Abstract: A course prediction device (1) includes a blind spot calculation unit (11) and a prediction unit (12). The blind spot calculation unit (11) acquires a sensing result sequentially from a sensor which is arranged in a mobile object and which detects whether an object exists, and calculates, based on sensor information, a blind spot area expressing such an area that an object existing therein cannot be detected by the sensor. The blind spot calculation unit (11) also detects an object entering the blind spot area based on the sensor information. The prediction unit (12) predicts a course in the blind spot area of the detected object based on the sensing result.

Abstract: An audio processing method by which input data are obtained that includes first sound data representing first components of a first frequency band, included in a first sound corresponding to a first sound source, second sound data representing second components of the first frequency band, included in a second sound corresponding to a second sound source, and mix sound data representing mix components of an input frequency band including a second frequency band, the mix components being included in a mix sound of the first sound and the second sound. The input data are then input to a trained estimation model, to generate at least one of first output data representing first estimated components within an output frequency band including the second frequency band, included in the first sound, or second output data representing second estimated components within the output frequency band, included in the second sound.

Abstract: One example method of operation may include identifying a content thread in a content sharing forum, assigning a bot to process messages of the content thread, identifying, via the bot, a question message, parsing the question message content to identify topics of interest, processing the messages of the content thread to identify response information related to the question message, determining the response information to the question message requires third party information, and transmitting a request, with the question message content, to one or more other bots operating in other content sharing forums.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for computing a backward looking surprise metric for autonomously driven vehicles. One of the methods includes obtaining first data representing one or more previously predicted states of an agent along one or more predicted trajectories of the agent at a first time step. Second data representing one or more states of the agent at a subsequent time step is obtained. A surprise score is computed from a measure of a difference between the first data computed for the one or more predicted trajectories for the prior time step and the second data computed for the one or more predicted states for the subsequent time step.

Abstract: A computing device, method, and system are provided of improving data quality to conserve computational resources. The computing device receives a raw dataset. One or more data quality metric goals corresponding to the received raw dataset are received. A schema of the dataset is determined. An initial set of validation nodes is identified based on the schema of the dataset. The initial set of validation nodes are executed. A next set of validation nodes are iteratively expanded and executed based on the schema of the dataset until a termination criterion is reached. A corrected dataset of the raw dataset is provided based on the iterative execution of the initial and next set of validation nodes.

Abstract: Techniques for implementing Bayesian model for refining and petrochemical wall thickness monitoring are presented. The construction of a model may be automated for each piping circuit or piece of major fixed equipment, utilizing, for example, specific component data, historical thickness measurements, inspection practices and related inspection program information. The model contains nodes describing the most significant sources of variability, namely component original thicknesses, wall thickness degradation over time, corrosion rates and thickness measurement error.

Abstract: Various embodiments include systems and methods to implement a security platform providing cyberattack detection. The security platform may, with respect to a cloud compute environment, use audit log data that is associated with a particular domain of operational activity within the cloud compute environment. Based on a domain specific graph representative of factor data and dependencies between factor data, the security platform may determine a behavioral anomaly that is associated with a cyberattack.

Abstract: A computerized method for detection of categorical drift within an incoming data stream. Herein, an error threshold is computed based on a first set of training data samples selected to detect categorical drift occurring for a data stream. Thereafter, probability distributions associated with content of a first and second data samples of the data stream are computed. Analytics are conducted to compute a difference between content of the first probability distribution that is based on a first data point of the first data sample and content of the second probability distribution that is based on a first data point of the second data sample. After computing the difference, that categorical drift is determined whether categorical drift detection has been conducted.

Abstract: A machine learning algorithm is trained on a number of microscopic images and a measure of outcome of each image. Each image is divided into tiles. The measure of outcome is assigned to each tile of the image. The tiles are then used to train the machine learning algorithm. The trained algorithm may then be used to evaluate images.

Abstract: Systems and methods are provided for authorizing an electronic transaction. In one implementation at least one processor is programmed to receive electronic transaction data and historical transaction data, the electronic transaction data including an entity identifier component and an amount component of an electronic transaction; determine, based on the entity identifier component and the amount component, a location of the electronic transaction in a space of a distributed representation space, the distributed representation space comprising a mapping of electronic transaction components in a high-order space; determine locations of the historical transaction data in the distributed representation space; determine a decision boundary in the distributed representation space based on the locations of the historical transaction data; and authorize the electronic transaction based on the location of the electronic transaction being within the decision boundary.

