SlideShare a Scribd company logo
1 of 16
4  Data-Applied.com: Technology Insight
Tools: Data Import data:  CSV File, Excel File, SalesForce.com, Dynamics CRM
Tools: Data Export Data: CSV File
Tools Super Pivots: An XML based API allowing multiple levels of grouping, binning and aggregation. Tree Maps: An aspect-ratio optimization recursive layout algorithm.
Tools Forecasts: An optimized formulation of a specialized neural network with monte-carlo simulation Monte Carlo methods are a class of computational algorithms that rely on repeated random sampling to compute their results. Monte Carlo methods are often used in simulating physical and mathematical systems.
Tools Correlations: A parallel formulation of Pearson product-moment correlation coefficient algorithm  Pearson product-moment correlation coefficient is a measure of the correlation between two variables X and Y, giving a value between +1 and −1 inclusive. It is widely used in the sciences as a measure of the strength of linear dependence between two variables.
Tools Outliers: An optimized formulation of the Bay and Schwabacher’s outlier detection algorithm Associations:An optimized formulation of the apriori-all association rule algorithm.
Tools Decisions:A parallel formulation of an algorithm based on information gain (discrete decision trees). A formulation of the Kruskal-Wallis statistic test (numeric trees) The Kruskal–Wallis one-way analysis of variance by ranks (named after William Kruskal and W. Allen Wallis) is a non-parametric method for testing equality of population medians among groups. It is identical to a one-way analysis of variance with the data replaced by their ranks
Tools Clusters: An optimized formulation for the BIRCH clustering algorithm.  BIRCH (balanced iterative reducing and clustering using hierarchies) is an unsupervised data mining algorithm used to perform hierarchical clustering over particularly large data-sets. An advantage of Birch is its ability to incrementally and dynamically cluster incoming, multi-dimensional metric data points in an attempt to produce the best quality clustering for a given set of resources (memory and time constraints)
Tools Similarity: A parallel formulation of a Kohonen artificial neural network.  Kohonen self-organizing network is a self-organizing map (SOM) invented by Teuvo Kohonen performs a form of unsupervised learning. A set of artificial neurons learn to map points in an input space to coordinates in an output space. The input space can have different dimensions and topology from the output space, and the SOM will attempt to preserve these.
Architecture: System Web Client Runs within a browser,  uses XML requests, visualization capabilities using Microsoft Silverlight, local data caching and compression. Web Service secure XML-based Web API, accept and process XML requests
Architecture: System Back-End Distributed computing, manage task priorities, detect abandoned tasks, restart failed tasks, terminate long-running tasks, and synchronize task execution between nodes Database SQL-based storage system,
Architecture: System
Architecture: System Data Users, Workspaces, Rights visual CAPTCHA challenge,  email confirmation, workspace sharing.  Databases, Tables, Fields Master-slave configuration in databases,
Architecture: System Data Nodes, Jobs, Tasks Keys, Licenses, Logs Comments, Downloads, Images, Settings
Architecture: System Security Right Enforcement License Restrictions Cryptographic Validations

More Related Content

What's hot

Cybertools stork-2009-cybertools allhandmeeting-poster
Cybertools stork-2009-cybertools allhandmeeting-posterCybertools stork-2009-cybertools allhandmeeting-poster
Cybertools stork-2009-cybertools allhandmeeting-posterbalmanme
 
Graph Based Machine Learning on Relational Data
Graph Based Machine Learning on Relational DataGraph Based Machine Learning on Relational Data
Graph Based Machine Learning on Relational DataBenjamin Bengfort
 
Parallel Key Value Pattern Matching Model
Parallel Key Value Pattern Matching ModelParallel Key Value Pattern Matching Model
Parallel Key Value Pattern Matching Modelijsrd.com
 
Context based Web Indexing for Storage of Relevant Web Pages
Context based Web Indexing for Storage of Relevant Web PagesContext based Web Indexing for Storage of Relevant Web Pages
Context based Web Indexing for Storage of Relevant Web PagesGopal Ji Singh
 
Tech Talk - Underutilized Resources in Distributed System
Tech Talk - Underutilized Resources in Distributed SystemTech Talk - Underutilized Resources in Distributed System
Tech Talk - Underutilized Resources in Distributed SystemRishabh Dugar
 
