RapidMiner is an environment for machine learning and data mining processes that follows a modular operator concept. It introduces transparent data handling and process modeling to ease configuration for end users. Additionally, its clear interfaces and scripting language based on XML make it an integrated developer environment for data mining and machine learning. To get started with RapidMiner, users download the file for their system from the website, install it by accepting the license agreement and specifying the installation directory, then launch it by double clicking the desktop icon.
The slides cover:
An Overview of RapidMiner Studio interface
Importing a dataset
Descriptive statistics and visualisation
Data modelling
Model evaluation
Data cleaning
Adding R script
M Chambers and RapidMiner Overview for Babson classmcAnalytics99
RapidMiner is a modern analytics platform that enables anyone to leverage big data and accelerate time-to-value. Unlike traditional analytics providers, RapidMiner allows users of any skill level to make the most of all data in all environments. It provides a code-free interface that is built by data scientists for data scientists, business analysts, and developers to simplify analytics. RapidMiner also utilizes a knowledge base of analytic best practices and machine learning to empower users to become data science heroes.
Rapid Miner is an open source platform for data mining that was first released in 2006. It has over 250,000 users including large companies like eBay, Intel, and PepsiCo. Rapid Miner offers different versions including Rapid Miner Studio, Rapid Miner Server, and Rapid Miner Cloud. It provides an integrated environment for all steps of data mining with features like loading data from various sources, preprocessing, modeling, and evaluation.
This document discusses using RFX (Reactive Function X), a design pattern and collection of open source tools, to solve fast data problems. It presents an example of using RFX for web analytics to count pageviews and unique users and detect DDOS attacks. The RFX approach applies the BEAM methodology for agile data warehousing. It demonstrates RFX concepts like event data actors, agents, collectors, routers, processors, storage and reactors using a pageview analytics demo with source code on GitHub.
The document provides information about obtaining accounts for using the Center for Cancer Computational Biology (CCCB) Cloud Services. It describes how to request a:
1. DFCI G Suite account through the Research Computing website to access the DFCI Google Virtual Private Cloud.
2. Partners Dropbox account, which provides unlimited encrypted storage for Partners community members through a Partners.org email.
3. Agilent CrossLab (iLab Solutions) account through their website to track projects using most DFCI cores and centers, including CCCB.
Orange is an open-source data visualization and analysis tool for novice and expert users. It was developed in Python and is available for Windows, Mac OS X, and Linux. Orange provides tools for data mining, machine learning, and statistical analysis through a graphical user interface and Python scripting. Some key features include visual programming, data visualization, interaction and analytics capabilities, a large toolbox of algorithms, and extensibility. Orange has been used by organizations like AstraZeneca for drug development.
RapidMiner is an environment for machine learning and data mining processes that follows a modular operator concept. It introduces transparent data handling and process modeling to ease configuration for end users. Additionally, its clear interfaces and scripting language based on XML make it an integrated developer environment for data mining and machine learning. To get started with RapidMiner, users download the file for their system from the website, install it by accepting the license agreement and specifying the installation directory, then launch it by double clicking the desktop icon.
The slides cover:
An Overview of RapidMiner Studio interface
Importing a dataset
Descriptive statistics and visualisation
Data modelling
Model evaluation
Data cleaning
Adding R script
M Chambers and RapidMiner Overview for Babson classmcAnalytics99
RapidMiner is a modern analytics platform that enables anyone to leverage big data and accelerate time-to-value. Unlike traditional analytics providers, RapidMiner allows users of any skill level to make the most of all data in all environments. It provides a code-free interface that is built by data scientists for data scientists, business analysts, and developers to simplify analytics. RapidMiner also utilizes a knowledge base of analytic best practices and machine learning to empower users to become data science heroes.
Rapid Miner is an open source platform for data mining that was first released in 2006. It has over 250,000 users including large companies like eBay, Intel, and PepsiCo. Rapid Miner offers different versions including Rapid Miner Studio, Rapid Miner Server, and Rapid Miner Cloud. It provides an integrated environment for all steps of data mining with features like loading data from various sources, preprocessing, modeling, and evaluation.
This document discusses using RFX (Reactive Function X), a design pattern and collection of open source tools, to solve fast data problems. It presents an example of using RFX for web analytics to count pageviews and unique users and detect DDOS attacks. The RFX approach applies the BEAM methodology for agile data warehousing. It demonstrates RFX concepts like event data actors, agents, collectors, routers, processors, storage and reactors using a pageview analytics demo with source code on GitHub.
The document provides information about obtaining accounts for using the Center for Cancer Computational Biology (CCCB) Cloud Services. It describes how to request a:
1. DFCI G Suite account through the Research Computing website to access the DFCI Google Virtual Private Cloud.
2. Partners Dropbox account, which provides unlimited encrypted storage for Partners community members through a Partners.org email.
3. Agilent CrossLab (iLab Solutions) account through their website to track projects using most DFCI cores and centers, including CCCB.
Orange is an open-source data visualization and analysis tool for novice and expert users. It was developed in Python and is available for Windows, Mac OS X, and Linux. Orange provides tools for data mining, machine learning, and statistical analysis through a graphical user interface and Python scripting. Some key features include visual programming, data visualization, interaction and analytics capabilities, a large toolbox of algorithms, and extensibility. Orange has been used by organizations like AstraZeneca for drug development.
Unlocking Value in Device Data Using Spark: Spark Summit East talk by John La...Spark Summit
HP ships millions of PCs, Printers, and other devices every year to customers in all market segments. More customers are seeking services provided with our products enabling new opportunities for HP to create services from the data we can collect from our devices. Every device we ship is an IoT endpoint with powerful CPU to capture rich data. Insights from this data are used internally to improve our products and focus on customer needs.
In this presentation, John will focus on HP’s journey to enabling Big Data analytics from within a large enterprise environment. He will review the challenges and how HP decided on AWS, Apache Spark and Databricks as the foundation for their entry into Big Data Analytics. John will also review how HP uses Spark to build analytic services from the data they generate from their devices.