Abstract: In one example of the technology, device information associated with a device upgrade and a plurality of devices includes risk parameters including values associated with a minimum health value that is associated with a minimum acceptable number of healthy devices among the plurality of devices and a confidence value associated with a minimum acceptable probability that the number of healthy devices among the plurality of devices is at least as great as the minimum health value; and, for each device a success probability value that is associated with a probability that the device will be healthy after the device upgrade is performed on the device. A Poisson binomial distribution is iteratively used to determine a set of devices among the plurality of device for which the largest possible number of devices are included in the set of devices while meeting the risk parameters. The set of devices is then upgraded.

Abstract: A method for discovering causality from data includes acquiring to-be-processed data, and obtaining a covariance matrix of the to-be-processed data; determining a first target column in the covariance matrix, taking the number of columns of the first target column as a first place in a rearrangement sequence, and obtaining a first upper triangular matrix according to the first target column; determining a position of the number of columns of the covariance matrix other than the first target column except the first place in the rearrangement sequence according to the first target column and the first upper triangular matrix, and obtaining an upper triangular matrix in each position determination; obtaining an adjacency matrix according to an upper triangular matrix and a rearrangement sequence obtained in final position determination; and generating directed acyclic graph (DAG) by using the adjacency matrix, and taking the DAG as causality discovery result of the to-be-processed data.

Abstract: An urban design pipeline automatically generates design options for an urban design project. The urban design pipeline includes a geometry engine and an evaluation engine. The geometry engine analyzes design criteria, design objectives, and design heuristics associated with the urban design project and then generates numerous candidate designs. The design criteria specify a property boundary associated with a region of land to be developed. The design objectives indicate a specific type of topology that is derived from an existing urban layout. The design heuristics include different sets of construction rules for generating designs with specific types of topologies. The geometry engine generates candidate designs that conform to the property boundary and have topological characteristics in common with the existing urban layout.

Abstract: Sound source enhancement technology is provided that is capable of improving sound source enhancement capabilities in accordance with settings of usage and applications. A PSD optimization device includes a PSD updating unit that takes a target sound PSD input value {circumflex over (?)}?S(?, ?), an interference noise PSD input value {circumflex over (?)}?IN(?, ?), and a background noise PSD input value {circumflex over (?)}?BN(?, ?) as input, and generates a target sound PSD output value ?S(?, ?), an interference noise PSD output value {circumflex over (?)}?IN(?, ?), and a background noise PSD output value {circumflex over (?)}?BN(?, ?), by solving an optimization problem for a cost function relating to a variable uS representing a target sound PSD, a variable uIN representing an interference noise PSD, and a variable uBN representing a background noise PSD.

Abstract: An association metric for record attributes associated with cardinalities that are not necessarily the same is used for training and/or applying an entity resolution (ER) model. A pair of records includes (a) a first record indicating a first set of values for a first attribute and (b) a second record indicating a second set of values for a second attribute. Each of the first set of values and each of the second set of values are compared to determine individual association metrics. A first-level reduction operation is applied to subsets of the individual association metrics to determine reduced association metrics. A second-level reduction operation is applied to the reduced association metrics to determine an association metric, for the pair of records, for training and/or applying an ER model.

Abstract: Techniques are described herein for collaborative search sessions through an automated assistant. A method includes: receiving, from a first user of a first client device, a first query in a query session; providing, to the first user, a first set of search results; determining, based on at least one term in the first query, that the first query is relevant to a second user of the first client device; providing, to the second user, a selectable option to join the query session; in response to receiving, from the second user, an indication of acceptance of the selectable option, adding the second user to the query session; receiving, from the second user, additional input; generating, based on the additional input received from the second user, a modified set of search results; and providing, to the first user and the second user, the modified set of search results.

Abstract: Computerized methods and systems, including computer programs encoded on a computer storage medium, may adaptively predict expression of emotions based on collected biometric data. For example, a computing system may receive first data indicative of a first time-evolving movement of a portion of a body during a collection period, and may obtain second data identifying predictive models that correlate default emotions with second feature values that characterize body movements during prior collection periods. Based on an outcome of the application of the least one pattern recognition algorithm or machine learning algorithm to portions of the first and second data, the system may determine a corresponding one of the default emotions represented by the first time-evolving movement, and may transmit data indicative of the corresponding one of the default emotions to the communications device for presentation to a user.

