Asthma is one of the most prevalent and costly chronic conditions in the United States which cannot be cured. However accurate and timely surveillance data could allow for timely and targeted interventions at the community or individual level. Current national asthma disease surveillance systems can have data availability lags of up to two weeks. Rapid progress has been made in gathering non-traditional, digital information to perform disease surveillance.
We introduce a novel method of using
multiple data sources for predicting the number of asthma related emergency
department (ED) visits in a specific area. Twitter data, Google search
interests and environmental sensor data were collected for this purpose. Our
preliminary findings show that our model can predict the number of asthma ED
visits based on near-real-time environmental and social media data with
approximately 70% precision. The results can be helpful for public health
surveillance, emergency department preparedness, and, targeted patient interventions.
1.2 INTRODUCTION:
Asthma is one of the most prevalent and costly chronic conditions in the United States, with 25 million people affected. Asthma accounts for about two million emergency department (ED) visits, half a million hospitalizations, and 3,500 deaths, and incurs more than 50 billion dollars in direct medical costs annually. Moreover, asthma is a leading cause of loss productivity with nearly 11 million missed school days and more than 14 million missed work days every year due to asthma. Although asthma cannot be cured, many of its adverse events can be prevented by appropriate medication use and avoidance of environmental triggers. The prediction of population- and individual-level risk for asthma adverse events using accurate and timely surveillance data could guide timely and targeted interventions, to reduce the societal burden of asthma. At the population level, current national asthma disease surveillance programs rely on weekly reports to the Centers for Disease Control and Prevention (CDC) of data collected from various local resources by state health departments.
Notoriously, such data have a lag-time of weeks, therefore providing retrospective information that is not amenable to proactive and timely preventive interventions. At the individual level, known predictors of asthma ED visits and hospitalizations include past acute care utilization, medication use, and sociodemographic characteristics. Common data sources for these variables include electronic medical records (EMR), medical insurance claims data, and population surveys, all of which, also, are subject to significant time lag. In an ongoing quality improvement project for asthma care, Parkland Center for Clinical Innovation (PCCI) researchers have built an asthma predictive model relying on a combination of EMR and claim data to predict the risk for asthma-related ED visits within three months of data collection [Unpublished reports from PCCI]. Although the model performance (C-statistic 72%) and prediction timeframe (three months) are satisfying, a narrower prediction timeframe potentially could provide additional risk-stratification for more efficiency and timeliness in resource deployment. For instance, resources might be prioritized to first serve patients at high risk for an asthma ED visit within 2 weeks of data collection, while being safely deferred for patients with a later predicted high-risk period.
Novel sources of timely data on population- and individual-level asthma activities are needed to provide additional temporal and geographical granularity to asthma risk stratification. Short of collecting information directly from individual patients (a time- and resource-intensive endeavor), readily available public data will have to be repurposed intelligently to provide the required information. There has been increasing interest in gathering non-traditional, digital information to perform disease surveillance. These include diverse datasets such as those stemming from social media, internet search, and environmental data. Twitter is an online social media platform that enables users to post and read 140-character messages called “tweets”. It is a popular data source for disease surveillance using social media since it can provide nearly instant access to real-time social opinions. More importantly, tweets are often tagged by geographic location and time stamps potentially providing information for disease surveillance.
Another notable non-traditional disease surveillance
systemhas been a data-aggregating tool called Google Flu Trends which uses
aggregated search data to estimate flu activity. Google Trends was quite
successful in its estimation of influenza-like illness. It is based on Google’s
search engine which tracks how often a particular search-term is entered
relative to the total search-volume across a particular area. This enables
access to the latest data from web search interest trends on a variety of
topics, including diseases like asthma. Air pollutants are known triggers for
asthma symptoms and exacerbations. The United States Environmental Protection
Agency (EPA) provides access to monitored air quality data collected at outdoor
sensors across the country which could be used as a data source for asthma
prediction. Meanwhile, as health reform progresses, the quantity and variety of
health records being made available electronically are increasing dramatically.
In contrast to traditional disease surveillance systems, these new data sources
have the potential to enable health organizations to respond to chronic
conditions, like asthma, in real time. This in turn implies that health
organizations can appropriately plan for staffing and equipment availability in
a flexible manner. They can also provide early warning signals to the people at
risk for asthma adverse events, and enable timely, proactive, and targeted
preventive and therapeutic interventions.
1.3 LITRATURE SURVEY:
USE OF HANGEUL TWITTER TO TRACK AND PREDICT HUMAN INFLUENZA INFECTION
AUTHOR: Kim, Eui-Ki, et al.
PUBLISH: PloS one vol. 8, no.7, e69305, 2013.
EXPLANATION:
Influenza epidemics arise through
the accumulation of viral genetic changes. The emergence of new virus strains
coincides with a higher level of influenza-like illness (ILI), which is seen as
a peak of a normal season. Monitoring the spread of an epidemic influenza in
populations is a difficult and important task. Twitter is a free social
networking service whose messages can improve the accuracy of forecasting
models by providing early warnings of influenza outbreaks. In this study, we
have examined the use of information embedded in the Hangeul Twitter stream to detect
rapidly evolving public awareness or concern with respect to influenza
transmission and developed regression models that can track levels of actual
disease activity and predict influenza epidemics in the real world. Our
prediction model using a delay mode provides not only a real-time assessment of
the current influenza epidemic activity but also a significant improvement in
prediction performance at the initial phase of ILI peak when prediction is of
most importance.
