Abstract: One embodiment provides a method for augmenting missing values in historical or market data for deals. The method comprises receiving information relating to a set of deals. For any service included in one or more deals of the set of deals but not included in one or more other deals of the set of deals, the method further comprises augmenting, for any or all of the one or more other deals that does not include the service, one or more missing values for the service with one or more recommended values based on a recommendation algorithm. The service may be at any service level of a hierarchy of services.

Abstract: A system for cognitive assessment of the competitiveness of deals may include a memory having stored thereon historical deal information for historical deals with each historical deal including a historical deal component. A historical deal component may include a historical work scope and associated historical work pricing. The system may also include a processor cooperating with the memory and configured to compare current deal information with the historical deal information. The current deal information may include a current deal component that may include a current work scope and associated current work pricing. The processor may use machine learning to determine whether the current deal component is non-competitive based upon the historical deal information, and for each non-competitive current deal component generate an alternative current deal component. The alternative current deal component may have at least one of a different current work scope and different associated current work pricing.

Abstract: Methods and systems for detecting mutation events include collecting change event pattern counts from one or more processing nodes. Unintended change events are identified based on the collected change event pattern counts. A corrective action is performed for the unintended change events.

Abstract: Guided word association based domain name detection may be performed by obtaining an original domain name, constructing a feature space from a corpus of text, wherein each word appearing in the corpus is represented as a vector in the feature space, detecting whether a domain name registration exists for each combination of the original domain name and each of a plurality of seed words from the feature space, determining, for each seed word included in an existing domain name registration, a plurality of nearest neighbor candidate words, based on vector distance in the feature space, and repeating, for one or more repetitions, the detecting and the determining, wherein the plurality of nearest neighbor candidate words are utilized as the plurality of seed words.

Abstract: One embodiment provides a method for augmenting missing values in historical or market data for deals. The method comprises receiving information relating to a set of deals. For any service included in one or more deals of the set of deals but not included in one or more other deals of the set of deals, the method further comprises augmenting, for any or all of the one or more other deals that does not include the service, one or more missing values for the service with one or more recommended values based on a recommendation algorithm. The service may be at any service level of a hierarchy of services.

Abstract: One embodiment provides a method for top-down pricing of an in-flight deal. The method comprises receiving a first set of information relating to the in-flight deal. The in-flight deal comprises multiple services. The method comprises, for each service of the in-flight deal, selecting a corresponding set of peer deals from historical and market data based on the first set of information, and mining costs for the service from the corresponding set of peer deals. The method further comprises determining a set of price points for the in-flight deal based on each cost mined.

Abstract: Monitoring a process on an Internet of Things (IoT) device is performed by receiving, by the IoT device, a network data transfer from an external device; and receiving, by the IoT device, meta-information of the network data transfer. The meta-information can be generated, based on the network data transfer, by a network monitoring device. Additionally, the IoT device detects a file storing data of the network data transfer based on the meta-information. Further, the IoT device monitors a process relating to the detected file on the IoT device.

Abstract: Predicting simulation parameters is performed by obtaining a plurality of datasets, each dataset including simulation parameters, time series data and a label. Wherein the time series data represents a simulation status for each time and the label represents a simulation result. Learning a metric of the simulation parameters including two datasets of the plurality of datasets. Wherein the metric imitates the similarity of time series data of the two datasets, and training a model for predicting the label for simulation parameters by using the metric.

Abstract: Guided character string alteration can be performed by obtaining an original character string and a plurality of altered character strings, traversing the original character string with a first Long Short Term Memory (LSTM) network to generate, for each character of the original character string, a hidden state of a partial original character string up to that character, and applying, during the traversing, an alteration learning process to each hidden state of a partial original character string to produce an alteration function for relating partial original character strings to partial altered character strings.

Abstract: Guided word association based domain name detection may be performed by obtaining an original domain name, constructing a feature space from a corpus of text, wherein each word appearing in the corpus is represented as a vector in the feature space, detecting whether a domain name registration exists for each combination of the original domain name and each of a plurality of seed words from the feature space, determining, for each seed word included in an existing domain name registration, a plurality of nearest neighbor candidate words, based on vector distance in the feature space, and repeating, for one or more repetitions, the detecting and the determining, wherein the plurality of nearest neighbor candidate words are utilized as the plurality of seed words.

Abstract: A method for selecting a set of information technology (IT) services peer deals to an in-flight deal for each first level service in the in-flight deal includes receiving a detailed cost structure for historical information, in-flight deals information, market deals information, services baselines and deals metadata, and multiple peer deals for selection. For historical information and market deals information, all missing baselines for all services in the in-flight deal and all missing unit cost for services are augmented at the first level service. The multiple peer deals are classified into different clusters at the first level service. A closest cluster to the in-flight deal at the first level service is selected. For each second level service of the in-flight deal the method: classifies the selected peer deals into different clusters. A predetermined number of peer deals that appear in a largest number of the selected clusters is selected.

