Observability - Wikipedia Observability is a measure of how well internal states of a system can be inferred from knowledge of its external outputs. In control theory, the ... https://en.wikipedia.org/wiki/Observability
16319017820000000
× ⁝⁝
Graph Language Processing Settings:
Specify the settings for your text-to-network conversion algorithm for this graph.
× ⁝⁝
one statement at a time
Dynamic Graph Settings
See the dynamic evolution of this graph: scroll or "play" the text entries to see how the text propagated through the network graph over time.
Play the Graph
current speed of the player:
0 2000
one statement at a time
× ⁝⁝
Export the Data
Network Graph Images:
The graph images for publishing on the web or in a journal. For embeds and URLs use the share menu.
PNG (Image) SVG (Hi-Res)
Visible Statements (Tagged):
Export the currently filtered (visible) statements with all the meta-data tags (topics, sentiment).
CSV (Spreadsheet) MD (e.g.Obsidian)
Text Mining Analytics:
Summary of insights from the analytics panel. For specific in-depth exports, see the download button in the Analytics panel.
TXT Analytics Report CSV Report N-Grams CSV
Network Graph Data:
The raw data with all the statistics for further analysis in another software.
JSON CSV Gexf (Gephi)
All the Text (for backup and duplicating):
Plain text used to create this graph without any meta-data
Download Plain Text (All Statements)
× ⁝⁝
Save This Graph View:
× ⁝⁝
Delete This Graph:
× ⁝⁝
Your Project Notes
Interpret graph data, save ideas, AI content, and analytics reports.
Auto-Generate from Analytics
Top keywords (global influence):
Top topics (local contexts):
Explore the main topics and terms outlined above or see them in the excerpts from this text below.
See the relevant data in context: click here to show the excerpts from this text that contain these topics below.
Tip: use the form below to save the most relevant keywords for this search query. Or start writing your content and see how it relates to the existing search queries and results.
Tip: here are the keyword queries that people search for but don't actually find in the search results.
What is Observability? | Splunk Observability is the ability to measure the internal states of a system by examining its outputs. A system is considered "observable” if the current state ... https://www.splunk.com/en_us/data-insider/what-is-observability.html
16319017820000000
What Full-Stack Observability Requires Today Observability is a lot more than a synonym for monitoring. Monitoring gives software teams instrumentation, collecting data about their systems and allowing ... https://newrelic.com/resources/ebooks/what-is-observability
16319017820000000
Observability: It's Not What You Think | Splunk May 4, 2021 ... What is Observability? ... Observability is a mindset that enables you to answer any question about your entire business through collection and ... https://www.splunk.com/en_us/blog/devops/observability-it-s-not-what-you-think.html
16319017820000000
Monitoring and Observability — What's the Difference and Why ... Apr 16, 2018 ... Basically, and as the definition states, it's a measure of how well internal states of a system can be inferred from knowledge of its external ... https://thenewstack.io/monitoring-and-observability-whats-the-difference-and-why-does-it-matter/
16319017820000000
Elastic Observability | Elastic Elastic Observability. Unified visibility across your entire ecosystem. Bring your logs, metrics, and APM traces together at scale in a single ... https://www.elastic.co/observability
16319017820000000
DevOps measurement: Monitoring and observability | Google Cloud Observability is tooling or a technical solution that allows teams to actively debug their system. Observability is based on exploring properties and patterns ... https://cloud.google.com/architecture/devops/devops-measurement-monitoring-and-observability
16319017820000000
Learn about observability | Honeycomb Observability is about being able to ask arbitrary questions about your environment without having to know ahead of time what you wanted to ask. Observability ... https://docs.honeycomb.io/getting-started/learning-about-observability/
16319017820000000
What is observability? | IBM Jan 12, 2021 ... In general, observability is the extent to which you can understand the internal state or condition of a complex system based only on knowledge ... https://www.ibm.com/cloud/learn/observability
16319017820000000
Intelligent observability | Dynatrace Turn data into answers, through intelligent observability. Remove blind spots, resolve problems rapidly and deliver superior customer experience. Start… https://www.dynatrace.com/platform/observability/
