WO2021055964A1 - Système et procédé d'affinement participatif d'un phénomène naturel pour une gestion des risques et une validation de contrat - Google Patents

Système et procédé d'affinement participatif d'un phénomène naturel pour une gestion des risques et une validation de contrat Download PDF

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Publication number
WO2021055964A1
WO2021055964A1 PCT/US2020/051840 US2020051840W WO2021055964A1 WO 2021055964 A1 WO2021055964 A1 WO 2021055964A1 US 2020051840 W US2020051840 W US 2020051840W WO 2021055964 A1 WO2021055964 A1 WO 2021055964A1
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data
suite
sensor fusion
network
sensor
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PCT/US2020/051840
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English (en)
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Jason Crabtree
Andrew Sellers
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Qomplx, Inc.
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Priority claimed from US16/575,929 external-priority patent/US11074652B2/en
Application filed by Qomplx, Inc. filed Critical Qomplx, Inc.
Publication of WO2021055964A1 publication Critical patent/WO2021055964A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2455—Query execution
    • G06F16/24568—Data stream processing; Continuous queries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/02—Knowledge representation; Symbolic representation
    • G06N5/022—Knowledge engineering; Knowledge acquisition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/08—Insurance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/088—Non-supervised learning, e.g. competitive learning

Definitions

  • the disclosure relates to the field of computer management, and more particularly to the field of cybersecurity and threat analytics. Discussion o f the State o f the Art
  • the inventor has developed a system and method system for crowd-sourced refinement of natural phenomenon for risk management and contract validation that is designed to take in a heterogeneous plurality of network-connected sensors and data inputs in a sensor fusion suite which may then transform and analyze them into relevant knowledge graphs or Directed Computational Graphs (DCG), and hone models of predicting outcomes of the sensor data or future related events, for instance gathering satellite imaging, weather data, information about the date and historical information about hurricanes in the Florida area, to deduce if a hurricane is likely to occur in the next month, and a possible trajectory it may take.
  • DCG Directed Computational Graphs
  • the disclosed invention aims to provide a system and methods for discerning an objective and factually supported view of reality in scenarios based on a mix of public and private data, for many purposes including verification of insurance claims, prediction of insurance claims and financial market movements, prediction of natural disasters or certain kinds of human events, and more. This is important for numerous entities seeking a reasonably objective independent party to establish event occurrence and impact for the purposes of direct financial transactions as well as to support model development and training based on the labels data associated which may influence key models, future underwriting or risk management decisions. In the presence of adversarial data where gaming open or public or social data is prevalent, the ability to identify factually-supported and/ or disputed information becomes paramount to public safety, private risk management activities, and risk transfer (e.g. insurance).
  • the disclosed invention provides a system and methods to collect, ingest, transform if needed, analyze, and model data associated with establishing a probabilistic description and dossier of evidence for a given occurrence.
  • a system for crowd-sourced refinement of natural phenomenon for risk management and contract validation comprising: at least one physical sensor; at webcrawler comprising at least a second plurality of programming instructions stored in the memory of, and operating on at least one processor of, a computing device, wherein the second plurality of programming instructions, when operating on the processor, cause the computing device to: automatically gather data from a plurality of Internet-enabled sources including social media, search engine results, website data, and news outlets; send gathered data to a sensor fusion suite over a network; a sensor fusion suite comprising at least a network adapter, a data ingester, data transformer, a data analyzer, a multidimensional time-series database, a third plurality of programming instructions stored in the memory of, and operating on at least one processor of, a computing device, wherein the third plurality of programming instructions, when operating on the processor, cause the computing device to: ingest data from at least a webcrawler and at least
  • a method for crowd-sourced refinement of natural phenomenon for risk management and contract validation comprising the steps of: automatically gather data from a plurality of Internet-enabled sources including social media, search engine results, website data, and news outlets, using a webcrawler; send gathered data to a sensor fusion suite over a network, using a webcrawler; ingest data from at least a webcrawler and at least one physical sensor over a network, using a data ingester and a network adapter in a sensor fusion suite; record received data and the time of reception, and the timestamp of data retrieval from the webcrawler and the at least one physical sensor, in the multidimensional time-series database, using a data ingester and multidimensional time-series database in a sensor fusion suite; reformat received data as needed, using a data transformer in a sensor fusion suite; perform analysis using machine learning techniques on the received data, using a data analyzer in a sensor fusion suite
  • FIG. 1 is a diagram of an exemplary architecture of a business operating system according to an embodiment of the invention.
  • FIG. 2 is a diagram of an exemplary architecture of a system for the capture and storage of time series data from sensors with heterogeneous reporting profiles according to an embodiment of the invention.
  • Fig. 3a is a process diagram showing business operating system functions in use to mitigate cyberattacks.
  • Fig. 3b is a process diagram showing business operating system functions in use to mitigate cyberattacks.
  • Fig. 4 is a process flow diagram of a method for segmenting cyberattack information to appropriate corporation parties.
  • Fig. 5 is a diagram of an exemplary architecture of a distributed system for rapid, large volume, search and retrieval of unstructured or loosely structured information found on sources such as the World Wide Web according to an embodiment of the invention.
  • Fig. 6 is a system diagram showing heterogeneous data sources connected through a network to a sensor fusion suit which is further connected to a business operating system, according to an embodiment.
  • Fig. 7 is a system diagram of components comprising a master sensor fuser, according to an embodiment.
  • Fig. 8 is a method diagram for crowd-sourced refinement of natural phenomenon for risk management and contract validation.
  • Fig. 9 is a method diagram illustrating testing and comparison of historical models and predictions with current models and predictions to find the most statistically accurate model for analyzing fused sensor data.
  • Fig. 10 is a process flow diagram of a method for the receipt, processing and predictive analysis of streaming data according to one aspect.
  • Fig. 11 is a process flow diagram of a method for representing the operation of the transformation pipeline as a directed graph function according to one aspect.
  • Fig. 12 is a method diagram illustrating an insurance use-case of the system.
  • Fig. 13 is a method diagram illustrating an emergency response use-case of the system.
  • Fig. 14 is a method diagram illustrating a financial market trading use-case of the system.
  • FIG. 15 is a process flow diagram of a method for processing a set of three or more data transformations within a data transformation pipeline where output of the last member transformation of the set serves as input of the first member transformation thereby creating a cyclical relationship according to one aspect.
  • Fig. 16 is a block diagram illustrating an exemplary hardware architecture of a computing device.
  • Fig. 17 is a block diagram illustrating an exemplary logical architecture for a client device.
  • Fig. 18 is a block diagram illustrating an exemplary architectural arrangement of clients, servers, and external services.
  • Fig. 19 is another block diagram illustrating an exemplary hardware architecture of a computing device.
  • the inventor has conceived, and reduced to practice, a system and method system for crowd- sourced refinement of natural phenomenon for risk management and contract validation that is designed to take in a heterogeneous plurality of network-connected sensors and data inputs in a sensor fusion suite which may then transform and analyze them into relevant knowledge graphs or Directed Computational Graphs (DCG), and hone models of predicting outcomes of the sensor data or future related events, for instance gathering satellite imaging, weather data, information about the date and historical information about hurricanes in the Florida area, to deduce if a hurricane is likely to occur in the next month, and a possible trajectory it may take.
