This paper presents the design and implementation of an architecture based on the combination of ontologies, rules, web services, and the autonomic computing paradigm to manage data in home-based telemonitoring scenarios.

The architecture includes two layers: 1) a conceptual layer and 2) a data and communication layer. On the one hand, the conceptual layer based on ontologies is proposed to unify the management procedure and integrate incoming data from all the sources involved in the telemonitoring process. On the other hand, the data and communication layer based on REST web service (WS) technologies is proposed to provide practical backup to the use of the ontology, to provide a real implementation of the tasks it describes and thus to provide a means of exchanging data (support communication tasks).

We study regarding chronic obstructive pulmonary disease data management is presented in order to evaluate the efficiency of the architecture. This proposed ontology-based solution defines a flexible and scalable architecture in order to address main challenges presented in home-based telemonitoring scenarios and thus provide a means to integrate, unify, and transfer data supporting both clinical and technical management tasks.

1.2 INTRODUCTION

Patient empowerment is considered as a philosophy of health care based on the perspective that better outcomes are achieved when patients become active participants in their own health management. This new paradigm is a central idea in the European Union (EU) health strategy supported by international health organizations including the World Health Organization among others, and its effectiveness in yielding quality of care is an obvious and essential area of research. This new idea invites to look for new ways of providing healthcare, e.g., by using information and communications technologies. In this context, home-based telemonitoring systems can be used as self-care management tools, while collaborative processes among healthcare personnel and patients are maintained, thus the patient’s safe control is guaranteed. Telemonitoring systems face the problem of delivering medicine to the current growing population with chronic conditions while at the same time covering the dimensions of quality of care and new paradigms such as empowerment can be supported.

By periodically collecting patients’ themselves clinical data (located at their home sites) and transferring them to physicians located in remote sites, patient’s health status supervision and feedback provision are possible. This type of telemedicine system guarantees patient control while reducing costs and avoiding hospital overflows. These two sites (home site and healthcare site) comprised a typical home-based telemonitoring system. At home site, data acquired by using MDs together with the patient’s feedback are collected in a concentrator device (HG) used to evaluate and/or transfer the acquired data outside the patient’s home if necessary. At the health-care site, a server device is used to manage information from the home site as well as to manage and store the patient’s monitoring guidelines defined by physicians (TS, telemonitoring server). In fact, this telemonitoring process, and consequently the evolution of the patient’s health status, ismanaged through the indications or monitoring guidelines provided by physicians.

Although significant contributions have been made in this field in recent decades, telemedicine and in e-health scenarios in general still pose numerous challenges that need to be addressed by researchers in order to take maximum advantage of the benefits that these systems provide and to support their long-term implementation. Interoperability and integration are critical challenges that also need to be addressed when developing monitoring systems in order to provide effective healthcare and to make possible seamless communication among the different heterogeneous health entities that participate in the monitoring process. This integration should be addressed at both end sites of the scenario but also in the communication link, thus integrating the way of transferring and exchanging information efficiently between them.

We providing personalized care services and taking into account the patient’s context have been identified as additional requirements. Furthermore, apart from clinical data aspects, technical issues should be also addressed in this scenario. Technical management of all the devices that comprise the telemonitoring scenario (e.g., the MDs and HG) is an important task that may or may not be integrated under the same architecture as clinical management. Hence, at this technical level, research is still required to address these challenges. Consequently there is a need for the development of new telemonitoring architectures.

Great efforts have been made in recent years in developing standards to deal with interoperability at different points of the e-health communication infrastructure such as the ISO/IEEE 11073 (X73) for MDs interoperability, the OpenEHR initiative for storage, management and retrieval of electronic health record (EHR) information or as the standardized Health Level Seven7 (HL7) messages to solve clinical data transferences. Nevertheless, additional efforts are required to enable them to work together and ultimately provide a higher level of integration.

Specifically, in this telemonitoring scenario, there is not a unique standard-based solution to address data and management integration. Since several standards can be used (some of them in combination with proprietary protocols or other standards) at different points of this scenario, the interoperability problem remains unsolved unless these standards would merge into one or alignments and combination of them would be done. According to Berges et al. interoperability does not mean to have a unique representation but a semantically acknowledged equivalent one. That is the reason to propose in this study an ontology-based architecture in order to provide with a common knowledge about the exchanged data and the management of such data. This ontology constitutes the knowledge equivalent one. Then, at both ends of the architecture other standards could be used for other managing purposes relating this model with the specific desired approach. Using this alternative, a knowledge model is first provided that avoids alignment of models two by two, while all being related through the main ontology.