16319017820000000
Intelligent observability | Dynatrace Turn data into answers, through intelligent observability. Remove blind spots, resolve problems rapidly and deliver superior customer experience. Start… https://www.dynatrace.com/platform/observability/
16319017820000000
Istio / Observability Observability · Metrics. Istio generates a set of service metrics based on the four "golden signals” of monitoring (latency, traffic, errors, and saturation). https://istio.io/latest/docs/concepts/observability/
16319017820000000
Welcome! — Splunk Observability Cloud documentation Observability Cloud provides a unified experience for collecting and monitoring metrics, logs, and traces from common data sources. https://docs.splunk.com/Observability/
16319017820000000
Application Monitoring and Observability | Sumo Logic Reduce downtime and solve customer-impacting issues faster with an integrated observability platform for all of your application data including logs, ... https://www.sumologic.com/solutions/application-monitoring/
16319017820000000
Monitoring and Observability. During lunch with a few friends in late ... Sep 4, 2017 ... That's not sexy enough anymore. — Observability, because rebranding Ops as DevOps wasn't bad enough, now they're devopsifying monitoring too. — ... https://copyconstruct.medium.com/monitoring-and-observability-8417d1952e1c
16319017820000000
Data Observability: The Next Frontier Of Data Engineering - Monte ... Aug 31, 2021 ... To keep pace with data's clock speed of innovation, data engineers need to invest in data observability, the next frontier of data ... https://www.montecarlodata.com/data-observability-the-next-frontier-of-data-engineering/
16319017820000000
Observability: A Complete Overview for 2021 - Lightstep There are three primary types of telemetry data through which systems are made observable. Logs – Structured or unstructured lines of text that are emitted by ... https://lightstep.com/observability/
16319017820000000
What is Enterprise Observability? - Instana May 17, 2021 ... The Muddy Messaging of Observability and Application Performance Management. Here's a question I get asked quite a bit: "How is Enterprise ... https://www.instana.com/blog/what-is-enterprise-observability/
16319017820000000
Observability is key to the future of software (and your DevOps ... Sep 8, 2021 ... Observability platforms enable you to easily figure out what's happening with every request and to identify the cause of issues fast. https://stackoverflow.blog/2021/09/08/observability-is-key-to-the-future-of-software-and-your-devops-career/
16319017820000000
What Is Observability in Software? Learn More From Honeycomb Observability in software is the ability to understand and explain any state a system can get into without deploying new code. https://www.honeycomb.io/what-is-observability/
16319017820000000
Observability: What IT should know as the technology heats up ... Feb 18, 2021 ... As modern software systems become more and more complex, gaining observability into how applications perform and what makes them fail is ... https://www.infoworld.com/article/3608249/observability-what-it-should-know-as-the-technology-heats-up.html
16319017820000000
Observability | Dapr Docs Observability. Monitor applications through tracing, metrics, logs and health. When building an application, understanding how the system is behaving is an ... https://docs.dapr.io/concepts/observability-concept/
16319017820000000
Towards platform observability | Internet Policy Review Dec 18, 2020 ... This article proposes the concept of platform observability to help systematically study complex algorithmic systems. https://policyreview.info/articles/analysis/towards-platform-observability
16319017820000000
Rise of Full-Stack Observability | AppDynamics Read our Agents of Transformation 2021 report on the rise of full-stack observability and learn how technologists can manage soaring IT complexity by ... https://www.appdynamics.com/resources/reports/rise-of-full-stack-observability
16319017820000000
What is Observability? - Cribl How do you determine your needs with Observability? Understanding the Environment You're In; Asking Questions About Your Data; Structuring Your Data; Finding ... https://cribl.io/observability/
16319017820000000
Observability of complex systems | PNAS Feb 12, 2013 ... The observability criteria can be formulated algebraically for dynamical systems consisting of polynomial or rational expressions (1, ... https://www.pnas.org/content/110/7/2460
16319017820000000
VMware Tanzu Observability by Wavefront | VMware Tanzu From smarter, proactive alerting to instant custom metrics, VMware Tanzu Observability by Wavefront delivers enterprise-grade cloud monitoring & analytics. https://tanzu.vmware.com/observability
