Download 1.Big Data - Bangalore Sunday

Big Data, Big Knowledge: Big Data for
Personalized Healthcare
Abstract:
The idea that the purely phenomenological knowledge that we can extract by
analyzing large amounts of data can be useful in healthcare seems to contradict the
desire of VPH researchers to build detailed mechanistic models for individual
patients. But in practice no model is ever entirely phenomenological or entirely
mechanistic. We propose in this position paper that big data analytics can be
successfully combined with VPH technologies to produce robust and effective in
silico medicine solutions. In order to do this, big data technologies must be further
developed to cope with some specific requirements that emerge from this
application. Such requirements are: working with sensitive data; analytics of
complex and heterogeneous data spaces, including non textual information;
distributed data management under security and performance constraints;
specialized analytics to integrate bioinformatics and systems biology information
with clinical observations at tissue, organ and organisms scales; and specialized
analytics to define the “physiological envelope” during the daily life of each
patient. These domain-specific requirements suggest a need for targeted funding, in
which big data technologies for in silico medicine becomes the research priority.
Existing System:
THE birth of big data, as a concept if not as a term, is usually associated
with a META Group report by Doug Laney entitled “3-D Data Management:
Controlling Data Volume, Velocity, and Variety” published in 2001 [1]. Further
developments now suggest big data problems are identified by the so-called
volume (quantity of data), variety (data from different categories), velocity (fast
generation of new data), veracity (quality of the data), and value (in the data) .
Disadvantages:
1.Complexity of Query Processing.
2.Mapping and MapReduce is complexity.
Proposed System:
This may be conceptually simple, the VPH vision contains a tremendous challenge,
namely, the development of mathematical models capable of accurately predicting
what will happen to a biological system. To tackle this huge challenge,
multifaceted research is necessary: around medical imaging and sensing
technologies (to produce quantitative data about the patient’s anatomy and
physiology) ,data processing to extract from such data information that in some
cases is not immediately available ,biomedical modeling to capture the available
knowledge into predictive.
Advantages:
1.Map Reduce
2.Query Proocessing Faster.
Implementation Modules:
1.Big Data
2.Mapping
3.Map Reduce
4.Searching(query Processing)
Big Data:
Big data is generating a lot of hype in every industry including healthcare. As my
colleagues and I talk to leaders at health systems, we’ve learned that they’re
looking for answers about big data. A number of use cases in healthcare are well
suited for a big data solution. Some academic- or research-focused healthcare
institutions are either experimenting with big data or using it in advanced research
projects. Those institutions draw upon data scientists, statisticians, graduate
students, and the like to wrangle the complexities of big data. In the following
sections, we’ll address some of those complexities and what’s being done to
simplify big data and make it more accessible.
Mapping:
In computing and data management, data mapping is the process of creating data
element mappings between two distinct data models. Data mapping is used as a
first step for a wide variety of data integration tasks including: Data transformation
or data mediation between a data source and a destination. ap objects to relational
databases the place to start is with the data attributes of a class. An attribute will
map to zero or more columns in a relational database. Remember, not all attributes
are persistent, some are used for temporary calculations. For example, a Student
object may have an average Mark attribute that is needed within your application
but isn’t saved to the database because it is calculated by the application. Because
some attributes of an objects are objects in their own right, a Customer object has
an Address object as an attribute.
Map Reduce:
Map-reduce is a programming model that has its roots in functional programming.
In addition to often producing short, elegant code for problems involving lists or
collections, this model has proven very useful for large-scale highly parallel data
processing.
Map function reads a stream of data and parses it into intermediate (key, value)
pairs. When that is complete, the Reduce function is called once for each unique
key that was generated by Map and is given the key and a list of all values that
were generated for that key as a parameter. The keys are presented in sorted order.
As an example of using Map Reduce, consider the task of counting the number of
occurrences of each word in a large collection of documents. The user-written Map
function reads the document data and parses out the words. For each word, it
writes the (key, value) pair of (word, 1). That is, the word is treated as the key and
the associated value of 1 means that we saw the word once. This intermediate data
is then sorted by Map Reduce by keys and the user's Reduce function is called for
each unique key.
Healthcare Search (query processing):
Search engine is the popular term for an information retrieval (IR) system. While
researchers and developers take a broader view of IR systems, consumers think of
them more in terms of what they want the systems to do — namely search the
Web, or an intranet, or a database. Actually consumers would really prefer a
finding engine, rather than a search engine. Search engines match queries against
an index that they create. The index consists of the words in each document, plus
pointers to their locations within the documents. This is called an inverted file. A
search engine or IR system comprises four essential modules:
A document
processor A query processor A search and matching function A ranking capability
While users focus on "search," the search and matching function is only one of the
four modules. Each of these four modules may cause the expected or unexpected
results that consumers get when they use a search engine.
System Specifictions:
Hardware Requirements:
System
-
Pentium –IV 2.4 GHz
Speed
-
1.1 Ghz
RAM
- 256MB(min)
Hard Disk
- 40 GB
Key Board
-
Mouse
- Logitech
Monitor
- 15 VGA Color.
Standard Windows Keyboard
Software Requirements:

Operating System
:Windows/XP/7.

Application Server
: Tomcat 5.0/6.0

Front End

Scripts

Server side Script
: Java Server Pages.

Database
: MongoDB

Database Connectivity
: Robomongo-0.8.5-i386.
: HTML, Java, Jsp
: JavaScript.