Ontologies-based solutions have been popularized over the past few years. Ontologies provide a higher level of abstraction and have been successfully used in telemonitoring scenarios and other areas to provide knowledge representation and semantic integration, thus a common understanding about data exchanged by all the entities. Furthermore, its combination with rules allows providing personalized management services and thus personalized care. Although there are works that describe the details of an ontology approach in this domain, they do not devote much attention to the architecture implementation and the communication used to exchange the information described. Consequently, fewworks have given details about this practical implementation of the ontology-based system which may be of interest for the development of other ontology-based applications in and outside the e-health domain.

This paper presents an ontology-driven architecture to integrate data management and enable its communication in a telemonitoring scenario. The proposed architecture includes two layers: the conceptual layer (the ontology) and the communication and data layer. The conceptual layer uses the HOTMES and its extensions introduced. Specifically, the OWL-DL language was selected to define this ontology model. The second layer is based on WS technologies. WSs have been successfully used in network management and also in other works to exchange data modeled by ontology. However, our proposal, inspired on the representational state transfer (REST) style and based on a generic communication method, provides a different design approach that may be reusable for other systems based on ontologies. Furthermore, security issues have been considered. The aim is to define a flexible and scalable architecture in order to address main challenges presented in home-based telemonitoring scenarios and thus provide a means to integrate and transfer data supporting both clinical and technical data management.

1.3 LITRATURE SURVEY

AUTHOR AND PUBLICATION: JD. Trigo, I. Mart´ınez, A. Alesanco, A. Kollmann, J. Escayola, D. Hayn, G. Schreier, and J. Garc´ıa, “AN INTEGRATED HEALTHCARE INFORMATION SYSTEM FOR END-TO-END STANDARDIZED EXCHANGE AND HOMOGENEOUS MANAGEMENT OF DIGITAL ECG FORMATS,” IEEE Trans. Inf. Technol. Biomed., vol. 16, no. 4, pp. 518–529, Jul. 2012.

EXPLANATION:

This paper investigates the application of the enterprise information system (EIS) paradigm to standardized cardiovascular condition monitoring. There are many specifications in cardiology, particularly in the ECG standardization arena. The existence of ECG formats, however, does not guarantee the implementation of homogeneous, standardized solutions for ECG management. In fact, hospital management services need to cope with various ECG formats and, moreover, several different visualization applications. This heterogeneity hampers the normalization of integrated, standardized healthcare information systems, hence the need for finding an appropriate combination of ECG formats and suitable EIS-based software architecture that enables standardized exchange and homogeneous management of ECG formats. Determining such a combination is one objective of this paper.

We develop the integrated healthcare information system that satisfies the requirements posed by the previous determination. The ECG formats selected include ISO/IEEE11073, Standard Communications Protocol for Computer-Assisted Electrocardiography, and an ECG ontology. The EIS-enabling techniques and technologies selected include web services, simple object access protocol, extensible markup language, or business process execution language. Such a selection ensures the standardized exchange of ECGs within, or across, healthcare information systems while providing modularity and accessibility.

AUTHOR AND PUBLICATION: D. Ria˜no, F. Real, J. A. L´opez-Vallverd´u, F. Campana, S. Ercolani, P. Mecocci, R. Annicchiarico, and C. Caltagirone, “AN ONTOLOGY-BASED PERSONALIZATION OF HEALTH-CARE KNOWLEDGE TO SUPPORT CLINICAL DECISIONS FOR CHRONICALLY ILL PATIENTS,” J. Biomed. Informat., vol. 45, no. 3, pp. 429–446, 2012.

EXPLANATION:

Chronically ill patients are complex health care cases that require the coordinated interaction of multiple professionals. A correct intervention of these sort of patients entails the accurate analysis of the conditions of each concrete patient and the adaptation of evidence-based standard intervention plans to these conditions. There are some other clinical circumstances such as wrong diagnoses, unobserved comorbidities, missing information, unobserved related diseases or prevention, whose detection depends on the capacities of deduction of the professionals involved. In this paper, we introduce ontology for the care of chronically ill patients and implement two personalization processes and a decision support tool. The first personalization process adapts the contents of the ontology to the particularities observed in the health-care record of a given concrete patient, automatically providing a personalized ontology containing only the clinical information that is relevant for health-care professionals to manage that patient. The second personalization process uses the personalized ontology of a patient to automatically transform intervention plans describing health-care general treatments into individual intervention plans. For comorbid patients, this process concludes with the semi-automatic integration of several individual plans into a single personalized plan. Finally, the ontology is also used as the knowledge base of a decision support tool that helps health-care professionals to detect anomalous circumstances such as wrong diagnoses, unobserved comorbidities, missing information, unobserved related diseases, or preventive actions. Seven health-care centers participating in the K4CARE project, together with the group SAGESA and the Local Health System in the town of Pollenza have served as the validation platform for these two processes and tool. Health-care professionals participating in the evaluation agree about the average quality 84% (5.9/7.0) and utility 90% (6.3/7.0) of the tools and also about the correct reasoning of the decision support tool, according to clinical standards.