Moa: Real Time Analytics for Data Streams
Moa: Real Time Analytics for Data StreamsMoa: Real Time Analytics for Data Streams
Moa: Real Time Analytics for Data StreamsAlbert Bifet
 
DMDW Lesson 04 - Data Mining Theory
DMDW Lesson 04 - Data Mining TheoryDMDW Lesson 04 - Data Mining Theory
DMDW Lesson 04 - Data Mining TheoryJohannes Hoppe
 
Enhancing Spark SQL Optimizer with Reliable Statistics
Enhancing Spark SQL Optimizer with Reliable StatisticsEnhancing Spark SQL Optimizer with Reliable Statistics
Enhancing Spark SQL Optimizer with Reliable StatisticsJen Aman
 
Scalable Distributed Real-Time Clustering for Big Data Streams
Scalable Distributed Real-Time Clustering for Big Data StreamsScalable Distributed Real-Time Clustering for Big Data Streams
Scalable Distributed Real-Time Clustering for Big Data StreamsAntonio Severien
 
Query evaluation and optimization
Query evaluation and optimizationQuery evaluation and optimization
Query evaluation and optimizationlavanya marichamy
 
La résolution de problèmes à l'aide de graphes
La résolution de problèmes à l'aide de graphesLa résolution de problèmes à l'aide de graphes
La résolution de problèmes à l'aide de graphesData2B
 
Rethinking data intensive science using scalable analytics systems
 Rethinking data intensive science using scalable analytics systems Rethinking data intensive science using scalable analytics systems
Rethinking data intensive science using scalable analytics systemsnewmooxx
 
Cloud k svd a collaborative dictionary learning algorithm for big, distribute...
Cloud k svd a collaborative dictionary learning algorithm for big, distribute...Cloud k svd a collaborative dictionary learning algorithm for big, distribute...
Cloud k svd a collaborative dictionary learning algorithm for big, distribute...ieeepondy
 
Applying stratosphere for big data analytics
Applying stratosphere for big data analyticsApplying stratosphere for big data analytics
Applying stratosphere for big data analyticsAvinash Pandu
 
Computing Scientometrics in Large-Scale Academic Search Engines with MapReduce
Computing Scientometrics in Large-Scale Academic Search Engines with MapReduceComputing Scientometrics in Large-Scale Academic Search Engines with MapReduce
Computing Scientometrics in Large-Scale Academic Search Engines with MapReduceLeonidas Akritidis
 
A Parallel Algorithm for Approximate Frequent Itemset Mining using MapReduce
A Parallel Algorithm for Approximate Frequent Itemset Mining using MapReduce A Parallel Algorithm for Approximate Frequent Itemset Mining using MapReduce
A Parallel Algorithm for Approximate Frequent Itemset Mining using MapReduce Fabio Fumarola
 

What's hot (20)

IR tutorial
IR tutorialIR tutorial
IR tutorial
 
Understanding Big Data Platform from Patents
Understanding Big Data Platform from PatentsUnderstanding Big Data Platform from Patents
Understanding Big Data Platform from Patents
 
Cybertools stork-2009-cybertools allhandmeeting-poster
Cybertools stork-2009-cybertools allhandmeeting-posterCybertools stork-2009-cybertools allhandmeeting-poster
Cybertools stork-2009-cybertools allhandmeeting-poster
 
ORE en Fedora Op Klompen
ORE en Fedora Op KlompenORE en Fedora Op Klompen
ORE en Fedora Op Klompen
 
Graph Based Machine Learning on Relational Data
Graph Based Machine Learning on Relational DataGraph Based Machine Learning on Relational Data
Graph Based Machine Learning on Relational Data
 
Parallel Key Value Pattern Matching Model
Parallel Key Value Pattern Matching ModelParallel Key Value Pattern Matching Model
Parallel Key Value Pattern Matching Model
 
Context based Web Indexing for Storage of Relevant Web Pages
Context based Web Indexing for Storage of Relevant Web PagesContext based Web Indexing for Storage of Relevant Web Pages
Context based Web Indexing for Storage of Relevant Web Pages
 