16319017820000000
Free eBook: Observability with AIOps For Dummies | Download Now Combine AIOps algorithmic analysis and automation with observability's detailed operational data. Written clearly and with a sense of humor by AIOps aficionado ... https://info.moogsoft.com/ebook-observability-aiops-for-dummies.html
16319017820000000
Observability Synonyms, Observability Antonyms | Merriam-Webster ... 18 synonyms and near synonyms of observability from the Merriam-Webster Thesaurus, plus 10 antonyms and near antonyms. Find another word for observability. https://www.merriam-webster.com/thesaurus/observability
16319017820000000
Domain-Oriented Observability Apr 9, 2019 ... Observability in our software systems has always been valuable and has become even more so in this era of cloud and microservices. https://martinfowler.com/articles/domain-oriented-observability.html
16319017820000000
Full Stack Observability Exam Do you know Observability on the New Relic platform? Put your knowledge to the test with our Full-Stack Observability Exam. https://learn.newrelic.com/full-stack-observability-exam
16319017820000000
Observability at Twitter Sep 9, 2013 ... Twitter's Observability stack is a distributed system just like the systems it provides visibility into. About 1.5% of the machines in a data ... https://blog.twitter.com/engineering/en_us/a/2013/observability-at-twitter
16319017820000000
Nonlinear controllability and observability | IEEE Journals & Magazine The properties of controllability, observability, and the theory of minimal realization for linear systems are well-understood and have been very useful in ... https://ieeexplore.ieee.org/document/1101601
16319017820000000
cncf/tag-observability: Technical Advisory Group for ... - GitHub Mission statement. The TAG Observability focuses on topics pertaining to the observation of cloud native workloads. Additionally, it produces supporting ... https://github.com/cncf/tag-observability
16319017820000000
Moral Hazard and Observability Moral hazard and observability. Bengt Holmstrom. Swedish School of Economics and Business Administration. The role of imperfect information in a ... https://www.jstor.org/stable/3003320
16319017820000000
Observability vs. Monitoring - A Guide for IT Operations | Devo.com Observability is the practice of achieving actionable insights from data that is generated by instrumented IT and software systems. https://www.devo.com/observability-vs-monitoring-guide/
16319017820000000
IT Observability Research Report | LogicMonitor Achieving unified observability in the age of digital transformation and technological convergence. https://www.logicmonitor.com/resource/it-observability-research-report
16319017820000000
Observability and Management for Cloud and IT environments | Oracle Oracle Cloud Observability and Management Platform. Oracle provides visibility and machine learning-driven actionable insights to ease management across all ... https://www.oracle.com/manageability/
16319017820000000
Observability | Grafana Labs Observing Loki Both Loki and Promtail expose a /metrics endpoint that expose Prometheus metrics. You will need a local Prometheus and add Loki and Promtail ... https://grafana.com/docs/loki/latest/operations/observability/
16319017820000000
Observability Report Page Jun 17, 2020 ... Contrast's 2020 Application Security Observability Report. Discover what are the top vulnerabilities and attacks over the past 12 months. https://www.contrastsecurity.com/appsec-observability-report-promo
16319017820000000
× ⁝⁝
Total Nodes Shown:
extend
to:
Recalculate Metrics Reset Filters
Reset to Default
Show Nodes with Degree > 0:
0 0
Total Nodes Shown:
extend
Filter Graphs:
Filter Time Range
from: to:
Recalculate Metrics Reset Filters
Show Labels for Nodes > 0 size:
0 0
Default Label Size: 0
0 20
Edges Type:
Layout Type:
Reset to Default
× ⁝⁝
× ⁝⁝
Semantic Variability Score
— modulates diversity of the discourse network how it works? The score is calculated based on how modular the structure of the graph is (> 0.4 means the clusters are distinct and separate from one another = multiple perspectives). It also takes into account how the most influential nodes are dispersed among those clusters (higher % = lower concentration of power in a particular cluster).
Actionable Insight:
N/A
We distinguish 4 states of variability in your discourse. We recommend that a well-formed discourse should go through every stage during its evolution (in several iterations).
1 - (bottom left quadrant) — biased — low variability, low diversity, one central idea (genesis and introduction stage).
