Open data - Download the Knowledge base
You are free to download the data of this Knowledge base.
To do this you must be an authenticated user: log in or sign in now.
All the data are licensed as Creative Common CC-BY 4.0.
KNIME Analytics Platform
KNIME Analytics Platform is the open source software for creating data science applications and services. Intuitive, open, and continuously integrating new developments, KNIME makes understanding data and designing data science workflows and reusable components accessible to everyone.
Type of content:
Assets
Type of asset:
Platform
Big data potential
Yes
Policy domains:
Urban Planning & Transport
Phase in the policy cycle:
Policy Design and Analysis
TRL
7
Implementation/customisation cost
Low
Open license availability
Yes
Ease of use
High
Tags:
BI
Data analytics
IT
IT potential
IT processes
| SWOT Analysis for KNIME Analytics Platform |
||
|---|---|---|
| Helpful | Harmful | |
Internal |
Strengths• KNIME Analytics Platform is not a cut-down version and there are no artificial limitations, such as machine processing size or numbers of data rows: If you have enough hard disk and memory, you can run projects with hundreds of millions of rows, as many KNIME users currently do. • Build end to end data science workflows: o Create visual workflows o Blend tools from different domains. o Choose from over 2000 modules ("nodes") to build your workflow. o Get up and running quickly. • Blend data from any source o Open and combine simple text formats, unstructured data types, or time series data. o Connect to a host of databases and data warehouses to integrate data o Access and retrieve data from sources such as Twitter, AWS S3, Google Sheets, and Azure. • Shape your data o Derive statistics, or apply statistical tests to validate a hypothesis. o Aggregate, sort, filter, and join data o Clean data through normalisation o Extract and select features. • Leverage Machine Learning and AI o Build machine learning models for classification, regression, dimension reduction, or clustering. o Optimise model performance o Validate models o Make predictions • Discover and share insights o Visualise data o Display summary o Export reports for presenting results to stakeholders. o Store processed data or analytics results in many common file formats or databases. • Scale execution with demands o Build workflow prototypes o Scale workflow performance. o Exercise the power of in-database processing or distributed computing on Apache Spark to further increase computation performance. |
Weaknesses• Intellectual property and patents issues are complicated and there is a risk that code is illegally used and propagated Also licenses are complex – there is over 60 different licenses that comply with the open source definition • Resources are required for switching to an open source from a proprietary system. This results in further expenses in the form of switching costs. • Migration of data • Retraining personnel • Less suitable option for large complex workflows. • Partitioning ability is limited for dataset. |
External |
Opportunities• Maintaining an open source platform containing all functionality that any individual might require and continue delivering extended functionality • Innovation: Opening previously closed or exclusive platforms, processes, tools, organisational boundaries, idea sourcing or funding can speed up innovation. • Open platforms, their very committed users and their advanced ecosystems will bring about the most interesting breakthroughs in data-driven innovation. • Increase of the number of large global organisations and institutions that actively consider and adopt open platforms for their data science teams. |
Threats• Intellectual property and patents issues are complicated and there is a risk that code is illegally used and propagated Also licenses are complex – there is over 60 different licenses that comply with the open source definition • Resources are required for switching to an open source from a proprietary system. This results in further expenses in the form of switching costs. • Migration of data • Retraining personnel |