Abstract: A client device processing received emoji messages using emoji-first messaging. Text messaging is automatically converted to emojis by an emoji-first application so that only emojis are communicated from one client device to another client device. Each client device has a library of emojis that are mapped to words, which libraries are customizable and unique to the users of the client devices, such that the users can communicate secretly in code. Upon receipt of a string of emojis, a user can select the emoji string to convert to text if desired, for a predetermined period of time.

Abstract: A method of generating a multi-modal prediction is disclosed herein. A computing system retrieves event data from a data store. The event data includes information for a plurality of events across a plurality of seasons. Computing system generates a predictive model using a mixture density network, by generating an input vector from the event data learning, by the mixture density network, a plurality of values associated with a next play following each play in the event data. The mixture density network is trained to output the plurality of values near simultaneously. Computing system receives a set of event data directed to an event in a match. The set of event data includes information directed to at least playing surface position and current score. Computing system generates, via the predictive model, a plurality of values associated with a next event following the event based on the set of event data.

Abstract: An urban design pipeline automatically generates design options for an urban design project. The urban design pipeline includes a geometry engine and an evaluation engine. The geometry engine analyzes design criteria, design objectives, and design heuristics associated with the urban design project and then generates numerous candidate designs. The design criteria specify a property boundary associated with a region of land to be developed. The design objectives indicate a specific type of topology that is derived from an existing urban layout. The design heuristics include different sets of construction rules for generating designs with specific types of topologies. The geometry engine generates candidate designs that conform to the property boundary and have topological characteristics in common with the existing urban layout.

Abstract: A method includes identifying, by pre-set criteria, a first plurality of optical channels and a second plurality of optical channels using optical data generated from a plurality of optical filters of a tester tool. The method also includes determining, from the optical data, a first plurality of fluid types detected by each channel in the first plurality of optical channels using a bootstrap fluid identification technique and determining, from the optical data and the first plurality of fluid types, a second plurality of fluid types detected by each channel in the second plurality of optical channels using a guided fluid identification technique. Further, the method includes generating a spectral signature indicating fluid types for each channel within the tester tool based on the first plurality of fluid types and second plurality of fluid types.

Abstract: Sequential hypothesis testing in a digital medium environment is described using continuous data. To begin, a model is received that defines at least one data distribution. Testing data is also received that describes an effect of user interactions with the plurality of options of digital content on achieving an action using continuous non-binary data. Values of parameters of the model are then estimated for each option of the plurality of options based on the testing data. In one example. A variance estimate is then generated based on the estimated values of the parameters of the model for each option of the plurality of options. From this, a determination is made as to a decision boundary based on the variance estimate and an estimate for a mean value of each option of the plurality of options based on the testing data.

Abstract: A remote control apparatus measures a communication delay in a communication network, generates a probability distribution of the communication delay, generates, based on the probability distribution of the communication delay, a plurality of arrival patterns, including an arrival time of a control input at a control target, predicts a state of the control target at a time of arrival of the control input, calculates a control input set by calculating a plurality of control inputs corresponding respectively to the plurality of states predicted, determines a control input to be outputted to the control target, based on a result of statistical processing applied on the control input set calculated.

Abstract: A system for electronic lead generation including a semantic graph database including a knowledge graph and a dynamic profiling module comprising automated computing machinery configured to identify a near-term surge in product interest for a number of companies of a particular size in a particular industry in a particular region of the world in dependence upon the knowledge graph and create a company profile in dependence upon the size of the identified companies, the industry of the identified companies, and the region of the world of the identified companies associated with the near-term surge. Embodiments also include a lead purchase module comprising automated computing machinery configured to generate an electronic lead purchase order in dependence upon the company profile; transmit the lead purchase order to a lead generation engine; and receive, from the lead generation engine, a plurality of leads in dependence upon the lead purchase order.

Abstract: A method for partitioning NMR T1-T2 data may comprise: identifying modes in NMR T1-T2 data from a plurality of samples with a multimodal deconvolution or decomposition with regularized nonlinear inversion; deriving a modal properties vector comprising modal properties for each of the modes; performing a cluster analysis of the modes to identify clusters; assigning a poro-fluid class to the clusters based on one or more of the modal properties of the modes in each of the clusters; and deriving partitioned representations for the clusters based on the cluster analysis.