A NEW AGE OF PUBLIC HEALTH: IDENTIFYING DISEASE OUTBREAKS BY ANALYZING TWEETS
AUTHOR: Krieck, Manuela, Johannes Dreesman, Lubomir Otrusina, and Kerstin Denecke.
PUBLISH: In Proceedings of Health Web-Science Workshop, ACM Web Science Conference. 2011.
EXPLANATION:
Traditional disease surveillance is a very time
consuming reporting process. Cases of notifiable diseases are reported to the
different levels in the national health care system before actions can be
taken. But, early detection of disease activity followed by a rapid response is
crucial to reduce the impact of epidemics. To address this challenge,
alternative sources of information are investigated for disease surveillance.
In this paper, the relevance of twitter messages outbreak detection is
investigated from two directions. First, Twitter messages potentially related
to disease outbreaks are retrospectively searched and analyzed. Second,
incoming twitter messages are assessed with respect to their relevance for
outbreak detection. The studies show that twitter messages can be – to a
certain extent – highly relevant for early detecting hints to public health
threats. According to the law on German Protection against Infection Act
(Infektionsschutzgesetz (IfSG), 2001) the traditional disease surveillance
relies on data from mandatory reporting of cases by physicians and
laboratories. They inform local county health departments (Landkreis) which in
turn report to state health departments (Land). At the end of the reporting
pipeline, the national surveillance institute (Robert Koch Institute) is
informed about the outbreak. It is clear that these different stages of
reporting take time and delay a timely reaction.
TOWARDS DETECTING INFLUENZA EPIDEMICS BY ANALYZING TWITTER MESSAGES
AUTHOR: Culotta, Aron.
PUBLISH: In Proceedings of the first workshop on social media analytics, pp. 115-122. ACM, 2010.
EXPLANATION:
Rapid response to a health epidemic
is critical to reduce loss of life. Existing methods mostly rely on expensive
surveys of hospitals across the country, typically with lag times of one to two
weeks for influenza reporting, and even longer for less common diseases. In
response, there have been several recently proposed solutions to estimate a
population’s health from Internet activity, most notably Google’s Flu Trends service,
which correlates search term frequency with influenza statistics reported by
the Centers for Disease Control and Prevention (CDC). In this paper, we analyze
messages posted on the micro-blogging site Twitter.com to determine if a
similar correlation can be uncovered. We propose several methods to identify
influenza-related messages and compare a number of regression models to
correlate these messages with CDC statistics. Using over 500,000 messages
spanning 10 weeks, we find that our best model achieves a correlation of .78
with CDC statistics by leveraging a document classifier to identify relevant
messages.
CHAPTER 2
2.0 SYSTEM ANALYSIS
2.1 EXISTING SYSTEM:
Existing methods in the increased availability of information in the Web, in the last years, a new research area has been developed, namely Infodemiology. It can be defined as the “science of distribution and determinants of information in an electronic medium, specifically the Internet, or in a population, with the ultimate aim to inform public health and public policy”. As part of this research area, several kinds of data have been studied for their applicability in the context of disease surveillance. Google flu trends exploit the search behavior to monitor the current flurelated disease activity. It could be shown by Carneiro and Mylonakis that Google Flu Trends can detect regional outbreaks of influenza 7–10 days before conventional Centers for Disease Control and Prevention surveillance systems.
Google messages and their relevance for
disease outbreak detection has been reported already that especially tweets are
useful to predict outbreaks such as a Norovirus outbreak at a university
analysed twitter news during the influenza epidemic 2009. They compared the use
of the term “H1N1” and “swine flu” over the time. Furthermore, they analysed
the content of the tweets (ten content concepts) and validated twitter as a the
real time content. They analysed the data via Infovigil an infosurveillance
system by using an automated coding. To find out if there is a relationship
between automated and manual coding, the tweets were evaluated by a Pearson´s
correlation. Chew et al. found a significant correlation between both coding in
seven content concept it needs to be investigated whether this source might be
of relevance for detecting disease outbreaks in Germany. Therefore, only German
keywords are exploited to identify Twitter messages. Further, we are not only interested
in influenza-like illnesses as the studies available so far, but also in other
infectious diseases (e.g. Norovirus and Salmonella).
2.1.1 DISADVANTAGES:
Existing methods have a common format:
[username]
[text] [date time client]. The length is restricted to 140
characters. In terms of linguistics, each twitter user can write as he or she
likes. Thus, the variety reaches from complete sentences to listing of
keywords. Hashtags, i.e. terms that are combined with a hash (e.g. #flu) denote
topics and are primarily utilized by experienced users categories google according
to their contents in more details, google messages can • Provide information, •
Express opinions or • Report personal issues is provided, the authority of that
information cannot normally not be determined, so it might be unverified
information. Opinions are often expressed with humor or sarcasm and may be
highly contradictive in the emotions that are expressed.
2.2 PROPOSED SYSTEM:
Our proposed methods to leverage social media, internet search, and environmental air quality data to estimate ED visits for asthma in a relatively discrete geographic area (a metropolitan area) within a relatively short time period (days) to this end, we have gathered asthma related ED visits data, social media data from Twitter, internet users’ search interests from Google and pollution sensor data from the EPA, all from the same geographic area and time period, to create a model for predicting asthma related ED visits. This work is different from extant studies that typically predict the spread of contagious diseases using social media such as Twitter. Unlike influenza or other viral diseases, asthma is a non-communicable health condition and we demonstrate the utility and value of linking big data from diverse sources in developing predictive models for non-communicable diseases with a specific focus on asthma.
