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Streaming Hypothesis Reasoning - William Smith, Jan 2016
- 1. SHyRe Streaming Hypothesis Reasoning WILLIAM SMITH, PATRICK PAULSON, MARK BORKUM, DEBORAH MCGUINNESS, BRENDA PRAGGASTIS, RUI YAN, YUE LIU DAML 2016 – Seattle, WA Smart Data Conference, 2015 – San Jose, California January 26, 2016 The legends PROTECTED INFORMATION and PROPRIETARY INFORMATION apply to information describing Subject Inventions as defined in Contract No. DE-AC05-76RL01830 and any other information which may be properly withheld from public disclosure thereunder
- 2. DOE’s National Laboratories are Solving America’s Toughest Challenges 4
- 3. Mission Drivers Analyzing Changing Online Landscapes Seed LDRD Projects - Signatures of Communities & Change - Digital Currency Graph Forensics - DarkNet Characterization - Signatures in the Cloud Signature Discovery Initiative (SDI) Analysis in Motion (AIM) National Security Computing Disrupting Illicit Trafficking Nuclear Security National Defense Homeland Security Special Programs Seattle Innovation District Asymmetric Resilient Cybersecurity (ARC) Cyber-Physical Systems Ubiquitous Sensing
- 4. Analysis in Motion 6 Streaming Data Characterization & Processing Library of foundational streaming algorithms, methods for extracting features from streams Data reduction techniques like semantic characterization Hypothesis Generation & Testing Scalable symbolic deduction & incremental machine learning to track a stream Generate, update, and validate human-understandable hypotheses from streaming classifiers Human-Machine Feedback Interaction with human interfaces to implicitly weight, tune, and modify underlying models Visual strategies for bidirectional communication of and interaction with multiple hypotheses Work Environments Integration framework and testing range Instrumentation to measure overall accuracy, utility, and throughput
- 5. January 27, 2016 7 AIM Program Area 1 Streaming Data Characterization Compression Analysis (CA) Video compression algorithms provide an efficient means of detecting and classifying events in a stream Nonstandard features Became full project at mid-year Scalable Feature Extraction and Sampling (SFE) Given a dataset, can we find a minimum subset that provides similar accuracy as the entire dataset? Parallel setting using MPI Open source library (MaTEX)
- 6. 8 AIM Program Area 3 Human-Machine Feedback User-Centered Hypothesis Definition (UCHD) Transitioned to new PM and new technical focus in February What does a machine-generated hypothesis look like to a human analyst? Science of Interaction (SOI) Use user clickstream data as an indicator of user sensemaking Developed and open-sourced the Streaming Canvas software UI engineering for use cases User studies January 27, 2016
- 7. January 27, 2016 9 AIM Program Area 3 Human-Machine Feedback Mitigating Cognitive Depletion in Streaming Environments (CD) Predict and mitigate human performance degradation Quantify increase in error and impulsivity based on time from last break Studies using Halo and exam data User study planned Kills / Deaths Halo: Reach
- 8. Streaming Analytics 10 CHALLENGE ____________________________________________________________________ Craft machine-generated hypotheses as data arrive, steering data collection and using human feedback to tune a multi-classifier system. PNNL IMPACT ____________________________________________________________ Developing niche in interactive streaming analytics at scale; basis for invited keynotes at IEEE HCBDR, AAAS Big Data in Life Science, Data Science Innovation Summit, Science of Multi-INT. Developed streaming automated detection of first point of failure in lithium battery through electron microscopy. PNNL streaming architecture used as reference model for special programs sponsors. Collaborators: Rensselaer Polytechnic, Laboratory for Analytic Sciences. TXT VIS STREAM GRAPH STATS DATA PROV CYBER
- 9. Data Provenance & Workflow at Extreme Scale 11 CHALLENGE ____________________________________________________________________ Ensuring reliable performance and reproducibility of complex and adaptive workflows in extreme scale environments. PNNL IMPACT ____________________________________________________________ Workflow Performance Provenance ontology captures performance and reproducibility metrics across the complete system and application stack, helping to identify causal relationships. ProvEn uses PNNL’s provenance ontology to record, correlate, and analyze events; distinguished from mainstream provenance by focusing on process not just data heritage. PNNL is informing ASCR directions for future provenance investments. TXT VIS STREAM GRAPH STATS DATA PROV CYBER
