Abstract: An example operation may include one or more of detecting, by a promotion processor node, a delivery of a trade promotion token by a retailer node on a blockchain, and executing, by the promotion processor node, a smart contract to: apply the trade promotion token to a purchase item and transfer incentive assets based on the trade promotion token to the retailer node and to a corresponding manufacturer node.

Abstract: An example operation may include one or more of acquiring, by a promotion processor node, consumer exploration of a product data from a blockchain, determining, by the promotion processor node, features of the product, receiving, by the promotion processor node, a promotion plan from at least one product retailer node, and executing a smart contract to generate a plurality of promotion tokens based on the features of the product and the promotion plan.

Abstract: Methods, apparatus, and processor-readable storage media for implementing dynamic widgets for displaying status information are provided herein. An example computer-implemented method includes executing a software widget configured to display, on a user device, status information associated with a plurality of items of an online data source; obtaining the status information from an application server via a first application programming interface, wherein the application server maintains at least a portion of the status information in a local database using a second application programming interface associated with the online data source; and updating a graphical user interface of the software widget to display the status information on the user device.

Abstract: An example operation may include one or more of collecting, by a first node, a plurality of permissioned data inputs from a plurality of second nodes of a supply-chain, performing, by the first node, a granular simulation based on the permissioned data inputs to generate a plurality of key performance indicators (KPIs), and executing a smart contract to adjust order processes of the supply-chain based on the KPIs.

Abstract: An example operation may include one or more of detecting, by a promotion processor node, a delivery of a trade promotion token by a retailer node on a blockchain, and executing, by the promotion processor node, a smart contract to: apply the trade promotion token to a purchase item and transfer incentive assets based on the trade promotion token to the retailer node and to a corresponding manufacturer node.

Abstract: An example operation may include one or more of acquiring, by a promotion processor node, consumer exploration of a product data from a blockchain, determining, by the promotion processor node, features of the product, receiving, by the promotion processor node, a promotion plan from at least one product retailer node, and executing a smart contract to generate a plurality of promotion tokens based on the features of the product and the promotion plan.

Abstract: An example operation may include one or more of storing data via a blockchain ledger based on a consensus of a set of anchor blockchain nodes which are subset of nodes included in larger set of nodes in a blockchain network, receiving an access request from a blockchain member not included in the set of anchor blockchain nodes and which does not maintain a copy of the blockchain ledger, verifying a previously stored portion of the blockchain ledger which is accessible to the blockchain member based on the received access request, and sharing the previously stored portion of the blockchain ledger with the a computing system of the blockchain member based on the verification.

Abstract: An example operation may include one or more of collecting, by a first node, a plurality of permissioned data inputs from a plurality of second nodes of a supply-chain, performing, by the first node, a granular simulation based on the permissioned data inputs to generate a plurality of key performance indicators (KPIs), and executing a smart contract to adjust order processes of the supply-chain based on the KPIs.

Abstract: An example operation may include one or more of collecting, by a first node, a subset of data inputs from a plurality of second nodes over a blockchain underlying a supply-chain, executing, by the first node, a smart contract to simulate a state of the supply-chain based on the subset of the data inputs, and generating, by the first node, at least one policy decision based on the simulated state of the supply-chain.

Abstract: An example operation may include one or more of receiving supply chain states of a plurality of nodes of a supply chain, respectively, where the supply chain states have restricted visibility among the plurality of nodes, determining a modification to a supply chain state of a target node in the supply chain based on a supply chain state of at least one other node in the supply chain, transmitting the determined modification of the supply chain state to the target node, and storing a reason for modifying the supply chain state of the target node via a block included among a hash-linked chain of blocks.

Abstract: An example operation may include one or more of storing data via a blockchain ledger based on a consensus of a set of anchor blockchain nodes which are subset of nodes included in larger set of nodes in a blockchain network, receiving an access request from a blockchain member not included in the set of anchor blockchain nodes and which does not maintain a copy of the blockchain ledger, verifying a previously stored portion of the blockchain ledger which is accessible to the blockchain member based on the received access request, and sharing the previously stored portion of the blockchain ledger with the a computing system of the blockchain member based on the verification.

Abstract: Described technologies automatically evaluate contact information and other submitted textual content to identify suspect data. Different data validation technologies alone or in combination identify frivolous content such as profanity, gibberish, and mismatched meanings. These technologies may perform regular expression recognition, naïve Bayes or other probabilistic classifications, named entity recognition, and subsidiary functions such as data cleaning, gibberish generation, and model training. A predictor value indicates how likely it is that the submitted data is valid input, e.g., a valid person name or valid company name, as requested. A trained machine learning based content characterizer embodies rules for content characterization, including implicit rules produced by supervised machine learning. Some content characterizers identify suspect content other than range violations or data type violations, by identifying profanity or gibberish.