Abstract: A method and system for intelligently ranking search results may include receiving a search request containing one or more terms for performing a search, providing the one or more terms for conducting a search, and receiving a search results index containing a list of a plurality of documents, each of the plurality of documents corresponding to one of the one or more terms or to one or more other terms associated with the at least one of the one or more terms.

Abstract: A multi-scale unsupervised seismic velocity inversion method based on an autoencoder for observation data. Large-scale information is extracted by the autoencoder, which is used for guiding an inversion network to complete the recovery of different-scale features in a velocity model, thereby reducing the non-linearity degree of inversion. A trained encoder part is embedded into the network to complete the extraction of seismic observation data information at the front end, so it can better analyze the information contained in seismic data, the mapping relationship between the data and velocity model is established better, then the inversion method is unsupervised, and location codes are added to the observation data to assist the network in perceiving the layout form of an observation system, which facilitates practical engineering application. Thus a relatively accurate inversion result of the seismic velocity model when no real geological model serves as a network training label can be achieved.

Abstract: In sequence level prediction of a sequence of frames of high dimensional data one or more affective labels are provided at the end of the sequence. Each label pertains to the entire sequence of frames. An action is taken with an agent controlled by a machine learning algorithm for a current frame of the sequence at a current time step. An output of the action represents affective label prediction for the frame at the current time step. A pool of actions taken up until the current time step including the action taken with the agent is transformed into a predicted affective history for a subsequent time step. A reward is generated on predicted actions up to the current time step by comparing the predicted actions against corresponding annotated affective labels.

Abstract: A computer-implemented method for managing datasets of a storage system is provided, wherein the datasets have respective sets of metadata, the method including: successively feeding first sets of metadata to a spiking neural network (SNN), the first sets of metadata fed corresponding to datasets of the storage system that are labeled with respect to classes they belong to, so as to be associated with class labels, for the SNN to learn representations of said classes in terms of connection weights that weight the metadata fed; successively feeding second sets of metadata to the SNN, the second sets of metadata corresponding to unlabeled datasets of the storage system, for the SNN to infer class labels for the unlabeled datasets, based on the second sets of metadata fed and the representations learned; and managing datasets in the storage system, based on class labels of the datasets, these including the inferred class labels.

Abstract: The present disclosure relates to system(s) and method(s) for tuning an analytical model. The system builds a global analytical model based on modelling data received from a user. Further, the system analyses a target eco-system to identify a set of target eco-system parameters. The system further selects a sub-set of model parameters, corresponding to the set of target eco-system parameters, from a set of model parameters. Further, the system generates a local analytical model based on updating the global analytical model, based on the sub-set of model parameters and one or more PMML wrappers. The system further deploys the local analytical model at each node, from a set of nodes, associated with the target eco-system. Further, the system gathers test results from each node based on executing the local analytical model. The system further tunes the sub-set of model parameters associated with the local analytical model using federated learning algorithms.

Abstract: An industrial information hub (IIH) serves as a single industrial ecosystem platform where multiple participants can deliver repeatable and standardized services relevant to their core competencies. The IIH system can interface with a work order management system to facilitate automated creation and submission of work orders for maintenance tasks. To this end, the IIH system can monitor real-time data from industrial devices and identify conditions indicative of a performance issue requiring a maintenance action. When such a condition is identified, the IIH system can initiate creation of a work order in the work order management system for tracking and recording of the maintenance task.

Abstract: An extracted hypothesis has logical formulas leading to a possible conclusion. If the logical formulas include one including a first parameter a value obtained from observation data and a second parameter that does not have a value obtained from the observation data, the logical formula is set as a target logical formula, and a value of the second parameter acquired based on the observation data is input. If there is no such logical formula, but there is a logical formula that has, for one or more parameters, a value set in advance or obtained using inference knowledge, whether this logical formula is true or false is determined. When whether the logical formula is true or false is determined, or, when the value of the second parameter is input, the logical formula is added to the observation data, and a hypothesis is derived again.