Accelerating Delivery of Data Products - The EBSCO WayMongoDB
EBSCO Information Services (EBSCO) is the leading provider of electronic journals, magazines, eBooks, audioBooks, and online research content for libraries, including hundreds of research databases, historical archives, point-of-care medical reference, and corporate learning tools serving millions of end users at tens of thousands of institutions worldwide. The EBSCO platform is a widely used platform serving the needs of researchers at all levels in academic institutions, schools, public libraries, hospitals, medical institutions, corporations and government institutions. Data is our business, and delivering new products quickly is our competitive advantage. We build hundreds of data products and accelerating the analysis, transformation of new datasets translates to revenue and competitiveness. And since our data is so varied, using MognoDB to store data flexibly and JSON Studio to analyze this data allows us to deliver products to market faster. In this session we will describe this process that helped us expedite delivery of new datasets, and give real examples of how data is used, analyzed and processed.
Scalable Data Management for Kafka and Beyond | Dan Rice, BigIDHostedbyConfluent
Data in motion has changed both the scale and scope of data and analytics - enabling organizations to capture more information and use it more effectively. But to get the most value from it - you need to know what’s there, make it risk aware, and take action on it. In this session, you’ll learn how to leverage modern ML-augmented data management solutions to automatically find, identify, and classify sensitive data across Spark, Databricks, and beyond - and how to apply policies for compliance and risk mitigation to get the most value from our data.
American Water shares how bringing IoT to fleet management can provide value to the customer. In the utilities industry, fleet management plays a major part in the business. The front line is one of the largest parts of the business whether it is the field employees working on mains, or those working on the customers' property. American Water strives to provide the best customer experience and part of that includes improving the effectiveness of our fleet.
Currently, there is no insight or active feedback on the effectiveness of the routes or driving behaviors. As a PoC, American Water leveraged NiFi to track metrics against a simulated truck, showing the initial values in capturing this type of data.
Technologies: NiFi, Druid, Hive
Converging Database Transactions and Analytics SingleStore
delivered at the Gartner Data and Analytics 2018 show in Texas. This presentation discusses real-time applications and their impact on existing data infrastructures
Implementing BigPetStore with Apache FlinkMárton Balassi
The document outlines how to implement BigPetStore, a blueprint for Flink users, in under 500 lines of code. It describes generating sample data, performing ETL with both the DataSet and Table APIs, training a matrix factorization model with FlinkML for recommendations, and serving recommendations with the DataStream API. The goal is to demonstrate end-to-end workflows in Flink that go beyond WordCount by mixing APIs for data generation, cleaning, machine learning, and streaming predictions.
John Hammink's Talk at Great Wide Open 2016. We discuss: 1.) the need for data analytics infrastructure that can scale exponentially and 2.) what such an infrastructure must contain and finally 3.) the need for an infrastructure to be able to handle un - and semi-structured data.
It’s All About The Cards: Sharing on Social Media Encouraged HTML Metadata G...Shawn Jones
In a perfect world, all articles consistently contain sufficient metadata to describe the resource. We know this is not the reality, so we are motivated to investigate the evolution of the metadata that is present when authors and publishers supply their own. Because applying metadata takes time, we recognize that each news article author has a limited metadata budget with which to spend their time and effort. How are they spending this budget? What are the top metadata categories in use? How did they grow over time? What purpose do they serve? We also recognize that not all metadata fields are used equally. What is the growth of individual fields over time? Which fields experienced the fastest adoption? In this paper, we review 227,726 HTML news articles from 29 outlets captured by the Internet Archive between 1998 and 2016. Upon reviewing the metadata fields in each article, we discovered that 2010 began a metadata renaissance as publishers embraced metadata for improved search engine ranking, search engine tracking, social media tracking, and social media sharing. When analyzing individual fields, we find that one application of metadata stands out above all others: social cards -- the cards generated by platforms like Twitter when one shares a URL. Once a metadata standard was established for cards in 2010, its fields were adopted by 20% of articles in the first year and reached more than 95% adoption by 2016. This rate of adoption surpasses efforts like schema.org and Dublin Core by a fair margin. When confronted with these results on how news publishers spend their metadata budget, we must conclude that it is all about the cards.
This presentation is to understand StreamSets ETL tool.
StreamSets is modern ETL tool designed to process streaming data.
StreamSets has 2 engines, 1 is Data Controller and Data Transformer(Based on Apache Spark).
Building the Foundation for a Latency-Free LifeSingleStore
The document discusses how MemSQL is able to process 1 trillion rows per second on 12 Intel servers running MemSQL. It demonstrates this throughput by running a query to count the number of trades for the top 10 most traded stocks from a dataset of over 115 billion rows of simulated NASDAQ trade data. The document argues that a latency-free operational and analytical data platform like MemSQL that can handle both high-volume operational workloads and complex queries is key to powering real-time analytics and decision making.
RightsDirect provides data-driven content solutions that help make copyright work for everyone. They offer document delivery, content workflow and analytics, text and data mining, licensing solutions, and copyright education for rightsholders and publishers with over 600 million rights. For content users, RightsDirect offers a Multinational Copyright License that provides a consistent set of rights from thousands of publishers to simplify content usage and sharing across borders. The license complements but does not replace publisher subscriptions. RightsDirect also offers document delivery through RightFind, personal and shared libraries, and content decision support services to help track content usage and spending.
To transform your organization and unlock the value of your data, you need a way to ingest, store and analyze every type of data in your organization.
This presentation covers the Data Access Layer of the Hadoop Ecosystem which enables you to achieve this.
We will use the HDP (Hortonworks Data Platform) reference architecture to walk through the Hadoop core and its ecosystem with focus on the data access layer.
We will cover some of the prominent tools of the ecosystem such as Pig, Hive, Sqoop, Flume and Oozie and how they are used for ingesting data into Hadoop from structured, unstructured and streaming sources.
Talk to us at +91 80 6567 9700 or send an email to training@springpeople.com for more information.