AUTHOR AND PUBLICATION: I.Berges, J. Bermudez, and A. Illarramendi, “TOWARDS SEMANTIC INTEROPERABILITY OF ELECTRONIC HEALTH RECORDS,” IEEE Trans. Inf. Technol. Biomed., vol. 16, no. 3, pp. 424–431, May 2012.

EXPLANATION:

Although the goal of achieving semantic interoperability of electronic health records (EHRs) is pursued by many researchers, it has not been accomplished yet. In this paper, we present a proposal that smoothes out the way toward the achievement of that goal. In particular, our study focuses on medical diagnoses statements. In summary, the main contributions of our ontology-based proposal are the following: first, it includes a canonical ontology whose EHR-related terms focus on semantic aspects. As a result, their descriptions are independent of languages and technology aspects used in different organizations to represent EHRs. Moreover, those terms are related to their corresponding codes in well-known medical terminologies. Second, it deals with modules that allow obtaining rich ontological representations of EHR information managed by proprietary models of health information systems. The features of one specific module are shown as reference. Third, it considers the necessary mapping axioms between ontological terms enhanced with so-called path mappings. This feature smoothes out structural differences between heterogeneous EHR representations, allowing proper alignment of information.

AUTHOR AND PUBLICATION: N. Lasierra,A.Alesanco, J.Garc´ıa, andD.O’Sullivan, “DATA MANAGEMENT IN HOME SCENARIOS USING AN AUTONOMIC ONTOLOGY-BASED APPROACH,” in Proc. of the 9th IEEE Int. Conf. Pervasive Workshop on Manag. Ubiquitous Commun. Services part of PerCom, 2012, pp. 94–99.

EXPLANATION:

An ontology-based approach to deal with data and management procedure integration in home-based scenarios is presented in this paper. The proposed ontology not only provides a means to represent exchanged data but also to unify the way of accessing, controlling, evaluating and transferring information remotely. The structure of this ontology has been inspired by the autonomic computing paradigm, thus it describes the tasks that comprise the MAPE (Monitor, Analyze, Plan and Execute) process. Furthermore the use of SPARQL (Simple Protocol and RDF Query Language) is proposed in this paper to express conditions and rules that determine the performance of these tasks according to each situation. Finally two practical application cases of the proposed ontology-based approach are presented.

CHAPTER 2

2.0 SYSTEM ANALYSIS

2.1 EXISTING SYSTEM:

Telemonitoring systems face the problem of delivering medicine to the current growing population with chronic conditions while at the same time covering the dimensions of quality of care and new paradigms such as empowerment can be supported. By periodically collecting patients’ themselves clinical data (located at their home sites) and transferring them to physicians located in remote sites, patient’s health status supervision and feedback provision are possible.

This type of telemedicine system guarantees patient control while reducing costs and avoiding hospital overflows. These two sites (home site and healthcare site) comprised a typical home-based telemonitoring system. At home site, data acquired by using MDs together with the patient’s feedback are collected in a concentrator device (HG) used to evaluate and/or transfer the acquired data outside the patient’s home if necessary.

2.1.1 DISADVANTAGES:

Existing models for chronic diseases pose several technology-oriented challenges for home-based care, where assistance services rely on a close collaboration among different stakeholders, such as health operators, patient relatives, and social community members.

An ontology-based context model and a related context management system providing a configurable and extensible service-oriented framework to ease the development of applications for monitoring and handling patient chronic conditions.

The system has been developed in a prototypal version, and integrated with a service platform for supporting operators of home-based care networks in cooperating and sharing patient-related information and coordinating mutual interventions for handling critical and alarm situations.

2.2 PROPOSED SYSTEM:

We present an ontology-driven architecture to integrate data management and enable its communication in a telemonitoring scenario. It enables to not only integrate patient’s clinical data management but also technical data management of all devices that are included in the scenario. The proposed architecture includes two layers: the conceptual layer (the ontology) and the communication and data layer.

The conceptual layer uses the HOTMES and its extensions introduced specifically in the OWL-DL language was selected to define this ontology model. The second layer is based on WS technologies. WSs have been successfully used in network management and also in other works to exchange data modeled by ontology is our proposal, inspired on the representational state transfer (REST) style and based on a generic communication method, provides a different design approach that may be reusable for other systems based on ontologies.

Furthermore, security issues have been considered. The aim is to define a flexible and scalable architecture in order to address main challenges presented in home-based telemonitoring scenarios and thus provide a means to integrate and transfer data supporting both clinical and technical data management.

2.2.1 ADVANTAGES:

Ontologies provide a higher level of abstraction and have been successfully used in telemonitoring scenarios and other areas to provide knowledge representation and semantic integration, thus a common understanding about data exchanged by all the entities. Furthermore, its combination with rules allows providing personalized management services and thus personalized care.

