI Applicati...
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
 
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
 
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
 
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, AdobeApidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
 
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
 
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWEREMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
 
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century education
 
GenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdfGenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdf
 
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
 

Geophone -- Data Mining Presentation

  • 1.     Geophone Data Mining Ricardo Aguirre
  • 3.     What does Data Mining is? ● The process for: – select – explore – model big data volumes. ● For discover periodicity and not knowing relations. ● It search useful and clear results ● Is many useful for the data base proprietary.
  • 4.     Knowledge Data Discovering
  • 5.     The Data mining process ● 1. Defining analysis goals. ● 2. Select, organize and prepare data. ● 3. Data exploration analysis and eventual transformation. ● 4. Establish statistical methods for elaboration phase: – Exploratory methods – Descriptive methods – Forecast methods – Local methods
  • 6.     The Data mining process (will continue) ● 5. Data elaboration, using the previously selected methods. ● 6. Evaluation and validation of statistical methods, select a final analysis model. ● 7. Model interpretation and future appliances on decision processes.
  • 7.     1. Defining analysis goals. ● shows citizen behaviors like: – Who lives/works in certain area – Which are their “working days” – Who like certain things (such as assist to the soccer stadium each two weeks) – Who buy in well known supermarkets. – Who has little childs, because go to kinder garden each day in the open/close hours. – Who is using train service, because he follow the rail lines. – Who use car daily, because follow the freeway route.
  • 8.     2. Select, organize and prepare data. ● Create Metadata Database ● Populate it – Delimitate problem just for a city – Make an database extraction just for considering that city. – Research entire city services and its addresses – Transform each addresses in geopositions – Create Relations between “Service Places” and “base stations”
  • 9.     3. Data exploration analysis and eventual transformation ● Transaction Data with Missing and Incomplete Fields – CELL_TO_LOCATION_TRACE() – lookUnlocalizedCells() ● Content changes along the time
  • 10.     4. Establish statistical methods for elaboration phase ● We decide to use a Business Rule-Engine – The underlying idea of a rule engine is to externalize the business or application logic
  • 11.     Data Mining Differs from Typical Operational Business
  • 12.     next steps? ● Finish Geophone Data Mining – Continue working with the Rule-Engine – Making Decision Trees – Link analysis – Cluster Analysis ● Create Real-time Embedded System, – this software piece will replace Mobile Application – will be installed on Base-Stations – will avoid all cell management problems and many of current data acquisition problems. ● Get ready for Anthropological Approach