Abstract: Techniques are described for employing a crowdsourcing framework to analyze data related to the performance or operations of computing systems, or to analyze other types of data. A question is analyzed to determine data that is relevant to the question. The relevant data may be decontextualized to remove or alter contextual information included in the data, such as sensitive, personal, or business-related data. The question and the decontextualized data may then be presented to workers in a crowdsourcing framework, and the workers may determine an answer to the question based on an analysis or an examination of the decontextualized data. The answers may be combined, correlated, or otherwise processed to determine a processed answer to the question. Machine learning techniques are employed to adjust and refine the decontextualization.

Abstract: A search device and method of a pertinent solution using a genetic algorithm that performs genetic operation(s) on a plurality of individuals each having an element of a candidate solution to a problem in the form of a gene sequence. Genetic information about target individuals includes whether all individuals are, regardless of their fitness values, in a state among a living state that is a target of genetic operation and a target of calculating a fitness value, and a dead state that is not the target of genetic operation nor the target of the calculation of the fitness value. Each target individual has a predetermined value of a lifespan. A breeding area that allows predation of leading an individual belonging to a lower layer to a dead state due to predation by an individual belonging to a higher layer in the breeding area is an aspect of generating new target individuals.

Abstract: Quantitative analysis is provided through the implementation of a graph that includes nodes having edges therebetween representing data dependencies between the nodes. The nodes include calculation objects such as programs, data, libraries, and/or other objects. The nodes provide for modular computation that provides for transparency, record-keeping, experimentation, and/or other functionalities.

Abstract: A method and associated systems for automatically generating an ontology and a set of axioms from a business-process model that represents the operations of a business. This ontology and set of axioms may be used to create the knowledgebase of an artificially intelligent expert system that emulates the business operations. A processor parses a representation of business processes stored in the business-process model, deriving a set of axioms and a set of entity classes from the parsed data. The processor uses these axioms and classes to identify concept nodes and process nodes, which it organizes into the ontology of the knowledgebase. The processor further identifies information derived from the parsed data to create a set of triple data items, each of which represents the information represented by one or more of the derived axioms. These triples are stored in the knowledgebase as a triple store data structure.

Abstract: According to an example, synthetic time series data generation may include receiving time series data for a plurality of users, and applying dimensionality reduction to transform the time series data from a high dimensional space n of the time series data to a low dimensional space m, where m<n. The transformed time series data may be used to estimate a time series data density in the low dimensional space m by generating a density function. The density function may be sampled for data, and the sampled data may be transformed back to the high dimensional space n to generate the synthetic time series data.

Abstract: Implementations include systems and methods generate data for training or evaluating semantic analysis engines. For example, a method may include receiving documents from a corpus that includes an authoritative set of documents from an authoritative source. Each document in the authoritative set may be associated with an entity. A second set of documents from the corpus that do not overlap with the first set may include at least one link to a document in the authoritative set, the at least one link being associated with anchor text. For each document in the second set, the method may include identifying entity mentions in the document based on the anchor text. The method may include associating the entity mention with the entity in a graph-structured knowledge base or associating entity types with the entity mention. The method may also include training a semantic analysis engine using the identified entity mentions and associations.

Abstract: A plurality of classifiers is identified. A set of test cases is selected based on time. The set of test cases are grouped into a plurality of datasets based on time where each of the plurality of datasets is associated with a corresponding interval of time. Each of the plurality of classifiers is applied to each of the plurality of datasets to generate classifications for test cases in each of the plurality of datasets. For each of the plurality of classifiers, a classification performance score is determined for each of the plurality of datasets based on the classifications generated for the test cases of each dataset. A classifier is selected from among the plurality of classifiers for production based on the classification performance scores of each of the plurality of classifiers across the plurality of datasets.