We describe the details of an ontology approach in this domain, they do not devote much attention to the architecture implementation and the communication used to exchange the information described.

Our implementation of the ontology-based system which may be of interest for the development of other ontology-based applications in and outside the e-health domain the ontology for interpreting the data transferred for the communication of end sources of the architecture. The data and communication layer deals with data management and transmission.

2.3 HARDWARE & SOFTWARE REQUIREMENTS:

2.3.1 HARDWARE REQUIREMENT:

v Processor – Pentium –IV

Speed – 1.1 GHz
- RAM – 256 MB (min)
- Hard Disk – 20 GB
- Floppy Drive – 1.44 MB
- Key Board – Standard Windows Keyboard
- Mouse – Two or Three Button Mouse
- Monitor – SVGA

2.3.2 SOFTWARE REQUIREMENTS:

Operating System : Windows XP or Win7
Front End : Microsoft Visual Studio .NET
Back End : MSSQL Server
Server : ASP .NET Web Server
Script : C# Script
Document : MS-Office 2007

CHAPTER 3

3.0 SYSTEM DESIGN:

Data Flow Diagram / Use Case Diagram / Flow Diagram:

The DFD is also called as bubble chart. It is a simple graphical formalism that can be used to represent a system in terms of the input data to the system, various processing carried out on these data, and the output data is generated by the system

The data flow diagram (DFD) is one of the most important modeling tools. It is used to model the system components. These components are the system process, the data used by the process, an external entity that interacts with the system and the information flows in the system.

DFD shows how the information moves through the system and how it is modified by a series of transformations. It is a graphical technique that depicts information flow and the transformations that are applied as data moves from input to output.

DFD is also known as bubble chart. A DFD may be used to represent a system at any level of abstraction. DFD may be partitioned into levels that represent increasing information flow and functional detail.

NOTATION:

SOURCE OR DESTINATION OF DATA:

External sources or destinations, which may be people or organizations or other entities

DATA SOURCE:

Here the data referenced by a process is stored and retrieved.

PROCESS:

People, procedures or devices that produce data’s in the physical component is not identified.

DATA FLOW:

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:

All processes must have at least one data flow in and one data flow out.
All processes should modify the incoming data, producing new forms of outgoing data.
Each data store must be involved with at least one data flow.
Each external entity must be involved with at least one data flow.
A data flow must be attached to at least one process.

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:

CHAPTER 4

4.0 IMPLEMENTATION:

ONTOLOGIES:

According to one of the most widely accepted definitions of ontologies in computer science, ontology can be described as “an explicit and formal specification of a shared conceptualization”. In simple words, ontologies represent concepts and basic relationships for the purpose of comprehension of a common knowledge area. To develop an ontology means to formalize a common view of a certain domain.

1) OWL Language: In computer science, there are plenty of formal languages that can be used to define and constructontologies. These languages allow encoding knowledge contained in ontology in a simple and formal way. However, the standardized RDF and OWL have been gaining popularity in the semantic web world. Ontology can be formally described in OWL using following basic elements: 1) classes; 2) individuals; and 3) properties. These elements are used in order to describe concepts, instances, or members of a class and relationships between individuals of two classes (object properties) or to link individuals with datatype values, respectively (data type properties). Apart from these basic elements OWL provides with class descriptors used to precisely describe OWL classes which includes properties restrictions (value and cardinality constraints), class axioms, properties axioms, and properties over individuals.

2) Rules: Generally, ontology-based solutions combine knowledge presented in ontologies with dynamic knowledge presented by the use of rules. A system based on the use of rules usually contains a set of if-then rules (which indicate what should be done according to a situation) and a rule engine used to apply them. By using rules, the behavior of individuals can be expressed inside a domain. Hence, they can be used to generate new knowledge and can also be used to provide personalized services. One of the most popular languages for rules definition is SWRL.

However, in our study, we used SPARQL to define some rules is a query language it can be used as a rule language by combining CONSTRUCT clause and FILTER restrictions. On the one hand, the CONSTRUCT query form returns a single RDF graph built based on the results of matching with the graph pattern of the query and by taking the specified graph template. On the other hand, the FILTER clause can be used to restrict solutions to those which the filter expression considers as TRUE. Only if the filter function evaluates to true is the solution to be included in the solution sequence. Note that although this language was good enough for our purpose, its limitations should be studied for other purposes (e.g., recursive tasks) and the adequacy of SWRL could be studied for complex applications.

WEB SERVICES

Web services are used in this study as software technology to access and exchange information modeled by the ontology. According to the W3C, a WS is a “software system designed to support interoperable machine-to-machine interaction over a communication network”. Systems may interact with the web services by exchanging SOAP messages serialized in XML for its message format and sent over other application layer protocols, usually HTTP. Although SOAP-based web services are the most popular types of WSs, there are other styles of programming a WS such as the REST style.