Tech Talk - Underutilized Resources in Distributed System
Tech Talk - Underutilized Resources in Distributed SystemTech Talk - Underutilized Resources in Distributed System
Tech Talk - Underutilized Resources in Distributed System
 
Moa: Real Time Analytics for Data Streams
Moa: Real Time Analytics for Data StreamsMoa: Real Time Analytics for Data Streams
Moa: Real Time Analytics for Data Streams
 
DMDW Lesson 04 - Data Mining Theory
DMDW Lesson 04 - Data Mining TheoryDMDW Lesson 04 - Data Mining Theory
DMDW Lesson 04 - Data Mining Theory
 
Enhancing Spark SQL Optimizer with Reliable Statistics
Enhancing Spark SQL Optimizer with Reliable StatisticsEnhancing Spark SQL Optimizer with Reliable Statistics
Enhancing Spark SQL Optimizer with Reliable Statistics
 
Scalable Distributed Real-Time Clustering for Big Data Streams
Scalable Distributed Real-Time Clustering for Big Data StreamsScalable Distributed Real-Time Clustering for Big Data Streams
Scalable Distributed Real-Time Clustering for Big Data Streams
 
Query evaluation and optimization
Query evaluation and optimizationQuery evaluation and optimization
Query evaluation and optimization
 
La résolution de problèmes à l'aide de graphes
La résolution de problèmes à l'aide de graphesLa résolution de problèmes à l'aide de graphes
La résolution de problèmes à l'aide de graphes
 
Rethinking data intensive science using scalable analytics systems
 Rethinking data intensive science using scalable analytics systems Rethinking data intensive science using scalable analytics systems
Rethinking data intensive science using scalable analytics systems
 
Cloud k svd a collaborative dictionary learning algorithm for big, distribute...
Cloud k svd a collaborative dictionary learning algorithm for big, distribute...Cloud k svd a collaborative dictionary learning algorithm for big, distribute...
Cloud k svd a collaborative dictionary learning algorithm for big, distribute...
 
Applying stratosphere for big data analytics
Applying stratosphere for big data analyticsApplying stratosphere for big data analytics
Applying stratosphere for big data analytics
 
Data structures 1
Data structures 1Data structures 1
Data structures 1
 
Computing Scientometrics in Large-Scale Academic Search Engines with MapReduce
Computing Scientometrics in Large-Scale Academic Search Engines with MapReduceComputing Scientometrics in Large-Scale Academic Search Engines with MapReduce
Computing Scientometrics in Large-Scale Academic Search Engines with MapReduce
 
A Parallel Algorithm for Approximate Frequent Itemset Mining using MapReduce
A Parallel Algorithm for Approximate Frequent Itemset Mining using MapReduce A Parallel Algorithm for Approximate Frequent Itemset Mining using MapReduce
A Parallel Algorithm for Approximate Frequent Itemset Mining using MapReduce
 

Similar to DATA-APPLIED INSIGHTS

Introduction to Machine Learning with SciKit-Learn
Introduction to Machine Learning with SciKit-LearnIntroduction to Machine Learning with SciKit-Learn
Introduction to Machine Learning with SciKit-LearnBenjamin Bengfort
 
Bra a bidirectional routing abstraction for asymmetric mobile ad hoc networks...
Bra a bidirectional routing abstraction for asymmetric mobile ad hoc networks...Bra a bidirectional routing abstraction for asymmetric mobile ad hoc networks...
Bra a bidirectional routing abstraction for asymmetric mobile ad hoc networks...Mumbai Academisc
 
Scalable Similarity-Based Neighborhood Methods with MapReduce
Scalable Similarity-Based Neighborhood Methods with MapReduceScalable Similarity-Based Neighborhood Methods with MapReduce
Scalable Similarity-Based Neighborhood Methods with MapReducesscdotopen
 
Web Information Extraction for the Database Research Domain
Web Information Extraction for the Database Research DomainWeb Information Extraction for the Database Research Domain
Web Information Extraction for the Database Research DomainMichael Genkin
 
A Web Extraction Using Soft Algorithm for Trinity Structure
A Web Extraction Using Soft Algorithm for Trinity StructureA Web Extraction Using Soft Algorithm for Trinity Structure
A Web Extraction Using Soft Algorithm for Trinity Structureiosrjce
 