  • DCG Directed Computational Graphs
  • Devices that are in communication with each other need not be in continuous communication with each other, unless expressly specified otherwise.
  • devices that are in communication with each other may communicate directly or indirectly through one or more communication means or intermediaries, logical or physical.
  • steps may be performed simultaneously despite being described or implied as occurring non-simultaneously (e.g., because one step is described after the other step).
  • the illustration of a process by its depiction in a drawing does not imply that the illustrated process is exclusive of other variations and modifications thereto, does not imply that the illustrated process or any of its steps are necessary to one or more of the aspects, and does not imply that the illustrated process is preferred.
  • steps are generally described once per aspect, but this does not mean they must occur once, or that they may only occur once each time a process, method, or algorithm is carried out or executed. Some steps may be omitted in some aspects or some occurrences, or some steps may be executed more than once in a given aspect or occurrence.
  • a “swimlane” is a communication channel between a time series sensor data reception and apportioning device and a data store meant to hold the apportioned data time series sensor data.
  • a swimlane is able to move a specific, finite amount of data between the two devices.
  • a single swimlane might reliably carry and have incorporated into the data store, the data equivalent of 5 seconds worth of data from 10 sensors in 5 seconds, this being its capacity. Attempts to place 5 seconds worth of data received from 6 sensors using one swimlane would result in data loss.
  • a “metaswimlane” is an as-needed logical combination of transfer capacity of two or more real swimlanes that is transparent to the requesting process. Sensor studies where the amount of data received per unit time is expected to be highly heterogeneous over time may be initiated to use metaswimlanes.
  • graph is a representation of information and relationships, where each primary unit of information makes up a “node” or “vertex” of the graph and the relationship between two nodes makes up an edge of the graph.
  • Nodes can be further qualified by the connection of one or more descriptors or “properties” to that node. For example, given the node “James R,” name information for a person, qualifying properties might be “ 183 cm tall”, “DOB 08/ 13/1965” and “speaks English”. Similar to the use of properties to further describe the information in a node, a relationship between two nodes that forms an edge can be qualified using a “label”.
  • transformation graphs which are highly variable in size and node, edge composition as the system processes data streams.
  • transformation graph may assume many shapes and sizes with a vast topography of edge relationships. The examples given were chosen for illustrative purposes only and represent a small number of the simplest of possibilities. These examples should not be taken to define the possible graphs expected as part of operation of the invention
  • transformation is a function performed on zero or more streams of input data which results in a single stream of output which may or may not then be used as input for another transformation. Transformations may comprise any combination of machine, human or machine-human interactions Transformations need not change data that enters them, one example of this type of transformation would be a storage transformation which would receive input and then act as a queue for that data for subsequent transformations. As implied above, a specific transformation may generate output data in the absence of input data. A time stamp serves as a example. In the invention, transformations are placed into pipelines such that the output of one transformation may serve as an input for another. These pipelines can consist of two or more transformations with the number of transformations limited only by the resources of the system.
  • transformation pipelines have been linear with each transformation in the pipeline receiving input from one antecedent and providing output to one subsequent with no branching or iteration.
  • Other pipeline configurations are possible.
  • the invention is designed to permit several of these configurations including, but not limited to: linear, afferent branch, efferent branch and cyclical.
  • a “database” or “data storage subsystem” (these terms may be considered substantially synonymous), as used herein, is a system adapted for the long-term storage, indexing, and retrieval of data, the retrieval typically being via some sort of querying interface or language.
  • “Database” may be used to refer to relational database management systems known in the art, but should not be considered to be limited to such systems.
  • Many alternative database or data storage system technologies have been, and indeed are being, introduced in the art, including but not limited to distributed non-relational data storage systems such as Hadoop, column-oriented databases, in memory databases, and the like.
  • any data storage architecture may be used according to the aspects.
  • one or more particular data storage needs are described as being satisfied by separate components (for example, an expanded private capital markets database and a configuration database), these descriptions refer to functional uses of data storage systems and do not refer to their physical architecture.
  • any group of data storage systems of databases referred to herein may be included together in a single database management system operating on a single machine, or they may be included in a single database management system operating on a cluster of machines as is known in the art.
  • any single database (such as an expanded private capital markets database) may be implemented on a single machine, on a set of machines using clustering technology, on several machines connected by one or more messaging systems known in the art, or in a master/ slave arrangement common in the art.
  • a “data context”, as used herein, refers to a set of arguments identifying the location of data. This could be a Rabbit queue, a xsv file in cloud-based storage, or any other such location reference except a single event or record. Activities may pass either events or data contexts to each other for processing. The nature of a pipeline allows for direct information passing between activities, and data locations or files do not need to be predetermined at pipeline start.
  • Each batch activity may contain a “source” data context (this may be a streaming context if the upstream activities are streaming), and a “destination” data context (which is passed to the next activity).
  • Streaming activities may have an optional “destination” streaming data context (optional meaning: caching/persistence of events vs. ephemeral), though this should not be part of the initial implementation.
  • Fig. 3a and Fig. 3b are a process diagram showing business operating system functions in use to mitigate cyberattacks.
  • Input network data which may include network flow patterns 321, the origin and destination of each piece of measurable network traffic 322, system logs from servers and workstations on the network 323, endpoint data 329, any security event log data from servers or available security information and event (SIEM) systems 324, external threat intelligence feeds 325, external network health or cybersecurity feeds 326, Kerberos domain controller or ACTIVE DIRECTORYTM server logs or instrumentation 327 and business unit performance related data 328, among many other possible data types for which the invention was designed to analyze and integrate, may pass into 315 the business operating system 310 for analysis as part of its cyber security function.
  • SIEM security information and event
  • These multiple types of data from a plurality of sources may be transformed for analysis 311, 312 using at least one of the specialized cybersecurity, risk assessment or common functions of the business operating system in the role of cybersecurity system, such as, but not limited to network and system user privilege oversight 331, network and system user behavior analytics 332, attacker and defender action timeline 333, SIEM integration and analysis 334, dynamic benchmarking 335, and incident identification and resolution performance analytics 336 among other possible cybersecurity functions; value at risk (VAR) modeling and simulation 341, anticipatory vs.
  • VAR value at risk
  • Output 317 can be used to configure network gateway security appliances 361, to assist in preventing network intrusion through predictive change to infrastructure recommendations 362, to alert an enterprise of ongoing cyberattack early in the attack cycle, possibly thwarting it but at least mitigating the damage 362, to record compliance to standardized guidelines or SLA requirements 363, to continuously probe existing network infrastructure and issue alerts to any changes which may make a breach more likely 364, suggest solutions to any domain controller ticketing weaknesses detected 365, detect presence of malware 366, perform one time or continuous vulnerability scanning depending on client directives 367, and thwart and mitigate the effects of cyber attacks including malware of various types 368. These examples are, of course, only a subset of the possible uses of the system, they are exemplary in nature and do not reflect any boundaries in the capabilities of the invention.
  • Fig. 4 is a process flow diagram of a method for segmenting cyberattack information to appropriate corporation parties 400.
  • one of the strengths of the advanced cyber-decision platform is the ability to finely customize reports and dashboards to specific audiences, concurrently is appropriate. This customization is possible due to the devotion of a portion of the business operating system’s programming specifically to outcome presentation by modules which include the observation and state estimation service 140 with its game engine 140a and script interpreter 140b.