2 - (top right) - focused - medium variability and diversity, several concepts form a cluster (coherent communication stage).
3 - (bottom right) - diversified — there are several distinct clusters of main ideas present in text, which interact on the global level but maintain specificity (optimization and reflection stage).
4 - (left top) — dispersed — very high variability — there are disjointed bits and pieces of unrelated ideas, which can be used to construct new ideas (creative reformulation stage).
Read more in the cognitive variability help article.
1 - (bottom left quadrant) — biased — low variability, low diversity, one central idea (genesis and introduction stage).
2 - (top right) - focused - medium variability and diversity, several concepts form a cluster (coherent communication stage).
3 - (bottom right) - diversified — there are several distinct clusters of main ideas present in text, which interact on the global level but maintain specificity (optimization and reflection stage).
4 - (left top) — dispersed — very high variability — there are disjointed bits and pieces of unrelated ideas, which can be used to construct new ideas (creative reformulation stage).
Read more in the cognitive variability help article.
Shows to what extent you explored all the different states of the graph, from uniform and regular to fractal and complex. Read more in the cognitive variability help article.
You can increase the score by adding content into the graph (your own and AI-generated), as well as removing the nodes from the graph to reveal latent topics and hidden patterns.
You can increase the score by adding content into the graph (your own and AI-generated), as well as removing the nodes from the graph to reveal latent topics and hidden patterns.
× ⁝⁝
Download: TXT Report CSV Report More Options
We provide LDA stats for comparison purposes only. It works with English-language texts at the moment. More languages are coming soon, subscribe @noduslabs to be informed.
positive: | negative: | neutral:
⤓ ? ⤓
Use the Bert AI model for English, Dutch, German, French, Spanish and Italian to get more precise results (slower). Standard model is faster, works for English only, is less precise, and is based on a fixed AFINN dictionary.
?
Main Topical Clusters & High-Level Ideas
? The topical clusters are comprised of the nodes (words) that tend to co-occur together in the same context (next to each other).
We use a combination of clustering and graph community detection algorithm (Blondel et al based on Louvain) to identify the groups of nodes are more densely connected together than with the rest of the network. They are aligned closer to each other on the graph using the Force Atlas algorithm (Jacomy et al) and are given a distinct color.
We use a combination of clustering and graph community detection algorithm (Blondel et al based on Louvain) to identify the groups of nodes are more densely connected together than with the rest of the network. They are aligned closer to each other on the graph using the Force Atlas algorithm (Jacomy et al) and are given a distinct color.
please, add your data to display the stats...
+ ⤓ ⤓ Most Influential Keywords & Concepts
?
The most influential nodes are either the ones with the highest betweenness centrality — appearing most often on the shortest path between any two randomly chosen nodes (i.e. linking the different distinct communities) — or the ones with the highest degree.
We use the Jenks elbow cutoff algorithm to select the top prominent nodes that have significantly higher influence than the rest.
Click the Reveal Underlying Ideas button to remove the most influential words (or the ones you select) from the graph, to see what terms are hiding behind them.
We use the Jenks elbow cutoff algorithm to select the top prominent nodes that have significantly higher influence than the rest.
Click the Reveal Underlying Ideas button to remove the most influential words (or the ones you select) from the graph, to see what terms are hiding behind them.
please, add your data to display the stats...
+ ⤓
Network Structure:
N/A
?The network structure indicates the level of its diversity. It is based on the modularity measure (>0.4 for medium, >0.65 for high modularity, measured with Louvain (Blondel et al 2008) community detection algorithm) in combination with the measure of influence distribution (the entropy of the top nodes' distribution among the top clusters), as well as the the percentage of nodes in the top community.
Download: TXT Report CSV Report More Options
Structural Gap Insight
(topics to connect) ?A structural gap shows the two distinct communities (clusters of words) in this graph that are important, but not yet connected. That's where the new potential and innovative ideas may reside.
This measure is based on a combination of the graph's connectivity and community structure, selecting the groups of nodes that would either make the graph more connected if it's too dispersed or that would help maintain diversity if it's too connected.
This measure is based on a combination of the graph's connectivity and community structure, selecting the groups of nodes that would either make the graph more connected if it's too dispersed or that would help maintain diversity if it's too connected.
