Abstract: The technique relates to a system and method for data-driven anomaly detection. This technique involves identifying region of interest from the data based on dimensionality reduction technique and change point detection algorithm. A reference data can be obtained separately or can be obtained from the test data also, wherein the reference data represent the normal operating condition of a system. The reference data are classified into different groups representing different modes of operation of the system. A control limit is determined for the different groups. The data within the region of interest are mapped with the different groups of the reference data and it is determined if the mapped data fall outside of the control limit of the mapped group. Finally, at least one abnormal event is detected by applying a heuristic algorithm on the data within the region of interest which are outside the control limit.

Abstract: The technique relates to a system and method for mining frequent and in-frequent items from a large transaction database to provide the dynamic recommendation of items. The method involves determining user interest for an item by monitoring short item behavior of at least one user then selecting a local category, a neighborhood category and a disjoint category with respect to the item clicked by the at least one user based on long term preferences data of a plurality of users of the ecommerce environment thereafter selecting one or more frequent and infrequent items from each of the selected local, neighborhood and disjoint category items and finally generating one or more dynamic recommendations based on the one or more items selected from the local category, the neighborhood category and the disjoint category and the one or more selected frequent and infrequent items.

Abstract: A system and a method for management of freight is disclosed. The method includes receiving (502) an input from a user. The input is mapped (504) with a network database and a route is calculated (506). The calculated route is displayed (508) to the user. An input in response to the calculated route is received (510). Based on the input, a list of route options is presented to the user (512). A response to the list of route options is collected (512). A result of the response to the list option is rendered to the user (514). A learning engine is updated based on the received input and collected response (516).

Abstract: The technique relates to a system and method for mining frequent and in-frequent items from a large transaction database to provide the dynamic recommendation of items. The method involves determining user interest for an item by monitoring short item behavior of at least one user then selecting a local category, a neighborhood category and a disjoint category with respect to the item clicked by the at least one user based on long term preferences data of a plurality of users of the ecommerce environment thereafter selecting one or more frequent and infrequent items from each of the selected local, neighborhood and disjoint category items and finally generating one or more dynamic recommendations based on the one or more items selected from the local category, the neighborhood category and the disjoint category and the one or more selected frequent and infrequent items.

Abstract: A system and method for logically masking data by implementing masking algorithms is provided. The method includes receiving one or more inputs from user regarding type of data masking to be implemented depending on type of data entry. Data entries include alphabetical data, data comprising unique codes, data comprising dates and numerical data. Based on inputs received, the data entries are classified and appropriate masking algorithms are executed. For masking numerical data entries, the data entries are first grouped using clustering algorithms and are then shuffled using shuffling algorithms. For low level of data masking selected by a user, numerical data entries are shuffled within groups and for high level of data masking selected by a user, numerical data entries are shuffled across groups.

Abstract: The technique relates to a system and method for data-driven anomaly detection. This technique involves identifying region of interest from the data based on dimensionality reduction technique and change point detection algorithm. A reference data can be obtained separately or can be obtained from the test data also, wherein the reference data represent the normal operating condition of a system. The reference data are classified into different groups representing different modes of operation of the system. A control limit is determined for the different groups. The data within the region of interest are mapped with the different groups of the reference data and it is determined if the mapped data fall outside of the control limit of the mapped group. Finally, at least one abnormal event is detected by applying a heuristic algorithm on the data within the region of interest which are outside the control limit.

Abstract: Systems, methods, and computer-readable code stored on a non-transitory media for mining association rules include determining a minimum support threshold and a minimum confidence threshold for association rule mining; determining a sampling model; sampling transactions from a transaction dataset; mining association rules from the sampled transactions; and transmitting mined association rules.

Abstract: A system, method and computer program product for forecasting one or more clinical pathways and resource requirements of at least one patient are provided. The system comprises an input module for receiving inputs pertaining to patient diagnostic data, the patient diagnostic data comprising information identified during diagnosis of the patient. The system also comprises a repository for storing data comprising at least one of patient data and pre-existing clinical pathways. The patient data comprises at least one of the patient diagnostic data received from the input module; patient historical data comprising historical treatment data of the patient and patient demographic data comprising demographic details of the patient. The system also comprises a clinical pathway forecasting module for forecasting clinical pathways by application of predetermined analytical models on the patient data and pre-existing clinical pathways.

Abstract: Systems, methods, and computer-readable code stored on a non-transitory media for mining association rules include determining a minimum support threshold and a minimum confidence threshold for association rule mining; determining a sampling model; sampling transactions from a transaction dataset; mining association rules from the sampled transactions; and transmitting mined association rules.

Abstract: A system, method and computer program product for forecasting one or more clinical pathways and resource requirements of at least one patient are provided. The system comprises an input module for receiving inputs pertaining to patient diagnostic data, the patient diagnostic data comprising information identified during diagnosis of the patient. The system also comprises a repository for storing data comprising at least one of patient data and pre-existing clinical pathways. The patient data comprises at least one of the patient diagnostic data received from the input module; patient historical data comprising historical treatment data of the patient and patient demographic data comprising demographic details of the patient. The system also comprises a clinical pathway forecasting module for forecasting clinical pathways by application of predetermined analytical models on the patient data and pre-existing clinical pathways.