KNIME Analytics Platform is an open source data analytics platform that allows users to discover insights from data through customizable workflows. It provides over 1000 analytic techniques through nodes for tasks like statistics, data mining, text mining, and more. Workflows can integrate various data sources, transform the data, apply models, and output results in standard formats. The platform is open source and free to use, customize, and extend through its community and commercial extensions.
Are you curious about KNIME Software?
Do you know the difference between KNIME Analytics Platform and KNIME Server?
Which data sources can KNIME connect to?
Can you run an R script from within a KNIME workflow? A Python script? Which other integrations are available?
How can KNIME help with ETL, data preparation, and general data manipulation? Which machine learning algorithms can KNIME offer?
This webinar answers all of these questions! There’s also information about connecting to big data clusters and how you can run the whole or part of your analysis on a big data platform. It also covers everything you need to know about Microsoft Azure and Amazon AWS
Security Incident Event Management
Real time monitoring of Servers, Network Devices.
Correlation of Events
Analysis and reporting of Security Incidents.
Threat Intelligence
Long term storage
A tremendous backlog of predictive modeling problems in the industry and short supply of trained data scientists have spiked interest in automation over the last few years. A new academic field, AutoML, has emerged. However, there is a significant gap between the topics that are academically interesting and automation capabilities that are necessary to solve real-world industrial problems end-to-end. An even greater challenge is enabling a non-expert to build a robust and trustworthy AI solution for their company. In this talk, we’ll discuss what an industry-grade AutoML system consists of and the scientific and engineering challenges of building it.
ATTACKers Think in Graphs: Building Graphs for Threat IntelligenceMITRE - ATT&CKcon
From MITRE ATT&CKcon Power Hour January 2021
By Valentine Mairet, Security Researcher, McAfee
The MITRE ATT&CK framework is the industry standard to dissect cyberattacks into used techniques. At McAfee, all attack information is disseminated into different categories, including ATT&CK techniques. What results from this exercise is an extensive repository of techniques used in cyberattacks that goes back many years. Much can be learned from looking at historical attack data, but how can we piece all this information together to identify new relationships between threats and attacks? In her recent efforts, Valentine has embraced analyzing ATT&CK data in graphical representations. One lesson learned is that it is not just about merely mapping out attacks and techniques used into graphs, but the strength lies in applying different algorithms to answer specific questions. In this presentation, Valentine will showcase the results and techniques obtained from her research journey using graph and graph algorithms.
Are you curious about KNIME Software?
Do you know the difference between KNIME Analytics Platform and KNIME Server?
Which data sources can KNIME connect to?
Can you run an R script from within a KNIME workflow? A Python script? Which other integrations are available?
How can KNIME help with ETL, data preparation, and general data manipulation? Which machine learning algorithms can KNIME offer?
This webinar answers all of these questions! There’s also information about connecting to big data clusters and how you can run the whole or part of your analysis on a big data platform. It also covers everything you need to know about Microsoft Azure and Amazon AWS
Security Incident Event Management
Real time monitoring of Servers, Network Devices.
Correlation of Events
Analysis and reporting of Security Incidents.
Threat Intelligence
Long term storage
A tremendous backlog of predictive modeling problems in the industry and short supply of trained data scientists have spiked interest in automation over the last few years. A new academic field, AutoML, has emerged. However, there is a significant gap between the topics that are academically interesting and automation capabilities that are necessary to solve real-world industrial problems end-to-end. An even greater challenge is enabling a non-expert to build a robust and trustworthy AI solution for their company. In this talk, we’ll discuss what an industry-grade AutoML system consists of and the scientific and engineering challenges of building it.
ATTACKers Think in Graphs: Building Graphs for Threat IntelligenceMITRE - ATT&CKcon
From MITRE ATT&CKcon Power Hour January 2021
By Valentine Mairet, Security Researcher, McAfee
The MITRE ATT&CK framework is the industry standard to dissect cyberattacks into used techniques. At McAfee, all attack information is disseminated into different categories, including ATT&CK techniques. What results from this exercise is an extensive repository of techniques used in cyberattacks that goes back many years. Much can be learned from looking at historical attack data, but how can we piece all this information together to identify new relationships between threats and attacks? In her recent efforts, Valentine has embraced analyzing ATT&CK data in graphical representations. One lesson learned is that it is not just about merely mapping out attacks and techniques used into graphs, but the strength lies in applying different algorithms to answer specific questions. In this presentation, Valentine will showcase the results and techniques obtained from her research journey using graph and graph algorithms.
