Abstract: A machine-implemented method for retrieval of a set of prioritized data is described. The method includes the steps of obtaining a scope of analysis, prescribed either by a user or a system, feeding the scope of analysis in a machine through an user interface wherein, the machine is configured to augment the scope of analysis with a set of required information obtained by extracting relevant information from a preliminary data source, creating a corpus of data from at least one data source based on the augmented scope of analysis, processing the corpus of data by employing at least one parameter wherein, the at least one parameter is selected from at least one rule or a weightage based on the scope of analysis or a combination thereof to obtain a prioritized set of data. A machine-implemented method for extraction of insights from a set of prioritized data is also described.

Abstract: This technology relates to real-time filtering of relevant events from a plurality of events distributed spatially. An event processing engine receives the plurality of events from one or more real-time data sources. The received plurality of events are each compared with significant events stored in a significant event database and an entity associated with each of the significant events is identified. Next, the event processing engine identifies related events associated with each of the identified entities. The related events are aggregated to form one or more temporal sequence structures which are then matched with a plurality of sequential event patterns to identify each of the temporal sequence structures which match at least one of the plurality sequential event patterns. The entity associated with the one or more temporal sequence structures is filtered for providing one or more actions.

Abstract: The present disclosure relates to a system and method for dynamic and contextual ranking of information facets of multi-dimensional data. In one embodiment, one or more information facets or dimensions are ranked based on the context of the user and state of the information being observed. The context of observer defines how the user attention will be distributed across multiple facets of information based on the intent, goal and responsibility. The state of the observer is represented by a User Attention vector that is computed offline for multiple users based on the profile of the users and stored in a directory. The state of the information being observed is defined by value or level of significance of various facets of information and is represented by Perspective of value vector (POV) that is computed independent of the users.

Abstract: The present disclosure is related to a method and system for real-time filtering of relevant events from plurality of events distributed spatially. The event processing engine disclosed in the present disclosure receives plurality of events from the one or more real-time data sources. The received plurality of events is compared with significant events stored in significant event database. An entity associated with each of the significant events is identified. Thereafter, the event processing engine identifies related events associated with each of the identified entity. The related events are aggregated to form temporal sequence structure. Then the plurality of temporal sequence structures is matched with plurality of sequential event patterns to identify one or more temporal sequence structures which matches at least one sequential event pattern. The entity associated with the one or more temporal sequence structure is filtered for providing one or more actions.

Abstract: Embodiment is related to a method and system for preferentially accessing of one or more critical entities. One or more event data sets, related to one or more entities stored in a database, are received from one or more data sources by a processing engine. The entities, which are associated to the received event data sets are identified. The identified entities are compared with a preconfigured list of critical entities to determine at least one of the one or more entities matching with at least one critical entity. The preconfigured list of critical entities is stored in a pattern based selective index. The critical entities are stored in the graph database and indexed by the pattern based selective index. The entities are accessed, using address information associated to the respective entities, from the database when identified entities are matched with the critical entity in preconfigured list of critical entities.