Abstract:
The present invention relates to systems and methods in which data is processed to identify important concepts, a network graph produced describing the concepts and connections between them, and identifying and analyzing evolution concepts. The system of the present invention comprises at least one data processing module (110) adapted to perform metadata analysis on inputted artifacts to identify temporal and geospatial information from each of the artifacts and to classify the artifacts as temporal and geospatial artifacts; at least one importance analysis module (120) in communication with a domain knowledge base (125) and adapted to identify domain specific important concepts from the artifacts using said domain knowledge base(125); at least one concept network generation module (150) adapted to construct a set of network graphs using the important concepts and the temporal and geospatial associations; and at least one concept evolution analysis module (140) adapted to analyse the network connections to discover evolution data for the important concepts from temporal network graphs and geospatial network graphs. The present invention automatically process raw data and identifies the important concepts; creates network graph that describes the concepts and connections between them; and identifies and analyse the evolution concepts over time and space using network analysis.
Abstract:
A system (200) and method (300) for dynamic generation of distribution plan for intensive social network analysis (SNA) tasks in a distributed environment comprising at least one Processing Environment Profiler (202); at least one Network Graph Analysis Task Profiler (204); at least one Resource Cost Analyzer (206); at least one Distribution Planner (208); and at least one Task Distributer (210). The at least one Network Graph Analysis Task Profiler (204) further comprises at least one Network Graph Pruning module having means to eliminate unnecessary links and nodes from network graph to produce accurate analysis. A distribution plan for Intensive Social Network (SNA) Tasks is achieved by utilizing a pruned network which extracts the Sub Graph from network graph based on feature set extraction (non-dependent Sub Graph) and estimating the resource cost required to perform each of the given tasks which further map the said task to the appropriate server.