Security Incident and Event Management (SIEM) - Managed and Hosted Solutions ...Sirius
SIEM technology has been around for years and continues to enjoy broad market adoption. Companies continue to rely on SIEM capabilities to handle proactive security monitoring, detection and response, and regulatory compliance. However, with today’s staggering volume of cyber-security threats and the number of security devices, network infrastructures and system logs, IT security staff can become quickly overwhelmed.
Gartner projects that by 2020:
-- 50% of new SIEM implementations will be delivered via SIEM as a service.
-- 60% of all advanced security analytics will be delivered from the cloud as part of SIEM-as-a-service offerings.
BDA306 Building a Modern Data Warehouse: Deep Dive on Amazon RedshiftAmazon Web Services
In this session, we take a deep dive on Amazon Redshift architecture and the latest performance enhancements that give you faster insights into your data. We also cover Redshift Spectrum, a feature of Redshift that enables you to analyze data across Redshift and your Amazon S3 data lake to deliver unique insights not possible by analyzing independent data silos. A customer is joining us to share how they were able to extend their data warehouse to their data lake to encompass multiple data sources and data formats. This modern architecture helps them tie together data sources to get actionable insights across their business units.
Effective Threat Hunting with Tactical Threat IntelligenceDhruv Majumdar
How to set up a Threat Hunting Team for Active Defense utilizing Cyber Threat Intelligence and how CTI can help a company grow and improve its security posture.
LTS Secure Security Information and Event Management (SIEM), is a technology that provides real-time analysis of security alerts generated by network hardware and applications.
Azure Key Vault, Azure Dev Ops and Azure Synapse - how these services work pe...Erwin de Kreuk
Can we store our Connectionstrings or BlobStorageKeys or other Secretvalues somewhere else then in Azure Synapse Pipelines? Yes you can! You can store these valuable secrets in Azure Key Vault(AKV).
• But how can we achieve this in Azure Synapse Analytics?
• How do we deploy our Synapse Pipelines in Azure Dev Ops to Test, Acceptance and Production environments with these Secrets ?
• Can this be setup dynamically?
During this session I will give answers on all these questions. You will learn how to setup your Azure Key Vault, connect these secrets in Azure Synapse Analytics and finally deploy these secrets dynamically in Azure Dev Ops. As you can see a lot to talk about during this session.
This talk was given during DevOps Con 2017.
Have you ever spent time digging through various terminals, greping, lessing, awking and trying to find that few log lines that may be important? Have you every done that under time pressure, because mission critical services were not working? Have you every heard from your developers that they can’t tell you anything, because they don’t have access to application logs? Have you ever considered a centralized storage for logs, but time and resources are not on your side?
If you said yes, to any of the above questions, than this talk is for you. During the talk we’ll introduce you to the world of log centralization and analysis, both when it comes to open source, but also commercial tools. We will go from top to bottom and learn how to setup log centralization and analysis for servers, virtualized environments and containers. We will get from log shipping, through centralized buffering to storage and analysis to show you, that having a centralized log analysis tool is not a rocket science.
Finally, you will see how useful is to combine the logs from all your servers in a single place for blazingly fast correlation.
This Edureka Python tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) gives an introduction to Machine Learning and how to implement machine learning algorithms in Python. Below are the topics covered in this tutorial:
1. Why Machine Learning?
2. What is Machine Learning?
3. Types of Machine Learning
4. Supervised Learning
5. KNN algorithm
6. Unsupervised Learning
7. K-means Clustering Algorithm
Best Practices and ROI for Risk-based Vulnerability ManagementResolver Inc.
Risk Vision explores the best practices and ROI of the most successful business risk-centric vulnerability management programs. Watch the full webcast here: https://youtu.be/gW_ZAFpTK20
Big Data with KNIME is as easy as 1, 2, 3, ...4!KNIMESlides
This presentation shows how we have re-engineered an old legacy workflow to run partially on Hadoop from within the KNIME Analytics Platform, to speed up dramatically the execution time.
