smart-context: a context ontology for pervasive mobile computing

Smart-Context: A Context Ontology

for Pervasive Mobile Computing

PHILIP MOORE*, BIN HU AND JIZHENG WAN

Department of Computing, Birmingham City University, Birmingham B42 2SU, UK

*Corresponding author: [email protected]

This paper addresses context in intelligent context-aware systems to support personalised service

provision and cooperative computing. Context processing, context modelling, ontology, and

OWL are introduced and a context reasoning ontology presented. Context implementation

reduces to a decision problem which is characterised as one of selecting from a number of potential

options based on the relationship between the values that describe the input and the solution, the

modelling school of decision analysis attempts to construct an explicit model of such relationships,

usually in the form of decision trees. An overview of decision trees with parametric design consider-

ations is presented. Comparisons with related research are drawn and an evaluation and simulation

of Smart-Context is presented. RDF/S with OWL and Jena provide an effective basis for auton-

omous decision making using processing rules, and the issue is one of implementation in adaptable

and tractable solutions. A conclusion with open research questions is presented with consideration

of potential directions for future research.

Keywords: context; context-awareness; pervasive computing

Received 21 February 2007; revised 06 July 2007

1. INTRODUCTION

This paper addresses the issue of personalised service

provision and cooperative computing based on defined need

and situated role, in mobile computing a user’s situated role

describes the current situation users experience during their

interaction with the system. Personalisation requires that an

individual’s profile (termed context) is created. The definition

of context presents many difficulties including identifying

individual users, accommodating users’ evolving preferences,

device and location sensing and managing the heterogeneous

nature of mobile and computational devices with their associ-

ated connectivity constraints and service infrastructure.

Accommodating these diverse demands requires an overall

context definition made up of sub-contextsthat describe and

define entities, an entity being defined in [1] as: a person,

place or physical or computational object.

In posing the question mobile, pervasive, ubiquitous or

wireless? [2], the observation has been made that the term

mobile wireless communication is often used in conjunction

with or interchangeably with pervasive computing; however,

the two are not necessarily synonymous.

Pervasive computing describes a paradigm whose primary

characteristics are (generally) ubiquity and invisibility such

that the opportunity for computation and connectivity are

constantly available while masking the presence of the

system from the user [3]. Pervasive computing is addressed

in [4], the anytime anywhere paradigm being extended to all

the time everywhere.

Currently, pervasive computing fails to realise the objectives

of the pervasive computing paradigm and is limited in its

capacity to implement the systemic requirements of flexibility,

autonomy and adaptability. Developments in hardware and

software have brought pervasive computing close to technical

and economic viability with the basic component technologies

all currently existing [4]. The challenges lie in the development

of intelligent agents implemented in intelligent systems [5] and

the integration of existing component technologies [4].

Pervasive computing must address a number of key issues

including: (1) accommodating a diverse range of applications,

(2) service provision based on relevance, (3) service provision

compliant with device constraints and users’ situated roles,

and (4) meeting users needs, objectives and beliefs, desires

and intentions (BDI). BDI represents mental attitudes repre-

senting, respectively, “information, motivational and delibera-

tive states” [6]. Accommodating these requirements demands

that users are identified based on their context (discussed in

Section 2.1), to enable pervasive computing to effectively

address the issues identified.

THE COMPUTER JOURNAL, Vol. 53 No. 2, 2010

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This paper introduces Smart-Context, an autonomous intelli-

gent context-aware pervasive system designed to enable

context matching between inputs (information, resources or

queries) and outputs (potential solutions, in this case suitable

users) based on contextual information. Implementation of

context matching essentially reduces to a decision problem,

the modelling school of decision analysis attempts to construct

an explicit model of the relationships between the input and

potential solutions, usually in the form of decision trees.

This paper presents a novel context process model to meet

the context processing needs with a rule-based semantic

context reasoning ontology to provide the basis upon which

effective intelligent context reasoning can be realised. A

brief illustrative location-based on-line cooperative computing

scenario is shown to demonstrate the reasoning process.

Implementation issues are considered with parametric design

considerations and decision trees are proposed as a potential

solution to enable effective intelligent context implemen-

tation. Smart-Context provides the basis upon which effective

implementation of context in intelligent systems can be

realised.

