Approaches for determining scheduling assignments for the movement of people along a multi-segment path from a starting location to a destination location, are used to manage crowds, predict crowd behavior, and mitigate crowd turbulence. For example, to mitigate crowd congestion, routing solutions specifying an amount of time to spend at a destination and a departure time can be provided. Itinerary assignments, crowd data, and data associated with an event can be analyzed and weighted to determine scheduling assignments. Scheduling assignments can be validated against current crowd data and event data. Current crowd data and event data and crowd simulation can be used to predict future crowd behavior or crowd problems. Scheduling assignments can be rescheduled to mitigate crowd problems or emergencies.

Approaches for determining scheduling assignments for the movement of people along a multi-segment path from a starting location to a destination location, are used to manage crowds, predict crowd behavior, and mitigate crowd turbulence. For example, to mitigate crowd congestion, routing solutions specifying an amount of time to spend at a destination and a departure time can be provided. Itinerary assignments, crowd data, and data associated with an event can be analyzed and weighted to determine scheduling assignments. Scheduling assignments can be validated against current crowd data and event data. Current crowd data and event data and crowd simulation can be used to predict future crowd behavior or crowd problems. Scheduling assignments can be rescheduled to mitigate crowd problems or emergencies.

Embodiments of the present invention relate to a method, system, and computer program product for multiplexing data operation. In some embodiments, a method is disclosed. A query for at least one table comprising a plurality of data records is received. The query indicating a plurality of data operations to be performed on the plurality of data records. The plurality of data operations are combined into a target data operation. An intermediate result of the query is generated by performing the target data operation on the plurality of data records. A final result of the query is determined based on the intermediate result. In other embodiments, a system and a computer program product are disclosed.

Embodiments of the present invention relate to a method, system, and computer program product for multiplexing data operation. In some embodiments, a method is disclosed. A query for at least one table comprising a plurality of data records is received. The query indicating a plurality of data operations to be performed on the plurality of data records. The plurality of data operations are combined into a target data operation. An intermediate result of the query is generated by performing the target data operation on the plurality of data records. A final result of the query is determined based on the intermediate result. In other embodiments, a system and a computer program product are disclosed.

According to an aspect, a method may include receiving a candidate value; in response to a received candidate value matching one of the entries in the table, incrementing a corresponding count; in response to the received candidate value not matching one of the entries in the table and the table not exceeding a threshold size, adding an entry to the table; in response to the received candidate value not matching one of the entries in the table and the table exceeding the threshold size, decrementing the counts in the table and deleting entries having a count of zero; adding noise to the corresponding counts in the entries of the table and deleting any noisy corresponding counts less than a threshold value; and outputting at least a portion of the table as the top-k value result set.

According to an aspect, a method may include receiving a candidate value; in response to a received candidate value matching one of the entries in the table, incrementing a corresponding count; in response to the received candidate value not matching one of the entries in the table and the table not exceeding a threshold size, adding an entry to the table; in response to the received candidate value not matching one of the entries in the table and the table exceeding the threshold size, decrementing the counts in the table and deleting entries having a count of zero; adding noise to the corresponding counts in the entries of the table and deleting any noisy corresponding counts less than a threshold value; and outputting at least a portion of the table as the top-k value result set.

Disclosed herein are system, method, and apparatus for generating labels for k-means clusters. The method includes accessing a plurality of data records from a database repository, and storing the plurality of data records into at least one of primary or secondary memory associated with at least one computer processor performing the method, along with a cluster number for each data record. All data records having a same cluster number form a cluster, and each record has been categorized or designated a cluster number out of a total K number of clusters. The method includes for each of a plurality of classification features, performing cluster-based analysis for a first cluster with respect to a single feature to generate a single feature overlap score. The method includes sorting, grouping, and generating a naming label for the first cluster based on the predetermined number of features having the lowest overlap scores.

Disclosed herein are system, method, and apparatus for generating labels for k-means clusters. The method includes accessing a plurality of data records from a database repository, and storing the plurality of data records into at least one of primary or secondary memory associated with at least one computer processor performing the method, along with a cluster number for each data record. All data records having a same cluster number form a cluster, and each record has been categorized or designated a cluster number out of a total K number of clusters. The method includes for each of a plurality of classification features, performing cluster-based analysis for a first cluster with respect to a single feature to generate a single feature overlap score. The method includes sorting, grouping, and generating a naming label for the first cluster based on the predetermined number of features having the lowest overlap scores.

Even if an existing learning dataset is limited, a new learning dataset with sufficient variation is generated. Therefore, for each of a plurality of learning data subsets, new input signals are generated from input signals of a plurality of pieces of learning data, and a plurality of pieces of new learning data that are respectively combinations of the new input signals and output signals of the corresponding learning data subset are generated. The input signals of the plurality of pieces of the learning data included in the corresponding learning data subset are divided into a first signal group and a second signal group, and the new input signals are generated by a learning device that is generated by performing learning by the first signal group set as an input signal set and the second signal group set as an output signal set.

Even if an existing learning dataset is limited, a new learning dataset with sufficient variation is generated. Therefore, for each of a plurality of learning data subsets, new input signals are generated from input signals of a plurality of pieces of learning data, and a plurality of pieces of new learning data that are respectively combinations of the new input signals and output signals of the corresponding learning data subset are generated. The input signals of the plurality of pieces of the learning data included in the corresponding learning data subset are divided into a first signal group and a second signal group, and the new input signals are generated by a learning device that is generated by performing learning by the first signal group set as an input signal set and the second signal group set as an output signal set.