Abstract: In at least one embodiment, the present description is directed to a computer system, having a processor to at least: electronically receive a model for one or more operating conditions, and facility operating data; iteratively perform one or more iterations of outlier bias reduction in the facility operating data based on the model, including: determining model predicted values, comparing the model predicted values to the facility operating data, removing bias facility operating data from the facility operating data of the plurality of facilities, and constructing, based at least in part on the non-biased facility operating a data, an updated model with one or more updated coefficients; determine, based on non-biased facility operating data, a non-biased performance standard for the one or more operating conditions; and track, based on the no-biased performance standard and the facility operating data, operating performance of each respective facility of the plurality of facilities.

Abstract: A device may receive training data that includes datasets associated with natural language processing, and may mask the training data to generate masked training data. The device may train a masked event C-BERT model, with the masked training data, to generate pretrained weights and a trained masked event C-BERT model, and may train an event aware C-BERT model, with the training data and the pretrained weights, to generate a trained event aware C-BERT model. The device may receive natural language text data identifying natural language events, and may process the natural language text data, with the trained masked event C-BERT model, to determine weights. The device may process the natural language text data and the weights, with the trained event aware C-BERT model, to predict causality relationships between the natural language events, and may perform actions, based on the causality relationships.

Abstract: A method for using a distributed ledger (DL) of a blockchain applicable to a network of blockchain nodes is provided.

Abstract: A method and a system process a stream of data in parallel across a plurality of nodes. The log processing system has a log module, a query language module, and a query processing module. The log module receives and organizes the stream of data into a sequential and nested data structure. The query language operator module defines operators that operate on the sequential and nested data structure. The query processing module processes in parallel across a plurality of nodes a query based on an operator on the stream of data.

Abstract: Certain embodiments involve using a machine-learning tool to generate metadata identifying segments and topics for text within a document. For instance, in some embodiments, a text processing system obtains input text and applies a segmentation-and-labeling model to the input text. The segmentation-and-labeling model is trained to generate a predicted segment for the input text using a segmentation network. The segmentation-and-labeling model is also trained to generate a topic for the predicted segment using a pooling network of the model to the predicted segment. The output of the model is usable for generating metadata identifying the predicted segment and the associated topic.

Abstract: An information processing apparatus includes: a processor configured to: when a request for recommendation information and situation information are input, identify one or more actions associated with a situation indicated by the situation information based on action history information in which a past action is associated with a situation where the past action is executed, the situation information indicating a situation of a user; for each target action among the one or more identified actions, calculate a first probability and a second probability, the first probability being a probability that each target action is included as a choice for the user, the second probability being a probability that the user selects each target action when the each target action is included as the choice; and output an action with a relatively low first probability and a relatively high second probability, as the recommendation information, among the one or more actions.

Abstract: A drug monitoring tool comprises a data receiver and an interactive user interface. The data receiver is configured to receive a pharmacokinetic (PK) profile of a patient. The interactive user interface is configured to display, to the patient, a time-varying therapeutic plasma protein level of the patient and delineate a plurality of zones within the interactive user interface associated with the time-varying therapeutic plasma protein level. The plurality of zones includes a safe zone indicating to the patient that the time-varying therapeutic plasma protein level is within a first concentration range considered safe for physical activity and a danger zone indicating to the patient that the time-varying therapeutic plasma protein level is within a second concentration range and physical activity should be limited.

Abstract: Provided is sound source enhancement technology that is capable of improving sound source enhancement capabilities in a configuration using a beamformer to suppress interference noise. A PSD optimization device includes a PSD updating unit that takes a target sound PSD input value, an interference noise PSD input value, and a background noise PSD input value as input, and generates a target sound PSD output value, an interference noise PSD output value, and a background noise PSD output value, by solving an optimization problem for a cost function relating to a variable representing a target sound PSD, a variable representing an interference noise PSD, and a variable representing a background noise PSD.

Abstract: Apparatuses, systems, program products, and method are disclosed for machine learning abstraction. An apparatus includes an objective module configured to receive an objective to be analyzed using machine learning. An apparatus includes a grouping module configured to select a logical grouping of one or more machine learning pipelines to analyze a received objective. An apparatus includes an adjustment module configured to dynamically adjust one or more machine learning settings for a logical grouping of one or more machine learning pipelines based on feedback generated in response to analyzing a received objective.