Testistanbul 2016 - Keynote: "Enterprise Challenges of Test Data" by Rex BlackTurkish Testing Board
If you are testing a simple mobile app, you may find it relatively easy to find representative test data. However, what if you are testing enterprise scale applications? In the enterprise data center, one hundred or more applications of various sizes, complexity, and criticality co-exist, operating on various data repositories, in some cases shared data repositories. In some cases, disparate data repositories hold related data, and the ability to test integration across applications that access these data sets is critical. In this keynote speech, Rex Black will talk about the challenges facing his clients as they deal with these testing problems. You’ll go away with a better understanding of the nature of the challenges, as well as ideas on how to handle them, grounded in lessons Rex has learned in over 30 years of software engineering and testing.
MongoDB .local Houston 2019: Building an IoT Streaming Analytics Platform to ...MongoDB
Corva's analytics platform enables real-time engineering and machine learning predictions and powers faster and safer drilling. The platform utilizes AWS serverless Lambda & extensible, data-driven API with MongoDB to handle 100,000+ requests per minute of streaming sensor data.
HBase Global Indexing to support large-scale data ingestion at UberDataWorks Summit
Danny Chen presented on Uber's use of HBase for global indexing to support large-scale data ingestion. Uber uses HBase to provide a global view of datasets ingested from Kafka and other data sources. To generate indexes, Spark jobs are used to transform data into HFiles, which are loaded into HBase tables. Given the large volumes of data, techniques like throttling HBase access and explicit serialization are used. The global indexing solution supports requirements for high throughput, strong consistency and horizontal scalability across Uber's data lake.
Дмитрий Попович "How to build a data warehouse?"Fwdays
To build a data warehouse, Tubular ingests raw data from multiple sources using Kafka and stores it permanently. The data is normalized using Spark - duplicates are removed, data is partitioned by time, and sources are joined. A metadata storage using Hive Metastore allows unified access to datasets discovered across various storage formats like Parquet and Avro. This centralized repository helps engineers, analysts and services access and analyze disparate data.
Presto, an open source distributed SQL engine, is widely recognized for its low-latency queries, high concurrency, and native ability to query multiple data sources. Proven at scale in a variety of use cases at Facebook, Airbnb, Netflix, Uber, Twitter, Bloomberg, and FINRA, Presto experienced an unprecedented growth in popularity in both on-premises and cloud deployments in the last few years.
Inspired by the increasingly complex SQL queries run by the Presto user community, engineers at Facebook and Starburst have recently focused on cost-based query optimization. In this talk we will present the initial design and implementation of the CBO, support for connector-provided statistics, estimating selectivity, and choosing efficient query plans. Then, our detailed experimental evaluation will illustrate the performance gains for several classes of queries achieved thanks to the optimizer. Finally, we will discuss our future work enhancing the initial CBO and present the general Presto roadmap for 2018 and beyond.
Speakers
Kamil Bajda-Pawlikowski, Starburst Data, CTO & Co-Founder
Martin Traverso
Mining the Web of Linked Data with RapidMinerHeiko Paulheim
Lots of data from different domains is published as Linked Open Data. While there are quite a few browsers for that data, as well as intelligent tools for particular purposes, a versatile tool for deriving additional knowledge by mining the Web of Linked Data is still missing. In this challenge entry, we introduce the RapidMiner Linked Open Data extension. The extension hooks into the powerful data mining platform RapidMiner, and offers operators for accessing Linked Open Data in RapidMiner, allowing for using it in sophisticated data analysis workflows without the need to know SPARQL or RDF. As an example, we show how statistical data on scientific publications, published as an RDF data cube, can be linked to further datasets and analyzed using additional background knowledge from various LOD datasets.
RapidMiner is an environment for machine learning and data mining processes that follows a modular operator concept. It introduces transparent data handling and process modeling to ease configuration for end users. Additionally, its clear interfaces and scripting language based on XML make it an integrated developer environment for data mining and machine learning. To get started with RapidMiner, users download the file for their system from the website, install it by accepting the license agreement and specifying the installation directory, then launch it by double clicking the desktop icon.
Unlocking Value in Device Data Using Spark: Spark Summit East talk by John La...Spark Summit
HP ships millions of PCs, Printers, and other devices every year to customers in all market segments. More customers are seeking services provided with our products enabling new opportunities for HP to create services from the data we can collect from our devices. Every device we ship is an IoT endpoint with powerful CPU to capture rich data. Insights from this data are used internally to improve our products and focus on customer needs.
In this presentation, John will focus on HP’s journey to enabling Big Data analytics from within a large enterprise environment. He will review the challenges and how HP decided on AWS, Apache Spark and Databricks as the foundation for their entry into Big Data Analytics. John will also review how HP uses Spark to build analytic services from the data they generate from their devices.
Accelerating Delivery of Data Products - The EBSCO WayMongoDB
EBSCO Information Services (EBSCO) is the leading provider of electronic journals, magazines, eBooks, audioBooks, and online research content for libraries, including hundreds of research databases, historical archives, point-of-care medical reference, and corporate learning tools serving millions of end users at tens of thousands of institutions worldwide. The EBSCO platform is a widely used platform serving the needs of researchers at all levels in academic institutions, schools, public libraries, hospitals, medical institutions, corporations and government institutions. Data is our business, and delivering new products quickly is our competitive advantage. We build hundreds of data products and accelerating the analysis, transformation of new datasets translates to revenue and competitiveness. And since our data is so varied, using MognoDB to store data flexibly and JSON Studio to analyze this data allows us to deliver products to market faster. In this session we will describe this process that helped us expedite delivery of new datasets, and give real examples of how data is used, analyzed and processed.
Scalable Data Management for Kafka and Beyond | Dan Rice, BigIDHostedbyConfluent
Data in motion has changed both the scale and scope of data and analytics - enabling organizations to capture more information and use it more effectively. But to get the most value from it - you need to know what’s there, make it risk aware, and take action on it. In this session, you’ll learn how to leverage modern ML-augmented data management solutions to automatically find, identify, and classify sensitive data across Spark, Databricks, and beyond - and how to apply policies for compliance and risk mitigation to get the most value from our data.