  • issuance of specialized alerts, updates and reports may significantly assist in getting the correct mitigating actions done in the most timely fashion while keeping all participants informed at predesignated, appropriate granularity.
  • the cybersecurity focused embodiment may create multiple targeted information streams each concurrently designed to produce most rapid and efficacious action throughout the enterprise during the attack and issue follow-up reports with and recommendations or information that may lead to long term changes afterward 403.
  • Examples of groups that may receive specialized information streams include but may not be limited to front line responders during the attack 404, incident forensics support both during and after the attack 405, chief information security officer 406 and chief risk officer 407 the information sent to the latter two focused to appraise overall damage and to implement both mitigating strategy and preventive changes after the attack.
  • Front line responders may use the cyber-decision platform’s analyzed, transformed and correlated information specifically sent to them to probe the extent of the attack, isolate such things as: the predictive attacker’s entry point onto the enterprise’s network, the systems involved or the predictive ultimate targets of the attack and may use the simulation capabilities of the system to investigate alternate methods of successfully ending the attack and repelling the attackers in the most efficient manner, although many other queries known to those skilled in the art are also answerable by the invention. Simulations run may also include the predictive effects of any attack mitigating actions on normal and critical operation of the enterprise’s IT systems and corporate users.
  • a chief information security officer may use the cyber-decision platform to predictively analyze what corporate information has already been compromised, predictively simulate the ultimate information targets of the attack that may or may not have been compromised and the total impact of the attack what can be done now and in the near future to safeguard that information.
  • the forensic responder may use the cyber-decision platform to clearly and completely map the extent of network infrastructure through predictive simulation and large volume data analysis.
  • the forensic analyst may also use the platform’s capabilities to perform a time series and infrastructural spatial analysis of the attack’s progression with methods used to infiltrate the enterprise’s subnets and servers.
  • the chief risk officer would perform analyses of what information was stolen and predictive simulations on what the theft means to the enterprise as time progresses. Additionally, the system’s predictive capabilities may be employed to assist in creation of a plan for changes of the IT infrastructural that should be made that are optimal for remediation of cybersecurity risk under possibly limited enterprise budgetary constraints in place at the company so as to maximize financial outcome.
  • Fig. 8 is a method diagram for crowd-sourced refinement of natural phenomenon for risk management and contract validation.
  • Sensors, internet data sources, and manual data entries are sent to the sensor fusion suite over a network 810, either through the sensor fusion suite polling them actively, or through the various data sources actively sending data to the sensor fusion suite, which receives and analyzes them.
  • Manual data entry and automated webcrawling of data can both be accomplished by purchasing the rights to data from other groups, individuals, or corporations, and configuring the sensor fusion suite to poll these sources or receive these manual data sources as required.
  • the sensor fusion suite then receives the collection of sensor data, data from internet or other networks, and manually entered data 820, causing it to ingest received data and record the data and timestamps of data in a connected or internal MDTSDB 830.
  • data from an ingestion component is sent to a data transformer 840, where the data transformer acts on received data if necessary, for instance by normalizing inputs, stripping unnecessary data away, and more if applicable 850. Since the ability to include subjective and qualitative assessments and quantitative observables from multiple sources (e.g.
  • the data transformer may further produce a directed computational graph based on the received data including knowledge graph creation via data semantification. Semantified views on incoming data (addressing cleanliness and deduplication type issues, for instance) support the use of such incoming heterogeneous data, including with probabilistic evaluation via uncertainty quantification or UQ, for use in triggers/truth and sensor validation. This may be performed by a data transformer and data analyzer in tandem, or separately.
  • the data after any transformations may have taken place, and any similar historical data from the MDTSDB are analyzed to draw correlations and develop models based on patterns in data 860 for the purposes of predicting events that may occur as a result of the analyzed data, said result of the data analysis including models and predictions on future data is then sent to a business operating system for further use and viewing by administratorss, and sent to internal MDTSDB for later historical model and data comparisons 870.
  • Fig. 9 is a method diagram illustrating testing and comparison of historical models and predictions with current models and predictions to find the most statistically accurate model for analyzing fused sensor data.
  • a data analysis component receives new data 910 from network- connected sensors and data sources, before a data analysis component utilizes the most recent relevant model of data to predict future values of data or events, based on current data 920.
  • a data analysis component queries an internal or connected MDTSDB for historical data and associated models and predictions 930, allowing the analysis component to compare current data and models with past data and models 940, to compare the certainty of a current model with the accuracy of the previous model that had similar data 950.
  • Such a comparison may take the form of a basic statistical analysis of a previous model’s accuracy and comparing the similarity to variables in the previous model predictions with the current data and model, or a neural network my be utilized to move from a model of less accuracy to higher accuracy over time, to find the highest certainty/ accuracy model for predicting future values based on current data, and based on historical comparisons with current data and models 960.
  • Fig. 10 is a process flow diagram of a method 1000 for predictive analysis of very large data sets using the distributed computational graph.
  • One or more streams of data from a plurality of sources which includes, but is in no way not limited to, a number of physical sensors, web based questionnaires and surveys, monitoring of electronic infrastructure, crowd sourcing campaigns, and direct human interaction, may be received by system 1001.
  • the received stream is filtered 1002 to exclude data that has been corrupted, data that is incomplete or misconfigured and therefore unusable, data that may be intact but nonsensical within the context of the analyses being run, as well as a plurality of predetermined analysis related and unrelated criteria set by the authors.
  • Filtered data may be split into two identical streams at this point (second stream not depicted for simplicity), wherein one substream may be sent for batch processing 1600 while another substream may be formalized 1003 for transformation pipeline analysis 1004 and retraining 1005.
  • Data formalization for transformation pipeline analysis acts to reformat the stream data for optimal, reliable use during analysis. Reformatting might entail, but is not limited to: setting data field order, standardizing measurement units if choices are given, splitting complex information into multiple simpler fields, and stripping unwanted characters, again, just to name a few simple examples.
  • the formalized data stream may be subjected to one or more transformations. Each transformation acts as a function on the data and may or may not change the data.
  • transformations working on the same data stream where the output of one transformation acts as the input to the next are represented as transformation pipelines. While the great majority of transformations in transformation pipelines receive a single stream of input, modify the data within the stream in some way and then pass the modified data as output to the next transformation in the pipeline, the invention does not require these characteristics.
  • individual transformations can receive input of expected form from more than one source or receive no input at all as would a transformation acting as a timestamp.
  • individual transformations may not modify the data as would be encountered with a data store acting as a queue for downstream transformations.
  • individual transformations may provide output to more than one downstream transformations.
  • transformations in a transformation pipeline backbone may form a linear, a quasi- linear arrangement or may be cyclical, where the output of one of the internal transformations serves as the input of one of its antecedents allowing recursive analysis to be run.
  • Fig. 11 is a process flow diagram of a method 1100 for an aspect of modeling the transformation pipeline module of the invention as a directed graph using graph theory.
  • the individual transformations 1102, 1104, 1106 of the transformation pipeline ti..t n such that each T are represented as graph nodes. Transformations belonging to T are discrete transformations over individual datasets di, consistent with classical functions.
  • each individual transformation t j receives a set of inputs and produces a single output.
  • the input of an individual transformation t h is defined with the function in: h di..d k such that m(U) — ⁇ di..d k ) and describes a transformation with k inputs.