1) Rest Style for DesigningWeb Services: REST is a style of software architecture for distributed hypermedia systems such as the World Wide Web first defined in 2000 by Fielding. This style is based on the idea of transferring the representations of resources, a resource being any item of interest. One of the key advantages of the REST architecture are scalability of components and generality of interfaces. Although REST was initially described in the context of HTTP, this paradigm can be applied to other protocols or implementations. Web services can also be described using this style. A WS implemented using HTTP and the principles of REST architecture is designated as REST(ful) WS. Requests made from the client and responses from the WS are used to transfer resources information. Each resource is identified through an URI. Stateless behavior of data using XML and/or JSON and explicitly used HTTP methods (PUT, GET, POST, DELETE) to exchange resources are the key characteristics of a REST(ful) WS.

4.1 MODULES:

MANAGEMENT PROFILE:

DATA AND COMMUNICATION LAYER:

HG AND TS MANAGEMENT MODULES:

COMMUNICATION FLOW AND WORKFLOW:

4.3 MODULE DESCRIPTION:

CLINICAL MANAGEMENT PROFILE:

COPD patients were identified as candidates to be monitored at home sites. From a clinical point of view, it was an interesting case study (some estimations suggest that up to 10% of the European population suffers COPD). From a technical point of view, the case of the COPD patient led to define a complex technical management profile (because different MDs are required to be used by the patient) and interesting option to test the performance of the agent. Hence, one patient profile was designed according to the clinical HOTMES ontology and one technical management profile was designed according to the technical HOTMES ontology.

The patient profile includes the required tasks to monitor a COPD patient such as controlling the FEV1 measurement in order to detect the presence and severity of the airway obstruction. It was configured by a primary care physician by means of published clinical guidelines in patient profile included 15 monitoring task, 11 analysis task, 9 planning task, and 3 execution task. This configuration led to include 144 new instances and to configure 18 rules. The details of this profile and its evaluation to configure other type of profiles can be technical management profile was designed to monitor the state of theMDs used by the COPD patient (weighing scale, a blood pressure monitor, a pulse-oximeter, and a glucometer) and the consumption of resources of the correspondent HG. In addition rules were configured and 83 new instances were required to be configured in the technical management profile in additional information of the application of the HOTMES ontology for technical tasks.

DATA AND COMMUNICATION LAYER:

In the data layer, the communication between the end sites is established using WS technologies. Consequently, a WS has been designed to be placed in the TS and also a web client to be installed in the HG (to establish a communication with the TS). This communication allows the HG to ask for its associated management profile to the TS and to transmit acquired information from the HG to the TS.

A REST WS was developed in order to enhance the scalability and flexibility of the architecture and improve the performance (efficiency). This WS comprises and defines a set of operations over the following resources: an OWL ontology, the rules (transferred by means of an XML), OWL individuals (sent by the IndividualWS structure), properties datatype values corresponding to an individual (identified by the URI of the individual and the URI of the property sent in a string generic type), and inform messages to provide some control functions to the web pair communication.

Each one of these resources was identified by an URI, and a set of operations was defined for each particular resource using HTTP methods (e.g., GET or PUT). This WS interface allows information described in the ontology to be exchanged in a generic manner. This is one key that contributes to the reusability and easy extension of the architecture. Described communication methods do not depend on the knowledge itself described in the ontology (related to the service) but on the fact of using an ontology to represent such knowledge. A summary of the resources and defined operations is depicted in Table I. As mentioned in the description of the converter module, individuals are exchanged by using a developed structure designated as IndividualWS. Using OWL language, an individual of the ontology can be described as a member of a class with individual axioms or facts as individual property values (datatype and object properties).

HG AND TS MANAGEMENT MODULES:

Two management modules and web technology modules inside the HG and the TS constitute the main parts of the telemedicine system (see Fig. 1). The modules that comprise the architecture have been developed using .NET technologies. Specifically, the .NET framework (version 3.5) has been used to process the ontology and create new instances, data acquisition, and manipulation when the rules are applied. Regarding the web modules the components of the remote management module installed in the TS are depicted in Fig. 1. This management module includes the following three components:

1) Ontology knowledge base module: This module contains the ontology knowledge models and the instances of the registered management profiles. The TDB triple-store has been used to store the ontology model and new instances in this knowledge base module.

2) Converter module: The communication module of this architecture is mainly based on OWL instances exchanged generically by means of a developed object structure named IndividualWS. The converter module is used to wrap and unwrap the individuals structure used to exchange information with web clients. Furthermore, this module incorporates some reasoning tasks. Ontology-based reasoning is used in order to check instances before including new information

in the model and to ensure the consistency of the model.

