| big data|
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|Related keyword(s)||data mining|
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big data is included as keyword or extra keyword in 0 datasets, 0 tools and 2 publications.
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|Title||Author(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|Utilizing semantic Wiki technology for intelligence analysis at the tactical edge||Little E.||Proceedings of SPIE - The International Society for Optical Engineering||English||2014||Challenges exist for intelligence analysts to efficiently and accurately process large amounts of data collected from a myriad of available data sources. These challenges are even more evident for analysts who must operate within small military units at the tactical edge. In such environments, decisions must be made quickly without guaranteed access to the kinds of large-scale data sources available to analysts working at intelligence agencies. Improved technologies must be provided to analysts at the tactical edge to make informed, reliable decisions, since this is often a critical collection point for important intelligence data. To aid tactical edge users, new types of intelligent, automated technology interfaces are required to allow them to rapidly explore information associated with the intersection of hard and soft data fusion, such as multi-INT signals, semantic models, social network data, and natural language processing of text. Abilities to fuse these types of data is paramount to providing decision superiority. For these types of applications, we have developed BLADE. BLADE allows users to dynamically add, delete and link data via a semantic wiki, allowing for improved interaction between different users. Analysts can see information updates in near-real-time due to a common underlying set of semantic models operating within a triple store that allows for updates on related data points from independent users tracking different items (persons, events, locations, organizations, etc.). The wiki can capture pictures, videos and related information. New information added directly to pages is automatically updated in the triple store and its provenance and pedigree is tracked over time, making that data more trustworthy and easily integrated with other users' pages.||0||0|
|Early Prediction of Movie Box Office Success based on Wikipedia Activity Big Data||Márton Mestyán
|English||2012||Use of socially generated "big data" to access information about collective states of the minds in human societies becomes a new paradigm in the emerging field of computational social science. One of the natural application of this would be prediction of the society's reaction to a new product in the sense of popularity and adoption rate. However, bridging between "real time monitoring" and "early predicting" remains as a big challenge. Here, we report on an endeavor to build a minimalistic predictive model for the financial success of movies based on collective activity data of online users. We show that the popularity of a movie could be predicted well in advance by measuring and analyzing the activity level of editors and viewers of the corresponding entry to the movie in Wikipedia, the well-known online encyclopedia.||0||0|