  • the output of an individual transformation is defined as the function out: h [Idi] to describe transformations that produce a single output (usable by other transformations).
  • a dependency function can now be defined such that dep(t a ,t b ) out(t a )in(t b )The messages carrying the data stream through the transformation pipeline 1101,1103, 1105 make up the graph edges.
  • Fig. 12 is a method diagram illustrating an insurance use-case of the system.
  • sensor data, manual entries, and automated web crawling components gather data to be ingested by sensor fusion suite 1210, either through active polling of data sources or by data sources sending data actively to the sensor fusion suite.
  • Data gathered will encompass an event occurrence or occurrences such as a hurricane, forest fire, house fire, earthquake, or other disaster or insurance-claimable event 1220, allowing an analysis module to analyze the probability of events coinciding and being false- positives 1230 given historical or newly developed event modelling.
  • Machine learning techniques such as supervised neural networks may be used in an analysis module to compare similar events from the past in the MDTSDB to current data readings and what the predictive accuracy of models historically were 1240, to produce gradually more and more accurate models for varying sets of data.
  • the system develops predictions of risk assessment for insurance purposes, possible damages, and scope of incidents 1250, to aid in insurance companies more adequately being able to predict costs and liability both during events and outside of the events (for instance, examining the likelihood that a given area with certain characteristics and history might encounter certain kinds of disasters in a given timeframe).
  • the system then makes predictions and projections available to business operating system and administrators for viewing and further analysis if necessary 1260.
  • sensor data, manual entries, and automated web crawling components gather data to be ingested by sensor fusion suite 1310, either through active polling of data sources or by data sources sending data actively to the sensor fusion suite.
  • Data gathered will encompass an event occurrence or occurrences such as a hurricane, forest fire, house fire, earthquake, or other disaster or insurance- claimable event 1320, allowing an analysis module to analyze the probability of events coinciding and being false-positives 1330 given historical or newly developed event modelling.
  • Machine learning techniques such as supervised neural networks may be used in an analysis module to compare similar events from the past in the MDTSDB to current data readings and what the predictive accuracy of models historically were 1340, to produce gradually more and more accurate models for varying sets of data.
  • the system must then develop predictions of risk assessment, scope of incidents, type of dangers or damages, and any data indicating availability of emergency services 1350, for instance by normalization of data a neural network can be devised with the proper inputs to predict a forest fire in an area and the rate of spread of the fire, allowing the system to generate predictions of the likelihood of incident(s) occurring and what sort of emergency personnel are needed, where, and when, with associated percentages indicating likelihood of outcome 1360.
  • Fig. 14 is a method diagram illustrating a financial market trading use-case of the system.
  • sensor data, manual entries, and automated web crawling components gather data to be ingested by sensor fusion suite 1410, either through active polling of data sources or by data sources sending data actively to the sensor fusion suite.
  • Data gathered will encompass an event occurrence or occurrences such as a hurricane, forest fire, house fire, earthquake, other disasters, financial data and stock movements, news headlines relating to trade etc.1420, allowing an analysis module to analyze the probability of events coinciding and being false-positives 1430 given historical or newly developed event modelling.
  • Machine learning techniques such as supervised neural networks may be used in an analysis module to compare similar events from the past in the MDTSDB to current data readings and what the predictive accuracy of models historically were 1440, to produce gradually more and more accurate models for varying sets of data.
  • the method in this embodiment then proceeds to develop predictions of financial market movements in the short and mid-term based on known events and current market behavior 1450, in a similar fashion to predicting possible natural disasters in other embodiments.
  • the system then makes predictions and projections available to the business operating system and administrators for viewing and further analysis if necessary 1460.
  • Fig. 15 is a process flow diagram of a method 1500 for one aspect of a transformation pipeline where the topology of all or part of the pipeline is cyclical 1501.
  • the output stream of one transformation node 1504 acts as an input of an antecedent transformation node within the pipeline 1502 serialization or decomposition linearizes this cyclical configuration by completing the transformation of all of the nodes that make up a single cycle 1502, 1503, 1504 and then storing the result of that cycle in a data store 1505. That result of a cycle is then reintroduced to the transformation pipeline as input 1506 to the first transformation node of the cycle.
  • this configuration is by nature recursive, special programming to unfold the recursions was developed for the invention to accommodate it.
  • FIG. 1 is a diagram of an exemplary architecture of a business operating system 100 according to an embodiment of the invention.
  • the directed computational graph module 155 retrieves one or more streams of data from a plurality of sources, which includes, but is in no way not limited to, a plurality of physical sensors, network service providers, web based questionnaires and surveys, monitoring of electronic infrastructure, crowd sourcing campaigns, and human input device information.
  • data may be split into two identical streams in a specialized pre-programmed data pipeline 155a, wherein one sub-stream may be sent for batch processing and storage while the other sub-stream may be reformatted for transformation pipeline analysis.
  • the data is then transferred to the general transformer service module 160 for linear data transformation as part of analysis or the decomposable transformer service module 150 for branching or iterative transformations that are part of analysis.
  • the directed computational graph module 155 represents all data as directed graphs where the transformations are nodes and the result messages between transformations edges of the graph.
  • the high volume web crawling module 115 uses multiple server hosted preprogrammed web spiders, which while autonomously configured are deployed within a web scraping framework 115a of which SCRAPYTM is an example, to identify and retrieve data of interest from web based sources that are not well tagged by conventional web crawling technology.
  • the multiple dimension time series data store module 120 may receive streaming data from a large plurality of sensors that may be of several different types.
  • the multiple dimension time series data store module may also store any time series data encountered by the system such as but not limited to enterprise network usage data, component and system logs, performance data, network service information captures such as, but not limited to news and financial feeds, and sales and service related customer data.
  • the module is designed to accommodate irregular and high volume surges by dynamically allotting network bandwidth and server processing channels to process the incoming data.
  • Inclusion of programming wrappers for languages examples of which are, but not limited to C++, PERL, PYTHON, and ERLANGTM allows sophisticated programming logic to be added to the default function of the multidimensional time series database 120 without intimate knowledge of the core programming, greatly extending breadth of function.
  • Data retrieved by the multidimensional time series database 120 and the high volume web crawling module 115 may be further analyzed and transformed into task optimized results by the directed computational graph 155 and associated general transformer service 150 and decomposable transformer service 160 modules.
  • data from the multidimensional time series database and high volume web crawling modules may be sent, often with scripted cuing information determining important vertexes 145a, to the graph stack service module 145 which, employing standardized protocols for converting streams of information into graph representations of that data, for example, open graph internet technology although the invention is not reliant on any one standard.
  • the graph stack service module 145 represents data in graphical form influenced by any pre-determined scripted modifications 145a and stores it in a graph-based data store 145b such as GIRAPHTM or a key value pair type data store REDISTM, or RIAKTM, among others, all of which are suitable for storing graph-based information.
  • Results of the transformative analysis process may then be combined with further client directives, additional business rules and practices relevant to the analysis and situational information external to the already available data in the automated planning service module 130 which also runs powerful information theory 130a based predictive statistics functions and machine learning algorithms to allow future trends and outcomes to be rapidly forecast based upon the current system derived results and choosing each a plurality of possible business decisions.