Research studies have explored the use
of novel data sources to propose rapid, cost-effective health status
surveillance methodologies. Some of the early studies rely on document
classification suggesting that Twitter data can be highly relevant for early
detection of public health threats. Others employ more complex linguistic
analysis, such as the Ailment Topic Aspect Model which is useful for syndrome
surveillance. This type of analysis is useful for demonstrating the
significance of social media as a promising new data source for health
surveillance. Other recent studies have linked social media data with real
world disease incidence to generate actionable knowledge useful for making
health care decisions. These include which analyzed Twitter messages related to
influenza and correlated them with reported CDC statistics validated Twitter as
a real-time content, sentiment, and public attention trend-tracking tool.
Collier employed supervised classifiers (SVM and Naive Bayes) to classify
tweets into four self-reported protective behavior categories. This study adds
to evidence supporting a high degree of correlation between pre-diagnostic
social media signals and diagnostic influenza case data.
2.2.1 ADVANTAGES:
Our work uses a combination of data from multiple sources to predict the number of asthma-related ED visits in near real-time. In doing so, we exploit geographic information associated with each dataset. We describe the techniques to process multiple types of datasets, to extract signals from each, integrate, and feed into a prediction model using machine learning algorithms, and demonstrate the feasibility of such a prediction.
The main contributions of this work are:
• Analysis of tweets with respect to their relevance for disease surveillance,
• Content analysis and content classification of tweets,
• Linguistic analysis of disease-reporting twitter messages,
• Recommendations on search patterns for tweet
search in the context of disease surveillance.
2.3.1 HARDWARE REQUIREMENT:
CHAPTER 3
3.0 SYSTEM DESIGN:
Data Flow Diagram / Use Case Diagram / Flow Diagram:
External sources or destinations, which may be people or organizations or other entities
Here the data referenced by a process is stored and retrieved.
People, procedures or devices that produce data’s in the physical component is not identified.
Data moves in a specific direction from an origin to a destination. The data flow is a “packet” of data.
MODELING RULES:
There are several common modeling rules when creating DFDs:
3.1 ARCHITECTURE DIAGRAM:
3.2 DATAFLOW DIAGRAM:
UML DIAGRAMS:
3.2 USE CASE DIAGRAM:
3.3 CLASS DIAGRAM:
3.4 SEQUENCE DIAGRAM:
3.5 ACTIVITY DIAGRAM:
| Alert Email |
| Login |
| Filter Tweet |
| NO |
| Accept |
| Asthma Tweets |
| New Tweet |
| Tweet |
| Login |
| Friend Follow |
| Friends list |
CHAPTER 4
4.0 IMPLEMENTATION:
DISEASE CONTROL AND PREVENTION (CDC):
Current national asthma disease surveillance programs rely on weekly reports to the Centers for Disease Control and Prevention (CDC) of data collected from various local resources by state health departments [4]. Notoriously, such data have a lag-time of weeks, therefore providing retrospective information that are not amenable to proactive and timely preventive interventions. At the individual level, known predictors of asthma ED visits and hospitalizations include past acute care utilization, medication use, and sociodemographic characteristics. Common data sources for these variables include electronic medical records (EMR), medical insurance claims data, and population surveys, all of which, also, are subject to significant time lag. In an ongoing quality improvement project for asthma care, Parkland Center for Clinical Innovation (PCCI) researchers have built an asthma predictive model relying on a combination of EMR and claim data to predict the risk for asthma-related ED visits within three months of data collection.
Although the model performance (C-statistic 72%) and prediction timeframe (three months) are satisfying, a narrower prediction timeframe potentially could provide additional risk-stratification for more efficiency and timeliness in resource deployment. For instance, resources might be prioritized to first serve patients at high risk for an asthma ED visit within 2 weeks of data collection, while being safely deferred for patients with a later predicted high-risk period. Novel sources of timely data on population- and individual-level asthma activities are needed to provide additional temporal and geographical granularity to asthma risk stratification. Short of collecting information directly from individual patients (a time- and resource-intensive endeavor), readily available public data will have to be repurposed intelligently to provide the required information.
4.1 ALGORITHM:
MACHINE LEARNING ALGORITHMS:
Our research objective is to leverage
social media, internet search, and environmental air quality data to estimate
ED visits for asthma in a relatively discrete geographic area (a metropolitan
area) within a relatively short time period (days). To this end, we have
gathered asthma related ED visits data, social media data from Twitter,
internet users’ search interests from Google and pollution sensor data from the
EPA, all from the same geographic area and time period, to create a model for
predicting asthma related ED visits. This work is different from extant studies
that typically predict the spread of contagious diseases using social media
such as Twitter. Unlike influenza or other viral diseases, asthma is a
non-communicable health condition and we demonstrate the utility and value of
linking big data from diverse sources in developing predictive models for
non-communicable diseases with a specific focus on asthma.