The remainder of the paper is structured as follows: Section

2 considers the background to the study with a discussion on

context, ontologies, the Semantic Web and the Web Ontology

Language (OWL). Section 3 discusses context modelling, the

context process model is presented and the application of

context is addressed with typical examples. Section 4

addresses Smart-Context, which with the context process

model forms the core concept of the paper. The context

factors, their identification and evaluation are discussed with

the RDF properties, the class structure, and the semantic

context reasoning ontology. An example of a reasoned scen-

ario presented. Section 5 considers related research with a

simulation and evaluation of the Smart-Context concept pre-

sented in Section 6. Section 7 looks at implementation

issues and considers decision trees (DT). The paper concludes

with conclusions and a discussion on future work in Section 8

and an appendix.

2. BACKGROUND

This Section introduces context with consideration of the

types of context and the elements that combine to create a

context definition. Ontologies and the issues that impact

their use are discussed with examples. There is a brief intro-

duction to the Semantic Web technologies and consideration

of the Web Ontology Language (OWL) from a context reason-

ing perspective.

2.1. Context

The concept of context is generally agreed, however, a

common definition is not [1], considerable confusion

surrounding the notion of context, its meaning and the role it

plays in interactive systems [7]. Context is purpose and appli-

cation specific requiring the identification of the function(s)

and properties (context factors) specific to each domain [8].

This is exemplified in the application of context-awareness

applied to mobile learning [9] where the starting point in the

definition of context is to identify the purpose of the context

we are interested in.

Contexts are variable, the degree of variability being

reflected in the classification applied to a context. There are

two general classifications [10]:

† A static context (termed customisation) describes a state

in which the ‘look-and-feel’ and content provision is

essentially user-driven, the user having an element of

control.

† A dynamic context (termed personalisation) describes a

state in which the user is passive, or at least somewhat

less in control, system functions monitoring analysing

and reacting to the users situated role [10], actions and

BDI [6].

The two types of context are reflected in the two principal

ways context is used, these are: (1) as a retrieval clue

(a static context) and (2) to tailor system behaviour to match

users’ system usage patterns (a dynamic context).

A context consists of properties (context factors) that

describe and define an entity in computer-readable form [8],

the properties describing any information that can be used to

characterise an entity [5]. A context definition must define

and describe a number of elements that combine to create an

overall context definition, these elements include: (1) spatio-

temporal, (2) personal, (3) device(s), (4) infrastructure and

connectivity constraints, and (5) resource(s). A context is

created by combining property values that describe a diverse

range of elements [4] [10] [11] [12] including: (1) the variable

tasks demanded by users, (2) the mobile and computational

devices, (3) the service infrastructure, (4) the physical situ-

ation including location, and (5) the social setting.

There are three primary types of contextual information

(referred to as context dimensions in [1]): (1) spatio-temporal,

(2) identity, and (3) activity. Primary contextual information is

used to derive secondary contextual information [1]. Second-

ary contextual information relates to property values that

describe context factors such as location, device character-

istics and identity information. Contextual information can

be derived from an array of diverse sources including location,

weather and traffic sensors, sensors monitoring computer net-

works and status sensors for human users or computing

devices in ad hoc networks.

2.2. Ontologies

The use of ontology in computer science can be traced to

Artificial Intelligence (AI) research in the 1960’s [13] [14].

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A formal dictionary definition of the term ontology1 when

used in AI is: An explicit formal specification of how to rep-

resent objects, concepts and other entities that are assumed

to exist in some area of interest and the relationships that

hold among them . . . for AI systems, what exists is that

which can be represented. Ontology is defined in [15] as: A

conceptualisation of a domain into a human-understandable

but machine-readable format consisting of entities, attributes,

relationships and axioms.

The ability of ontologies to represent entities has recognised

benefits; these are the capacity to communicate information

and the ability to name concepts in machine-readable form

[15]. There are however disadvantages [16], and these include:

(i) The potential size and scale of an ontology

(ii) Ontologies are based on well-defined concepts and

logic making concept definition difficult

In considering (i), adding concepts and facts can result in

ontologies growing very large very quickly resulting in pos-

sible tractability issues. In considering (ii), humans (generally)

understand what is meant by different concepts such as

at_home or at_work. Computationally it is necessary to

define such concepts using inference rules on an ontology

which can present difficulties.

Ontology languages allow users to write explicit formal

conceptualisations of domain models. The main requirements

of such models are [17]:

† Well-designed syntax

† Well-designed semantics

† Efficient reasoning support

† Sufficient expressive power

† Convenience of expression

There have been a number of ontology languages devel-

oped, and the most widely known is the Web Ontology

Language OWL2 (a component in the W3c Semantic Web),

which is an extension of RDF/S (this is a generalisation as

there are 3 OWL sub-languages—see Section 2.4). OWL is

(partially) mapped onto a description logic [17] which is a

subset of predicate logic for which efficient reasoning

support is possible [18].