  • the automated planning service module 130 may propose business decisions most likely to result is the most favorable business outcome with a usably high level of certainty.
  • the action outcome simulation module 125 with its discrete event simulator programming module 125a coupled with the end user facing observation and state estimation service 140 which is highly scriptable 140b as circumstances require and has a game engine 140a to more realistically stage possible outcomes of business decisions under consideration, allows business decision makers to investigate the probable outcomes of choosing one pending course of action over another based upon analysis of the current available data.
  • the Information Assurance department is notified by the system 100 that principal X is using credentials K (Kerberos Principal Key) never used by it before to access service Y.
  • Service Y utilizes these same credentials to access secure data on data store Z.
  • Ad hoc simulations of these traffic patterns are run against the baseline by the action outcome simulation module 125 and its discrete event simulator 125a which is used here to determine probability space for likelihood of legitimacy.
  • the system 100 based on this data and analysis, was able to detect and recommend mitigation of a cyberattack that represented an existential threat to all business operations, presenting, at the time of the attack, information most needed for an actionable plan to human analysts at multiple levels in the mitigation and remediation effort through use of the observation and state estimation service 140 which had also been specifically preprogrammed to handle cybersecurity events 140b
  • Fig. 2 is a diagram of an exemplary architecture of a system for the capture and storage of time series data from sensors with heterogeneous reporting profiles according to an embodiment of the invention 200.
  • a plurality of sensor devices 210a-n stream data to a collection device, in this case a web server acting as a network gateway 215.
  • These sensors 210a-n can be of several forms, some non-exhaustive examples being: physical sensors measuring humidity, pressure, temperature, orientation, and presence of a gas; or virtual such as programming measuring a level of network traffic, memory usage in a controller, and number of times the word “refill” is used in a stream of email messages on a particular network segment, to name a small few of the many diverse forms known to the art.
  • the sensor data is passed without transformation to the data management engine 220, where it is aggregated and organized for storage in a specific type of data store 225 designed to handle the multidimensional time series data resultant from sensor data.
  • Raw sensor data can exhibit highly different delivery characteristics. Some sensor sets may deliver low to moderate volumes of data continuously. It would be infeasible to attempt to store the data in this continuous fashion to a data store as attempting to assign identifying keys and the to store real time data from multiple sensors would invariably lead to significant data loss.
  • the data stream management engine 220 would hold incoming data in memory, keeping only the parameters, or “dimensions” from within the larger sensor stream that are pre decided by the administrator of the study as important and instructions to store them transmitted from the administration device 212.
  • the data stream management engine 220 would then aggregate the data from multiple individual sensors and apportion that data at a predetermined interval, for example, every 10 seconds, using the the timestamp as the key when storing the data to a multidimensional time series data store over a single swimlane of sufficient size.
  • This highly ordered delivery of a foreseeable amount of data per unit time is particularly amenable to data capture and storage but patterns where delivery of data from sensors occurs irregularly and the amount of data is extremely heterogeneous are quite prevalent. In these situations, the data stream management engine cannot successfully use strictly single time interval over a single swimlane mode of data storage.
  • the invention also can make use of event based storage triggers where a predetermined number of data receipt events, as set at the administration device 212, triggers transfer of a data block consisting of the apportioned number of events as one dimension and a number of sensor ids as the other.
  • the system time at commitment or a time stamp that is part of the sensor data received is used as the key for the data block value of the value-key pair.
  • the invention can also accept a raw data stream with commitment occurring when the accumulated stream data reaches a predesigned size set at the administration device 212.
  • the embodiment of the invention can, if capture parameters pre-set at the administration device 212, combine the data movement capacity of two or more swimlanes, the combined bandwidth dubbed a metaswimlane, transparently to the committing process, to accommodate the influx of data in need of commitment. All sensor data, regardless of delivery circumstances are stored in a multidimensional time series data store 225 which is designed for very low overhead and rapid data storage and minimal maintenance needs to sap resources.
  • the embodiment uses a key-value pair data store examples of which are Riak, Redis and Berkeley DB for their low overhead and speed, although the invention is not specifically tied to a single data store type to the exclusion of others known in the art should another data store with better response and feature characteristics emerge. Due to factors easily surmised by those knowledgeable in the art, data store commitment reliability is dependent on data store data size under the conditions intrinsic to time series sensor data analysis. The number of data records must be kept relatively low for the herein disclosed purpose. As an example one group of developers restrict the size of their multidimensional time series key-value pair data store to approximately 8.64 x 20 4 records, equivalent to 24 hours of 1 second interval sensor readings or 60 days of 1 minute interval readings. In this development system the oldest data is deleted from the data store and lost.
  • the archival storage is included 230.
  • This archival storage might be locally provided by the user, might be cloud based such as that offered by Amazon Web Services or Google or could be any other available very large capacity storage method known to those skilled in the art.
  • data spec might be replaced by a list of individual sensors from a larger array of sensors and each sensor in the list might be given a human readable identifier in the format “sensor AS identifier” “unit” allows the researcher to assign a periodicity for the sensor data such as second (s), minute (m), hour (h).
  • transformational filters which include but a not limited to: mean, median, variance, standard deviation, standard linear interpolation, or Kalman filtering and smoothing, may be applied and then data formatted in one or more formats examples of with are text, JSON, KML, GEOJSON and TOPOJSON among others known to the art, depending on the intended use of the data.
  • Fig. 5 is a diagram of an exemplary architecture of a distributed system 500 for rapid, large volume, search and retrieval of unstructured or loosely structured information found on sources such as the World Wide Web, according to a preferred embodiment of the invention.
  • scrape campaign requests which are comprised of a plurality of scrape agent (spider) configuration parameters as well as scrape campaign control directives, may be entered from a connected computer terminal 520 or by terminal-like commands issued by external software applications 510 using a built in command line interface 530.
  • similar scrape campaign requests may enter the system through an HTTP REST-based API usingJSON-compliant instructions 540.
  • Scrape campaign parameters enter a distributed scrape campaign controller module 550, where they are formalized and stored in a scrape request data store 570 as one or more scrape campaign-related spider configurations 572, 573, 574 and associated scrape campaign control directives 571.
  • Scrape campaigns remain persistently stored 560 until a command to run one or more of them is received through command line interface 530 or HTTP-based API 540, at which time request parameters 571, 572, etc. for a campaign are retrieved by distributed scrape campaign controller module 550 from scrape request data store 570. Persistent storage of scrape campaign request parameters also allows the same scrape campaign to be run multiple times and used as a starting point for design of similar scrape campaigns.
  • distributed scrape campaign controller module 550 Upon receipt of a command to run a specific scrape campaign and retrieval of that scrape campaign’s configuration and control parameters, coordinates the scrape campaign in regards to the number of spiders 582, 583, 584 to be used, and the number of distributed scrape servers 580, 590, 5100 to be used based upon the control directives for that campaign. Distributed scrape campaign controller module 550 then sends appropriate instructions to scrape servers 580, 590, 5100 to initiate and run the requested scrape campaign.
  • scrape controller module 550 which directs the scrape servers 580, 590, 5100 accordingly to initiate and run the requested multipage or multisite scrape campaign.
  • scrape controller module 581, 591, 5101 of each scrape server 580, 590, 5110 executes the required scrapes.
