Abstract: A computer-implemented method according to one embodiment includes receiving snapshot data for a node within a data center; determining one or more candidate labels for one or more software applications running on the node, utilizing the snapshot data; implementing a validation of the one or more candidate labels to determine one or more validated labels; and training a machine learning model, utilizing the one or more validated labels and the snapshot data.

Abstract: Technology for using a computing device to interpret entity and relationship occurrences a natural language understanding system that includes the following operations (not necessarily in the following order): (i) receiving a corpus that includes unstructured data and/or structured data; (ii) parsing the corpus to obtain parsed corpus information; (iii) applying artificial intelligence to the parsed corpus information to determine a plurality of logical relationships manifested by the corpus; and (iv) expressing, by machine logic, the plurality of logical relationships as a respectively corresponding plurality of logical rule expressions, with each logical rule expression of the plurality of logical rule expressions expressing the respectively corresponding logical relationship as fact(s) with regard to the corpus.

Abstract: Disclosed aspects relate to analytics engine selection management. A set of reference data may be ingested by a first analytics engine to compile a first set of characteristic data. The set of reference data may be ingested by a second analytics engine to compile a second set of characteristic data. The first set of characteristic data may be compiled for the first analytics engine. The second set of characteristic data may be compiled for the second analytics engine. A set of distinct attributes related to the first and second analytics engines may be determined based on the first and second sets of characteristic data. An analytics engine selection operation may be executed.

Abstract: Embodiments relate to a system, program product, and method for use with a physical computing device to process a data access request. The requested data is encrypted with two keys, including a physical device authentication key and a transient key. Access to the data requires authentication on both the device level and situational level. Device situational data is monitored, which includes selectively enabling access to the requested data and de-activation of the transient key in response to a change in the monitored situational data. The transient key de-activation removes access to the requested data.

Abstract: Embodiments relate to a computer system, computer program product, and method to prevent unauthorized file dissemination and replication. A file parameter is defined, with the defined file parameter including a file dissemination characteristic. The file is encoded with the defined file parameter as file metadata. Dissemination and replication of the file is managed responsive to the encoded file parameter. The defined parameter is assessed along with a physical replication destination. The file is selectively replicated or transmitted responsive to the file parameter and the destination assessment.

Abstract: Embodiments relate to a system, program product, and method for use with a physical computing device to process a data access request. The associated data is encrypted with a key pair that includes both a persistent key and a transient key. Both keys require authentication to access the requested data. The transient key is subject to real-time monitoring, with changes in situational data selectively affecting the validity of the transient key, and selectively changing the physical state of the physical computing device.

Abstract: One embodiment provides a method including identifying all computing nodes and connections associated with the computing nodes in a data center based on running processes in the data center that communicate with one another. For each computing node, running processes are identified using natural language processing (NLP) by: iteratively refining a rule set that enables processing of surveillance information from the data center into an initial map of systems and applications in the data center, and extracting known process entities according to predetermined rules from the rule set. A visual dependency representation of the computing nodes and the processes running on the computing nodes is generated.

Abstract: Embodiments relate to a system, program product, and method for iterative expansion and application of a domain-specific dictionary. One or more dictionary instances are applied against a text corpus. The dictionary is iteratively expanded and selectively populated with one or more additional dictionary instances, including semantically similar instances to the applied dictionary instances and extension instances contextually related to the applied dictionary instances. The iteratively expanded dictionary is applied to an unexplored corpus to identify matching corpus data to populated instances of the dictionary.

Abstract: One embodiment provides a method including identifying all computing nodes and connections associated with the computing nodes in a data center. For each computing node, running processes are identified using natural language processing (NLP) by: extracting known process entities according to predetermined rules; extracting unknown process entities by: grouping process logs that share process entities and identifying hints in parameters and directory paths; receiving annotations to the hints to identify an application a process is running; and creating a new rule based on the annotations and propagating the new rule to other process logs. A visual representation of the computing nodes and the processes running on the computing nodes is generated.

Abstract: Embodiments relate to a system, program product, and method for knowledge resource management. A first document is subjected to a first semantic annotation and one or more entities, relations, and textual annotations of interest are identified. A neural model is built with the first document and trained with the first document and one or more of the first semantic annotations. An un-annotated document is applied to the neural model, and one or more second semantic annotations are produced. The un-annotated document is enriched with the produced second semantic annotation(s) and is subjected to adjudication. The neural model is selectively amended responsive to the adjudication.