American Water shares how bringing IoT to fleet management can provide value to the customer. In the utilities industry, fleet management plays a major part in the business. The front line is one of the largest parts of the business whether it is the field employees working on mains, or those working on the customers' property. American Water strives to provide the best customer experience and part of that includes improving the effectiveness of our fleet.
Currently, there is no insight or active feedback on the effectiveness of the routes or driving behaviors. As a PoC, American Water leveraged NiFi to track metrics against a simulated truck, showing the initial values in capturing this type of data.
Technologies: NiFi, Druid, Hive
Converging Database Transactions and Analytics SingleStore
delivered at the Gartner Data and Analytics 2018 show in Texas. This presentation discusses real-time applications and their impact on existing data infrastructures
Implementing BigPetStore with Apache FlinkMárton Balassi
The document outlines how to implement BigPetStore, a blueprint for Flink users, in under 500 lines of code. It describes generating sample data, performing ETL with both the DataSet and Table APIs, training a matrix factorization model with FlinkML for recommendations, and serving recommendations with the DataStream API. The goal is to demonstrate end-to-end workflows in Flink that go beyond WordCount by mixing APIs for data generation, cleaning, machine learning, and streaming predictions.
John Hammink's Talk at Great Wide Open 2016. We discuss: 1.) the need for data analytics infrastructure that can scale exponentially and 2.) what such an infrastructure must contain and finally 3.) the need for an infrastructure to be able to handle un - and semi-structured data.
It’s All About The Cards: Sharing on Social Media Encouraged HTML Metadata G...Shawn Jones
In a perfect world, all articles consistently contain sufficient metadata to describe the resource. We know this is not the reality, so we are motivated to investigate the evolution of the metadata that is present when authors and publishers supply their own. Because applying metadata takes time, we recognize that each news article author has a limited metadata budget with which to spend their time and effort. How are they spending this budget? What are the top metadata categories in use? How did they grow over time? What purpose do they serve? We also recognize that not all metadata fields are used equally. What is the growth of individual fields over time? Which fields experienced the fastest adoption? In this paper, we review 227,726 HTML news articles from 29 outlets captured by the Internet Archive between 1998 and 2016. Upon reviewing the metadata fields in each article, we discovered that 2010 began a metadata renaissance as publishers embraced metadata for improved search engine ranking, search engine tracking, social media tracking, and social media sharing. When analyzing individual fields, we find that one application of metadata stands out above all others: social cards -- the cards generated by platforms like Twitter when one shares a URL. Once a metadata standard was established for cards in 2010, its fields were adopted by 20% of articles in the first year and reached more than 95% adoption by 2016. This rate of adoption surpasses efforts like schema.org and Dublin Core by a fair margin. When confronted with these results on how news publishers spend their metadata budget, we must conclude that it is all about the cards.
This presentation is to understand StreamSets ETL tool.
StreamSets is modern ETL tool designed to process streaming data.
StreamSets has 2 engines, 1 is Data Controller and Data Transformer(Based on Apache Spark).
Building the Foundation for a Latency-Free LifeSingleStore
The document discusses how MemSQL is able to process 1 trillion rows per second on 12 Intel servers running MemSQL. It demonstrates this throughput by running a query to count the number of trades for the top 10 most traded stocks from a dataset of over 115 billion rows of simulated NASDAQ trade data. The document argues that a latency-free operational and analytical data platform like MemSQL that can handle both high-volume operational workloads and complex queries is key to powering real-time analytics and decision making.
RightsDirect provides data-driven content solutions that help make copyright work for everyone. They offer document delivery, content workflow and analytics, text and data mining, licensing solutions, and copyright education for rightsholders and publishers with over 600 million rights. For content users, RightsDirect offers a Multinational Copyright License that provides a consistent set of rights from thousands of publishers to simplify content usage and sharing across borders. The license complements but does not replace publisher subscriptions. RightsDirect also offers document delivery through RightFind, personal and shared libraries, and content decision support services to help track content usage and spending.
To transform your organization and unlock the value of your data, you need a way to ingest, store and analyze every type of data in your organization.
This presentation covers the Data Access Layer of the Hadoop Ecosystem which enables you to achieve this.
We will use the HDP (Hortonworks Data Platform) reference architecture to walk through the Hadoop core and its ecosystem with focus on the data access layer.
We will cover some of the prominent tools of the ecosystem such as Pig, Hive, Sqoop, Flume and Oozie and how they are used for ingesting data into Hadoop from structured, unstructured and streaming sources.
Talk to us at +91 80 6567 9700 or send an email to training@springpeople.com for more information.
Testistanbul 2016 - Keynote: "Enterprise Challenges of Test Data" by Rex BlackTurkish Testing Board
If you are testing a simple mobile app, you may find it relatively easy to find representative test data. However, what if you are testing enterprise scale applications? In the enterprise data center, one hundred or more applications of various sizes, complexity, and criticality co-exist, operating on various data repositories, in some cases shared data repositories. In some cases, disparate data repositories hold related data, and the ability to test integration across applications that access these data sets is critical. In this keynote speech, Rex Black will talk about the challenges facing his clients as they deal with these testing problems. You’ll go away with a better understanding of the nature of the challenges, as well as ideas on how to handle them, grounded in lessons Rex has learned in over 30 years of software engineering and testing.
MongoDB .local Houston 2019: Building an IoT Streaming Analytics Platform to ...MongoDB
Corva's analytics platform enables real-time engineering and machine learning predictions and powers faster and safer drilling. The platform utilizes AWS serverless Lambda & extensible, data-driven API with MongoDB to handle 100,000+ requests per minute of streaming sensor data.
HBase Global Indexing to support large-scale data ingestion at UberDataWorks Summit
Danny Chen presented on Uber's use of HBase for global indexing to support large-scale data ingestion. Uber uses HBase to provide a global view of datasets ingested from Kafka and other data sources. To generate indexes, Spark jobs are used to transform data into HFiles, which are loaded into HBase tables. Given the large volumes of data, techniques like throttling HBase access and explicit serialization are used. The global indexing solution supports requirements for high throughput, strong consistency and horizontal scalability across Uber's data lake.