  • Scrape controller module 580590, 5110 hosts the programming for the spiders into which it loads scrape campaign spider configuration parameters sent to scrape server 580, 590, 5110 from distributed scrape campaign controller module 550 using the co-sent scrape campaign control directives to determine the number of spider instances 582, 583, 584 to create and the resource usage priority each spider is given on the server. It is possible that all spider 582, 583, 584 instances on a given scrape server 580 will be scraping the same web target 613; however, the invention does not require this and is instead set up to make efficient use of scrape server resources.
  • a single scrape server 590; 5110 may execute spiders scraping different web targets 592, 593, 594; 5102, 5103, 5104 and the spiders scraping a single web target 582, 583, 593; 592, 5102; 594, 5103 may be distributed across multiple servers 580; 590; 5100.
  • Scrape controller module 581, 591, 5101 of each scrape server 580, 590, 5100 monitors the progress and operational status of the spiders it has executed and returns that information back to distributed scrape controller module 550.
  • Both the progress and operational data is stored as log data 575 in scrape request store 570 and is made available to the authors of the scrape campaign during its operation, which may result in directives being issued that change one or more aspects of the scrape campaign.
  • the invention is designed to allow such mid-campaign parameter changes without downtime or loss of collected, intermediate, data.
  • Results of the scrapes returned to scrape controller module 581, 591, 5100 by individual spiders 582, 583, 584, 592, 593, 594, 5102, 5103, 5104 are sent to persistence service server 5120, which aggregates the data from individual scrape server spiders 582, 583, 584, 592, 593,
  • 594, 5102, 5103, 5104 performs any transformations pre-designed by the authors of the scrape campaign prior to outputting the data in a format determined by the authors of the campaign. This may involve sending the output to external software applications for further processing.
  • the data may also be processed for storage by persistence service server 5120 and sent to a persistence data store for more permanent archival 5130.
  • the core distributed scrape campaign system distributes load across a pool of scrape servers, coordinates the number of spiders employed within a scrape campaign, and prioritizes allotment of scrape server resources among spiders, it does not internally manage or control spider web page and link follow restrictions, crawling frequencies, and so forth. Individual spiders must implement suitable controls and crawling orchestration (which is external to the distributed scrape campaign system). All of these considerations are part of the scrape campaign spider configuration parameters that are received from the authors of scrape campaigns 510, 520 by distributed scrape campaign controller module 550. This is done to give the authors of the scrape maximal flexibility in the behavior of the spiders during a scrape campaign while allowing the use of a robust yet easily deployed spider programming interface.
  • FIG. 6 is a system diagram showing heterogeneous data sources connected through a network to a sensor fusion suit which is further connected to a business operating system, according to an embodiment.
  • a business operating system 100 connects to a sensor fusion suite 605, such a connection being in the form of a network connection over either wireless or wired LAN or WAN, including but not limited to the Internet.
  • a sensor fusion suite 605 serves to perform initial ingestion, transformation, and analysis of incoming sensory data from a network 610 which may be a LAN, WAN, the Internet, or some other network.
  • Sources of sensory data may be from network-connected cameras 620, physical sensors 630 including earthquake sensors or satellite sensors that may relay various readings of different kinds, a webcrawler 640 which may gather data from social media 641, a search engine or search engines 642, or news sources 643, or some other web-accessible source.
  • Other sources of sensor data include a market crawler 650 for financial market data gathering, and manual data inputs 660, which may include such things as users manually typing database entries or queries into an online portal, or individuals reporting events as they happen on the ground during disasters using a specific portal or a generalized portal, or any other manual entry of data. These sources may all be both polled actively by a sensor fusion suite 605 or may send their data to a sensor fusion suite 605 themselves over a network 610.
  • Fig. 7 is a system diagram of components comprising a master sensor fuser, according to an embodiment.
  • a network adapter 710 allows the sensor fusion suite 605 to connect to a network and gather data from sources including physical sensors, webcrawling, and more, and communicate with internal components comprising a data ingester 720 and data analyzer 750.
  • a data ingester 720 communicates further with a multidimensional time-series database (MDTSDB) 730, and a data transformer 740, sending any received data and included metadata as necessary to the MDTSDB for storage and marking with the time the data matches, and the same data being sent to a data transformer 740, where transformation including stripping of extraneous data, stripping of data formatting that is no longer relevant, stripping of data that is malformed or corrupted, or normalizing of ingested data, before sending the data with any transformations to a data analysis component 750.
  • MDTSDB multidimensional time-series database
  • a data analyzer 750 takes the data after any transformations are applied (if any are applied) and may draw correlations between datapoints and sensor input, draw on past data and models from the MDTSDB 730, and develop models for predicting other data values or future data values, using machine learning techniques such as unsupervised neural networks.
  • transformed data may include normalized inputs from house fire alarms in a neighborhood, news sources indicating a fire in the neighborhood happened, and webcrawling that indicates the average worth of homes in the neighborhood, to result in statistical predictions of what possible damages an insurance provider may incur, compared with past events and predictions if possible so as to train a more accurate data prediction analysis.
  • This analysis is then sent both to the MDTSDB 730 and the network adapter 710 to be sent to a business operating system for any viewing or further processing or storage.
  • the techniques disclosed herein may be implemented on hardware or a combination of software and hardware. For example, they may be implemented in an operating system kernel, in a separate user process, in a library package bound into network applications, on a specially constructed machine, on an application-specific integrated circuit (ASIC), or on a network interface card.
  • ASIC application-specific integrated circuit
  • Software/hardware hybrid implementations of at least some of the aspects disclosed herein may be implemented on a programmable network-resident machine (which should be understood to include intermittently connected network-aware machines) selectively activated or reconfigured by a computer program stored in memory.
  • Such network devices may have multiple network interfaces that may be configured or designed to utilize different types of network communication protocols.
  • a general architecture for some of these machines may be described herein in order to illustrate one or more exemplary means by which a given unit of functionality may be implemented.
  • At least some of the features or functionalities of the various aspects disclosed herein may be implemented on one or more general-purpose computers associated with one or more networks, such as for example an end-user computer system, a client computer, a network server or other server system, a mobile computing device (e.g., tablet computing device, mobile phone, smartphone, laptop, or other appropriate computing device), a consumer electronic device, a music player, or any other suitable electronic device, router, switch, or other suitable device, or any combination thereof.
  • at least some of the features or functionalities of the various aspects disclosed herein may be implemented in one or more virtualized computing environments (e.g., network computing clouds, virtual machines hosted on one or more physical computing machines, or other appropriate virtual environments).
  • FIG. 16 there is shown a block diagram depicting an exemplary computing device 10 suitable for implementing at least a portion of the features or functionalities disclosed herein.
  • Computing device 10 may be, for example, any one of the computing machines listed in the previous paragraph, or indeed any other electronic device capable of executing software- or hardware -based instructions according to one or more programs stored in memory.
  • Computing device 10 may be configured to communicate with a plurality of other computing devices, such as clients or servers, over communications networks such as a wide area network a metropolitan area network, a local area network, a wireless network, the Internet, or any other network, using known protocols for such communication, whether wireless or wired.
  • communications networks such as a wide area network a metropolitan area network, a local area network, a wireless network, the Internet, or any other network, using known protocols for such communication, whether wireless or wired.
  • computing device 10 includes one or more central processing units (CPU) 12, one or more interfaces 15, and one or more busses 14 (such as a peripheral component interconnect (PCI) bus).