2.3. The Semantic Web

The Semantic Web is a concept predicated on the goal of trans-

forming the world wide web (www) into “. . . a set of con-

nected applications . . . forming a consistent logical web of

data . . .” [19] realised by adding ‘semantic annotations’ to

the XML/RDF tagging schema to impart meaning to web

content [20]. In the Semantic Web information has explicit

meaning enabling easier automated processing (as opposed

to merely presenting content to users).

The Semantic Web functions using Semantic Metadata and

is predicated on the idea that data can be arranged in the form

of an ontology and can be viewed in terms of “a set of logical

assertions about specific things and their relationships” [17,

21]. Ontologies establish a common conceptual description

and a joint terminology between members of communities

of interest (human and autonomous software agents) [22].

An example of a semantic ontology constructed to represent

an animal kingdom built using logical assertions is presented

in [17].

The Semantic Web consists of four W3C recommendations:

† The Extensible Markup Language (XML)

† XML Schema

† The Resource Description Framework (RDF/S)

† The Web Ontology Language (OWL)

XML, XML Schema and RDF/S3 are well-understood con-

cepts. OWL is addressed in the following Section.

2.4. The Web Ontology Language (OWL)

The expessivity of RDF and RDF Schema is (deliberately)

very limited [17]. RDF is (generally) limited to binary

ground predicates, RDF Schema being (again generally)

limited to subclass and property hierarchy with domain and

range definitions of these properties [17]. OWL extends the

expressive power of RDF and RDF Schema, adding additional

vocabulary enabling constraints including: (1) relationships,

(2) equality, (3) richer typing of properties, (4) characteristics

of properties, and (5) enumerated classes. OWL provides three

sub-languages [17]:

† OWL Full: uses all OWL language primitives enabling

their use with RDF and RDF Schema. The disadvantage

is that OWL Full is undecidable, making effective

reasoning support at best impractical and at worst

impossible.

† OWL DL (Description Logic): to enable effective

reasoning OWL DL restricts the use of OWL and RDF

constructors, thus ensuring that the language corresponds

to a description logic. The disadvantage is that full com-

patibility with RDF is lost, RDF documents requiring

extending or restricting before they are legal OWL DL

documents. Conversely, every OWL DL document is a

legal RDF document.

† OWL Lite: represents a further restriction in limiting

OWL to a subset of the language constructors. OWL

Lite exclusions include: (1) enumerated classes, (2) dis-

joint statements, and (3) cardinality. The disadvantage1Dictionary.com: http://dictionary.reference.com/2OWL: http://www.w3.org/TR/owl-features/ 3W3c: http://www.w3.org/TR

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of OWL Lite is its restricted expressivity and limited

reasoning capability.

From an ontological perspective there are strict design

notions of upward compatibility between the three sub-

languages [17]:

† Every legal OWL Lite ontology is a legal OWL DL

ontology.

† Every legal OWL DL ontology is a legal OWL Full

ontology.

† Every valid OWL Lite conclusion is a valid OWL DL

conclusion.

† Every valid OWL DL conclusion is a valid OWL Full

conclusion.

Recall that ontological modelling requires sufficient expres-

sive power to enable effective reasoning support, OWL Full and

Lite fail in this regard. OWL DL, being predicated on descrip-

tion logic, does however support effective reasoning.

3. CONTEXT MODELLING

There are many approaches to the modelling of context. A

comprehensive survey of context modelling from a ubiquitous

computing perspective can be found in [23], the context mod-

elling approaches are categorised under 5 headings: (1) Key-

value Models (KVM), (2) Markup Scheme Models (MSM),

(3) Graphical Models (GM), (4) Object-Oriented Models

(OOM), (5) Logic Based Models (LBM), and (6) Ontology

Based Models (OBM). The modelling approaches are classi-

fied based on their data structures, and an analysis in [23]

resulted in the conclusion that Ontology Based Models

represent the optimal approach.

In addition to the categories listed in [23] there are machine

learning (ML) approaches using both supervised and unsuper-

vised learning [16] [24]. There are inherent issues and poten-

tial advantages in using an ML approach, addressing these

forms the basis of future work and are discussed in Sections

5.1, 7 and 8.

The modelling categories are not independent, there being

an element of interdependence. For example, in developing

systems using ontologies OBM may be used to model the

relationships and constraints that exist between entities.

OBM approaches share elements from LBM, OOM, GM and

MSM approaches including the following:

† OBM (using the Semantic Web) uses RDF/S and OWL

(markup-languages)

† The Semantic Web is predicated on OO, OBM therefore

inherits the class-based structure of the OOM approach

† OOM generally employs graphical representations (such

as UML) which exhibit similarities with the GM approach

(using, for example, Entity Relationship diagrams).

