Abstract: Methods and systems for Predictive Malware Defense (PMD) are described. The systems and methods can utilize advanced machine-learning (ML) techniques to generate malware defenses preemptively. Embodiments of PMD can utilize models, which are trained on features extracted from malware families, to predict possible courses of malware evolution. PMD captures these predicted future evolutions in signatures of as yet unseen malware variants to function as a malware vaccine. These signatures of predicted future malware “evolutions” can be added to the training set of a machine-learning (ML) based malware detection and/or mitigation system so that it can detect these new variants as they arrive.

Abstract: Methods and systems for Predictive Malware Defense (PMD) are described. The systems and methods can utilize advanced machine-learning (ML) techniques to generate malware defenses preemptively. Embodiments of PMD can utilize models, which are trained on features extracted from malware families, to predict possible courses of malware evolution. PMD captures these predicted future evolutions in signatures of as yet unseen malware variants to function as a malware vaccine. These signatures of predicted future malware “evolutions” can be added to the training set of a machine-learning (ML) based malware detection and/or mitigation system so that it can detect these new variants as they arrive.

Abstract: Embodiments of the present invention provide tools and facilities for definition and population of segments to facilitate automated data analysis and automated experimentation based on user interaction with web pages, web sites, and other user interfaces, as well as for carrying out automated tasks related to users who can be partitioned into well-defined segments. Embodiments of the present invention provide a segment-definition language (“SDL”) that allows users and developers to abstractly define segments in a data-independent manner. The SDL provides many operators and constructs for creating and defining segments. SDL-based subsystem components execute SDL segment definitions to assemble segments on behalf of application programs.

Abstract: A distributed analytics method and system for processing hit data and producing reports. The hit data includes attributes and values representing activities of a visitor on a web site. The distributed analytics system may include log processors to read and parse the hit data. Analytics generators are configured to read and process the parsed hit data and store the processed data in one or more analytics data stores, which are associated with bands. Analytics processors read and merge data from the analytics data stores associated with the bands, and produce intermediate report deltas. Report generators are configured to receive the intermediate report deltas and produce and store one or more report segments in report data stores. External data can be combined with the data being processed. Report processors are configured to read data from the report data stores and produce a final result based on the report segments.

Abstract: A method and system for efficient storage and retrieval of current and historical analytics data. The method includes reading current event data and historical event data associated with a visitor from an analytics data store and producing one or more metrics based on the current or historical event data. Delta data is generated using the current and historical event data. The delta data is then combined with previously aggregated data to produce new aggregated data. A system includes an analytics data store. The analytics data store includes a plurality of analytics data store entities arranged chronologically in time. Each analytics data store entity includes a plurality of sub bands of data. Each sub band of data is associated with configurable data blocks. The analytics data store entities also include meta data portions for increasing the efficiency of storage and retrieval of information to and from the analytics data store entities.

Abstract: A method of multi-enterprise optimization at a buyer computer includes accessing a forecasted demand for at least one item and generating one or more proposed flexible trade contracts using the forecasted demand for the item. The proposed flexible trade contract is communicated to a seller computer and subsequently executed after acceptance of the proposed flexible trade contract at the seller computer to create a flexible trade contract. Each proposed flexible trade contract may be a forward contract, an option contract, or a flexible forward contract.

Abstract: A method of multi-enterprise optimization at a buyer computer includes accessing a forecasted demand for at least one item and generating one or more proposed flexible trade contracts using the forecasted demand for the item. The proposed flexible trade contract is communicated to a seller computer and subsequently executed after acceptance of the proposed flexible trade contract at the seller computer to create a flexible trade contract. Each proposed flexible trade contract may be a forward contract, an option contract, or a flexible forward contract.

Abstract: A method of multi-enterprise optimization at a buyer computer includes accessing a forecasted demand for at least one item and generating one or more proposed flexible trade contracts using the forecasted demand for the item. The proposed flexible trade contract is communicated to a seller computer and subsequently executed after acceptance of the proposed flexible trade contract at the seller computer to create a flexible trade contract. Each proposed flexible trade contract may be a forward contract, an option contract, or a flexible forward contract.

Abstract: A method of multi-enterprise optimization at a buyer computer includes accessing a forecasted demand for at least one item and generating one or more proposed flexible trade contracts using the forecasted demand for the item. The proposed flexible trade contract is communicated to a seller computer and subsequently executed after acceptance of the proposed flexible trade contract at the seller computer to create a flexible trade contract. Each proposed flexible trade contract may be a forward contract, an option contract, or a flexible forward contract.

Abstract: Generating a price schedule involves generating a graph having paths that include states with values. The graph is generated by determining the values of a successor state from the values of a predecessor state. An optimal path is selected, and a price schedule is determined from the optimal path. Computing an elasticity curve involves having a demand model, values for demand model, and filter sets that restrict the values. Elasticity curves are determined by filtering the values using filter sets, and calculating the elasticity curve using the demand model. A best-fitting elasticity curve is selected. Adjusting a demand forecast value includes estimating an inventory and a demand at a number of locations. An expected number of unrealized sales at each location is calculated. An sales forecast value is determined according to the expected number.

