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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.