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BDCaM: Big Data for Context-aware Monitoring

- A Personalized Knowledge Discovery

Framework for Assisted Healthcare

ABSTRACT:

Context-aware monitoring is an emerging technology that provides real-time

personalised health-care services and a rich area of big data application. In this paper, we

propose a knowledge discovery-based approach that allows the context-aware system to

adapt its behaviour in runtime by analysing large amounts of data generated in ambient

assisted living (AAL) systems and stored in cloud repositories. The proposed BDCaM

model facilitates analysis of big data inside a cloud environment. It first mines the trends

and patterns in the data of an individual patient with associated probabilities and utilizes

that knowledge to learn proper abnormal conditions. The outcomes of this learning method

are then applied in context-aware ecision-making processes for the patient. A use case is

implemented to illustrate the applicability of the framework that discovers the knowledge of

classification to identify the true abnormal conditions of patients having variations in blood

pressure (BP) and heart rate (HR). The evaluation shows a much

INTRODUCTION

AN ambient assisted living (AAL) system consists of heterogeneous sensors and devices

which generate huge amounts of patient-specific unstructured raw data everyday. Due to

diversity of sensors and devices, the captured data also have wide variations. A data

element can be from a few bytes of numerical value (e.g. HR = 72 bpm) to several

gigabytes of video stream. For example, if we assume a single AAL system generates 100

kilobytes data every second on average then it will become 2.93 abytes in one year. If any

system targets to support say, 5 million patients, then the data amount will be 14 exabytes

per year. Even if a healthcare system targets to analyse only continuous ECG of cardiac

patients in real-time inside the cloud environment, then it will produce around 7 PetaBytes

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data everyday from 3.5 million patients. Including these dynamically generated continuous

monitoring data there are also huge amounts of persistent data such as patient profile,

medical records, disease histories and social contacts.

EXISTING SYSTEM

In Existing System an attribute value set Ai is converted to a numerical value. Some

context attributes already have numeric values (e.g. HR, BP, room perature). Numerical

annotations are used for contexts having nominal value (e.g. activity). The static or

historical context that have boolean values (e.g. symptoms) are combined in a single

binary string which results a decimal value (e.g. 001100 converted to 12). So, after such

numerical conversion every Ai has the value set described in Definition 1.

PROPOSED SYSTEM

In Proposed System we developed BDCaM, an extended version of the

CoCaMAAL model. This includes the functionalities of learning and the knowledge

discovery process to find patient-specific anomalies using large amounts of data

ADVANTAGE OF PROPOSED SYSTEM

Faster learning with greater knowledge

Reduce the transmission of repeated false alerts

Innovative architectural model for context-aware monitoring

Step learning methodology

Demonstrate the performance and efficiency of BDCaM model





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ARCHITECTURE:





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HARDWARE REQUIREMENTS:

System : Pentium IV 2.4 GHz.

Hard Disk : 40 GB.

Floppy Drive : 44 Mb.

Monitor : 15 VGA Colour.

SOFTWARE REQUIREMENTS:

Operating system : Windows 7.

Coding Language : Java 1.7 ,Hadoop 0.8.1

Database : MySql 5

IDE : Eclipse



Download - Bd ca m big data for context-aware monitoring - a personalized knowledge discovery framework for assisted healthcare

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