Abstract:
An observation information processing apparatus calculates, for each mesh, a support and a confidence. The observation information processing apparatus generates an adjacent mesh set by merging adjacent ones of the meshes. The observation information processing apparatus calculates, based on a support and a confidence of each mesh included in the adjacent mesh set, a confidence for each adjacent mesh, and sets the smallest one of the confidences calculated as a new confidence threshold value. The observation information processing apparatus detects and excludes meshes to be excluded from meshes included in the adjacent mesh set, based on the confidences and supports of the meshes included in the adjacent mesh set and the confidence threshold value.
Abstract:
A non-transitory computer-readable recording medium stores an information processing program for causing a computer to execute processing including: generating, from first data in which values of a plurality of attributes included in each sample are accumulated for each sample, second data obtained by binarizing, for each sample, the values of the plurality of attributes included in each sample based on an attribute condition set in advance; enumerating, by using the second data, sets of attribute conditions in which all sample sets indicate true values; computing, for each set of attribute conditions, a correlation between the plurality of attributes in the first data in a sample set associated with each set of attribute conditions; and selecting a set of attribute conditions determined to have a correlation as a condition to be causally searched.
Abstract:
A non-transitory computer-readable recording medium storing a rule update program for causing a computer to execute processing including: receiving first appropriateness determination for at least some determination rules among a plurality of determination rules derived through machine learning by using training data; performing second appropriateness determination for a determination rule other than the some determination rules, based on a result of the first appropriateness determination and similarity between the some determination rules and the determination rule other than the some determination rules; and updating the plurality of determination rules, based on the result of the first appropriateness determination and a result of the second appropriateness determination.
Abstract:
An analysis method executed by a computer, the analysis method includes: detecting a plurality of staying points where one or more mobile bodies stayed in accordance with a plurality of trace data associated with trajectories of the one or more mobile bodies; comparing, in accordance with the plurality of trace data, a first ending time of stay in a first staying point selected from among the plurality of staying points with second ending times of stay in one or more second staying points which are similar to the first staying point; and determining feature of the first staying point in accordance with a result of the comparison.
Abstract:
A non-transitory computer-readable recording medium stores therein an allocation program that causes a computer to execute a process including: performing, by using a part of data including an objective variable and one or more explanatory variables corresponding to the objective variable as training data, training of a model that predicts the objective variable from the explanatory variables of the data; classifying test data obtained by excluding the training data from the data into a group according to a classification condition regarding at least a part of the explanatory variables of the data; predicting the objective variable from the explanatory variables of the test data using the trained model for each of groups by which classification has been performed at the classifying; and calculating a predetermined resource amount to be allocated to each of the groups based on the objective variable for each of the groups predicted at the predicting.
Abstract:
When a second pattern is to be generated by adding an event to a first pattern including events, an extraction program causes a computer to execute the following process based on combinations of events. That is, the extraction program causes the computer to generate the second pattern when the number of occurrence, in the second pattern, of each of the events included in the combinations is not more than a threshold. The extraction program causes the computer to calculate, based on data including a plurality of events, a frequency at which one or more of the generated second patterns occur in the data. The extraction program causes the computer to extract the second pattern having the frequency satisfying a predetermined condition. The extraction program causes the computer to add a new event to the extracted second pattern.
Abstract:
A computer-readable storage medium storing an update program that causes a computer to execute a process includes: acquiring an automaton performing matching of data in an input stream hierarchized by tags to a keyword in a query, the automaton in which an initial state, a start state indicating a start tag symbol, an end state indicating an end tag symbol, a transition between the initial state and the start state, a transition between the initial state and the end state, and a transition from the initial state to the initial state are defined; generating a path identifying the position of a start tag based on a hierarchy in the input stream when the start tag is read from the input stream; judging whether or not the generated path meets a condition corresponding to the keyword in the query; generating, when the generated path is judged to meet a condition.
Abstract:
A non-transitory computer-readable recording medium storing an information processing program for causing a computer to execute a process includes receiving a hypothesis to be interpreted, by using a first storage that includes, for each piece of knowledge that indicates a plurality of resources and a relationship between the resources, basis information that serves as a basis of the knowledge and a rule identifier connected with a rule used to interpret the hypothesis, acquiring the basis information and the rule identifier that correspond to the hypothesis to be interpreted, and by using a second storage that includes, for each rule identifier, a probability that the rule and the hypothesis coincide with existing knowledge, acquiring the probability of coinciding with the existing knowledge that corresponds to the acquired rule identifier.
Abstract:
An extraction program causes a computer to execute a process. The process includes adding an event to a first pattern including the events according to the sequential order, thus generating a second pattern in such a manner that the second pattern is generated by adding the event when a first value is less than a predetermined threshold; when the event is added, adding a predetermined value to the first value, and adding the predetermined value to a second value in a column corresponding to an end of the added event among second values corresponding to respective columns of a table; extracting the second pattern that satisfies a predetermined condition; and when an event in a second or subsequent column in the table is added.
Abstract:
A non-transitory computer readable recording medium storing a machine learning program for causing a computer to execute a process includes extracting a feature related to a surface structure of a substance based on an atomic arrangement of the substance, and training a machine learning model that predicts information regarding a chemical reaction that occurs in a substance that corresponds to an input explanatory variable using training data that includes, as an explanatory variable, atomic arrangement information regarding the atomic arrangement of the substance and the extracted feature and includes, as an objective variable, information regarding the chemical reaction that occurs in the substance.