- 10. Project Approach Protected Information | Proprietary Information 12
- 11. National Security Computing Program Areas 13 INFRASTRUCTURE Data and workflow management HPC programming models and libraries Power, performance, and reliability modeling Resiliency theory Mobile and edge computing Embedded systems Systems engineering and agile development Cloud and streaming architectures Modeling and simulation Data quality and provenance Sampling strategies Experimental design Human language technology Computer vision Large graph analysis Recommender systems Social and behavioral science ANALYTICS DECISION SUPPORT Visualization Human-computer interaction User experience design Semantic computing Operations research Test environments Analytic tradecraft and critical thinking Situational awareness Collaborative systems Training systems MISSION AREAS AND OPERATIONAL DEPLOYMENT Cyber analysis | Bio-surveillance | Social media analysis | Forensics | Emergency preparedness and response Law enforcement | Critical infrastructure resiliency | Trafficking networks | Power grid management
- 12. January 27, 2016 14 Project Goals Research Question How do we structure the Semantic technology stack to consume and reason over a volatile data stream, and what are the effects of this configuration when expressing streaming data models through common-of- the-shelf (COTS) reasoners? Goals of Project Build prototype frameworks created to consume streaming data into a Semantic Web stack Model streaming data in a Description Logic (DL) ontology and reason over the new graph using a set of DL compliant reasoners Model streaming data into an ontology, DL or comparable rule set, that can be compared across reasoning clients Study the effects of cache maintenance, primarily data eviction, on the Semantic Web stack and results across reasoners Develop engineering proposal to convert prototypes into singular platform that can be deployed on cloud networks (AWS, PIC)
- 13. January 27, 2016 15 Project Approach Propositional data are streaming in at a certain rate, and we can only see some “window” of them at any given time. We sample the data in the window and add them to a fixed-size cache. We need effective methods of sampling. The fixed-size cache differentiates our framing of the problem from agglomerative databases (i.e., “just store everything”). Deductive reasoning is continuously performed over the cache in order to try and answer queries and corroborate/refute hypotheses as quickly as possible. Low-latency, high-throughput reasoning on ephemeral data is a hard, open problem. There will likely be many conclusions to bring to the attention of the user, and so ranking is needed in order to prioritize attention. The idea of ranking is not so hard, but determining the correct ordering is.
- 14. Approach Fixed-size Cache Data Stream Window Size Data Rate Pellet StarDog AllegroG DINTNMR USE CASE Symbolic Reasoning Hypotheses / Questions Ranked Conclusions cache maintenance sampling 16
- 15. Approach Fixed-size Cache Data Stream Window Size Data Rate Pellet StarDog AllegroG DINTNMR USE CASE Symbolic Reasoning Hypotheses / Questions Ranked Conclusions cache maintenance sampling 17
- 16. January 27, 2016 18 Engineering Approach J2EE Pipeline AVRO Packet StreamStream JAVA Stream “Pull” Client Use Case JAVA - Streaming Design Pattern Use Case JAVA - Streaming Design Pattern JAVA Pellet Reasoner StarDog TripleStore / Reasoner AllegroGraph TripleStore / Reasoner Not Implemented / Reasoner
- 17. January 27, 2016 19 Four Concurrent States Ingestion Annotation Query Cache Mangement Initialize Load Process
- 18. January 27, 2016 20 Four Concurrent States Ingestion Annotation Query Cache Mangement Initialize Load Process FAST SLOW
- 19. January 27, 2016 21 SHyRe Decision Tree
- 20. January 27, 2016 22 SHyRe Decision Tree
- 21. January 27, 2016 23 SHyRe Decision Tree 5 Possible Outcomes: 1. Query Pellet with built in JENA RDF functionality 2. Query Pellet with SPARQL Query 3. Encode SPARQL to URL format and CURL a triplestore endpoint. 4. Use SNARL protocol to query StarDog with SPARQL Query 5. Use AGQuery protocol to query AllegroGraph with SPARQL Query a. *RDFS++ Reasoning
- 22. January 27, 2016 24 Engineering Approach J2EE Pipeline AVRO Packet StreamStream JAVA Stream “Pull” Client Use Case JAVA - Streaming Design Pattern Use Case JAVA - Streaming Design Pattern JAVA Pellet Reasoner StarDog TripleStore / Reasoner AllegroGraph TripleStore / Reasoner Not Implemented / Reasoner
- 23. Use Case 1: Nuclear Magnetic Resonance Protected Information | Proprietary Information 25
- 24. January 27, 2016 26 What is Nuclear Magnetic Resonance?