Дмитрий Попович "How to build a data warehouse?"Fwdays
To build a data warehouse, Tubular ingests raw data from multiple sources using Kafka and stores it permanently. The data is normalized using Spark - duplicates are removed, data is partitioned by time, and sources are joined. A metadata storage using Hive Metastore allows unified access to datasets discovered across various storage formats like Parquet and Avro. This centralized repository helps engineers, analysts and services access and analyze disparate data.
Presto, an open source distributed SQL engine, is widely recognized for its low-latency queries, high concurrency, and native ability to query multiple data sources. Proven at scale in a variety of use cases at Facebook, Airbnb, Netflix, Uber, Twitter, Bloomberg, and FINRA, Presto experienced an unprecedented growth in popularity in both on-premises and cloud deployments in the last few years.
Inspired by the increasingly complex SQL queries run by the Presto user community, engineers at Facebook and Starburst have recently focused on cost-based query optimization. In this talk we will present the initial design and implementation of the CBO, support for connector-provided statistics, estimating selectivity, and choosing efficient query plans. Then, our detailed experimental evaluation will illustrate the performance gains for several classes of queries achieved thanks to the optimizer. Finally, we will discuss our future work enhancing the initial CBO and present the general Presto roadmap for 2018 and beyond.
Speakers
Kamil Bajda-Pawlikowski, Starburst Data, CTO & Co-Founder
Martin Traverso
Mining the Web of Linked Data with RapidMinerHeiko Paulheim
Lots of data from different domains is published as Linked Open Data. While there are quite a few browsers for that data, as well as intelligent tools for particular purposes, a versatile tool for deriving additional knowledge by mining the Web of Linked Data is still missing. In this challenge entry, we introduce the RapidMiner Linked Open Data extension. The extension hooks into the powerful data mining platform RapidMiner, and offers operators for accessing Linked Open Data in RapidMiner, allowing for using it in sophisticated data analysis workflows without the need to know SPARQL or RDF. As an example, we show how statistical data on scientific publications, published as an RDF data cube, can be linked to further datasets and analyzed using additional background knowledge from various LOD datasets.
RapidMiner is an environment for machine learning and data mining processes that follows a modular operator concept. It introduces transparent data handling and process modeling to ease configuration for end users. Additionally, its clear interfaces and scripting language based on XML make it an integrated developer environment for data mining and machine learning. To get started with RapidMiner, users download the file for their system from the website, install it by accepting the license agreement and specifying the installation directory, then launch it by double clicking the desktop icon.
The document discusses setting up a process in RapidMiner including retrieving data from various formats, applying operators by dragging them from the menu, specifying operator attributes in the attributes menu, running the process and viewing results such as by importing an Excel file, applying modeling and cross-validation, and viewing prediction results. An example process is provided that imports data from an Excel file, retrieves the data, and views results in a scatter plot by changing plot view attributes.
The document discusses data mining tools and techniques for analyzing diffraction pattern data. It provides examples of analyzing an unknown catalytic converter specimen using x-ray diffraction data mining of the Powder Diffraction File. Through phase identification, Rietveld refinement, and searching the PDF database, it is able to determine that the specimen contains cordierite, cerium-stabilized zirconia, and around 3% rhodium oxide. Additional online searching finds a patent that matches these materials and likely synthesis process. The analysis demonstrates how data mining of diffraction data can be used to elucidate unknown sample compositions and properties.
Hadoop World 2011: Radoop: a Graphical Analytics Tool for Big Data - Gabor Ma...Cloudera, Inc.
Hadoop is an excellent environment for analyzing large data sets, but it lacks an easy-to-use graphical interface for building data pipelines and performing advanced analytics. RapidMiner is an excellent open-source tool for data analytics, but is limited to running on a single machine.In this presentation, we will introduce Radoop, an extension to RapidMiner that lets users interact with a Hadoop cluster. Radoop combines the strengths of both projects and provides a user-friendly interface for editing and running ETL, analytics, and machine learning processes on Hadoop. We will also discuss lessons learned while integrating HDFS, Hive, and Mahout with RapidMiner.
Orange, R, RapidMiner, and WEKA are open source data mining and machine learning tools. Orange has an elegant scripting interface and can be run in GUI or ETL mode. R has elegant scripting integrated with extensive statistical libraries. RapidMiner has many features and good connectivity. WEKA has the easiest GUI but more limited connectivity than the other tools. The document compares the tools on factors such as supported data formats, user interfaces, connectivity, and provides examples of companies using the different tools.
The presentation covers the use of Scalable Predictive Analysis in Critically Ill Patients using a Visual Open Data Analysis Platform (RapidMiner).
With the accumulation of large amounts of health related data, predictive analytics could stimulate the transformation of reactive medicine towards Predictive, Preventive and Personalized (PPPM) Medicine, ultimately affecting both cost and quality of care. However, high-dimensionality and high-
complexity of the data involved, prevents data-driven methods from easy translation into clinically relevant models. Additionally, the application of cutting edge predictive methods and data manipulation require substantial programming skills, limiting its direct exploitation by medical domain experts. This leaves a gap between potential and actual data usage. The presentation addresses the problem by focusing on an open, visual environment, suited to be applied by the medical community (RapidMiner). As a showcase, a framework was developed for the meaningful use of data from critical care patients by integrating the MIMIC-II / III database in a data mining environment (RapidMiner) supporting scalable predictive analytics using visual tools (RapidMiner’s Radoop extension). Guided by the CRoss-Industry Standard Process for Data Mining (CRISP-DM), the ETL process (Extract, Transform, Load) was initiated by retrieving data from the MIMIC-II tables of interest. Using visual tools for ETL on Hadoop and predictive modeling in RapidMiner, robust processes for automatic building, parameter optimization and evaluation of various
predictive models, under different feature selection schemes can be developed. Because these processes can be easily adopted in other projects, this environment is attractive for scalable predictive analytics in health research.