3) Rules module: This module is used to store rules associated with each management profile. These rules are subsequently transferred by means of an XML file. As shown in Fig. 1, an additional GUI is required in order to make easier for EM, technical or clinical (physician), the process of defining the profiles and the rules. We are currentlyworking in the development of this GUI combining ontology visualization techniques and usability methods. The methodology used to design this interface components of the management module installed in the HG are equally depicted in Fig. 1. This last management module has been designated the “Semantic Autonomic Agent.” This module plays a key role in the architecture. It is in charge of integrating incoming data and executing the management tasks described in the management profile.

The communication between this agent and the management module installed at the remote site is established through a web client connection to the WS installed in the remote TS. The architecture of the agent comprises the ontology knowledge base module, the rules module, the converter module, and the following modules.

1) MAPE module: This module constitutes the computing core of the agent. It will be used to run the tasks specified in each management profile, hence to execute the closed loop from the MAPE loop process.

2) Integrator module: Information transferred by MDs and also contextual data provided by patients will be acquired in this module, which integrates data coming from different data sources.

3) Reminders and alarms module: This module includes clock functionalities to ask patients about data (reminders) or to collect information from a specific software resource.

4) Actions module: This last module is used to execute actions described within the execution tasks of the management profile if an abnormal finding occurs.

FLOW AND WORKFLOW PERFORMANCE:

All the modules and sources involved in the management procedure. The first step (see Fig. 3) consists in the download of the management profile (patient profile or technical profile). First of all, an instance of the management profile should be configured by an EM placed at a remote site. Furthermore, a set of individual rules should be configured for each particular management purpose. As shown in Fig. 3, the designed GUI helps the physician with the ontology instantiation process and the rules definition. The outputs of this interface (which uses selected classes of the ontology as a navigation tool) are a personalized management profile and a set of rules gathered in an XML file. Other functionalities such as queries over acquired data or crossing data among patients to take some decisions could be of interest to be included in this tool.

The communication is always initiated by the user (web client at HG). Through a connection to the web service, the user (the patient in the telemonitoring scenario) situated at home site will acquire the required management profile. As shown in Fig. 3, if the user requests for an update of his/her management profile, then the version of the available profile at the TS will be requested for its evaluation (GET property value). When the user requests a new management profile, first, it is checked whether the ontology to download it is available (GET ontology). After that, the rules and the management profile will be downloaded when required.

The methods involved are 1) GET (rules) and 2) GET (individual). Note that the TLS authentication phase is not depicted in Fig. 3, but it is initially carried out in order to allow the web client connection to the web service. As depicted in Fig. 3, the associated management profile is extracted from the ontology and the instances of the ontology managed by Jena are wrapped into the IndividualWS structure through the converter module. Once the management profile is in the HG, it will be processed into the converter module, unwrapped, and inserted as individuals managed by Jena in the ontology. Once the management profile has been included in the ontology knowledge base module of the HG, it will be evaluated in the MAPE module and the management procedure will be performed by running the tasks specified in the profile.

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

ECONOMICAL FEASIBILITY
TECHNICAL FEASIBILITY
SOCIAL FEASIBILITY

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.

5.1.2 TECHNICAL FEASIBILITY:

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:

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.

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.

5.1.3 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.

FUNCTIONAL TESTING:

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. 4 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:

Load testing
Performance testing
Usability testing
Reliability testing
Security testing

5.1.5 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.

Load Testing

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.

PERFORMANCE TESTING:

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.

RELIABILITY TESTING:

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.

SECURITY TESTING:

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.

5.1.7 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.

5.1.8 WHITE BOX TESTING:

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.

5.1.10 BLACK BOX TESTING:

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 7

7.0 SOFTWARE SPECIFICATION:

7.1 FEATURES OF .NET:

Microsoft .NET is a set of Microsoft software technologies for rapidly building and integrating XML Web services, Microsoft Windows-based applications, and Web solutions. The .NET Framework is a language-neutral platform for writing programs that can easily and securely interoperate. There’s no language barrier with .NET: there are numerous languages available to the developer including Managed C++, C#, Visual Basic and Java Script.

The .NET framework provides the foundation for components to interact seamlessly, whether locally or remotely on different platforms. It standardizes common data types and communications protocols so that components created in different languages can easily interoperate.

“.NET” is also the collective name given to various software components built upon the .NET platform. These will be both products (Visual Studio.NET and Windows.NET Server, for instance) and services (like Passport, .NET My Services, and so on).