- 25. January 27, 2016 27 NMR Accomplishments to Date Research Question Answered By consuming an undefined count of scans, can we assemble a NMR run, model compounds within an ontology of background data, and then reason across this new combined model of compound and spectrum ontology? Logic Constraints Answered Streaming data – When is a spectrum fully assembled? How do we decide which functions to model in the ontology, and which to apply to a query? SHyRe NMR Model Description Logic background ontology of compound classes and peaks (Pellet implementation) RDFS background ontology of compound classes and peaks (StarDog / AllegroGraph implementations) Consume and model a NMR run from a stream of spectrum scans Query the NMR run after applying the compound background ontology
- 26. 28 NMR Accomplishments to Date
- 27. 29 NMR Accomplishments to Date
- 28. Use Case 2: Shipping a Strategic Surprise Protected Information | Proprietary Information 30
- 29. January 27, 2016 31 How do we detect a Strategic Surprise? Ford Exemplar HS-10237 HS-10238 HS-10239 HS-10240 HS-10241 HS-10242 HS-10237 HS-10238 HS-10239 HS-10246 HS-10248 HS-10243 Import Stream HS-10243 HS-10244 HS-10245 HS-10246 HS-10247 HS-10248 Nike Exemplar HS-10301 HS-10302 HS-10303 HS-10304 HS-10305 HS-10306 HS-10307 HS-10308 HS-10309 HS-10310 HS-10311 HS-10312
- 30. January 27, 2016 32 How do we detect a Strategic Surprise? Ford Exemplar HS-10237 HS-10238 HS-10239 HS-10240 HS-10241 HS-10242 HS-10237 HS-10238 HS-10239 HS-10246 HS-10248 HS-10243 Import Stream HS-10243 HS-10244 HS-10245 HS-10246 HS-10247 HS-10248 Nike Exemplar HS-10301 HS-10302 HS-10303 HS-10304 HS-10305 HS-10306 HS-10307 HS-10308 HS-10309 HS-10310 HS-10311 HS-10312
- 31. January 27, 2016 33 How do we detect a Strategic Surprise? Ford Exemplar HS-10237 HS-10238 HS-10239 HS-10240 HS-10241 HS-10242 HS-10246 HS-10248 HS-10243 HS-10303 HS-10311 HS-10307 Import Stream HS-10243 HS-10244 HS-10245 HS-10246 HS-10247 HS-10248 Nike Exemplar HS-10301 HS-10302 HS-10303 HS-10304 HS-10305 HS-10306 HS-10307 HS-10308 HS-10309 HS-10310 HS-10311 HS-10312
- 32. January 27, 2016 34 How do we detect a Strategic Surprise? Ford Exemplar HS-10237 HS-10238 HS-10239 HS-10240 HS-10241 HS-10242 HS-10303 HS-10311 HS-10307 HS-10304 HS-10305 HS-10312 Import Stream HS-10243 HS-10244 HS-10245 HS-10246 HS-10247 HS-10248 Nike Exemplar HS-10301 HS-10302 HS-10303 HS-10304 HS-10305 HS-10306 HS-10307 HS-10308 HS-10309 HS-10310 HS-10311 HS-10312
- 33. January 27, 2016 35 Strategic Surprise Accomplishments to Date Research Question Answered Based on a company’s import records, can we determine if they are entering a new LOB? Logic Constraints Answered Streaming data – have to determine if record might be important in future Explain reasoning to enable user intervention / interaction and integration with other models SHyRe Strategic Surprise Model Model each company by the HSCODEs it imports Identify companies that represent all companies in a LOB Exemplar of the LOB Use training data to get HSCODEs used by each exemplar Count the number of matching HSCODEs between monitored company and exemplars
- 34. 36 Strategic Surprise Accomplishments to Date 0 5000 10000 15000 20000 25000 30000 35000 40000 45000 Outputs 0 0 15 88 129 Inputs Outputs Required Input Records to Produce Output
- 35. January 27, 2016 37 Strategic Surprise Accomplishments to Date Input Import Records Output Results CPU (seconds) CPU (inputs / second) 0 0 1.292 1 0 1.693 10,000 15 77.619 128.834 20,000 88 185.553 107.786 30,000 129 330.895 90.663 40,000 169 508.902 78.601 Required Input Records to Produce Output
- 36. Project Challenges Protected Information | Proprietary Information 39
- 37. Challenges Reasoning Differences in Standards (RDFS / OWL EL/DL / RDFS++) January 27, 2016 40 Reasoner Difficulty Pellet Nearly complete OWL DL, but not currently maintained. StarDog Strict separation of A-Box / T-Box reasoning within OWL DL across embedded Pellet and StarDog systems. Creates oddly formed, verbose SPARQL queries. AllegroGraph Proprietary reasoning with inconsistent standards. Complex cache eviction algorithms and unsupported SPARQL standards Reasoner Difficulty Pellet Requires complex internal storage algorithms to manipulate memory graphs StarDog SPARQL DELETE can only support literal triples. Variables within a DELETE invoke background graph indexing and frequently fail.
