This study presents a comprehensive method for rapidly processing, storing, retrieving, and analyzing big healthcare data. Based on NoSQL (not only SQL), a patientdriven data architecture is suggested to enable the rapid storing and flexible expansion of data. Thus, the schema differences of various hospitals can be overcome, and the flexibility for field alterations and addition is ensured. The timeline mode can easily be used to generate a visual representation of patient records, providing physicians with a reference for patient consultation. The sharding-key is used for data partitioning to generate data on patients of various populations. Subsequently, data reformulation is conducted as a first step, producing additional temporal and spatial data, providing cloud computing methods based on queryMapReduce-shard, and enhancing the search performance of data mining. Target data can be rapidly searched and filtered, particularly when analyzing temporal events and interactive effects.
The position of a node in a social network, or node centrality, can be quantified in several ways. Traditionally, it can be defined by considering the local connectivity of a node (degree) and some non-local characteristics (distance). Here, we present an approach that can quantify the interaction structure of signed digraphs and we define a node centrality measure for these networks. The basic principle behind our approach is to determine the sign and strength of direct and indirect effects of one node on another along pathways. Such an approach allows us to elucidate how a node is structurally connected to other nodes in the social network, and partition its interaction structure into positive and negative components. Centrality here is quantified in two ways providing complementary information: total effect is the overall effect a node has on all nodes in the same social network; while net effect describes, whether predominately positive or negative, the manner in which a node can exert on the social network. We use Sampson's like-dislike relation network to demonstrate our approach and compare our result to those derived from existing centrality indices. We further demonstrate our approach by using Hungarian school classroom social networks.
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