Presentation at Laboratory for Computational Physiology (LCP)
Massachusetts Institute of Technology (MIT),
Building E25 room 101; December 8th 12-noon
Sven Van Poucke, MD, Anesthesiologist, Emergency Physician
Department of Anesthesiology, Intensive Care, Emergency Medicine and Pain Therapy, Ziekenhuis Oost-Limburg, Genk, Belgium
Data Mining: Implementation of Data Mining Techniques using RapidMiner softwareMohammed Kharma
K-means and k-medoids clustering techniques are illustrated using RapidMiner tool and a Java application. K-means partitions data into k groups based on minimizing distance between data points and cluster centers. It assigns each data point to exactly one cluster. K-medoids is similar but uses actual data points as centers instead of means. Both require specifying the number of clusters k in advance and can be impacted by outliers, though k-medoids is less sensitive to outliers. The document demonstrates implementing both techniques using different software and compares the results.
Exploiting Linked Open Data as Background Knowledge in Data MiningHeiko Paulheim
The document summarizes an approach to exploiting linked open data as background knowledge in data mining tasks. It describes using LOD to generate additional features for machine learning algorithms from entity names in datasets. Experiments show this approach can improve results for classification tasks. Applications discussed include classifying events from Wikipedia and tweets by leveraging background knowledge from DBpedia to prevent overfitting. The document also proposes using LOD to help explain statistics by enriching datasets and analyzing correlations.
RapidMiner offers several products for data mining and machine learning including the Community Edition, Enterprise Edition, and Enterprise Analytics Server. The Community Edition provides over 400 operators that can be combined through a graphical user interface or XML scripting. The Enterprise Edition adds more features, services, and guarantees. The Enterprise Analytics Server allows running RapidMiner processes on powerful hardware for remote execution, collaborative work, and web-based access to results. RapidMiner plug-ins can extend its functionality and some available plug-ins include text mining and social media analysis.
The document defines several key machine learning and neural network terminology including:
- Activation level - The output value of a neuron in an artificial neural network.
- Activation function - The function that determines the output value of a neuron based on its net input.
- Attributes - Properties of an instance that can be used to determine its classification in machine learning tasks.
- Axon - The output part of a biological neuron that transmits signals to other neurons.
Analyzing the World's Largest Security Data Lake!DataWorks Summit
The document discusses Symantec's CloudFire Analytics platform for analyzing security data at scale. It describes how CloudFire provides Hadoop ecosystem tools on OpenStack virtual machines across 50+ data centers to support security product analytics. Key points covered include analytics services and data, administration and monitoring using tools like Ambari and OpsView, and plans for self-service analytics using dynamic clusters provisioned through CloudBreak integration.
Organizational success depends on our ability to sense the environment, grab opportunities and eliminate threats that are present in real-time. Such real-time processing is now available to all organizations (with or without a big data background) through the new WSO2 Stream Processor.
This slides presents WSO2 Stream Processor’s new features and improvements and explains how they make an organization excel in the current competitive marketplace. Some key features we will consider are:
* WSO2 Stream Processor’s highly productive developer environment, with graphical drag-and-drop, and the Streaming SQL query editor
* The ability to process real-time queries that span from seconds to years
* Its interactive visualization and dashboarding features with improved widget generation
* Its ability to processing at scale via distributed deployments with full observability
* Default support for HTTP analytics, distributed message trace analytics, and Twitter analytics
This slide deck explores WSO2 Stream Processor’s new features and improvements and explain how they make an organization excel in the current competitive marketplace.
The document discusses Microsoft System Center 2012 R2 and its components for managing IT infrastructure and automating processes. It provides an overview of System Center capabilities for data center and client automation. Key components described include System Center Configuration Manager for device management, Operations Manager for monitoring, Virtual Machine Manager for hypervisor management, and Service Manager for IT service management. The document demonstrates System Center's unified management capabilities and how customers can get started or advance their use of System Center.
How Pixid dropped Oracle and went hybrid with MariaDBMariaDB plc
Pixid replaced Oracle Database with MySQL in 2011, then soon migrated to MariaDB to get better performance, more features and synchronous clustering for high availability. In addition to high-performance transactions, their customers needed access to fast analytics for self-service reporting and data exploration. Pixid started with a separate columnar database for analytics, but with the release of MariaDB ColumnStore, they found a more elegant solution – deploying a single database platform to handle both transactions and analytics. In this session, Antoine Gosset and Jérôme Mouret share how Pixid went from Oracle Database to handling both transactional and analytical workloads with MariaDB.
Hadoop Infrastructure and SoftServe Experience by Vitaliy Bashun, Data ArchitectSoftServe
This document discusses Hadoop infrastructure and SoftServe's experience with it. It provides an overview of various Hadoop components like HDFS, YARN, Pig, Hive, Sqoop and HBase. It also discusses popular Hadoop distributions and the Lambda architecture. Finally, it shares three case studies where SoftServe implemented Hadoop solutions for clients in log analysis, web analytics and an online analytics platform.
Pacemaker hadoop infrastructure and soft serve experienceVitaliy Bashun
This document discusses Hadoop infrastructure and SoftServe's experience working with Hadoop. It provides an overview of Hadoop components like HDFS, YARN, Pig, Hive, Sqoop and HBase. It also discusses popular Hadoop distributions and the Lambda architecture. The document then presents three case studies where SoftServe implemented Hadoop solutions for clients - one for log analysis, one for clickstream analysis of a retail website, and one for an online analytics platform. It provides details on the technologies used, architecture and business goals for each case study.
Power Big Data Analytics with Informatica Cloud Integration for Redshift, Kin...Amazon Web Services
Companies are dealing with increasingly large data sets and looking for ways to significantly improve the scale and cost of Big Data analysis with AWS. This hands-on session shows you how you can achieve that. With hundreds of pre-built connectors, you will learn how to get your on-premise and cloud data into Redshift in minutes, not days, and at a significantly reduced costs using Informatica Cloud Integration. With fully certified support for large scale RDS deployments and Informatica’s Vibe Data Stream solution for automated streaming data collection for Kinesis, Informatica offers a comprehensive cloud integration solution for Big Data analytics with AWS. The ability to seamlessly migrate Informatica’s PowerCenter to Amazon Cloud (EC2) offers customers a Cloud migration path, with even higher performance and lower costs.