Clustering for Stream and Parallelism (DATA ANALYTICS)
Clustering for Stream and Parallelism (DATA ANALYTICS)Clustering for Stream and Parallelism (DATA ANALYTICS)
Clustering for Stream and Parallelism (DATA ANALYTICS)DheerajPachauri
 
Annotating Search Results from Web Databases
Annotating Search Results from Web DatabasesAnnotating Search Results from Web Databases
Annotating Search Results from Web DatabasesSWAMI06
 
Task Adaptive Neural Network Search with Meta-Contrastive Learning
Task Adaptive Neural Network Search with Meta-Contrastive LearningTask Adaptive Neural Network Search with Meta-Contrastive Learning
Task Adaptive Neural Network Search with Meta-Contrastive LearningMLAI2
 
Information Extraction
Information ExtractionInformation Extraction
Information Extractionbutest
 
IEEE Datamining 2016 Title and Abstract
IEEE  Datamining 2016 Title and AbstractIEEE  Datamining 2016 Title and Abstract
IEEE Datamining 2016 Title and Abstracttsysglobalsolutions
 
Paper id 25201463
Paper id 25201463Paper id 25201463
Paper id 25201463IJRAT
 
Discovery Engines for Big Data: Accelerating Discovery in Basic Energy Sciences
Discovery Engines for Big Data: Accelerating Discovery in Basic Energy SciencesDiscovery Engines for Big Data: Accelerating Discovery in Basic Energy Sciences
Discovery Engines for Big Data: Accelerating Discovery in Basic Energy SciencesIan Foster
 
Machine Learning Algorithms
Machine Learning AlgorithmsMachine Learning Algorithms
Machine Learning AlgorithmsDezyreAcademy
 

Similar to DATA-APPLIED INSIGHTS (20)

Introduction to Machine Learning with SciKit-Learn
Introduction to Machine Learning with SciKit-LearnIntroduction to Machine Learning with SciKit-Learn
Introduction to Machine Learning with SciKit-Learn
 
Final proj 2 (1)
Final proj 2 (1)Final proj 2 (1)
Final proj 2 (1)
 
Bra a bidirectional routing abstraction for asymmetric mobile ad hoc networks...
Bra a bidirectional routing abstraction for asymmetric mobile ad hoc networks...Bra a bidirectional routing abstraction for asymmetric mobile ad hoc networks...
Bra a bidirectional routing abstraction for asymmetric mobile ad hoc networks...
 
Scalable Similarity-Based Neighborhood Methods with MapReduce
Scalable Similarity-Based Neighborhood Methods with MapReduceScalable Similarity-Based Neighborhood Methods with MapReduce
Scalable Similarity-Based Neighborhood Methods with MapReduce
 
Data mining weka
Data mining wekaData mining weka
Data mining weka
 
OpenML 2019
OpenML 2019OpenML 2019
OpenML 2019
 
Web Information Extraction for the Database Research Domain
Web Information Extraction for the Database Research DomainWeb Information Extraction for the Database Research Domain
Web Information Extraction for the Database Research Domain
 
Apresent
ApresentApresent
Apresent
 
A Web Extraction Using Soft Algorithm for Trinity Structure
A Web Extraction Using Soft Algorithm for Trinity StructureA Web Extraction Using Soft Algorithm for Trinity Structure
A Web Extraction Using Soft Algorithm for Trinity Structure
 
G017334248
G017334248G017334248
G017334248
 
Clustering for Stream and Parallelism (DATA ANALYTICS)
Clustering for Stream and Parallelism (DATA ANALYTICS)Clustering for Stream and Parallelism (DATA ANALYTICS)
Clustering for Stream and Parallelism (DATA ANALYTICS)
 
Annotating Search Results from Web Databases
Annotating Search Results from Web DatabasesAnnotating Search Results from Web Databases
Annotating Search Results from Web Databases
 
Task Adaptive Neural Network Search with Meta-Contrastive Learning
Task Adaptive Neural Network Search with Meta-Contrastive LearningTask Adaptive Neural Network Search with Meta-Contrastive Learning
Task Adaptive Neural Network Search with Meta-Contrastive Learning
 