- 38. January 27, 2016 41 Conclusions Contract with Rensselaer Polytechnic Institute Rui Yan and Yue Liu joined SHyRe team advised by Prof. Deborah McGuinness Complete: International Conference for Biomedical Ontologies Paper William Smith, Alan Chapell, Courtney Courley Complete: Smart Data 2015 Conference William Smith, Deborah McGuinness, Rui Yan Complete: Conference on Information Knowledge Management 2015 Paper Mark Borkum, William Smith, Deborah McGuinness, Rui Yan, Yue Liu Complete: ISWC 2015 Workshop Paper Rui Yan, Brenda Praggastis, William Smith, Deborah McGuinness In Progress: Skolemization/Currying to enable decidable reasoning Patrick Paulson In Progress: Journal of Web Semantics, Streaming Edition Paper
- 39. William Smith Human Centered Analytics william.smith@pnnl.gov +1.206.528.3356 SHYRE: Streaming Hypothesis Reasoning aim.pnnl.gov Protected Information | Proprietary Information
Notas del editor
- 2 Outside views - Civil servants – Amusing during election season, which stopped being a season long ago and is now just the perpetual state of things Department of Defense – Controversial when at peace, and necessary when somebody is somewhere they’re not supposed to be and it’s going to take a pretty penny to get them out. Third Arm of Government DOE – Infrastructure and Science. 17 labs, national highway system, power plants, green energy, smart grids, environmental regulation, cyber security, disease tracking… Point out some of the labs PNNL Nuclear Labs Sanida (z-machine) Fermi – (collider) NREL Energy Grid Lab We are the support system for the myriad of other internal US departments that support state governments and national projects.
- 2 Outside views - Civil servants – Amusing during election season, which stopped being a season long ago and is now just the perpetual state of things Department of Defense – Controversial when at peace, and necessary when somebody is somewhere they’re not supposed to be and it’s going to take a pretty penny to get them out. Third Arm of Government DOE – Infrastructure and Science. 17 labs, national highway system, power plants, green energy, smart grids, environmental regulation, cyber security, disease tracking… Point out some of the labs PNNL Nuclear Labs Sanida (z-machine) Fermi – (collider) NREL Energy Grid Lab We are the support system for the myriad of other internal US departments that support state governments and national projects. PNNL strengths and cultural focus on … Focus on strengthening & leveraging the science base Focus on impacting mission & developing next generation History – 3 innovations that won WWII DOE labs overall role Accelerate the rate of innovation (user facilities, next gen, scientific leadership) Address enduring, S&T centered mission challenges (naval reactors, nonpro, energy, stockpile) Ensure ability to react to rapid change or crises (critical materials, Fukushima, 9/11, cyber) Achieve and prevent technology surprise (security mission-centric) Enhance economic competitiveness
- Oppy here for analytic provenance in SP sponsors – big challenge there. one other thing that makes ProvEn research different is that we are more focused on how the captured provenance can provide real actionable insights for the users e.g. why is my workflow performance so variable, what was different in this process from different instances, how can I improve workflow performance etc.