This system introduced a new concept for search engines - a chance for users to win prizes! Search engines are integral part of IT, Marketing and all fields of work. Additionally, this project has two specific requirements - web and toolbar. Currently there is no search engine that rewards users for hits. So, each search has a chance to win prizes. see more at http://www.greymatterindia.com/search-engine-and-toolbar-with-a-chance-to-win-prizes
This document provides information on Microsoft's cloud computing solutions and capabilities across four requirements:
1) Help customers solve complex problems and provide deep application insight and management.
2) Offer comprehensive management of heterogeneous IT environments across platforms.
3) Enable customers to build a true cloud platform that goes beyond virtualization.
4) Allow customers to distribute IT across public and private cloud models with common tools.
Accelerating the Path to Digital with a Cloud Data StrategyMongoDB
This document discusses accelerating digital transformation through a cloud data strategy using MongoDB.
It begins by outlining MongoDB's capabilities as a cloud data platform, including its use by over 3000 enterprises. The document then discusses how time to market has replaced cost as the primary driver for cloud adoption. It also outlines considerations for choosing a cloud data platform like deployment flexibility, reducing complexity, agility, resiliency, scalability, cost, and security.
The document then provides an overview of MongoDB's cloud offerings, including MongoDB Atlas on public clouds, MongoDB Ops Manager for private clouds, and MongoDB Stitch for backend services. It also discusses best practices for replatforming applications from relational databases to MongoDB in the cloud.
ALT-F1.BE : The Accelerator (Google Cloud Platform)Abdelkrim Boujraf
The Accelerator is an IT infrastructure able to collect and analyze a massive amount of public data on the WWW.
The Accelerator leverages the untapped potential of web data with the first solution designed for diverse sectors,
completely scalable, available on-premise, and cloud-provider agnostic.
R+Hadoop - Ask Bigger (and New) Questions and Get Better, Faster AnswersRevolution Analytics
The business cases for Hadoop can be made on the tremendous operational cost savings that it affords. But why stop there? The integration of R-powered analytics in Hadoop presents a totally new value proposition. Organizations can write R code and deploy it natively in Hadoop without data movement or the need to write their own MapReduce. Bringing R-powered predictive analytics into Hadoop will accelerate Hadoop’s value to organizations by allowing them to break through performance and scalability challenges and solve new analytic problems. Use all the data in Hadoop to discover more, grow more quickly, and operate more efficiently. Ask bigger questions. Ask new questions. Get better, faster results and share them.
This presentation gives an overview of StreamCentral technology targeted for IT professionals. StreamCentral is software to model and build Big Data Solutions. StreamCentral consists of a Big Data Solutions Modeler that not only makes it easy to model traditional BI/DW and Big Data solutions but also auto deploys the model on the latest innovations in Big Data Management solutions (like HP Vertica and SQL Server Parallel Data Warehouse). StreamCentral Big Data Server executes the model definition in real-time. StreamCentral drastically reduces the time to market, risk and cost associated with building traditional BI/DW and Big Data solutions!
Accelerating a Path to Digital With a Cloud Data StrategyMongoDB
The document describes a conference on accelerating a path to digital transformation with a cloud data strategy. It provides an agenda for the conference including speakers on executing a cloud data strategy, customer stories from De Persgroep and Toyota Motor Europe, and a session on landing in the cloud with MongoDB Atlas. The document also provides background on the speakers and their companies.
Find out why hosting service providers choose Jelastic for their cloud business and what technologies they offer to the users based on this PaaS and CaaS solution.
Whither the Hadoop Developer Experience, June Hadoop Meetup, Nitin MotgiFelicia Haggarty
The document discusses challenges with building operational data applications on Hadoop and introduces the Cask Data Application Platform (CDAP) as a solution. It provides an agenda that covers data applications, challenges, CDAP motivation and goals, use cases, and an introduction and architecture overview of CDAP. The document aims to demonstrate how CDAP provides a unified platform that simplifies application development and lifecycle while supporting reusable data and processing patterns.
Applications need data, but the legacy approach of n-tiered application architecture doesn’t solve for today’s challenges. Developers aren’t empowered to build and iterate their code quickly without lengthy review processes from other teams. New data sources cannot be quickly adopted into application development cycles, and developers are not able to control their own requirements when it comes to data platforms.
Part of the challenge here is the existing relationship between two groups: developers and DBAs. Developers are trying to go faster, automating build/test/release cycles with CI/CD, and thrive on the autonomy provided by microservices architectures. DBAs are stewards of data protection, governance, and security. Both of these groups are critically important to running data platforms, but many organizations deal with high friction between these teams. As a result, applications get to market more slowly, and it takes longer for customers to see value.
What if we changed the orientation between developers and DBAs? What if developers consumed data products from data teams? In this session, Pivotal’s Dormain Drewitz and Solstice’s Mike Koleno will speak about:
- Product mindset and how balanced teams can reduce internal friction
- Creating data as a product to align with cloud-native application architectures, like microservices and serverless
- Getting started bringing lean principles into your data organization
- Balancing data usability with data protection, governance, and security
Presenter : Dormain Drewitz, Pivotal & Mike Koleno, Solstice
The Summer 2016 release of Informatica Cloud is packed with many new platform features including :
- Cloud Data Integration Hub that supports publish and subscribe integration patterns that automate and streamline integration across cloud and on-premise sources
- Innovative features like stateful time sensitive variables, and advanced data transformations like unions and sequences
- Intelligent and dynamic data masking of sensitive data to save development and QA time.
-Cloud B2B Gateway is the leading data exchange platform for enterprises and it’ partners and customers providing end-to-end data monitoring capabilities and support for highest level of data quality.