4.2 MODULES:
EMERGENCY DEPARTMENT VISITS:
ENVIRONMENTAL SENSORS (EMR):
OUR PREDICTION SENSOR DATA:
ASTHMA
PREDICTION RESULTS:
4.3 MODULE DESCRIPTION:
EMERGENCY DEPARTMENT VISITS:
We introduce a novel method of using multiple data sources for predicting the number of asthma related emergency department (ED) visits in a specific area. Twitter data, Google search interests and environmental sensor data were collected for this purpose. Moreover, asthma is a leading cause of loss productivity with nearly 11 million missed school days and more than 14 million missed work days every year due to asthma. Although asthma cannot be cured, many of its adverse events can be prevented by appropriate medication use and avoidance of environmental triggers.
The prediction of population- and
individual-level risk for asthma adverse events using accurate and timely
surveillance data could guide timely and targeted interventions, to reduce the
societal burden of asthma. At the population level, current national asthma
disease surveillance programs rely on weekly reports to the Centers for Disease
Control and Prevention (CDC) of data collected from various local resources by
state health departments. Notoriously, such data have a lag-time of weeks,
therefore providing retrospective information that is not amenable to proactive
and timely preventive interventions. At the individual level, known predictors
of asthma ED visits and hospitalizations include past acute care utilization,
medication use, and sociodemographic characteristics.
ENVIRONMENTAL SENSORS (EMR):
Common data sources for these variables include electronic medical records (EMR), medical insurance claims data, and population surveys, all of which, also, are subject to significant time lag. In an ongoing quality improvement project for asthma care, Parkland Center for Clinical Innovation (PCCI) researchers have built an asthma predictive model relying on a combination of EMR and claim data to predict the risk for asthma-related ED visits within three months of data collection [Unpublished reports from PCCI]. Although the model performance (C-statistic 72%) and prediction timeframe (three months) are satisfying, a narrower prediction timeframe potentially could provide additional risk-stratification for more efficiency and timeliness in resource deployment.
For instance, resources might be
prioritized to first serve patients at high risk for an asthma ED visit within
2 weeks of data collection, while being safely deferred for patients with a
later predicted high-risk period. Novel sources of timely data on population-
and individual-level asthma activities are needed to provide additional temporal
and geographical granularity to asthma risk stratification. Short of collecting
information directly from individual patients (a time- and resource-intensive
endeavor), readily available public data will have to be repurposed
intelligently to provide the required information. There has been increasing
interest in gathering non-traditional, digital information to perform disease
surveillance. These include diverse datasets such as those stemming from social
media, internet search, and environmental data. Twitter is an online social
media platform that enables users to post and read 140-character messages
called “tweets”. It is a popular data source for disease surveillance using
social media since it can provide nearly instant access to real-time social opinions.
More importantly, tweets are often tagged by geographic location and time
stamps potentially providing information for disease surveillance.
OUR PREDICTION SENSOR DATA:
We first analyzed the association between the asthma-related ED visits and data from Twitter, Google trends, and Air Quality sensors, using the Pearson correlation coefficient. We also examined the association between asthma-related tweet counts and ED visit counts for abdominal pain/constipation patients, to control for non-asthma-specific variations in ED visit counts. Then, we designed and implemented a prediction model to estimate the incidence of asthma ED visits at CMC using a combination of independent variables from the above data sources.
Twitter offers streaming APIs to give developers and researchers low latency access to its global stream of data. Public streams, which can provide access to the public data flowing through Twitter, were used in this study. Studies have estimated that using Twitter’s Streaming API, researchers can expect to receive 1% of the tweets in near real-time. Twitter4j, an unofficial Java library for the Twitter API, was used to access tweet information from the Twitter Streaming API.
Two different Twitter data sets were collected in this study:
(1) The general twitter stream: a simple collection of JSON grabbed from the general Twitter stream. The general tweet counts were used to estimate the Twitter population and further normalize asthma tweet counts.
(2) The asthma-related stream: to collect
only tweets containing any of 19 related keywords that were suggested by our
clinical collaborators from PCCI. The asthma stream is limited to 1% of full
tweets as well.
ASTHMA PREDICTION RESULTS:
Our results from the correlation analysis, asthma tweets, CO, NO2 and PM2.5 were selected as inputs into our prediction model. We are only reporting results for the Decision Tree and Artificial Neural Networks (ANN) techniques, as the Naive Bayes and SVM techniques did not yield good prediction results. First, backward feature selection algorithm was used to examine if the addition of Twitter data would improve the prediction. As shown in Table VI, combining air quality data with tweets resulted in higher prediction accuracy. Additionally, we evaluated prediction precision. Given that our prediction task is for a three-way classification, each technique resulted in different prediction and/or precision in different classes (Table VII). Decision Tree performed well in predicting the “High” class, while ANN, after Adaptive Boosting, worked well for the “Low” class. Stacking the two techniques performed well for the “Medium” class.
The results of our analysis are
promising because they perform with a fairly high level of accuracy overall. As
noted in the introduction, traditional asthma ED visit models are useful for
predicting events in a three month window and have an accuracy of approximately
70%. It is to be noted that “traditional models” estimate a risk score for
asthma ED visit for each individual patient, whereas our “Twitter/
Environmental data model” predicts the risk for a daily number of ED visits
being High, Low, or Medium. The former is an individual-level risk model, while
the latter is a population-level risk model. Our population-level asthma risk
prediction model has the potential for complementing current individual-level
models, and may lead to a shorter time window and better accuracy of
prediction. This in turn has implications for better planning and proactive
treatment in specific geo-locations at specific time periods.