It also shows how easy it has been to move the ETL part of the workflow to Hadoop using the KNIME big data access nodes and in-database processing nodes.
KNIME big data nodes are Cloudera, Hortonworks, and MapR certified, as of today (October 21 2015)
Security Incident and Event Management (SIEM) - Managed and Hosted Solutions ...Sirius
SIEM technology has been around for years and continues to enjoy broad market adoption. Companies continue to rely on SIEM capabilities to handle proactive security monitoring, detection and response, and regulatory compliance. However, with today’s staggering volume of cyber-security threats and the number of security devices, network infrastructures and system logs, IT security staff can become quickly overwhelmed.
Gartner projects that by 2020:
-- 50% of new SIEM implementations will be delivered via SIEM as a service.
-- 60% of all advanced security analytics will be delivered from the cloud as part of SIEM-as-a-service offerings.
BDA306 Building a Modern Data Warehouse: Deep Dive on Amazon RedshiftAmazon Web Services
In this session, we take a deep dive on Amazon Redshift architecture and the latest performance enhancements that give you faster insights into your data. We also cover Redshift Spectrum, a feature of Redshift that enables you to analyze data across Redshift and your Amazon S3 data lake to deliver unique insights not possible by analyzing independent data silos. A customer is joining us to share how they were able to extend their data warehouse to their data lake to encompass multiple data sources and data formats. This modern architecture helps them tie together data sources to get actionable insights across their business units.
Effective Threat Hunting with Tactical Threat IntelligenceDhruv Majumdar
How to set up a Threat Hunting Team for Active Defense utilizing Cyber Threat Intelligence and how CTI can help a company grow and improve its security posture.
LTS Secure Security Information and Event Management (SIEM), is a technology that provides real-time analysis of security alerts generated by network hardware and applications.
Azure Key Vault, Azure Dev Ops and Azure Synapse - how these services work pe...Erwin de Kreuk
Can we store our Connectionstrings or BlobStorageKeys or other Secretvalues somewhere else then in Azure Synapse Pipelines? Yes you can! You can store these valuable secrets in Azure Key Vault(AKV).
• But how can we achieve this in Azure Synapse Analytics?
• How do we deploy our Synapse Pipelines in Azure Dev Ops to Test, Acceptance and Production environments with these Secrets ?
• Can this be setup dynamically?
During this session I will give answers on all these questions. You will learn how to setup your Azure Key Vault, connect these secrets in Azure Synapse Analytics and finally deploy these secrets dynamically in Azure Dev Ops. As you can see a lot to talk about during this session.
This talk was given during DevOps Con 2017.
Have you ever spent time digging through various terminals, greping, lessing, awking and trying to find that few log lines that may be important? Have you every done that under time pressure, because mission critical services were not working? Have you every heard from your developers that they can’t tell you anything, because they don’t have access to application logs? Have you ever considered a centralized storage for logs, but time and resources are not on your side?
If you said yes, to any of the above questions, than this talk is for you. During the talk we’ll introduce you to the world of log centralization and analysis, both when it comes to open source, but also commercial tools. We will go from top to bottom and learn how to setup log centralization and analysis for servers, virtualized environments and containers. We will get from log shipping, through centralized buffering to storage and analysis to show you, that having a centralized log analysis tool is not a rocket science.
Finally, you will see how useful is to combine the logs from all your servers in a single place for blazingly fast correlation.
This Edureka Python tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) gives an introduction to Machine Learning and how to implement machine learning algorithms in Python. Below are the topics covered in this tutorial:
1. Why Machine Learning?
2. What is Machine Learning?
3. Types of Machine Learning
4. Supervised Learning
5. KNN algorithm
6. Unsupervised Learning
7. K-means Clustering Algorithm
Best Practices and ROI for Risk-based Vulnerability ManagementResolver Inc.
Risk Vision explores the best practices and ROI of the most successful business risk-centric vulnerability management programs. Watch the full webcast here: https://youtu.be/gW_ZAFpTK20
Big Data with KNIME is as easy as 1, 2, 3, ...4!KNIMESlides
This presentation shows how we have re-engineered an old legacy workflow to run partially on Hadoop from within the KNIME Analytics Platform, to speed up dramatically the execution time.