- A strategic surprise is a material that doesn’t relate to the line of business of a company, or that is embargoed from that specific company receiving. Infant incubators. Great for premature babies, and also not bad for creating bio weapons. So if you’re a country with 10 hospitals and 4 premature babies why would your state company need 10,000 infant incubators? Same with glove boxes. Great for specific industries… that your country doesn’t have… so why do you need 10,000 glove boxes? Company that needs ONE industrial piece of equipment very rarely. Like a french bakery. Once every 20 years it will need an industrial HVAC system… so why did this one just import 50 of them? Loading dock switches. Let’s say I buy up the loading dock right next to a company that does need to import something, and make a deal about things moving from loading dock 1-AC to 1-AD. We took a much broader point of view before the fine grained loading dock switch…
- We started with a central research question like all PNNL projects… <read question> Through this question, and a slight project reorganization, the following goals were aligned to FY15. <read slides>
- Data pipe & sampling window – Provided by outside Java enterprise framework. Fixed size cache. There were a couple different of caching algorithms depending on in memory caching, application caching, and triple store caching Several reasoning platforms were selected, depending mainly on claims from manuals and marketing resources End user. Human in the loop was a large requirement, but we did focus on an analyst function.
- We chose 3 core areas for FY15: We selected three COTS – common off the shelf – reasoners with different degrees of the OWL and RDFS specification implemented and expressivity for testing. We researched cache maintenance and have placed emphasis on cache eviction and how do you maintain a stable, but relevant, graph for queries Communication with the underlying infrastructure. The original build of SHYRE was good at consuming and producing metrics about the query operation. FY15 focused not only on the use cases, but providing results back to the infrastructure for user review: a. Talk to automated entity extraction agent b. Provided conclusions to analytics UIs c. Create propositions for future use within Shyre
- The mile high picture of the design pattern is a workflow engine (or state machine) that runs four concurrent processes. MOST IMPORTANTLY, after starting each process they MUST run independently and not require a synchronization mechanisms beyond thread safe programming. The ingestion client is responsible for connecting to a stream, decoding an byte-stream encoded packet, completing an initial test on data conformity to the use case upon initialization. It then provides the decoded data to the annotation mechanism. Ingestion is not responsible for establishing a medium term storage solution, and all decoded data is immediately stored in a FIFO list for annotation. This is the state composed of processes for encoding data into an RDF graph and providing the graph to a reasoner. Data annotation to Semantic Web RDF identifiers creates a unique challenge, as each use case generally requires a different markup for decoded data. The ability to create a question and propose it in a way a reasoner can answer using an RDF graph or triplestore. Querying the Semantic Web, after annotating and storing a data stream, is a variable state intended to run in tandem with all states after the initial ingestion. Because the graphs are created and modified as the stream arrives, annotation and query design must be composed for a streaming architecture Cache maintenance… that’s really really hard.
- Ingestion can run really really fast Querying can run really really slow, especially depending on how you structure the query and logic. DL almost exponentially adds to query time as more triples are added to the graph, and RDFS you have to take special care on how you construct the query to ensure something is always returned for cache management And cache management never works.
- These 4 states create a decision tree - Data consumption from architecture Now that we have the data we have to make a decision based on annotation requirements, use case, and reasoner package In-memory systems quickly assembling RDF models, no metering of FIFO cache consumption is necessary Attempts to meter access to the temporary cache providing time to complete the string building process necessary String manipulation into RDF triples Template variable substitution into valid RDF/XML/TURTLE/ETC Graphs
- Query Pellet with built in JENA/OPEN RDF functionality Query Pellet across the file system with SPARQL Query Encode SPARQL to URL format and CURL a triplestore endpoint. Use SNARL protocol to query StarDog with SPARQL Query Use AGQuery protocol to query AllegroGraph with SPARQL Query a. *RDFS++ Reasoning
- And here are the 5 outcomes you have to account for when evaluating this kind of system. 1 and 2, while in memory (or on the file system if you’re particularly pressed for time) Pellet is slow, but it isn’t hand cuffed when it comes to pretty much complete DL logic. This is the easiest to create a static query, that always returns an expectation of true / false per entity within the query and ontology. 3 is the grand mystery highly dependent on triplestore. How did you populate it? Could you curl a query? Was there a proprietary loading method like ISQL or mark logic pipeline demo’d yesterday? How much file system is used? How do we know when we can query? 4-5 Both StarDog and AllegroGraph have similar custom protocols for populating the graphs, and it becomes much more of an issue of query composition, especially with Standard SPARQL, RDFS or OWL EL.
- Now that the broad overview is covered, we will be focusing on this yellow strip as our use case applications. This is where the majority of the custom SHYRE LOGIC – not stream ingestion - had to be created, and where the majority of the design pattern and decisions we just discussed were deployed.
- Nuclear Magnetic Resonance (NMR) spectroscopy is an analytic technique that exploits the magnetic properties of certain atomic nuclei in order to determine the physical and chemical properties of the molecules in which they are contained (e.g., the chemical structure).