CHAPTER 5
5.0 SYSTEM STUDY:
5.1 FEASIBILITY STUDY:
The feasibility of the project is analyzed in this phase and business proposal is put forth with a very general plan for the project and some cost estimates. During system analysis the feasibility study of the proposed system is to be carried out. This is to ensure that the proposed system is not a burden to the company. For feasibility analysis, some understanding of the major requirements for the system is essential.
Three key considerations involved in the feasibility analysis are
5.1.1 ECONOMICAL FEASIBILITY:
This study is carried out to check the economic impact that the system will have on the organization. The amount of fund that the company can pour into the research and development of the system is limited. The expenditures must be justified. Thus the developed system as well within the budget and this was achieved because most of the technologies used are freely available. Only the customized products had to be purchased.
This study is carried out to check the technical feasibility, that is, the technical requirements of the system. Any system developed must not have a high demand on the available technical resources. This will lead to high demands on the available technical resources. This will lead to high demands being placed on the client. The developed system must have a modest requirement, as only minimal or null changes are required for implementing this system.
5.1.3 SOCIAL FEASIBILITY:
The aspect of study is to check the level of acceptance of the system by the user. This includes the process of training the user to use the system efficiently. The user must not feel threatened by the system, instead must accept it as a necessity. The level of acceptance by the users solely depends on the methods that are employed to educate the user about the system and to make him familiar with it. His level of confidence must be raised so that he is also able to make some constructive criticism, which is welcomed, as he is the final user of the system.
5.2 SYSTEM TESTING:
Testing is a process of checking whether the developed system is working according to the original objectives and requirements. It is a set of activities that can be planned in advance and conducted systematically. Testing is vital to the success of the system. System testing makes a logical assumption that if all the parts of the system are correct, the global will be successfully achieved. In adequate testing if not testing leads to errors that may not appear even many months.
This creates two problems, the time lag between the cause and the appearance of the problem and the effect of the system errors on the files and records within the system. A small system error can conceivably explode into a much larger Problem. Effective testing early in the purpose translates directly into long term cost savings from a reduced number of errors. Another reason for system testing is its utility, as a user-oriented vehicle before implementation. The best programs are worthless if it produces the correct outputs.
5.2.1 UNIT TESTING:
| Description | Expected result |
| Test for application window properties. | All the properties of the windows are to be properly aligned and displayed. |
| Test for mouse operations. | All the mouse operations like click, drag, etc. must perform the necessary operations without any exceptions. |
A program represents the
logical elements of a system. For a program to run satisfactorily, it must
compile and test data correctly and tie in properly with other programs.
Achieving an error free program is the responsibility of the programmer.
Program testing checks
for two types
of errors: syntax
and logical. Syntax error is a
program statement that violates one or more rules of the language in which it
is written. An improperly defined field dimension or omitted keywords are
common syntax errors. These errors are shown through error message generated by
the computer. For Logic errors the programmer must examine the output
carefully.
5.1.2 FUNCTIONAL TESTING:
Functional testing of an application is used to prove the application delivers correct results, using enough inputs to give an adequate level of confidence that will work correctly for all sets of inputs. The functional testing will need to prove that the application works for each client type and that personalization function work correctly.When a program is tested, the actual output is compared with the expected output. When there is a discrepancy the sequence of instructions must be traced to determine the problem. The process is facilitated by breaking the program into self-contained portions, each of which can be checked at certain key points. The idea is to compare program values against desk-calculated values to isolate the problems.
| Description | Expected result |
| Test for all modules. | All peers should communicate in the group. |
| Test for various peer in a distributed network framework as it display all users available in the group. | The result after execution should give the accurate result. |
5.1. 3 NON-FUNCTIONAL TESTING:
The Non Functional software testing encompasses a rich spectrum of testing strategies, describing the expected results for every test case. It uses symbolic analysis techniques. This testing used to check that an application will work in the operational environment. Non-functional testing includes:
5.1.4 LOAD TESTING:
An important tool for implementing system tests is a Load generator. A Load generator is essential for testing quality requirements such as performance and stress. A load can be a real load, that is, the system can be put under test to real usage by having actual telephone users connected to it. They will generate test input data for system test.
| Description | Expected result |
| It is necessary to ascertain that the application behaves correctly under loads when ‘Server busy’ response is received. | Should designate another active node as a Server. |
5.1.5 PERFORMANCE TESTING:
Performance tests are utilized in order to determine the widely defined performance of the software system such as execution time associated with various parts of the code, response time and device utilization. The intent of this testing is to identify weak points of the software system and quantify its shortcomings.
| Description | Expected result |
| This is required to assure that an application perforce adequately, having the capability to handle many peers, delivering its results in expected time and using an acceptable level of resource and it is an aspect of operational management. | Should handle large input values, and produce accurate result in a expected time. |
5.1.6 RELIABILITY TESTING:
The software reliability is the ability of a system or component to perform its required functions under stated conditions for a specified period of time and it is being ensured in this testing. Reliability can be expressed as the ability of the software to reveal defects under testing conditions, according to the specified requirements. It the portability that a software system will operate without failure under given conditions for a given time interval and it focuses on the behavior of the software element. It forms a part of the software quality control team.
| Description | Expected result |
| This is to check that the server is rugged and reliable and can handle the failure of any of the components involved in provide the application. | In case of failure of the server an alternate server should take over the job. |
5.1.7 SECURITY TESTING:
Security testing evaluates system characteristics that relate to the availability, integrity and confidentiality of the system data and services. Users/Clients should be encouraged to make sure their security needs are very clearly known at requirements time, so that the security issues can be addressed by the designers and testers.