7.2 THE .NET FRAMEWORK

The .NET Framework has two main parts:

1. The Common Language Runtime (CLR).

2. A hierarchical set of class libraries.

The CLR is described as the “execution engine” of .NET. It provides the environment within which programs run. The most important features are

Conversion from a low-level assembler-style language, called Intermediate Language (IL), into code native to the platform being executed on.
Memory management, notably including garbage collection.
Checking and enforcing security restrictions on the running code.
Loading and executing programs, with version control and other such features.
The following features of the .NET framework are also worth description:

Managed Code

The code that targets .NET, and which contains certain extra Information – “metadata” – to describe itself. Whilst both managed and unmanaged code can run in the runtime, only managed code contains the information that allows the CLR to guarantee, for instance, safe execution and interoperability.

Managed Data

With Managed Code comes Managed Data. CLR provides memory allocation and Deal location facilities, and garbage collection. Some .NET languages use Managed Data by default, such as C#, Visual Basic.NET and JScript.NET, whereas others, namely C++, do not. Targeting CLR can, depending on the language you’re using, impose certain constraints on the features available. As with managed and unmanaged code, one can have both managed and unmanaged data in .NET applications – data that doesn’t get garbage collected but instead is looked after by unmanaged code.

Common Type System

The CLR uses something called the Common Type System (CTS) to strictly enforce type-safety. This ensures that all classes are compatible with each other, by describing types in a common way. CTS define how types work within the runtime, which enables types in one language to interoperate with types in another language, including cross-language exception handling. As well as ensuring that types are only used in appropriate ways, the runtime also ensures that code doesn’t attempt to access memory that hasn’t been allocated to it.

Common Language Specification

The CLR provides built-in support for language interoperability. To ensure that you can develop managed code that can be fully used by developers using any programming language, a set of language features and rules for using them called the Common Language Specification (CLS) has been defined. Components that follow these rules and expose only CLS features are considered CLS-compliant.

7.3 THE CLASS LIBRARY

.NET provides a single-rooted hierarchy of classes, containing over 7000 types. The root of the namespace is called System; this contains basic types like Byte, Double, Boolean, and String, as well as Object. All objects derive from System. Object. As well as objects, there are value types. Value types can be allocated on the stack, which can provide useful flexibility. There are also efficient means of converting value types to object types if and when necessary.

The set of classes is pretty comprehensive, providing collections, file, screen, and network I/O, threading, and so on, as well as XML and database connectivity.

The class library is subdivided into a number of sets (or namespaces), each providing distinct areas of functionality, with dependencies between the namespaces kept to a minimum.

7.4 LANGUAGES SUPPORTED BY .NET

The multi-language capability of the .NET Framework and Visual Studio .NET enables developers to use their existing programming skills to build all types of applications and XML Web services. The .NET framework supports new versions of Microsoft’s old favorites Visual Basic and C++ (as VB.NET and Managed C++), but there are also a number of new additions to the family.

Visual Basic .NET has been updated to include many new and improved language features that make it a powerful object-oriented programming language. These features include inheritance, interfaces, and overloading, among others. Visual Basic also now supports structured exception handling, custom attributes and also supports multi-threading.

Visual Basic .NET is also CLS compliant, which means that any CLS-compliant language can use the classes, objects, and components you create in Visual Basic .NET.

Managed Extensions for C++ and attributed programming are just some of the enhancements made to the C++ language. Managed Extensions simplify the task of migrating existing C++ applications to the new .NET Framework.

C# is Microsoft’s new language. It’s a C-style language that is essentially “C++ for Rapid Application Development”. Unlike other languages, its specification is just the grammar of the language. It has no standard library of its own, and instead has been designed with the intention of using the .NET libraries as its own.

Microsoft Visual J# .NET provides the easiest transition for Java-language developers into the world of XML Web Services and dramatically improves the interoperability of Java-language programs with existing software written in a variety of other programming languages.

Active State has created Visual Perl and Visual Python, which enable .NET-aware applications to be built in either Perl or Python. Both products can be integrated into the Visual Studio .NET environment. Visual Perl includes support for Active State’s Perl Dev Kit.

Other languages for which .NET compilers are available include

FORTRAN
COBOL
Eiffel

ASP.NET XML WEB SERVICES	Windows Forms
Base Class Libraries
Common Language Runtime
Operating System

Fig1 .Net Framework

C#.NET is also compliant with CLS (Common Language Specification) and supports structured exception handling. CLS is set of rules and constructs that are supported by the CLR (Common Language Runtime). CLR is the runtime environment provided by the .NET Framework; it manages the execution of the code and also makes the development process easier by providing services.

C#.NET is a CLS-compliant language. Any objects, classes, or components that created in C#.NET can be used in any other CLS-compliant language. In addition, we can use objects, classes, and components created in other CLS-compliant languages in C#.NET .The use of CLS ensures complete interoperability among applications, regardless of the languages used to create the application.

CONSTRUCTORS AND DESTRUCTORS:

Constructors are used to initialize objects, whereas destructors are used to destroy them. In other words, destructors are used to release the resources allocated to the object. In C#.NET the sub finalize procedure is available. The sub finalize procedure is used to complete the tasks that must be performed when an object is destroyed. The sub finalize procedure is called automatically when an object is destroyed. In addition, the sub finalize procedure can be called only from the class it belongs to or from derived classes.