Abstract: Monitoring a process on an Internet of Things (IoT) device is performed by receiving, by the IoT device, a network data transfer from an external device; and receiving, by the IoT device, meta-information of the network data transfer. The meta-information can be generated, based on the network data transfer, by a network monitoring device. Additionally, the IoT device detects a file storing data of the network data transfer based on the meta-information. Further, the IoT device monitors a process relating to the detected file on the IoT device.

Abstract: A computer-implemented method, according to one embodiment, includes: receiving information which corresponds to an offering, and generating an offering definition by defining basic properties of the offering. Defining the basic properties of the offering includes: defining one or more cost drivers which correspond to the offering, and defining one or more cost components which correspond to the offering. The basic properties of the offering are further used to generate one or more cost model output structures. The one or more cost model output structures are also merged with a single cost model, and the single cost model is updated. Other systems, methods, and computer program products are described in additional embodiments.

Abstract: A code dependency influenced bug localization apparatus and method receive bug reports and source code changes of a software program. Each source code change includes a syntax component and a location component. Each bug report includes a functional description of an aspect of the software program. The apparatus and method obtain a vectorized feature representation of each bug report, apply a learning process to the source code changes based on a code dependency among the source code changes, obtain a vectorized feature representation of each source code change based on the code dependency, merge the feature representations of the bug reports and the feature representations of the source code changes into a plurality of unified feature representations, and apply a ranking process to the unified feature representations to produce a source code relating function for relating a bug report and a source code change.

Abstract: A system for cognitive assessment of the competitiveness of deals may include a memory having stored thereon historical deal information for historical deals with each historical deal including a historical deal component. A historical deal component may include a historical work scope and associated historical work pricing. The system may also include a processor cooperating with the memory and configured to compare current deal information with the historical deal information. The current deal information may include a current deal component that may include a current work scope and associated current work pricing. The processor may use machine learning to determine whether the current deal component is non-competitive based upon the historical deal information, and for each non-competitive current deal component generate an alternative current deal component. The alternative current deal component may have at least one of a different current work scope and different associated current work pricing.

Abstract: A system for cognitive prioritization for report generation may include a processor and a memory cooperating therewith. The processor may be configured to accept a request for a new report from a user, the request having a user profile importance associated therewith and generate a predicted completion time for the new report based upon a historical completion time prediction model based upon historical data for prior reports. The processor may be configured to generate a predicted importance of the new report based upon a historical importance prediction model based upon the historical data for prior reports and determine a combined predicted importance based upon the user profile importance and the predicted importance. The processor may also be configured to generate a prioritization of the new report among other reports based upon the predicted completion time and the combined predicted importance and generate the new report based upon the prioritization.

Abstract: A method for selecting a set of information technology (IT) services peer deals to an in-flight deal for each first level service in the in-flight deal includes receiving a detailed cost structure for historical information, in-flight deals information, market deals information, services baselines and deals metadata, and multiple peer deals for selection. For historical information and market deals information, all missing baselines for all services in the in-flight deal and all missing unit cost for services are augmented at the first level service. The multiple peer deals are classified into different clusters at the first level service. A closest cluster to the in-flight deal at the first level service is selected. For each second level service of the in-flight deal the method: classifies the selected peer deals into different clusters. A predetermined number of peer deals that appear in a largest number of the selected clusters is selected.

Abstract: One embodiment provides a method for estimating unit price reduction of services in a new in-flight deal using data of historical deals and market reference deals cost structures. The method includes receiving a detailed cost structure for historical information, market deals information, services quantity information and deals metadata for a first year. For each service: peer deals to the in-flight deal are selected based on the detailed cost structure; missing cost data values in the peer deals are augmented; unit cost reduction values for the peer deals estimated; the unit cost reduction for the in-flight deal from each year in total contract years to a next year without a last contract year are estimated; and a total cost for the in-flight deal for all years in the total contract years beyond the first year are estimated.

Abstract: One embodiment provides a method for top-down pricing of an in-flight deal. The method comprises receiving a first set of information relating to the in-flight deal. The in-flight deal comprises multiple services. The method comprises, for each service of the in-flight deal, selecting a corresponding set of peer deals from historical and market data based on the first set of information, and mining costs for the service from the corresponding set of peer deals. The method further comprises determining a set of price points for the in-flight deal based on each cost mined.

Abstract: One embodiment provides a method for augmenting missing values in historical or market data for deals. The method comprises receiving information relating to a set of deals. For any service included in one or more deals of the set of deals but not included in one or more other deals of the set of deals, the method further comprises augmenting, for any or all of the one or more other deals that does not include the service, one or more missing values for the service with one or more recommended values based on a recommendation algorithm. The service may be at any service level of a hierarchy of services.