Abstract: A method of multi-enterprise optimization at a buyer computer includes accessing a forecasted demand for at least one item and generating one or more proposed flexible trade contracts using the forecasted demand for the item. The proposed flexible trade contract is communicated to a seller computer and subsequently executed after acceptance of the proposed flexible trade contract at the seller computer to create a flexible trade contract. Each proposed flexible trade contract may be a forward contract, an option contract, or a flexible forward contract.

Abstract: A method of multi-enterprise optimization at a buyer computer includes accessing a forecasted demand for at least one item and generating one or more proposed flexible trade contracts using the forecasted demand for the item. The proposed flexible trade contract is communicated to a seller computer and subsequently executed after acceptance of the proposed flexible trade contract at the seller computer to create a flexible trade contract. Each proposed flexible trade contract may be a forward contract, an option contract, or a flexible forward contract.

Abstract: A method of multi-enterprise optimization at a buyer computer includes accessing a forecasted demand for at least one item and generating one or more proposed flexible trade contracts using the forecasted demand for the item. The proposed flexible trade contract is communicated to a seller computer and subsequently executed after acceptance of the proposed flexible trade contract at the seller computer to create a flexible trade contract. Each proposed flexible trade contract may be a forward contract, an option contract, or a flexible forward contract.

Abstract: Generating a price schedule involves generating a graph having paths that include states with values. The graph is generated by determining the values of a successor state from the values of a predecessor state. An optimal path is selected, and a price schedule is determined from the optimal path. Computing an elasticity curve involves having a demand model, values for demand model, and filter sets that restrict the values. Elasticity curves are determined by filtering the values using filter sets, and calculating the elasticity curve using the demand model. A best-fitting elasticity curve is selected. Adjusting a demand forecast value includes estimating an inventory and a demand at a number of locations. An expected number of unrealized sales at each location is calculated. An sales forecast value is determined according to the expected number.

Abstract: A method of multi-enterprise optimization at a buyer computer (20) includes accessing a forecasted demand for at least one item and generating one or more proposed flexible trade contracts (500, 530, 560) using the forecasted demand for the item. The proposed flexible trade contract (500, 530, 560) is communicated to a seller computer (40) and subsequently executed after acceptance of the proposed flexible trade contract (500, 530, 560) at the seller computer (40) to create a flexible trade contract (500, 530, 560). Each proposed flexible trade contract (500, 530, 560) may be a forward contract (500), an option contract (530), or a flexible forward contract (560).

Abstract: Generating a price schedule involves generating a graph having paths that include states with values. The graph is generated by determining the values of a successor state from the values of a predecessor state. An optimal path is selected, and a price schedule is determined from the optimal path. Computing an elasticity curve involves having a demand model, values for demand model, and filter sets that restrict the values. Elasticity curves are determined by filtering the values using filter sets, and calculating the elasticity curve using the demand model. A best-fitting elasticity curve is selected. Adjusting a demand forecast value includes estimating an inventory and a demand at a number of locations. An expected number of unrealized sales at each location is calculated. An sales forecast value is determined according to the expected number.

Abstract: A method of multi-enterprise optimization at a buyer computer includes accessing a forecasted demand for at least one item and generating one or more proposed flexible trade contracts using the forecasted demand for the item. The proposed flexible trade contract is communicated to a seller computer and subsequently executed after acceptance of the proposed flexible trade contract at the seller computer to create a flexible trade contract. Each proposed flexible trade contract may be a forward contract, an option contract, or a flexible forward contract.

Abstract: A method of multi-enterprise optimization at a buyer computer includes accessing a forecasted demand for at least one item and generating one or more proposed flexible trade contracts using the forecasted demand for the item. The proposed flexible trade contract is communicated to a seller computer and subsequently executed after acceptance of the proposed flexible trade contract at the seller computer to create a flexible trade contract. Each proposed flexible trade contract may be a forward contract, an option contract, or a flexible forward contract.

Abstract: A method of multi-enterprise optimization at a buyer computer includes accessing a forecasted demand for at least one item and generating one or more proposed flexible trade contracts using the forecasted demand for the item. The proposed flexible trade contract is communicated to a seller computer and subsequently executed after acceptance of the proposed flexible trade contract at the seller computer to create a flexible trade contract. Each proposed flexible trade contract may be a forward contract, an option contract, or a flexible forward contract.

Abstract: A method and a computer implemented system for improving an active decision making process by using a simulation model of the decision making process. The simulation model is used to evaluate the impact of alternative decisions at a choice point, in order to select one alternative. The method or system may be integrated with an external system, like a manufacturing execution system.