2 research outputs found
DySky: Dynamic Skyline Queries on Uncertain Graphs
Given a graph, and a set of query vertices (subset of the vertices), the
dynamic skyline query problem returns a subset of data vertices (other than
query vertices) which are not dominated by other data vertices based on certain
distance measure. In this paper, we study the dynamic skyline query problem on
uncertain graphs (DySky). The input to this problem is an uncertain graph, a
subset of its nodes as query vertices, and the goal here is to return all the
data vertices which are not dominated by others. We employ two distance
measures in uncertain graphs, namely, \emph{Majority Distance}, and
\emph{Expected Distance}. Our approach is broadly divided into three steps:
\emph{Pruning}, \emph{Distance Computation}, and \emph{Skyline Vertex Set
Generation}. We implement the proposed methodology with three publicly
available datasets and observe that it can find out skyline vertex set without
taking much time even for million sized graphs if expected distance is
concerned. Particularly, the pruning strategy reduces the computational time
significantly
A Disease Diagnosis and Treatment Recommendation System Based on Big Data Mining and Cloud Computing
It is crucial to provide compatible treatment schemes for a disease according
to various symptoms at different stages. However, most classification methods
might be ineffective in accurately classifying a disease that holds the
characteristics of multiple treatment stages, various symptoms, and
multi-pathogenesis. Moreover, there are limited exchanges and cooperative
actions in disease diagnoses and treatments between different departments and
hospitals. Thus, when new diseases occur with atypical symptoms, inexperienced
doctors might have difficulty in identifying them promptly and accurately.
Therefore, to maximize the utilization of the advanced medical technology of
developed hospitals and the rich medical knowledge of experienced doctors, a
Disease Diagnosis and Treatment Recommendation System (DDTRS) is proposed in
this paper. First, to effectively identify disease symptoms more accurately, a
Density-Peaked Clustering Analysis (DPCA) algorithm is introduced for
disease-symptom clustering. In addition, association analyses on
Disease-Diagnosis (D-D) rules and Disease-Treatment (D-T) rules are conducted
by the Apriori algorithm separately. The appropriate diagnosis and treatment
schemes are recommended for patients and inexperienced doctors, even if they
are in a limited therapeutic environment. Moreover, to reach the goals of high
performance and low latency response, we implement a parallel solution for
DDTRS using the Apache Spark cloud platform. Extensive experimental results
demonstrate that the proposed DDTRS realizes disease-symptom clustering
effectively and derives disease treatment recommendations intelligently and
accurately