It also shows how easy it has been to move the ETL part of the workflow to Hadoop using the KNIME big data access nodes and in-database processing nodes.
KNIME big data nodes are Cloudera, Hortonworks, and MapR certified, as of today (October 21 2015)
This tutorial was presented at a Boston KNIME User Meetup in 2014 and offers a crash course in KNIME, text processing, text mining, and topic classification.
The presentation, provides a brief overview of the evaluation of Knime, a business intelligence tool. The evaluation was done as a part of my summer internship 2011 at Sanofi-Aventis. Kindly do not use the presentation for commercial purposes.
A tour of the evolution of data analytics use cases through time, from the early CRM data analysis to the more complex IoT sensor-based applications, of course using KNIME.
Knime customer intelligence on social media: Text Analytics vs. Network MiningKNIMESlides
These slides describe the steps in a project about social media analysis. The goal was to identify the positive and negative influencers inside a user forum. We combined sentiment analysis, to find positive and negative contributors, with network mining, to find the influencers.
This presentation is about Sentiment analysis Using Machine Learning which is a modern way to perform sentiment analysis operation. it has various techniques and algorithm described and compared for SA
Advanced analytics for the Internet of Things. Restocking Rental Bike StationsKNIMESlides
This presentation shows how to apply advanced analytics to forecast the need for timely restocking of bike stations in Washington DC, by taking into account past history, weather, calendar, traffic conditions, and additional external information. A model is created to predict the restocking need of each station by the hour and based on the minimum bare number of input parameters. The minimum bare number of parameters, necessary to guarantee an adequate prediction accuracy turned out to be: bike station, current bike ratio at the station (# bikes/# slots), time of the day, and weekend vs. business day.
SearchLove Boston 2016 | Paul Shapiro | How to Automate Your Keyword ResearchDistilled
Are you tapping into automation for keyword research? If not, why not? When it comes to SEO, automation is awesome. For starters, it can help free up a lot of time that is normally spent on menial tasks. What’s more, it can also aid deep analysis, and even facilitate innovation. If you are still doing keyword research manually, this is a must-attend session. Paul will show you how to get started with automated keyword research, using some easy-to-use tools. You’ll see first-hand how they can help you uncover valuable insights automatically. Overall, you will walk away with an immediately actionable plan to start automating your keyword research today.
The Actionable Guide to Doing Better Semantic Keyword Research #BrightonSEO (...Paul Shapiro
For a detailed recap: http://pshapi.ro/SemanticKWR
My BrightonSEO presentation...
1st Half: What is semantic search and why does it matter to SEOs.
2nd Half: Using KNIME to do semantic keyword research using SERP and Twitter data.
Comparing Machine Learning Algorithms in Text MiningAndrea Gigli
In this project I compare different Machine Learning Algorithm on different Text Mining Tasks.
ML algorithms: Naive Bayes, Support Vector Machine, Decision Trees, Random Forest, Ordinal Regression as ML task
Tasks considered: Classifying Positive and Negative Reviews, Predicting Review Stars, Quantifying Sentiment Over Time, Detecting Fake Reviews
Knime es una plataforma de minería de datos que permite el desarrollo de modelos en un entorno visual y amigable. Su desarrollo está bajo licencia GPL y está programado sobre la plataforma Eclipse y Java.En la charla se mostrará cómo realizar un proyecto de minería de datos y análisis con algoritmos conocidos para clasificación, asociación o predicción de datos empresariales.
Presentado por Diego García :
Ingeniero informático e investigador en la Universidad de Cantabria. Profesor de asignaturas del grado en informática de DataMining, Inteligencia Artificial y Algoritmia. En el campo de la investigación busca detectar patrones de comportamiento en plataformas E-learning para mejorar la docencia.
Data analytics is an indispensable part of modern businesses. It allows companies to make informed decisions and gain a competitive edge in their respective industries. With the proliferation of data, organizations need powerful tools to extract insights quickly and efficiently. This has led to the rise of several data analytics platforms, including Alteryx and Knime.