Information Extraction
Information ExtractionInformation Extraction
Information Extraction
 
IEEE Datamining 2016 Title and Abstract
IEEE  Datamining 2016 Title and AbstractIEEE  Datamining 2016 Title and Abstract
IEEE Datamining 2016 Title and Abstract
 
IJET-V3I2P2
IJET-V3I2P2IJET-V3I2P2
IJET-V3I2P2
 
Paper id 25201463
Paper id 25201463Paper id 25201463
Paper id 25201463
 
Discovery Engines for Big Data: Accelerating Discovery in Basic Energy Sciences
Discovery Engines for Big Data: Accelerating Discovery in Basic Energy SciencesDiscovery Engines for Big Data: Accelerating Discovery in Basic Energy Sciences
Discovery Engines for Big Data: Accelerating Discovery in Basic Energy Sciences
 
Machine Learning Algorithms
Machine Learning AlgorithmsMachine Learning Algorithms
Machine Learning Algorithms
 
PPT
PPTPPT
PPT
 

More from dataapplied content

More from dataapplied content (9)

Data Applied:Tree Maps
Data Applied:Tree MapsData Applied:Tree Maps
Data Applied:Tree Maps
 
Data Applied:Similarity
Data Applied:SimilarityData Applied:Similarity
Data Applied:Similarity
 
Data Applied:Outliers
Data Applied:OutliersData Applied:Outliers
Data Applied:Outliers
 
Data Applied: Forecast
Data Applied: ForecastData Applied: Forecast
Data Applied: Forecast
 
Data Applied: Decision
Data Applied: DecisionData Applied: Decision
Data Applied: Decision
 
Data Applied: Correlation
Data Applied: CorrelationData Applied: Correlation
Data Applied: Correlation
 
Data Applied: Clustering
Data Applied: ClusteringData Applied: Clustering
Data Applied: Clustering
 
Data Applied: Association
Data Applied: AssociationData Applied: Association
Data Applied: Association
 
Introduction to Data-Applied.com
Introduction to Data-Applied.comIntroduction to Data-Applied.com
Introduction to Data-Applied.com
 

Recently uploaded

Artificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxArtificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxhariprasad279825
 
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsKotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticscarlostorres15106
 
Vector Databases 101 - An introduction to the world of Vector Databases
Vector Databases 101 - An introduction to the world of Vector DatabasesVector Databases 101 - An introduction to the world of Vector Databases
Vector Databases 101 - An introduction to the world of Vector DatabasesZilliz
 
Powerpoint exploring the locations used in television show Time Clash
Powerpoint exploring the locations used in television show Time ClashPowerpoint exploring the locations used in television show Time Clash
Powerpoint exploring the locations used in television show Time Clashcharlottematthew16
 
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks..."LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...Fwdays
 
Training state-of-the-art general text embedding
Training state-of-the-art general text embeddingTraining state-of-the-art general text embedding
Training state-of-the-art general text embeddingZilliz
 
SAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxSAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxNavinnSomaal
 
My Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationMy Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationRidwan Fadjar
 
Search Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdfSearch Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdfRankYa
 
My INSURER PTE LTD - Insurtech Innovation Award 2024
My INSURER PTE LTD - Insurtech Innovation Award 2024My INSURER PTE LTD - Insurtech Innovation Award 2024
My INSURER PTE LTD - Insurtech Innovation Award 2024The Digital Insurer
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii SoldatenkoFwdays
 
Connect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationConnect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationSlibray Presentation
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsSergiu Bodiu
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfAlex Barbosa Coqueiro
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Mattias Andersson
 
SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024Lorenzo Miniero
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brandgvaughan
 

Recently uploaded (20)

Artificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxArtificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptx
 
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsKotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
 
Vector Databases 101 - An introduction to the world of Vector Databases
Vector Databases 101 - An introduction to the world of Vector DatabasesVector Databases 101 - An introduction to the world of Vector Databases
Vector Databases 101 - An introduction to the world of Vector Databases
 
Powerpoint exploring the locations used in television show Time Clash
Powerpoint exploring the locations used in television show Time ClashPowerpoint exploring the locations used in television show Time Clash
Powerpoint exploring the locations used in television show Time Clash
 
