DISCRN: A Distributed Storytelling Framework for Intelligence Analysis.
Big Data
; 5(3): 225-245, 2017 09.
Article
en En
| MEDLINE
| ID: mdl-28933944
Storytelling connects entities (people, organizations) using their observed relationships to establish meaningful storylines. This can be extended to spatiotemporal storytelling that incorporates locations, time, and graph computations to enhance coherence and meaning. But when performed sequentially these computations become a bottleneck because the massive number of entities make space and time complexity untenable. This article presents DISCRN, or distributed spatiotemporal ConceptSearch-based storytelling, a distributed framework for performing spatiotemporal storytelling. The framework extracts entities from microblogs and event data, and links these entities using a novel ConceptSearch to derive storylines in a distributed fashion utilizing key-value pair paradigm. Performing these operations at scale allows deeper and broader analysis of storylines. The novel parallelization techniques speed up the generation and filtering of storylines on massive datasets. Experiments with microblog posts such as Twitter data and Global Database of Events, Language, and Tone events show the efficiency of the techniques in DISCRN.
Palabras clave
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Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Narración
/
Inteligencia
Tipo de estudio:
Qualitative_research
Límite:
Female
/
Humans
/
Male
Idioma:
En
Revista:
Big Data
Año:
2017
Tipo del documento:
Article
Pais de publicación:
Estados Unidos