| Description | Expected result |
| Checking that the user identification is authenticated. | In case failure it should not be connected in the framework. |
| Check whether group keys in a tree are shared by all peers. | The peers should know group key in the same group. |
5.1.8 WHITE BOX TESTING:
White box testing, sometimes called glass-box testing is a test case design method that uses the control structure of the procedural design to derive test cases. Using white box testing method, the software engineer can derive test cases. The White box testing focuses on the inner structure of the software structure to be tested.
| Description | Expected result |
| Exercise all logical decisions on their true and false sides. | All the logical decisions must be valid. |
| Execute all loops at their boundaries and within their operational bounds. | All the loops must be finite. |
| Exercise internal data structures to ensure their validity. | All the data structures must be valid. |
5.1.9 BLACK BOX TESTING:
Black box testing, also called behavioral testing, focuses on the functional requirements of the software. That is, black testing enables the software engineer to derive sets of input conditions that will fully exercise all functional requirements for a program. Black box testing is not alternative to white box techniques. Rather it is a complementary approach that is likely to uncover a different class of errors than white box methods. Black box testing attempts to find errors which focuses on inputs, outputs, and principle function of a software module. The starting point of the black box testing is either a specification or code. The contents of the box are hidden and the stimulated software should produce the desired results.
| Description | Expected result |
| To check for incorrect or missing functions. | All the functions must be valid. |
| To check for interface errors. | The entire interface must function normally. |
| To check for errors in a data structures or external data base access. | The database updation and retrieval must be done. |
| To check for initialization and termination errors. | All the functions and data structures must be initialized properly and terminated normally. |
All
the above system testing strategies are carried out in as the development,
documentation and institutionalization of the proposed goals and related
policies is essential.
CHAPTER 6
6.0 SOFTWARE DESCRIPTION:
Java technology is both a programming language and a platform.
With most programming languages, you either compile or interpret a program so that you can run it on your computer. The Java programming language is unusual in that a program is both compiled and interpreted. With the compiler, first you translate a program into an intermediate language called Java byte codes —the platform-independent codes interpreted by the interpreter on the Java platform. The interpreter parses and runs each Java byte code instruction on the computer. Compilation happens just once; interpretation occurs each time the program is executed. The following figure illustrates how this works.
You can think of Java byte codes as the machine code instructions for the Java Virtual Machine (Java VM). Every Java interpreter, whether it’s a development tool or a Web browser that can run applets, is an implementation of the Java VM. Java byte codes help make “write once, run anywhere” possible. You can compile your program into byte codes on any platform that has a Java compiler. The byte codes can then be run on any implementation of the Java VM. That means that as long as a computer has a Java VM, the same program written in the Java programming language can run on Windows 2000, a Solaris workstation, or on an iMac.
A platform is the hardware or software environment in which a program runs. We’ve already mentioned some of the most popular platforms like Windows 2000, Linux, Solaris, and MacOS. Most platforms can be described as a combination of the operating system and hardware. The Java platform differs from most other platforms in that it’s a software-only platform that runs on top of other hardware-based platforms.
The Java platform has two components:
You’ve already been introduced to the Java VM. It’s the base for the Java platform and is ported onto various hardware-based platforms.
The Java API is a large collection of ready-made software components that provide many useful capabilities, such as graphical user interface (GUI) widgets. The Java API is grouped into libraries of related classes and interfaces; these libraries are known as packages. The next section, What Can Java Technology Do? Highlights what functionality some of the packages in the Java API provide.
The following figure depicts a program that’s running on the Java platform. As the figure shows, the Java API and the virtual machine insulate the program from the hardware.
Native code is code that after you compile it, the compiled code runs on a specific hardware platform. As a platform-independent environment, the Java platform can be a bit slower than native code. However, smart compilers, well-tuned interpreters, and just-in-time byte code compilers can bring performance close to that of native code without threatening portability.
The most common types of programs written in the Java programming language are applets and applications. If you’ve surfed the Web, you’re probably already familiar with applets. An applet is a program that adheres to certain conventions that allow it to run within a Java-enabled browser.
However, the Java programming language is not just for writing cute, entertaining applets for the Web. The general-purpose, high-level Java programming language is also a powerful software platform. Using the generous API, you can write many types of programs.
An application is a standalone program that runs directly on the Java platform. A special kind of application known as a server serves and supports clients on a network. Examples of servers are Web servers, proxy servers, mail servers, and print servers. Another specialized program is a servlet.
A servlet can almost be thought of as an applet that runs on the server side. Java Servlets are a popular choice for building interactive web applications, replacing the use of CGI scripts. Servlets are similar to applets in that they are runtime extensions of applications. Instead of working in browsers, though, servlets run within Java Web servers, configuring or tailoring the server.
How does the API support all these kinds of programs? It does so with packages of software components that provides a wide range of functionality. Every full implementation of the Java platform gives you the following features:
The Java platform also has APIs for 2D and 3D graphics, accessibility, servers, collaboration, telephony, speech, animation, and more. The following figure depicts what is included in the Java 2 SDK.
