Research any topic before you write.
Find related topics. | Discover entities. | See connections. | Build a topical map.
Apache SystemDS (Previously, Apache SystemML) is an open source ML system for the end-to-end data science lifecycle.
The analysis highlights History, Science and Products as prominent areas in the source structure around Apache SystemDS.
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
The extracted context around Apache SystemDS shows recurring relationship patterns in the source. For example, Apache SystemDS → CONTRIBUTING, The Another extracted example is Apache SystemDS → Apache Software Foundation, IBM. Use these groups to spot repeated connection types before inspecting the individual relationships.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
data systemml new apache systemds including support improvements release spark algorithm ibm machine learning system science python algorithms scale big
TTTA extracted 14 structured relationships around Apache SystemDS. Examples in this analysis include Apache SystemDS → Developers → Apache Software Foundation, IBM and Apache SystemDS → License → Apache License 2.0. The table shows each extracted connection, where it came from and its confidence.
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Apache SystemDS | Developers | Apache Software Foundation, IBM | 1.00 | infobox |
| Apache SystemDS | License | Apache License 2.0 | 1.00 | infobox |
| Apache SystemDS | Operating system | Linux, macOS, Windows | 1.00 | infobox |
| Apache SystemDS | Release | November 2, 2015; 10 years ago (2015-11-02) | 1.00 | infobox |
| Apache SystemDS | Repository | SystemDS Repository | 1.00 | infobox |
| Apache SystemDS | Stable release | 3.0.0 / July 5, 2022; 4 years ago (2022-07-05) | 1.00 | infobox |
| Apache SystemDS | Type | Machine Learning, Deep Learning, Data Science | 1.00 | infobox |
| Apache SystemDS | Website | systemds.apache.org | 1.00 | infobox |
| Apache SystemDS | Written in | Java, Python, DML, C | 1.00 | infobox |
| R | instance of | It was observed that data scientists would write machine learning algorithms in languages | 0.80 | text |
| Python for small data | instance of | It was observed that data scientists would write machine learning algorithms in languages | 0.80 | text |
| Scala | instance of | a systems programmer would be needed to scale the algorithm in a language | 0.80 | text |
The concept neighborhoods around Apache SystemDS bring nearby vocabulary together. In this analysis, examples include Systemml, Ibm and Project. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
For Apache SystemDS, one of the stronger structural bridges in this analysis connects Apache SystemDS with History. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
TTTA analyzes the structure around Apache SystemDS to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History, Science & Products, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
Source: Wikipedia — Apache SystemDS · EN edition · Analysis: TopicsToTalkAbout