- Enhancements to native connectors for popular cloud applications like Workday, SAP Success Factors, Oracle, SugarCRM, MongoDB, Teradata Cloud, SAP Concur, Salesforce Financial Services Cloud
And much more!
Neo4j GraphTalks Oslo - Graph Your Business - Rik Van Bruggen, Neo4jNeo4j
The Neo4j graph database is the fastest growing database engine in the market and has hundreds of customer references across Europe and globally, solving significant technology problems for large Enterprises in Finance, Telco, Retail, Utilities, Logistics and Internet sectors. Typical use cases are Recommendations, Fraud Detection, MDM, Network and Software Analysis and Optimization, Identity and Access Management.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Project Management Semester Long Project - Acuityjpupo2018
Acuity is an innovative learning app designed to transform the way you engage with knowledge. Powered by AI technology, Acuity takes complex topics and distills them into concise, interactive summaries that are easy to read & understand. Whether you're exploring the depths of quantum mechanics or seeking insight into historical events, Acuity provides the key information you need without the burden of lengthy texts.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
OpenID AuthZEN Interop Read Out - AuthorizationDavid Brossard
During Identiverse 2024 and EIC 2024, members of the OpenID AuthZEN WG got together and demoed their authorization endpoints conforming to the AuthZEN API
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
2. RapidMiner Company Overview
2
Easy-to-use, blazing fast, and very easy to integrate with any IT infrastructure
Support from a thriving communityof contributors creating new extensions and applications
Processes designed in RapidMiner can be one-click deployedto RapidMiner Server or RapidMiner Cloud
A unique Marketplacefor independent developers to publish their innovative extensions
RapidMiner delivers the power of predictive analytics to business users. No programming required.
More than 60 connectors (incl. SAP, Hadoop, Cloud connectors like Twitter and Zapier) allowing easy access to structured and unstructured data.
3. RapidMiner History
3
Cloud
•Cloud
•Hadoop
Business Source
•Commercial Editions
•Community Editions
•Client and Server
Open Source
•Command Line
•Initial Workbench
Open Source
•Complete Workbench
•CommunityExtensions
•Marketplace
Community Growth
2007
2010
2013
2014
5,000
30,000
150,000
250,000
4. RapidMiner Metrics
4
60+
Employees
Worldwide
100+
Active Developers
600+
Customers in over 50 Countries
40,000+
Downloads per Month
35,000+
Active Deployments with over 250,000 Users
6. RapidMiner Studio
•With access to over 1500 different operators, the Java-based visual environment of RapidMiner allows for rapid data mining process development
6
Visual Process Design Environment
7. Accelerators
7
Wizard
•Selection of data and label (e.g. churn) column.
•Label column contains missings values if unknown –those will be predicted
Results
•Predictions (individuals, churn predictions)
•Descriptive model
•Model accuracy and lift chart
9. RapidMiner Server
9
The RapidMiner Server provides enterprise-wide process development and process to web- service conversion with dynamic dashboards and data visualizations.
11. ExistingExtensions
11
Edda–Extensions for Binominal Text Classification
Instance selection and Prototype based rules
RapidMiner Finance and Economics Extension
Multimedia Mining Extension
12. RapidMiner Finance and Economics Extension
Edda–Extensions for Binominal Text Classification
ExistingExtensions
Confidential
12
Instance selection and Prototype based rules
Multimedia Mining Extension
13. Linked Open Data Extension
•Assume a rating system for books giving us an ISBN number and a rating from 1 to 5
•Goal: Predict the popularity of new books
13
…
14. Linked Open Data Extension
•Assume a rating system for books giving us an ISBN number and a rating from 1 to 5
•Goal: Predict the popularity of new books
14
…
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX ontology: <http://dbpedia.org/ontology/>
select distinct ?book ?author ?isbn?country ?abstract ?pages ?language
where {
?book rdf:typeontology:Book.
?book ontology:author?author .
?book ontology:abstract?abstract .
?book ontology:isbn?isbn.
?book ontology:numberOfPages?pages .
?book ontology:language?language .
?book ontology:country?country .
}
15. Linked Open Data Extension
•Assume a rating system for books giving us an ISBN number and a rating from 1 to 5
•Goal: Predict the popularity of new books
15
…
…
22. HowtoextendRapidMiner Studio
Confidential 22
gitclone https://github.com/rapidminer/rapidminer-extension-tutorial.gitgradleinstallExtension
•Live Demo:
–Extension skeleton
–Operators
–Special data objects
–Advanced Extension elements
–Accelerators
•Documentation
http://www.rapidminer.com/documentation
23. HowtointegrateRapidMiner
•By web services:
23
Web Service API
1.Export process as a web
service in RM Server
2.Select output format
(JSON, XML, PNG, …)
3.
•HTTP POST to that URL
•Read process results from HTTP response
or
•<iframe> into other Website
24. HowtointegrateRapidMiner
•OEM:
24
Java
1.RapidMiner can be easily invoked
2.Call RapidMiner.init()
3.Use the code:
Create processes, run processes or transform data
25. RapidMinerUSA
RapidMiner, Inc. (Headquarters)
10 Fawcett St
Cambridge, MA 02138
United States
E-mailcontact-us@rapidminer.com
Phone+1 -617 -401 -7708
Fax+1 -617 -401 -7709
THANK YOU
25
RapidMinerGermany
RapidMinerGmbH
StockumerStr. 475
44227 Dortmund
Germany
E-mailcontact-de@rapidminer.com
Phone+49 -231 -425 786 9-0
Fax+49 -231 -425 786 9-9
RapidMinerUK
RapidMinerLtd.
QuatroHouse, Frimley Road
CamberleyGU16 7ER
United Kingdom
E-mailcontact-uk@rapidminer.com
Phone+44 1276 804 426
Fax+1 -617 -401 –7709
www.rapidminer.com
RapidMiner Hungary
RapidMiner Kft
Iparutca5
1095 Budapest
Hungary
E-mailcontact-hu@rapidminer.com
Phone+44 1276 804 426
Fax+1 -617 -401 -7709