  • CPU 12 may be responsible for implementing specific functions associated with the functions of a specifically configured computing device or machine.
  • a computing device 10 may be configured or designed to function as a server system utilizing CPU 12, local memory 11 and/or remote memory 16, and interface(s) 15.
  • CPU 12 may be caused to perform one or more of the different types of functions and/ or operations under the control of software modules or components, which for example, may include an operating system and any appropriate applications software, drivers, and the like.
  • CPU 12 may include one or more processors 13 such as, for example, a processor from one of the Intel, ARM, Qualcomm, and AMD families of microprocessors.
  • processors 13 may include specially designed hardware such as application-specific integrated circuits (ASICs), electrically erasable programmable read-only memories (EEPROMs), field-programmable gate arrays (FPGAs), and so forth, for controlling operations of computing device 10.
  • ASICs application-specific integrated circuits
  • EEPROMs electrically erasable programmable read-only memories
  • FPGAs field-programmable gate arrays
  • a local memory 11 such as non-volatile random access memory (RAM) and/ or read-only memory (ROM), including for example one or more levels of cached memory
  • RAM non-volatile random access memory
  • ROM read-only memory
  • Memory 11 may be used for a variety of purposes such as, for example, caching and/ or storing data, programming instructions, and the like. It should be further appreciated that CPU 12 may be one of a variety of system-on-a-chip (SOC) type hardware that may include additional hardware such as memory or graphics processing chips, such as a QUALCOMM SNAPDRAGONTM or SAMSUNG EXYNOSTM CPU as are becoming increasingly common in the art, such as for use in mobile devices or integrated devices.
  • SOC system-on-a-chip
  • processor is not limited merely to those integrated circuits referred to in the art as a processor, a mobile processor, or a microprocessor, but broadly refers to a microcontroller, a microcomputer, a programmable logic controller, an application-specific integrated circuit, and any other programmable circuit.
  • interfaces 15 are provided as network interface cards (NICs).
  • NICs control the sending and receiving of data packets over a computer network; other types of interfaces 15 may for example support other peripherals used with computing device 10.
  • interfaces that may be provided are Ethernet interfaces, frame relay interfaces, cable interfaces, DSL interfaces, token ring interfaces, graphics interfaces, and the like.
  • interfaces may be provided such as, for example, universal serial bus (USB), Serial, Ethernet, FIREWIRETM, THUNDERBOLTTM, PCI, parallel, radio frequency (RF), BLUETOOTHTM, near-field communications (e.g., using near-field magnetics), 802.11 (WiFi), frame relay, TCP/IP, ISDN, fast Ethernet interfaces, Gigabit Ethernet interfaces, Serial ATA (SATA) or external SATA (ESATA) interfaces, high-defmition multimedia interface (HDMI), digital visual interface (DVI), analog or digital audio interfaces, asynchronous transfer mode (ATM) interfaces, high-speed serial interface (HSSI) interfaces, Point of Sale (POS) interfaces, fiber data distributed interfaces (FDDIs), and the like.
  • USB universal serial bus
  • RF radio frequency
  • BLUETOOTHTM near-field communications
  • near-field communications e.g., using near-field magnetics
  • WiFi WiFi
  • frame relay TCP/IP
  • Such interfaces 15 may include physical ports appropriate for communication with appropriate media. In some cases, they may also include an independent processor (such as a dedicated audio or video processor, as is common in the art for high-fidelity A/V hardware interfaces) and, in some instances, volatile and/or non-volatile memory (e.g., RAM).
  • an independent processor such as a dedicated audio or video processor, as is common in the art for high-fidelity A/V hardware interfaces
  • volatile and/or non-volatile memory e.g., RAM
  • FIG. 16 illustrates one specific architecture for a computing device 10 for implementing one or more of the aspects described herein, it is by no means the only device architecture on which at least a portion of the features and techniques described herein may be implemented.
  • architectures having one or any number of processors 13 may be used, and such processors 13 may be present in a single device or distributed among any number of devices.
  • a single processor 13 handles communications as well as routing computations, while in other aspects a separate dedicated communications processor may be provided.
  • different types of features or functionalities may be implemented in a system according to the aspect that includes a client device (such as a tablet device or smartphone running client software) and server systems (such as a server system described in more detail below).
  • the system of an aspect may employ one or more memories or memory modules (such as, for example, remote memory block 16 and local memory
  • Memory 16 or memories 11, 16 may also be configured to store data structures, configuration data, encryption data, historical system operations information, or any other specific or generic non-program information described herein.
  • At least some network device aspects may include nontransitory machine-readable storage media, which, for example, may be configured or designed to store program instructions, state information, and the like for performing various operations described herein.
  • nontransitory machine- readable storage media include, but are not limited to, magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD-ROM disks; magneto-optical media such as optical disks, and hardware devices that are specially configured to store and perform program instructions, such as read-only memory devices (ROM), flash memory (as is common in mobile devices and integrated systems), solid state drives (SSD) and “hybrid SSD” storage drives that may combine physical components of solid state and hard disk drives in a single hardware device (as are becoming increasingly common in the art with regard to personal computers), memristor memory, random access memory (RAM), and the like.
  • ROM read-only memory
  • flash memory as is common in mobile devices and integrated systems
  • SSD solid state drives
  • hybrid SSD hybrid SSD
  • such storage means may be integral and non-removable (such as RAM hardware modules that may be soldered onto a motherboard or otherwise integrated into an electronic device), or they may be removable such as swappable flash memory modules (such as “thumb drives” or other removable media designed for rapidly exchanging physical storage devices), “hot-swappable” hard disk drives or solid state drives, removable optical storage discs, or other such removable media, and that such integral and removable storage media may be utilized interchangeably.
  • swappable flash memory modules such as “thumb drives” or other removable media designed for rapidly exchanging physical storage devices
  • hot-swappable hard disk drives or solid state drives
  • removable optical storage discs or other such removable media
  • program instructions include both object code, such as may be produced by a compiler, machine code, such as may be produced by an assembler or a linker, byte code, such as may be generated by for example a JAVATM compiler and may be executed using a Java virtual machine or equivalent, or files containing higher level code that may be executed by the computer using an interpreter (for example, scripts written in Python, Perl, Ruby, Groovy, or any other scripting language).
  • interpreter for example, scripts written in Python, Perl, Ruby, Groovy, or any other scripting language.
  • FIG. 17 there is shown a block diagram depicting a typical exemplary architecture of one or more aspects or components thereof on a standalone computing system.
  • Computing device 20 includes processors 21 that may run software that carry out one or more functions or applications of aspects, such as for example a client application 24.
  • Processors 21 may carry out computing instructions under control of an operating system 22 such as, for example, a version of MICROSOFT WINDOWSTM operating system, APPLE macOSTM or iOSTM operating systems, some variety of the Linux operating system, ANDROIDTM operating system, or the like.
  • an operating system 22 such as, for example, a version of MICROSOFT WINDOWSTM operating system, APPLE macOSTM or iOSTM operating systems, some variety of the Linux operating system, ANDROIDTM operating system, or the like.
  • one or more shared services 23 may be operable in system 20, and may be useful for providing common services to client applications 24.
  • Services 23 may for example be WINDOWSTM services, user-space common services in a Linux environment, or any other type of common service architecture used with operating system 21.