N/A
Discourse Entrance Points
(concepts with the highest influence / frequency ratio) ?
These nodes have unusually high rate of influence (betweenness centrality) to their frequency — meaning they may appear not as often as the most influential nodes but they are important narrative shifting points.
These are usually effective entrance points into the discourse, as they link different topics together and have high inlfuence, but not too many connections, which makes them more accessible.
These are usually effective entrance points into the discourse, as they link different topics together and have high inlfuence, but not too many connections, which makes them more accessible.
N/A
Emerging Keywords
N/A
Evolution of Topics
(number of occurrences per text segment) ?
↻
The chart shows how the main topics and the most influential keywords evolved over time. X-axis: time period (split into 10% blocks). Y-axis: cumulative number of occurrences.
Drag the slider to see how the narrative evolved over time. Select the checkbox to recalculate the metrics at every step (slower, but more precise).
Drag the slider to see how the narrative evolved over time. Select the checkbox to recalculate the metrics at every step (slower, but more precise).
Main Topics
(according to Latent Dirichlet Allocation):loading...
⤓ ? ⤓LDA stands for Latent Dirichlet Allocation — it is a topic modelling algorithm based on calculating the maximum probability of the terms' co-occurrence in a particular text or a corpus.
We provide this data for you to be able to estimate the precision of the default InfraNodus topic modeling method based on text network analysis.
We provide this data for you to be able to estimate the precision of the default InfraNodus topic modeling method based on text network analysis.
Most Influential Words
(main topics and words according to LDA):loading...
We provide LDA stats for comparison purposes only. It works with English-language texts at the moment. More languages are coming soon, subscribe @noduslabs to be informed.
Sentiment Analysis
positive: | negative: | neutral:
⤓ ? ⤓
We analyze the sentiment of each statement to see whether it's positive, negative, or neutral. You can filter the statements by sentiment (clicking above) and see what kind of topics correlate with every mood.
The approach is based on AFINN and Emoji Sentiment Ranking
The approach is based on AFINN and Emoji Sentiment Ranking
Use the Bert AI model for English, Dutch, German, French, Spanish and Italian to get more precise results (slower). Standard model is faster, works for English only, is less precise, and is based on a fixed AFINN dictionary.
Concept Relation Analysis:
please, select the node(s) on the graph or in the table below to see their connections...
+ ?
Top Relations in 4-grams
(bidirectional, for directional bigrams see the CSV table below):
The most prominent relations between the nodes that exist in this graph are shown above. We treat the graph as undirected by default. Occurrences shows the number of the times a relationship appears in a 4-gram window. Weight shows the weight of that relation.
As an option, you can also downloaded directed bigrams above, in case the direction of the relations is important (for any application other than language).
As an option, you can also downloaded directed bigrams above, in case the direction of the relations is important (for any application other than language).
Text Statistics:
| Word Count | Unique Lemmas | Characters | Lemmas Density |
0 |
0 |
0 |
0 |
Text Network Statistics:
The higher is the network's structure diversity and the higher is the alpha in the influence propagation score, the higher is its mind-viral immunity — that is, such network will be more resilient and adaptive than a less diverse one.
In case of a discourse network, high mind-viral immunity means that the text proposes multiple points of view and propagates its influence using both highly influential concepts and smaller, secondary topics.
We recommend to try to increase mind-viral immunity for texts that have a low score and to decrease it for texts that have a high score. This ensures that your discourse will be open, but not dispersed.
In case of a discourse network, high mind-viral immunity means that the text proposes multiple points of view and propagates its influence using both highly influential concepts and smaller, secondary topics.
We recommend to try to increase mind-viral immunity for texts that have a low score and to decrease it for texts that have a high score. This ensures that your discourse will be open, but not dispersed.
The higher is the diversity, the more distinct communities (topics) there are in this network, the more likely it will be pluralist.
The network structure indicates the level of its diversity. It is based on the modularity measure (>0.4 for medium, >0.65 for high modularity, measured with Louvain (Blondel et al 2008) community detection algorithm) in combination with the measure of influence distribution (the entropy of the top nodes' distribution among the top clusters), as well as the the percentage of nodes in the top community.