MANY CHANNELS.
ONE CONVERSATION.
The leading technology for engaging your audiences with personalized, relevant communications across Print and Digital media touchpoints.
XMPie Catalog - Personalized Cross Media MarketingBlakeBrown39
1 to 1 Personalized Cross-Media Marketing Platform, scalable for every size business and type. Why? To generate more revenue from highly effective personal marketing campaigns--that your marketing team can automate and analyze data from.
XMPie - Getting started with VDP, Personalized Web-to-Print and Cross-Media M...Fuji Xerox Singapore
Variable data printing (VDP), Web-to-print, integrated cross-media publishing, and other marketing-focused services that use relevant, personalized communications and responsebased campaign optimization are dominating the agendas of marketing professionals and their service providers.
Knowage official presentation 2018. Knowage is the only open source and full capabilities suite for any modern business analysis.
Feel free to use this presentation to present and promote Knowage open source suite!
Open text media management cloud edition enterprise digital asset management ...Jasmine C.
In a Digital First world, DAM is integral for enabling
rich experiences, better engagement and higher
productivity. OpenText Media Management is the
underlying engine to media enable your organization
– in the cloud or on premise
We are a IT consulting company providing services to clients across geographies in Data Engineering, AI/ML, Cloud & DevOps, Platform Engineering, and Process Hyper automation.
Here is a case study that I developed to explain the different sets of functionality with the Pentaho Suite. I focused on the functionality, features, illustrative tools and key strengths. I've provided an understanding toward evaluating BI tools when selecting vendors. Enjoy!
ChannelCandy is a custom branded mobile app designed for Vendor, Distributor and Associations to deliver Channel highlights, company news and sales tools into the hands of Channel Partners. Developed by the team at ChannelEyes, it is reinventing Channel communication for leading firms in our industry.
ChannelCandy runs on iPhone, iPad, Android as well as all mobile web enabled platforms such as BlackBerry, Windows and the PC Web Browser.
The mobile app delivers an innovative way to drive:
Channel Sales Enablement – Deploying the tools and resources necessary to make your Channel act as an extension of your own sales team. Vendors can even send motivational messages to drive the sales cycle forward!
Channel Education – Publishing rich media such as videos, whitepapers, case studies and certification courses, Vendors can raise the level of knowledge and capabilities of their Partners.
Channel Incentives – Partners admit that they leave money on the table because they don’t regularly stay up to date. With a mobile app, they can set alerts and be notified each time a new incentive is launched.
Channel Tools – by customizing the app with external tools such as configurators, calculators, quoting tools, deal registration and product information, Vendors will drive better sell-thru, stronger options and accessories attach, as well as more robust program participation.
Technical Updates – keep your Channel Partners up to date with tech bulletins, service fixes and other critical updates in real-time. Technicians can customize the app to receive push notifications and collaborate with other members of the community, leveraging the wisdom of the crowd.
DevOps is more than an automated software development approach and a collaborative culture nowadays. Cloud computing, the internet of things, artificial intelligence, and machine learning are among the cutting-edge technologies used.
Businesses are constantly modernising their operations to increase efficiency and deliver unique client experiences. The digital transformation has accelerated the timeframes for interactions, transactions, and choices.
Companies can benefit from this data by utilising machine learning. Similarly, Machine learning (ML) models can detect patterns in massive volumes of data, allowing them to make choices faster and more correctly than people.
In this week’s Tech Tuesday, we present our pick of DevOps and Machine learning tools to pick for your business.
Building a guided analytics forecasting platform with KnimeKnoldus Inc.
Maintaining inventory and ensuring that stock is consumed efficiently is a key decision that many companies - particularly those in retail - have to make. Explore how you can do it easily with KNIME Platform.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
1. Présenté par : - TARGHI Amal
- SAHMOUDI Yahya
Encadrée par : Mme. FRIKH
2. KNIME Analytics Platform
KNIME Analytics Platform is the leading
open solution for data-driven innovation,
helping you discover the potential hidden in
your data, mine for fresh insights, or
predict new futures.
3. KNIME Analytics Platform
Your Open Platform for Data Innovation
- It’s yours: Get the most out of our powerful analytics
platform by customizing your tool kit with a broad range of
free or commercial applications. Or create and share your
own. It’s easy!