Abstract: Systems, methods and apparatus are provided through which in some aspects an ocular scene is imaged and changes in the ocular scene relative to a calibration image, are tracked. In one aspect, an imaging instrument includes an eyepiece, a light source operable to project light, an imaging system operable to image an ocular scene; and a processing system operable to receive a set of calibration images, and further operable to capture an image of the imaged ocular scene associated with a calibration image. In another aspect, a method to track changes in an ocular scene includes receiving a set of calibration images, receiving a set of landmarks on each calibration image, capturing a set of images of an ocular scene, associating each captured image with a calibration image, and detecting landmarks on each captured image wherein each detected landmark corresponds to a landmark on the associated calibration image.

Abstract: Systems, methods and apparatus are provided through which in some aspects an ocular scene is imaged and changes in the ocular scene relative to a calibration image, are tracked. In one aspect, an imaging instrument includes an eyepiece, a light source operable to project light, an imaging system operable to image an ocular scene; and a processing system operable to receive a set of calibration images, and further operable to capture an image of the imaged ocular scene associated with a calibration image. In another aspect, a method to track changes in an ocular scene includes receiving a set of calibration images, receiving a set of landmarks on each calibration image, capturing a set of images of an ocular scene, associating each captured image with a calibration image, and detecting landmarks on each captured image wherein each detected landmark corresponds to a landmark on the associated calibration image.

Abstract: Techniques are provided for ranking-based information processing. Method steps can include integrating information from at least one source (a plurality of heterogeneous sources can also be handled), to obtain integrated information; mapping the integrated information to at least one application; ranking the integrated information based on the mapping and on ranking criteria from a knowledge base; and processing the integrated information, based on the ranking, to obtain processed integrated information. Optionally, the processing step includes formatting the processed integrated information for a plurality of applications.

Abstract: Assignment scheduling for service projects, in one aspect, may comprise preparing input parameter data for servicing a client service request; generating a schedule for servicing the client service request by executing an optimization algorithm with the input parameter data; determining whether the schedule is acceptable by the client; and repeating automatically the preparing, the generating, the transmitting and the determining until it is determined that the schedule is acceptable by the client, wherein each iteration automatically prepares different input parameter data for inputting to the optimization algorithm and generates a different schedule based on the different input parameter data.

Abstract: Assignment scheduling for service projects, in one aspect, may comprise preparing input parameter data for servicing a client service request; generating a schedule for servicing the client service request by executing an optimization algorithm with the input parameter data; determining whether the schedule is acceptable by the client; and repeating automatically the preparing, the generating, the transmitting and the determining until it is determined that the schedule is acceptable by the client, wherein each iteration automatically prepares different input parameter data for inputting to the optimization algorithm and generates a different schedule based on the different input parameter data.

Abstract: Financial transformation in one aspect establishes a hub and spoke network of models including at least a process model as the hub and at least components model, value driver model, applications model, solutions models as the spoke connected to the process model. A plurality of correlations is inferred among said models using daisy-chain analysis that navigate the hub and spoke network of models, and heat map is generated. Heat map analysis, application shortfall analysis, organization shortfall analysis, solution analysis, and business case analysis are performed.

Abstract: A system and method allow a user to extract the set of customizations performed on an application and use these to estimate the time and effort and cost of (a) migrating to a new version of the application and/or (b) consolidating systems. The user can browse the extracted data and select configuration elements for re-use. After downloading the one or more configurations and comparing them, the user selects elements of the configurations for re-use. The first step is to scan one or more application systems and extract the configuration data using a surveyor. The method according to the invention automatically identifies configuration differences. The user then selects configuration elements for re-use. A graphic user interface (GUI) can be provided which allows the user to make these selections by dragging and dropping selected elements to a “To Be” configuration. The selected configurations are then uploaded and installed on an instance of the application.

Abstract: Assignment scheduling for service projects, in one aspect, may comprise preparing input parameter data for servicing a client service request; generating a schedule for servicing the client service request by executing an optimization algorithm with the input parameter data; determining whether the schedule is acceptable by the client; and repeating automatically the preparing, the generating, the transmitting and the determining until it is determined that the schedule is acceptable by the client, wherein each iteration automatically prepares different input parameter data for inputting to the optimization algorithm and generates a different schedule based on the different input parameter data.