We can’t promise you fame, fortune, or even a job if you learn the Java programming language. Still, it is likely to make your programs better and requires less effort than other languages. We believe that Java technology will help you do the following:
Microsoft Open Database Connectivity (ODBC) is a standard programming interface for application developers and database systems providers. Before ODBC became a de facto standard for Windows programs to interface with database systems, programmers had to use proprietary languages for each database they wanted to connect to. Now, ODBC has made the choice of the database system almost irrelevant from a coding perspective, which is as it should be. Application developers have much more important things to worry about than the syntax that is needed to port their program from one database to another when business needs suddenly change.
Through the ODBC Administrator in Control Panel, you can specify the particular database that is associated with a data source that an ODBC application program is written to use. Think of an ODBC data source as a door with a name on it. Each door will lead you to a particular database. For example, the data source named Sales Figures might be a SQL Server database, whereas the Accounts Payable data source could refer to an Access database. The physical database referred to by a data source can reside anywhere on the LAN.
The ODBC system files are not installed on your system by Windows 95. Rather, they are installed when you setup a separate database application, such as SQL Server Client or Visual Basic 4.0. When the ODBC icon is installed in Control Panel, it uses a file called ODBCINST.DLL. It is also possible to administer your ODBC data sources through a stand-alone program called ODBCADM.EXE. There is a 16-bit and a 32-bit version of this program and each maintains a separate list of ODBC data sources.
From a programming perspective, the beauty of ODBC is that the application can be written to use the same set of function calls to interface with any data source, regardless of the database vendor. The source code of the application doesn’t change whether it talks to Oracle or SQL Server. We only mention these two as an example. There are ODBC drivers available for several dozen popular database systems. Even Excel spreadsheets and plain text files can be turned into data sources. The operating system uses the Registry information written by ODBC Administrator to determine which low-level ODBC drivers are needed to talk to the data source (such as the interface to Oracle or SQL Server). The loading of the ODBC drivers is transparent to the ODBC application program. In a client/server environment, the ODBC API even handles many of the network issues for the application programmer.
The advantages
of this scheme are so numerous that you are probably thinking there must be
some catch. The only disadvantage of ODBC is that it isn’t as efficient as
talking directly to the native database interface. ODBC has had many detractors
make the charge that it is too slow. Microsoft has always claimed that the
critical factor in performance is the quality of the driver software that is
used. In our humble opinion, this is true. The availability of good ODBC
drivers has improved a great deal recently. And anyway, the criticism about
performance is somewhat analogous to those who said that compilers would never
match the speed of pure assembly language. Maybe not, but the compiler (or
ODBC) gives you the opportunity to write cleaner programs, which means you
finish sooner. Meanwhile, computers get faster every year.
6.6 JDBC:
In an effort to set an independent database standard API for Java; Sun Microsystems developed Java Database Connectivity, or JDBC. JDBC offers a generic SQL database access mechanism that provides a consistent interface to a variety of RDBMSs. This consistent interface is achieved through the use of “plug-in” database connectivity modules, or drivers. If a database vendor wishes to have JDBC support, he or she must provide the driver for each platform that the database and Java run on.
To gain a wider acceptance of JDBC, Sun based JDBC’s framework on ODBC. As you discovered earlier in this chapter, ODBC has widespread support on a variety of platforms. Basing JDBC on ODBC will allow vendors to bring JDBC drivers to market much faster than developing a completely new connectivity solution.
JDBC was announced in March of 1996. It was released for a 90 day public review that ended June 8, 1996. Because of user input, the final JDBC v1.0 specification was released soon after.
The remainder of this section will cover enough information about JDBC for you to know what it is about and how to use it effectively. This is by no means a complete overview of JDBC. That would fill an entire book.
Few software packages are designed without goals in mind. JDBC is one that, because of its many goals, drove the development of the API. These goals, in conjunction with early reviewer feedback, have finalized the JDBC class library into a solid framework for building database applications in Java.
The goals that were set for JDBC are important. They will give you some insight as to why certain classes and functionalities behave the way they do. The eight design goals for JDBC are as follows:
SQL Level API
The designers felt that their main goal was to define a SQL interface for Java. Although not the lowest database interface level possible, it is at a low enough level for higher-level tools and APIs to be created. Conversely, it is at a high enough level for application programmers to use it confidently. Attaining this goal allows for future tool vendors to “generate” JDBC code and to hide many of JDBC’s complexities from the end user.
SQL Conformance
SQL syntax varies as you move from database vendor to database vendor. In an effort to support a wide variety of vendors, JDBC will allow any query statement to be passed through it to the underlying database driver. This allows the connectivity module to handle non-standard functionality in a manner that is suitable for its users.
JDBC must be implemental on top of common database interfaces
The JDBC SQL API must “sit” on top of other common SQL level APIs. This goal allows JDBC to use existing ODBC level drivers by the use of a software interface. This interface would translate JDBC calls to ODBC and vice versa.
Because of Java’s acceptance in the user community thus far, the designers feel that they should not stray from the current design of the core Java system.
This goal probably appears in all software design goal listings. JDBC is no exception. Sun felt that the design of JDBC should be very simple, allowing for only one method of completing a task per mechanism. Allowing duplicate functionality only serves to confuse the users of the API.
Strong typing allows for more error checking to be done at compile time; also, less error appear at runtime.
Because more often than not, the usual SQL calls
used by the programmer are simple SELECT’s,
INSERT’s,
DELETE’s
and UPDATE’s,
these queries should be simple to perform with JDBC. However, more complex SQL
statements should also be possible.
Finally we decided to precede the implementation using Java Networking.
And for dynamically updating the cache table we go for MS Access database.