- Accelerate your learning curve: KNIME helps build on
what you’ve already developed and learned, without
having to start from scratch.
- More thinking, less tinkering: Its code-free setup and
intuitive interface mean you get to spend more quality time
with your data.
4. KNIME Analytics Platform
- Low cost, zero risk: Set up a collaborative platform for
joint development, analytics, and effortless sharing of
knowledge, tools and insights – with minimal investment.
- Deploy & enjoy: Save time and money by deploying and
scaling KNIME’s capabilities on top of, alongside, or
within your existing systems.
6. KNIME Analytics Platform
Powerful Data Analytics.
KNIME Analytics Platform provides over
1000 data analytic routines, either
natively or through R and Weka, for
such topics as:
Univariate and Multivariate Statistics,
DataMining, Time Series, Image
Processing, Web Analytics, Text
Mining, Network Analysis, Social Media
Analysis.
KNIME analytic workflows can be run
not only through the interactive user
interface but also in a batch execution
mode, enabling the data analysis
process to be easily integrated into your
local job management and executed
on a periodic basis.
Open Source & Free.
KNIME Analytics Platform is released
under an open source license (GPL).
KNIME.com AG, the company behind
KNIME, firmly believes that opening
up previously closed or exclusive
platforms, processes, tools,
organizational boundaries, idea
sourcing or funding can speed up
innovation. At KNIME, we believe
that open platforms accelerate data-
driven innovation. We stand firmly
behind the open platform model and
offer numerous KNIME Open Source
Extensions to the KNIME Analytics
Platform.
7. KNIME Analytics Platform
Open Integration Platform
Data integration from various sources
including text files, databases, and web
services is a basic feature of KNIME.
Data transformation and new data
creation are all important components of
KNIME.
KNIME Analytics Platform provides a wide
variety of techniques for using the new
data, ranging from applying models, to
scoring within KNIME or in other
databases, and full support of PMML.
Through its open API, KNIME is also a
powerful tools integration platform. KNIME
can be extended not only by other open
source projects like Weka or R, but also
by legacy tools. The active KNIME
community is constantly extending KNIME
to include capabilities such as text
processing, image analysis functionality,
and many others. For even tighter
integration, the free SDK allows
you to build custom nodes.
8. KNIME Analytics Platform
Integrated Reporting
KNIME provides a wide choice of tools
for reporting. The Business Intelligence
and Reporting Tool (BIRT) can be
automatically installed within your
KNIME workbench. It enables you to use
data and results as input for
comprehensive reports, which you can store
as templates. In addition, KNIME can
create output in a large variety of industry
standard formats including SVG, MS
Office, PDF, etc.
You can easily integrate KNIME into your
existing reporting environment in a way
that best suits you – either with KNIME as
your source, or KNIME as your integrator.
Commercial Extensions
KNIME provides commercial extensions
to increase your productivity and allow
you and your teams to collaborate. All
KNIME commercial products are
extensions to the open source KNIME
Analytics Platform.
KNIME Productivity Extensions are
designed to help you be even more
productive with KNIME Analytic
Platform.
KNIME Collaborative Extensions are
designed to extend individual usage of
KNIME Analytics Platform to a team or
into an organization.
Knme Analytics Platform est la première solution open source pour data-driven innovation ; qui nous aide à decouvrir le potenciel caché dans nos explorer de nouvelle perspective ansi que predire l avenir.
Vous pouvez avoir le maximum de notre puissante plate-forme d'analyse en personnalisant votre tool kit avec un large éventail d'applications gratuites ou commerciales. Ou créer et partager votre propre. C'est facile!...
Ameloirer vote courbe d appretissatge : knime vous aide a construit et appris et developpé dans avoir besoin de commencer a zero
Sa configuration sans code et l'interface intuitive
Moint couteux , 0 risque : installer une plateforme collaborative pour joindre le developpement l analyse et le partage sans effort des connaissances, des outils et des idées - avec un investissement minimal.
Gagnez du temps et de l'argent par le déploiement et scaling les capacité de .
Knime workflow combine les different data source et utilise les mining techinique pour analyser et explorer tes données