- Let’s run through our accomplishments with each use case - we will begin with NMR. 1. <read question 1> - Yes, we can. However there is a large scalability issue as scans become more complex ballooning our query time from 10 seconds to 19 minutes. 2. <read logic constraints> - a. Yes, we tracked on run numbers and did an additional test using query result completeness. IE – When is a query not returning any new results? b. OWL DL vs. RDFS – This was decided for us generally by the reasoner being used. StarDog has an interesting quirk where it breaks queries into A-Box / T-Box reasoning forcing the query author to be careful when composing queries and modeling an ontology 3. Finally, this is what we came up with - <read slide>
- MAJOR POINTS 25 Time Linear Runs out of 25,000+ This should be a straight line with no bumps. There should not have been this much change in such a small amount of time so something isn’t tracking between the graphs, ontology and queries.
- Roughly 1,730 seems to be golden graph size where we start to lose results and compound confirmations However, 1,698 / 1,722 returns all 11 positives here. This brings up the question of graph utilization – every triple in this graph applies to a compound we’re searching Each of these queries took roughly 20 seconds on an NMR run composed of between 30 and 50 scans. As scans increase query time increases dramatically due to ranging functions in the DL ontology having to search every triple 30 times. By the time we’re at 250+ scans (~20K triples in graph) queries take around 19 minutes and only return “possible” for all 30 compounds. This brings up the question of a “data deluge” never negating or affirming a chemical, while also providing enough information the probability is high – or nearly certain.
- A strategic surprise is a material that doesn’t relate to the line of business of a company, or that is embargoed from that specific company receiving. Infant incubators. Great for premature babies, and also not bad for creating bio weapons. So if you’re a country with 10 hospitals and 4 premature babies why would your state company need 10,000 infant incubators? Same with glove boxes. Great for specific industries… that your country doesn’t have… so why do you need 10,000 glove boxes? Company that needs ONE industrial piece of equipment very rarely. Like a french bakery. Once every 20 years it will need an industrial HVAC system… so why did this one just import 50 of them? Loading dock switches. Let’s say I buy up the loading dock right next to a company that does need to import something, and make a deal about things moving from loading dock 1-AC to 1-AD. We took a much broader point of view before the fine grained loading dock switch…
- Import stream – just your normal company pulling things in Ford is good for automotive Nike is good for athletic gear Poor examples: Samsung Toyota General Electric Walmart is actually on the line, but can be an exemplar. Sure they import a lot of finished consumer goods, but how many stores do they own and how often would they need raw materials and construction systems replaced (HVAC)? Walmart doesn’t sell cars or reactors, and they don’t need the raw goods to create their consumer goods. So there is a superstore category for finished consumer goods.
- Company A looks like Exemplar Ford so they’re importing automotive goods
- Well…. Now they don’t…
- Company A looks like Exemplar Nike so they’re importing athletic goods
- Next we move onto the Strategic Surprise accomplishments From the questions earlier in the talk, and established Strategic Surprise DL Ontology, our problem became how do we model so much data – record packets of 30+ values – and which are useful for determining drift in business category? This was accomplished by establishing a set of exemplar companies, or companies that never or very infrequently import outside of a given business category. Constraints on logic included – Record decomposition – common problem in the streaming data . More importantly, and the problem I frequently have, is explaining when and why the SHYRE reasoner creates an output record. Records are created ONLY when a company begins aligning itself with an exemplar, and this drift toward a specific industry provides the metrics for SOI from Shyre. That means in the SOI demonstration you’re only going to see SHYRE lines stepping up as a company becomes more and more like a specific exemplar company within an industry. 3. Finally, this is what we came up with - <read slide>
- This follows common logic in the fact most companies aren’t rapidly entering different lines of business and importing goods that don’t pertain to their business model.
- CPU seconds are nearly doubling as company import tendencies are modeled. Processing power is halved by the time we ingest roughly a magnitude of 4 from the original 10,000 records. Good because it doesn’t happen at 20,000, but an obvious bottle neck at some point.
- 30 seconds it starts – What is OPA What is Shyre What is the movement in between Talk threshold 1:30 shyre detects appliances 2:10 Detects a change Go to the background 5:20 Talk console
- So.. What challenges did we have. Beyond the aforementioned scalability covered previously…. I’m just reading this slide because it’s my favorite slide and the bane of my last year of research. Read Slide