We recommend to aim for Diversified structure if you're in the Biased or Focused score range and to aim for the Focused structure if you're in the Dispersed score range.
We recommend to aim for Diversified structure if you're in the Biased or Focused score range and to aim for the Focused structure if you're in the Dispersed score range.
| Modularity | 0 |
Influence Distribution | 0 % |
|---|
| Topics | Nodes in Top Topic | Components | Nodes in Top Comp |
0 |
0 % |
0 |
0 % |
| Nodes | Av Degree | Density | Weighed Betweenness |
0 |
0 |
0 |
0 |
Discourse Advice:
N/A
Narrative Influence Propagation:
? The chart above shows how influence propagates through the network. X-axis: lemma to lemma step (narrative chronology). Y-axis: change of influence.
The more even and rhythmical this propagation is, the stronger is the central idea or agenda (see alpha exponent below ~ 0.5 or less).
The more variability can be seen in the propagation profile, the less is the reliance on the main concepts (agenda), the stronger is the role of secondary topical clusters in the narrative.
The more even and rhythmical this propagation is, the stronger is the central idea or agenda (see alpha exponent below ~ 0.5 or less).
The more variability can be seen in the propagation profile, the less is the reliance on the main concepts (agenda), the stronger is the role of secondary topical clusters in the narrative.
We plot the narrative as a time series of influence (using the words' betweenness score). We then apply detrended fluctuation analysis to identify fractality of this time series, plotting the log2 scales (x) to the log2 of accumulated fluctuations (y). If the resulting loglog relation can be approximated on a linear polyfit, there may be a power-law relation in how the influence propagates in this narrative over time (e.g. most of the time non-influential words, occasionally words with a high influence).
Using the alpha exponent of the fit (which is closely related to Hurst exponent)), we can better understand the nature of this relation: uniform (pulsating | alpha <= 0.65), variable (stationary, has long-term correlations | 0.65 < alpha <= 0.85), fractal (adaptive | 0.85 < alpha < 1.15), and complex (non-stationary | alpha >= 1.15).
For maximal diversity, adaptivity, and plurality, the narrative should be close to "fractal" (near-critical state). For fiction, essays, and some forms of poetry — "uniform". Informative texts will often have "variable + stationary" score. The "complex" state is an indicator that the text is always shifting its state.
Using the alpha exponent of the fit (which is closely related to Hurst exponent)), we can better understand the nature of this relation: uniform (pulsating | alpha <= 0.65), variable (stationary, has long-term correlations | 0.65 < alpha <= 0.85), fractal (adaptive | 0.85 < alpha < 1.15), and complex (non-stationary | alpha >= 1.15).
For maximal diversity, adaptivity, and plurality, the narrative should be close to "fractal" (near-critical state). For fiction, essays, and some forms of poetry — "uniform". Informative texts will often have "variable + stationary" score. The "complex" state is an indicator that the text is always shifting its state.
Degree Distribution:
? (based on kolmogorov-smirnov test) ? switch to linear
Using this information, you can identify whether the network has scale-free / small-world (long-tail power law distribution) or random (normal, bell-shaped distribution) network properties.
This may be important for understanding the level of resilience and the dynamics of propagation in this network. E.g. scale-free networks with long degree tails are more resilient against random attacks and will propagate information across the whole structure better.
This may be important for understanding the level of resilience and the dynamics of propagation in this network. E.g. scale-free networks with long degree tails are more resilient against random attacks and will propagate information across the whole structure better.
If a power-law is identified, the nodes have preferential attachment (e.g. 20% of nodes tend to get 80% of connections), and the network may be scale-free, which may indicate that it's more resilient and adaptive. Absence of power law may indicate a more equalized distribution of influence.
Kolmogorov-Smirnov test compares the distribution above to the "ideal" power-law ones (^1, ^1.5, ^2) and looks for the best fit. If the value d is below the critical value cr it is a sign that the both distributions are similar.
Kolmogorov-Smirnov test compares the distribution above to the "ideal" power-law ones (^1, ^1.5, ^2) and looks for the best fit. If the value d is below the critical value cr it is a sign that the both distributions are similar.