Abstract: A method includes executing operations to generate a first enhancement function based on a parent-child link in a content hierarchy including a link between a parent node in a first level of the content hierarchy to a child node in a second level of the content hierarchy below the first level. A second enhancement function is generated based on a sibling link in the content hierarchy including a link between a sibling node in a third level of the content hierarchy and a sibling node in the third level of the content hierarchy sharing a common parent node with the first sibling node in a fourth level of the content hierarchy above the third level. A user content consumption metric is generated based on the first and second enhancement functions. A content list including a set of candidate content items ranked based on the user content consumption metric is generated.

Abstract: The disclosed embodiments relate to a system that updates a context that facilitates evaluating qualitative search terms for an attribute during query processing. During operation, the system extracts a value for the attribute from each data item. Next, the system updates the context based on the extracted attribute values, wherein the context includes a concept-mapping for one or more qualitative search terms applied to the attribute, and wherein each concept-mapping associates a given attribute value with a compatibility index that indicates a compatibility between the given attribute value and a corresponding qualitative search term.

Abstract: Methods and apparatus for using one or more feature recognition system(s) to identify features in a scanned image and to associate tags with identified features are described. Probabilities of a feature being present are taken into consideration in some embodiments. In various embodiments once a tag has been determined as corresponding to a feature which has been confirmed as being in the scanned image synonyms for the tag word are identified and also associated with the scanned image. By using results of multiple automated feature recognition systems and generating overall probabilities that a feature is present in an image more reliable tagging can be implemented in an automated manner than in system which rely on a single AI system without the need for extensive operator input with respect to identification of all features in the image which are to be tagged.

Abstract: Technologies and implementations for facilitating behavior prediction based on analysis of surveillance data are generally disclosed. The technologies and implementations include identification of subjects, associating the subjects with behavior, predicting future behavior of the subject, and providing a behavior influencing incentive to the subject.

Abstract: Techniques for synthetic data generation in computer-based reasoning systems are discussed and include receiving a request for generation of synthetic data based on a set of training data cases. One or more focal training data cases are determined. For undetermined features (either all of them or those that are not subject to conditions), a value for the feature is determined based on the focal cases. In some embodiments, the generated synthetic data may be checked for similarity against the training data, and if similarity conditions are met, it may be modified (e.g., resampled), removed, and/or replaced.

Abstract: Systems and methods for tracking EEG data and providing enhanced seizure detection and prediction are disclosed. The systems and methods use input sensors for receiving and collecting data from a plurality of EEG channels in association with a subject and processing said data to calculate and average Lyapunov exponents for a composite EEG data set. The systems and methods convert the average Lyapunov exponents into graphical representations that are displayed against a time axis. The graphical output adjusts in real-time according to the input data obtained from EEG channels. The systems and methods utilize pattern recognition to output alarms based upon input data and recommend diagnoses related to seizures.

Abstract: Methods and apparatus for using one or more feature recognition system(s) to identify features in a scanned image and to associate tags with identified features are described. Probabilities of a feature being present are taken into consideration in some embodiments. In various embodiments once a tag has been determined as corresponding to a feature which has been confirmed as being in the scanned image synonyms for the tag word are identified and also associated with the scanned image. By using results of multiple automated feature recognition systems and generating overall probabilities that a feature is present in an image more reliable tagging can be implemented in an automated manner than in system which rely on a single AI system without the need for extensive operator input with respect to identification of all features in the image which are to be tagged.

Abstract: A heating, ventilation, and air conditioning (HVAC) control device is configured to record a plurality of actual occupancy statuses, to determine a plurality of corresponding predicted occupancy statuses, and to compare the plurality of predicted occupancy statuses to the plurality of actual occupancy statuses. The device is further configured to identify conflicting occupancy statuses based on the comparison. A conflicting occupancy status indicates a difference between an actual occupancy status and a corresponding predicted occupancy status. The device is further configured to identify timestamps corresponding with the conflicting occupancy statuses, to identify historical occupancy statuses corresponding with the identified timestamps, and to update the conflicting occupancy statuses in the predicted occupancy schedule with the historical occupancy statuses.

Abstract: Aspects of the present disclosure involve systems, methods, devices, and the like for generating highly scalable temporal graph databases. In one embodiment, a novel architecture is presented that enables the identification of mutation or changes in graphs. For the identification a combination of graph-based modeling and journal entry is used. Events occurring are consumed and changes are ingested, transformed for use by a graph simulation system. The changes are journaled using a vertex centric temporal journaling schema.