Java ha two things: a programming language and a platform.
Java is a high-level programming language that is all of the following
Simple Architecture-neutral
Object-oriented Portable
Distributed High-performance
Interpreted Multithreaded
Robust Dynamic Secure
Java is also unusual in that each Java program is both compiled and interpreted. With a compile you translate a Java program into an intermediate language called Java byte codes the platform-independent code instruction is passed and run on the computer.
Compilation happens just once; interpretation occurs each time the program is executed. The figure illustrates how this works.
The TCP/IP stack is shorter than the OSI one:
TCP is a connection-oriented protocol; UDP (User Datagram Protocol) is a connectionless protocol.
The IP layer provides a connectionless and unreliable delivery system. It considers each datagram independently of the others. Any association between datagram must be supplied by the higher layers. The IP layer supplies a checksum that includes its own header. The header includes the source and destination addresses. The IP layer handles routing through an Internet. It is also responsible for breaking up large datagram into smaller ones for transmission and reassembling them at the other end.
UDP is also connectionless and unreliable. What it adds to IP is a checksum for the contents of the datagram and port numbers. These are used to give a client/server model – see later.
TCP supplies logic to give a reliable connection-oriented protocol above IP. It provides a virtual circuit that two processes can use to communicate.
In order to use a service, you must be able to find it. The Internet uses an address scheme for machines so that they can be located. The address is a 32 bit integer which gives the IP address.
Class A uses 8 bits for the network address with 24 bits left over for other addressing. Class B uses 16 bit network addressing. Class C uses 24 bit network addressing and class D uses all 32.
Internally, the UNIX network is divided into sub networks. Building 11 is currently on one sub network and uses 10-bit addressing, allowing 1024 different hosts.
8 bits are finally used for host addresses within our subnet. This places a limit of 256 machines that can be on the subnet.
The 32 bit address is usually written as 4 integers separated by dots.
A service exists on a host, and is identified by its port. This is a 16 bit number. To send a message to a server, you send it to the port for that service of the host that it is running on. This is not location transparency! Certain of these ports are “well known”.
A socket is a data structure maintained by the system
to handle network connections. A socket is created using the call socket. It returns an integer that is like a file
descriptor. In fact, under Windows, this handle can be used with Read File and Write File
functions.
#include <sys/types.h>
#include <sys/socket.h>
int socket(int family, int type, int protocol);
Here “family” will be AF_INET for IP communications, protocol will be zero, and type will depend on whether TCP or UDP is used. Two
processes wishing to communicate over a network create a socket each. These are
similar to two ends of a pipe – but the actual pipe does not yet exist.
6.8 JFREE CHART:
JFreeChart is a free 100% Java chart library that makes it easy for developers to display professional quality charts in their applications. JFreeChart’s extensive feature set includes:
A consistent and well-documented API, supporting a wide range of chart types;
A flexible design that is easy to extend, and targets both server-side and client-side applications;
Support for many output types, including Swing components, image files (including PNG and JPEG), and vector graphics file formats (including PDF, EPS and SVG);
JFreeChart is “open source” or, more specifically, free software. It is distributed under the terms of the GNU Lesser General Public Licence (LGPL), which permits use in proprietary applications.
Charts showing values that relate to geographical areas. Some examples include: (a) population density in each state of the United States, (b) income per capita for each country in Europe, (c) life expectancy in each country of the world. The tasks in this project include: Sourcing freely redistributable vector outlines for the countries of the world, states/provinces in particular countries (USA in particular, but also other areas);
Creating an appropriate dataset interface (plus
default implementation), a rendered, and integrating this with the existing
XYPlot class in JFreeChart; Testing, documenting, testing some more,
documenting some more.
Implement a new (to JFreeChart) feature for interactive time series charts — to display a separate control that shows a small version of ALL the time series data, with a sliding “view” rectangle that allows you to select the subset of the time series data to display in the main chart.
There is currently a lot of interest in dashboard displays. Create a flexible dashboard mechanism that supports a subset of JFreeChart chart types (dials, pies, thermometers, bars, and lines/time series) that can be delivered easily via both Java Web Start and an applet.
The property editor mechanism in JFreeChart only
handles a small subset of the properties that can be set for charts. Extend (or
reimplement) this mechanism to provide greater end-user control over the
appearance of the charts.
CHAPTER 7
7.0 APPENDIX
7.1 SAMPLE SCREEN SHOTS:
7.2
SAMPLE SOURCE CODE:
CHAPTER 8
8.1 CONCLUSION AND FUTURE:
In this study, we have provided preliminary evidence that social media and environmental data can be leveraged to accurately predict asthma ED visits at a population level. We are in the process of confirming these preliminary findings by collecting larger clinical datasets across different seasons and multiple hospitals. Our continued work is focused on extending this research to propose a temporal prediction model that analyzes the trends in tweets and air quality index changes, and estimates the time lag between these changes and the number of asthma ED visits.
We also are collecting air quality index
data over a longer time period to examine the effects of seasonal variations.
In addition, we would like to explore the effect of relevant data from other
types of social media interactions, e.g. blogs and discussion forums, on our
asthma visit prediction model. Additional studies are needed to examine how
combining real-time or near-real-time social media and environmental data with
more traditional data might affect the performance and timing of current
individual-level prediction models for asthma, and eventually, for other
chronic conditions. In future projects, we intend to extend our work to
diseases with geographical and temporal variability, e.g., COPD and diabetes.