GARBAGE COLLECTION

Garbage Collection is another new feature in C#.NET. The .NET Framework monitors allocated resources, such as objects and variables. In addition, the .NET Framework automatically releases memory for reuse by destroying objects that are no longer in use.

In C#.NET, the garbage collector checks for the objects that are not currently in use by applications. When the garbage collector comes across an object that is marked for garbage collection, it releases the memory occupied by the object.

OVERLOADING

Overloading is another feature in C#. Overloading enables us to define multiple procedures with the same name, where each procedure has a different set of arguments. Besides using overloading for procedures, we can use it for constructors and properties in a class.

MULTITHREADING:

C#.NET also supports multithreading. An application that supports multithreading can handle multiple tasks simultaneously, we can use multithreading to decrease the time taken by an application to respond to user interaction.

STRUCTURED EXCEPTION HANDLING

C#.NET supports structured handling, which enables us to detect and remove errors at runtime. In C#.NET, we need to use Try…Catch…Finally statements to create exception handlers. Using Try…Catch…Finally statements, we can create robust and effective exception handlers to improve the performance of our application.

7.5 THE .NET FRAMEWORK

The .NET Framework is a new computing platform that simplifies application development in the highly distributed environment of the Internet.

OBJECTIVES OF .NET FRAMEWORK

1. To provide a consistent object-oriented programming environment whether object codes is stored and executed locally on Internet-distributed, or executed remotely.

2. To provide a code-execution environment to minimizes software deployment and guarantees safe execution of code.

3. Eliminates the performance problems.

There are different types of application, such as Windows-based applications and Web-based applications.

7.6 FEATURES OF SQL-SERVER

The OLAP Services feature available in SQL Server version 7.0 is now called SQL Server 2000 Analysis Services. The term OLAP Services has been replaced with the term Analysis Services. Analysis Services also includes a new data mining component. The Repository component available in SQL Server version 7.0 is now called Microsoft SQL Server 2000 Meta Data Services. References to the component now use the term Meta Data Services. The term repository is used only in reference to the repository engine within Meta Data Services

SQL-SERVER database consist of six type of objects,

They are,

1. TABLE

2. QUERY

3. FORM

4. REPORT

5. MACRO

7.7 TABLE:

A database is a collection of data about a specific topic.

VIEWS OF TABLE:

We can work with a table in two types,

1. Design View

2. Datasheet View

Design View

To build or modify the structure of a table we work in the table design view. We can specify what kind of data will be hold.

Datasheet View

To add, edit or analyses the data itself we work in tables datasheet view mode.

QUERY:

A query is a question that has to be asked the data. Access gathers data that answers the question from one or more table. The data that make up the answer is either dynaset (if you edit it) or a snapshot (it cannot be edited).Each time we run query, we get latest information in the dynaset. Access either displays the dynaset or snapshot for us to view or perform an action on it, such as deleting or updating.

CHAPTER 7

APPENDIX

7.1 SAMPLE SOURCE CODE

7.2 SAMPLE OUTPUT

CHAPTER 8

8.1 CONCLUSION:

This study describes architecture to enable data integration and its management in an ontology-driven telemonitoring solution implemented in home-based scenarios. This is an innovative architecture that facilitates the integration of several management services at home sites using the same software engine. The architecture has been specifically studied to support both technical and clinical services in the telemonitoring scenario, thus avoiding installing additional software for technical purposes.

HOTMES ontology used at the conceptual layer to describe a management profile on the one hand, our ontology contributes to integrate data and its management offering benefits in terms of knowledge representation, workflow organization, and self-management capabilities to the system. Its combination with rules allows providing personalized services.

This application ontology could be in future improved by introducing concepts from domain ontology. On the other hand, the data and communication layer of the architecture, based on the REST WS, was oriented to minimizing the consumption of resources and providing reusable key ideas for future ontology-based architecture developments.

8.2 FUTURE ENHANCEMENT

This solution represents a further step toward the possibility of establishing more effective home-based telemonitoring systems and thus improving the remote care of patientswith chronic diseases. As it was reported in, good telemedicine implementations are developed after a process where the dynamic interaction among a combination of socio-technical and also clinical factors is optimized. It means that additional work should be done (e.g., to measure the interaction of the

patient–doctor using the system and also the truthfulness of the system for a long period of time) before adopting this solution in a real scenario its complete development, first, a concordance study should be conducted in order to determine its clinical efficiency. Then, a social impact study should be conducted in order to determine how the system allowed improving patient’s quality of life. Regarding these last studies, the results presented in evidence the benefits of telemonitoring systems while linking their success to the usability design issues and features.