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks..."LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
 
Training state-of-the-art general text embedding
Training state-of-the-art general text embeddingTraining state-of-the-art general text embedding
Training state-of-the-art general text embedding
 
SAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxSAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptx
 
My Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationMy Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 Presentation
 
Search Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdfSearch Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdf
 
My INSURER PTE LTD - Insurtech Innovation Award 2024
My INSURER PTE LTD - Insurtech Innovation Award 2024My INSURER PTE LTD - Insurtech Innovation Award 2024
My INSURER PTE LTD - Insurtech Innovation Award 2024
 
DMCC Future of Trade Web3 - Special Edition
DMCC Future of Trade Web3 - Special EditionDMCC Future of Trade Web3 - Special Edition
DMCC Future of Trade Web3 - Special Edition
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko
 
Connect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationConnect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck Presentation
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platforms
 
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptxE-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdf
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?
 
SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brand
 

DATA-APPLIED INSIGHTS

  • 1. 4 Data-Applied.com: Technology Insight
  • 2. Tools: Data Import data: CSV File, Excel File, SalesForce.com, Dynamics CRM
  • 3. Tools: Data Export Data: CSV File
  • 4. Tools Super Pivots: An XML based API allowing multiple levels of grouping, binning and aggregation. Tree Maps: An aspect-ratio optimization recursive layout algorithm.
  • 5. Tools Forecasts: An optimized formulation of a specialized neural network with monte-carlo simulation Monte Carlo methods are a class of computational algorithms that rely on repeated random sampling to compute their results. Monte Carlo methods are often used in simulating physical and mathematical systems.
  • 6. Tools Correlations: A parallel formulation of Pearson product-moment correlation coefficient algorithm  Pearson product-moment correlation coefficient is a measure of the correlation between two variables X and Y, giving a value between +1 and −1 inclusive. It is widely used in the sciences as a measure of the strength of linear dependence between two variables.
  • 7. Tools Outliers: An optimized formulation of the Bay and Schwabacher’s outlier detection algorithm Associations:An optimized formulation of the apriori-all association rule algorithm.
  • 8. Tools Decisions:A parallel formulation of an algorithm based on information gain (discrete decision trees). A formulation of the Kruskal-Wallis statistic test (numeric trees) The Kruskal–Wallis one-way analysis of variance by ranks (named after William Kruskal and W. Allen Wallis) is a non-parametric method for testing equality of population medians among groups. It is identical to a one-way analysis of variance with the data replaced by their ranks
  • 9. Tools Clusters: An optimized formulation for the BIRCH clustering algorithm. BIRCH (balanced iterative reducing and clustering using hierarchies) is an unsupervised data mining algorithm used to perform hierarchical clustering over particularly large data-sets. An advantage of Birch is its ability to incrementally and dynamically cluster incoming, multi-dimensional metric data points in an attempt to produce the best quality clustering for a given set of resources (memory and time constraints)
  • 10. Tools Similarity: A parallel formulation of a Kohonen artificial neural network. Kohonen self-organizing network is a self-organizing map (SOM) invented by Teuvo Kohonen performs a form of unsupervised learning. A set of artificial neurons learn to map points in an input space to coordinates in an output space. The input space can have different dimensions and topology from the output space, and the SOM will attempt to preserve these.
  • 11. Architecture: System Web Client Runs within a browser, uses XML requests, visualization capabilities using Microsoft Silverlight, local data caching and compression. Web Service secure XML-based Web API, accept and process XML requests
  • 12. Architecture: System Back-End Distributed computing, manage task priorities, detect abandoned tasks, restart failed tasks, terminate long-running tasks, and synchronize task execution between nodes Database SQL-based storage system,
  • 14. Architecture: System Data Users, Workspaces, Rights visual CAPTCHA challenge, email confirmation, workspace sharing. Databases, Tables, Fields Master-slave configuration in databases,
  • 15. Architecture: System Data Nodes, Jobs, Tasks Keys, Licenses, Logs Comments, Downloads, Images, Settings
  • 16. Architecture: System Security Right Enforcement License Restrictions Cryptographic Validations