Abstract: Embodiments relate to a system, program product, and method for dictionary membership management directed at identifying ambiguity in semantic resources. A dictionary of seed terms is applied to a text corpus and matching items in the corpus are identified. The linguistic properties for each matching item are characterized and a context pattern of each matching item is constructed. Each context pattern is applied to the dictionary and matching content between the seed terms and the context pattern is identified and quantified. Lexicon items from the dictionary that have anomalous behavior reflected in the quantification are identified. One or more seed words identified as having anomalous behavior are selectively removed from the dictionary.

Abstract: One embodiment provides generating a similarity matrix corresponding to an input collection including initializing, by a processor, a working set as a collection of a multiple items. Until the similarity matrix converges: receiving a seed for similarity for at least one pair of items of the multiple items, and obtaining a similarity value for all other item pairs using a Naive Triangle Inequality process. The similarity is generated with obtained similarity values.

Abstract: Embodiments relate to a system, program product, and method for use with a computer platform to process and physically convey a notification to a physical medium. In response to receipt, a notification is processed against a hierarchical arrangement of categories. The notification is processed in real-time, including translation of the notification with respect to a tier in the hierarchy. A delivery mechanism and associated instruction is dynamically determined, which includes adaptively assigning the translated notification together with a delivery instruction to a destination device. A physical medium and associated physical device are identified, and the notification is physically conveyed to the physical device across the physical medium.

Abstract: One embodiment provides a method including identifying all computing nodes and connections associated with the computing nodes in a data center. For each computing node, running processes are identified using natural language processing (NLP) by: extracting known process entities according to predetermined rules; extracting unknown process entities by: grouping process logs that share process entities and identifying hints in parameters and directory paths; receiving annotations to the hints to identify an application a process is running; and creating a new rule based on the annotations and propagating the new rule to other process logs. A visual representation of the computing nodes and the processes running on the computing nodes is generated.

Abstract: Embodiments relate to a system, program product, and method for information extraction and annotation of a data set. Neural models are utilized to automatically attach machine annotations to data elements within an unlabeled data set. The attached machine annotations are evaluated and a score is attached to the annotations. The score reflects a confidence of correctness of the annotations. A labeled data set is iteratively expanded with selectively evaluated annotations based on the attached score. The labeled data set is applied to an unexplored corpus to identify matching corpus data to populated instances of the labeled data set.

Abstract: Embodiments relate to a system, program product, and method for iterative expansion and application of a domain-specific dictionary. One or more dictionary instances are applied against a text corpus. The dictionary is iteratively expanded and selectively populated with one or more additional dictionary instances, including semantically similar instances to the applied dictionary instances and extension instances contextually related to the applied dictionary instances. The iteratively expanded dictionary is applied to an unexplored corpus to identify matching corpus data to populated instances of the dictionary.

Abstract: A method, system and computer program product for disambiguating meaning of terms. A natural language processing system obtains a first corpora of words used in a first sense and a second corpora of the same words that are used in a second sense. Each of these corpora of words may be associated with different linguistic domains. The natural language processing system generates a first and a second set of patterns using both the first and second corpora of words, respectively. A question passage is then received by the natural language processing system. The natural language processing system examines a word of interest in the question passage. The user is then notified that the word of interest is being used in the first sense or the second sense in response to identifying the word of interest in only the first set of patterns or the second set of patterns, respectively.

Abstract: A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement an automated lexicon expansion for an identified corpus. For a selected corpus in a set of corpora, the mechanism determines an estimated number of new terms in the selected corpus that are not in the lexicon based on a frequency count known terms in the selected corpus. Responsive to the estimated number of new terms in the selected corpus being greater than a threshold, the mechanism performs lexicon expansion using the selected corpus to form an expanded lexicon. Responsive to the estimated number of new terms in the selected corpus not being greater than the threshold, the mechanism halts lexicon expansion.

Abstract: A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement an automated lexicon expansion for an identified corpus. For a selected corpus in a set of corpora, the mechanism determines an estimated number of new terms in the selected corpus that are not in the lexicon based on a frequency count known terms in the selected corpus. Responsive to the estimated number of new terms in the selected corpus being greater than a threshold, the mechanism performs lexicon expansion using the selected corpus to form an expanded lexicon. Responsive to the estimated number of new terms in the selected corpus not being greater than the threshold, the mechanism halts lexicon expansion.

Abstract: An example operation may include one or more of connecting, by a contract execution node, to a blockchain network comprised of a plurality of Internet of things (IoT) nodes connected to a contract execution node, receiving, by the contract execution node, a contract between a sender and a recipient registered on the blockchain network, the contract contains a plurality of conditions, acquiring, by the contract execution node, data from the plurality of the IoT nodes, executing, by the contract execution node, a smart contract to compare the data against the plurality of the conditions of the contract, and in response to a match, executing a smart contract to transfer a blockchain asset from the sender to the recipient.