  • Input devices 28 may be of any type suitable for receiving user input, including for example a keyboard, touchscreen, microphone (for example, for voice input), mouse, touchpad, trackball, or any combination thereof.
  • Output devices 27 may be of any type suitable for providing output to one or more users, whether remote or local to system 20, and may include for example one or more screens for visual output, speakers, printers, or any combination thereof.
  • Memory 25 may be random-access memory having any structure and architecture known in the art, for use by processors 21, for example to run software.
  • Storage devices 26 may be any magnetic, optical, mechanical, memristor, or electrical storage device for storage of data in digital form (such as those described above, referring to Fig. 16). Examples of storage devices 26 include flash memory, magnetic hard drive, CD-ROM, and/ or the like.
  • systems may be implemented on a distributed computing network, such as one having any number of clients and/ or servers.
  • a distributed computing network such as one having any number of clients and/ or servers.
  • Fig. 18 there is shown a block diagram depicting an exemplary architecture 30 for implementing at least a portion of a system according to one aspect on a distributed computing network.
  • any number of clients 33 may be provided.
  • Each client 33 may run software for implementing client-side portions of a system; clients may comprise a system 20 such as that illustrated in Fig. 17.
  • any number of servers 32 may be provided for handling requests received from one or more clients 33.
  • Clients 33 and servers 32 may communicate with one another via one or more electronic networks 31 , which may be in various aspects any of the Internet, a wide area network, a mobile telephony network (such as CDMA or GSM cellular networks), a wireless network (such as WiFi, WiMAX, LTE, and so forth), or a local area network (or indeed any network topology known in the art; the aspect does not prefer any one network topology over any other).
  • Networks 31 may be implemented using any known network protocols, including for example wired and/ or wireless protocols.
  • servers 32 may call external services 37 when needed to obtain additional information, or to refer to additional data concerning a particular call. Communications with external services 37 may take place, for example, via one or more networks 31.
  • external services 37 may comprise web-enabled services or functionality related to or installed on the hardware device itself. For example, in one aspect where client applications 24 are implemented on a smartphone or other electronic device, client applications 24 may obtain information stored in a server system 32 in the cloud or on an external service 37 deployed on one or more of a particular enterprise’s or user’s premises.
  • clients 33 or servers 32 may make use of one or more specialized services or appliances that may be deployed locally or remotely across one or more networks 31.
  • one or more databases 34 may be used or referred to by one or more aspects. It should be understood by one having ordinary skill in the art that databases 34 may be arranged in a wide variety of architectures and using a wide variety of data access and manipulation means.
  • one or more databases 34 may comprise a relational database system using a structured query language (SQL), while others may comprise an alternative data storage technology such as those referred to in the art as “NoSQL” (for example, HADOOP CASSANDRATM, GOOGLE BIGTABLETM, and so forth).
  • SQL structured query language
  • variant database architectures such as column-oriented databases, in-memory databases, clustered databases, distributed databases, or even flat file data repositories may be used according to the aspect. It will be appreciated by one having ordinary skill in the art that any combination of known or future database technologies may be used as appropriate, unless a specific database technology or a specific arrangement of components is specified for a particular aspect described herein. Moreover, it should be appreciated that the term “database” as used herein may refer to a physical database machine, a cluster of machines acting as a single database system, or a logical database within an overall database management system.
  • security systems 36 and configuration systems 35 may make use of one or more security systems 36 and configuration systems 35.
  • Security and configuration management are common information technology (IT) and web functions, and some amount of each are generally associated with any IT or web systems. It should be understood by one having ordinary skill in the art that any configuration or security subsystems known in the art now or in the future may be used in conjunction with aspects without limitation, unless a specific security 36 or configuration system 35 or approach is specifically required by the description of any specific aspect.
  • Fig. 19 shows an exemplary overview of a computer system 40 as may be used in any of the various locations throughout the system. It is exemplary of any computer that may execute code to process data. Various modifications and changes may be made to computer system 40 without departing from the broader scope of the system and method disclosed herein.
  • Central processor unit (CPU) 41 is connected to bus 42, to which bus is also connected memory 43, nonvolatile memory 44, display 47, input/ output (1/ O) unit 48, and network interface card (NIC) 53.
  • 1/ O unit 48 may, typically, be connected to keyboard 49, pointing device 50, hard disk 52, and real-time clock 51.
  • NIC 53 connects to network 54, which may be the Internet or a local network, which local network may or may not have connections to the Internet.
  • power supply unit 45 connected, in this example, to a main alternating current (AC) supply 46.
  • AC alternating current

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Abstract

L'invention concerne un système et un procédé d'affinement participatif d'un phénomène naturel pour une gestion des risques et une validation de contrat, comprenant au moins un mélange hétérogène de capteurs et de techniques de collecte de données, une suite de fusion de capteurs, et un système d'exploitation commerciale, qui ingère, transforme le cas échéant, et analyse les données reçues et développe et applique des modèles de prédiction de conséquences des données de capteur et des événements futurs sur la base de telles données à des fins telles que la responsabilité d'assurance et l'évaluation des risques, la planification de services d'urgence et des prédictions de marché financier, et la comparaison de modèles historiques et de données avec des données actuelles et des modèles pour tenter d'affiner et d'utiliser un modèle prédictif plus précis à ces fins.
PCT/US2020/051840 2019-09-19 2020-09-21 Système et procédé d'affinement participatif d'un phénomène naturel pour une gestion des risques et une validation de contrat WO2021055964A1 (fr)

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US16/575,929 2019-09-19
US16/575,929 US11074652B2 (en) 2015-10-28 2019-09-19 System and method for model-based prediction using a distributed computational graph workflow

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115632840A (zh) * 2022-10-08 2023-01-20 北京天融信网络安全技术有限公司 基于零信任的风险处理方法、装置、设备及存储介质

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100223226A1 (en) * 2009-02-27 2010-09-02 International Business Machines Corporation System for monitoring global online opinions via semantic extraction
US20120296845A1 (en) * 2009-12-01 2012-11-22 Andrews Sarah L Methods and systems for generating composite index using social media sourced data and sentiment analysis
US20160119365A1 (en) * 2014-10-28 2016-04-28 Comsec Consulting Ltd. System and method for a cyber intelligence hub
US20180218453A1 (en) * 2015-10-28 2018-08-02 Fractal Industries, Inc. Platform for autonomous management of risk transfer

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100223226A1 (en) * 2009-02-27 2010-09-02 International Business Machines Corporation System for monitoring global online opinions via semantic extraction
US20120296845A1 (en) * 2009-12-01 2012-11-22 Andrews Sarah L Methods and systems for generating composite index using social media sourced data and sentiment analysis
US20160119365A1 (en) * 2014-10-28 2016-04-28 Comsec Consulting Ltd. System and method for a cyber intelligence hub
US20180218453A1 (en) * 2015-10-28 2018-08-02 Fractal Industries, Inc. Platform for autonomous management of risk transfer

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115632840A (zh) * 2022-10-08 2023-01-20 北京天融信网络安全技术有限公司 基于零信任的风险处理方法、装置、设备及存储介质
CN115632840B (zh) * 2022-10-08 2023-07-04 北京天融信网络安全技术有限公司 基于零信任的风险处理方法、装置、设备及存储介质

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