HCloud, a Healthcare-Oriented Cloud System with Improved Efficiency in Biomedical Data Processing - Cloud Computing with e-Science Applications - page 175

TABLE 8.1
Algorithm of MQonMapReduce
Input: iSize,oSize
Output: Boolean
BEGIN
initial a thread
while (message from MQ is Null)
if (message is DONE) return true;
// DONE is a particular message from MQ
else
loop;
load Mapper(message)
// parallel Map function for data processing and
analysis according to different message
iSize ← sizeof (files of input directory)
//compute the total size of files under input directory
if iSize>SIZE
MQonMapReduce(iSize, oSize);
//immerge trivial input files to a larger one
else {
oSize ← sizeof(output directory)
//compute the total size of files under output directory
if oSize>SIZE
MQonMapReduce(iSize, oSize);
//immerge trivial output files to a larger one
else
send a message DONE to MQ;
// process of all data are accomplished; }
END
A parallel computing framework based on the Map-Reduce framework should
be combined with the data stored in the NoSQL database (e.g., MongoDB) to
deliver complex analytics, and data processing for such physiological data
processing is always bound to the CPU (central processing unit). The map
function can be designed to handle part of the data, while the reduce func-
tion is to merge the output produced by the map function and then output
all of the filtered results. According to our six-layer HCloud, MQ can be uti-
lized as a scheduler to cooperate with the Map-Reduce scheme. Generally,
data analysis flow on this scheme can be described as in Table 8.1. Please
note that this algorithm is a recursive procedure. The constant SIZE is a
threshold according to the different scales of physiological data processing,
which indicates the Map-Reduce procedure is to merge with the size of data
scale. It will not be finished until all files are generated by the threshold of
SIZE. Hadoop and the Map-Reduce programming paradigm already have
a substantial base in the bioinformatics community [ 17 ] (e.g., monitoring of
long-term ECG for individuals). The next section introduces the details of the
ECG data process with this paradigm.