![WHAT IS MACHINE LEARNING?
GraphAware®
[Machine Learning is the] field of study that gives
computers the ability to learn without being explicitly
programmed.
— Arthur Samuel, 1959](https://image.slidesharecdn.com/graphpoweredmachinelearning-paris-180308153046/75/Graph-Powered-Machine-Learning-2-2048.jpg)
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Graph-Powered Machine Learning
The document discusses machine learning and its integration with graph databases, particularly Neo4j, highlighting its importance in providing the source of truth for accurate predictions. It explores various machine learning algorithms that can be implemented using graph structures, emphasizing the benefits of unsupervised methods and graph-native algorithms. Additionally, it invites engagement with Graphaware through meet-ups and online content for further learning.
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- 1. GraphAware® GRAPH POWERED MACHINE LEARNING VlastaKůs, Data Scientist @ GraphAware graphaware.com @graph_aware, @VlastaKus
- 2. WHAT IS MACHINELEARNING? GraphAware® [Machine Learning is the] field of study that gives computers the ability to learn without being explicitly programmed. — Arthur Samuel, 1959
- 3. WHAT IS AGRAPH? GraphAware® G = (V, E)
- 4. WHY NEO4J? GraphAware® It isa proper graph database It is a proper database
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- 6. MACHINE LEARNING CHALLENGES TheSource of Truth Performance Model Storing Real Time
- 7. THE SOURCE OFTRUTH GraphAware® “Even the best learning algorithm on wrong data produces wrong results.” — Alessandro Negro, 2015
- 8. THE SOURCE OFTRUTH GraphAware® Source: Michele Banko and Eric Brill Scaling to Very Very Large Corpora for Natural Language Disambiguation - 2001
- 9. THE SOURCE OFTRUTH GraphAware®Source: https://www.domo.com/learn/data-never-sleeps-5
- 10. Predictive accuracy Training Performance PredictionPerformance Ability to scale PERFOMANCE GraphAware®
- 11. NEO4J CAUSAL CLUSTER GraphAware®Source:https://neo4j.com/docs/operations-manual/
- 12. Store the resultsof the training phase Provide multiple access patterns Mix models The size depends on the algorithm STORING THE RESULTS & MODEL GraphAware®
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- 15. STORING DATA SOURCES:TENSOR GraphAware® Simple Recommendation f: User x Item -> Relevance Score Context Aware Recommendation f: User x Item x Context1 x Context2 x Context3 -> Relevance Score
- 16. The results ofmachine learning process can be stored in a graph as well. Some examples are: ‣ Similarity (k-Nearest Neighbours) ‣ Cluster ‣ Spanning Tree ‣ Decision Tree ‣ Random forest ‣ Markov Chain STORING RESULTS & MODELS GraphAware®
- 17. STORING RESULTS &MODELS GraphAware® K-Nearest Neighbours Markov Chain Decision Tree
- 18. STORING DATA SOURCES:KNOWLEDGE GRAPH GraphAware®
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- 20. ‣ NLP andgraphs: natural fit GRAPH-BASED NATURAL LANGUAGE PROCESSING GraphAware® ‣ Knowledge enrichment Source: http://nlp.stanford.edu:8080/corenlp/process
- 21. Unsupervised techniques tendto be underestimated … ‣ No need for time & money to get massive labeled training datasets ‣ Often faster to train & faster to predict ‣ Unsupervised deep learning UNSUPERVISED ML ALGORITHMS GraphAware®
- 22. Some graph-native algorithmsthat are relevant to machine learning processes: ‣ Random Walk ‣ Page Rank ‣ Graph Matching ‣ Shortest Path ‣ Depth-First Graph Traversal ‣ Breadth-First Graph Traversal ‣ Minimum Spanning Tree ‣ Graph Clustering ‣ Node2vec GRAPH-BASED ML ALGORITHMS GraphAware®
- 23. pressure information system telemetry spacecraft power critical ground command performance component drive connector standard software operation testing environmental flight damage spare subsystem orbiter posit flight software control instrument propellant flight project program resource equipment fluid re condition analysis servicemaintenance device electrical circuit area accident number management risk implem significant personnel exploration rover facility human effect event life lack loss potential review ground system engineering different configuration control configuration logistics datum NASA ground processing processing safety verification risk management part t launch pipe shuttle leak lesson load environment space check line source training factor material due practice capability battery flight hardware government assembly figure evm space shuttle surface operational thermal installation many set one inspection propulsion system actuator impact flight system launch vehicle flow current leakage shuttle program digital mishap propulsion engine complete NASA program fault attitude science flight operation mission operation independent investigation JPL fire procedure torque incident mars space system general critical hardware fault protection mer user system engineering expertisespace flight manager project manager mitigation Pegasus actuator system amp capability legacy actuator system output control system different manager many review purpose appropriate f integration important ground testing computer flight equipment independent review space hardware software development ssme technical content value management aerodynamic load aerodynamic ppe deceased successful implementation formal mishap investigation lax GRAPH-BASEDML ALGORITHMS: PAGE RANK GraphAware® Keywords Extraction Rada Mihalcea, Paul Tarau. 2004. TextRank: Bringing Order into Texts. Proceedings of EMNLP 2004, pages 404–411, Barcelona, Spain. Association for Computational Linguistics. http://www.aclweb.org/anthology/W04-3252.
- 24. GRAPH-BASED ML ALGORITHMS: GRAPHCLUSTERING GraphAware® Continuous Cellular Tower Data Analysis Eagle N., Quinn J.A., Clauset A. (2009) Methodologies for Continuous Cellular Tower Data Analysis. In: Tokuda H., Beigl M., Friday A., Brush A.J.B., Tobe Y. (eds) Pervasive Computing. Pervasive 2009. Lecture Notes in Computer Science, vol 5538. Springer, Berlin, Heidelberg
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- 27. ‣ Meet ustomorrow at Neo4j GraphTour ‣ Come to our meet-ups graphaware.com/events ‣ Visit our blog graphaware.com/blog ‣ Watch us youtube.com -> GraphAware channel ‣ And most importantly … Get in touch! INTERESTED IN MORE? GraphAware®
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