† Logic (the basis of LBM) is important in ontologies

therefore aspects of LBM are used in OBM.

In Section 2 ontologies, the Semantic Web, and OWL have

been introduced; the issues and advantages have been dis-

cussed with illustrative examples. There are clearly issues in

the use of ontologies to model context including issues ident-

ified in [16] (discussed in Sections 2.2 and 5.1). Effective

implementation of intelligent context requires sufficient com-

putational intelligence (discussed in Sections 2.2 and 2.4) to

enable inference and reasoning. The ontological modelling

approach using Semantic Web technologies (see Section 2)

provides the optimal modelling approach as shown in [23].

An ontology-based approach to context modelling and reason-

ing in pervasive computing is presented in [25], using a

semantic ontology in a rule-based, event-driven system, the

OBM approach is used to good effect.

The efficacy of ontological context modelling has been

demonstrated, the issues lie in the implementation. The pro-

posed approach to address these issues is addressed in Section 7.

3.1. Context process model

Ontological context modelling represents the optimal solution to

the modelling of context [23]. The modelling function however

fails to address the issue of the processing of contextual infor-

mation (context processing). This Section discusses context pro-

cessing and presents a novel context process model which forms

a pivotal element in the Smart-Context concept.

The domain-specific nature of context [8] extends to the

processing of contextual information. In our research there

are domain-specific system prerequisites, principal amongst

these is the need to access context and update entity context

definitions stored in heterogeneous back-end systems. To

meet the context processing needs a context process model

has been developed, this is presented graphically in Fig. 1.

A context can be viewed as a state with transitional states.

Fig. 1. models this process, commencing with a stored context

(Current Context [A]) and terminating with the replacement of

the initial stored context (Current Context [A]) with the

context (Implemented (updated) Context [D]). The process

is cyclic with a back-up context always retained. Notes 1–6

(see Fig. 1) are expanded below:

† Note 1: The initial stored Current Context [A] is created

(in the first instance default values will be used to create

an initial current context [A]).

† Note 2: Upon an event triggering the system the sensory

input data is processed and the Updated Context [B] is

created to reflect the new situated role.

† Note 3: Based on context processing rules if no further

context processing is required, the Updated Context [B]

will replace the initial stored Current Context [A]. The

new Current Context [A] is also stored in a back-up

system.

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† Note 4: In situations where context-processing rules

dictate the Updated Context [B] will be replaced by the

Updated Context [C] (following context processing) to

reflect the changing situated role.

† Note 5: The Updated Context [C] is implemented, the

Updated Context [C] becomes the Implemented

Context [D].

† Note 6: Finally, the Implemented Context [D] replaces

the initial Current Context [A]. The new Current

Context [A] is also stored in a back-up system.

The implementation of the process model requires an intel-

ligent multi-agent context middleware using context reasoning

in a rule-based system (discussed in later Sections), the intel-

ligent agent(s) autonomously applying the context rules to

manage the context processing and implementation thus auto-

mating context updating and implementation.

3.2. The application of context

Context-aware solutions have been applied in many diverse

domains where the provision of personalised services

mapped to an entities context is a system requirement. The fol-

lowing examples whilst not comprehensive, are representative

applications in the field of pervasive context-aware

computing.

† CRUMPET [26]: is a project to create user-

friendly mobile services personalised for tourism.

Context-awareness is used in the integration of four

technology domains: (1) location-aware services, (2)

personalised user interaction, (3) multi-media mobile

communication, and (4) smartware using multi-agent

technology.

† MOBllearn [9]: is an initiative to explore context-

awareness using contextual information in mobile

learning.

† Cooltown4: is a pervasive computing initiative focusing

on extending web technology, wireless networks and

portable devices to create a virtual bridge between

mobile users and physical entities and electronic

services.

† Cyberguid [27]: applies context-awareness to the devel-

opment of a mobile context-aware tour guide.

† Solar [28]: is a middleware platform to assist

context-aware applications aggregate desired context

from heterogeneous sources and to locate environmental

services depending on the current context.

† Group Interaction Support [29]: implements a

context-aware system to support group interaction in

mobile-distributed computing environments.

† Portolano5: A University of Washington project that

emphasises invisible, Internet- based computing. Infer-

ence is drawn from users intentions via their actions in

the environment and their interactions with everyday

objects.

† Oxygen6: is an MIT project to create an infrastructure of

mobile and stationary devices connected to a self-

configuring network.

The projects identified point to location as the predominant

application of context, a survey of context-aware applications

[30] confirms this conclusion. The use of location-based

context as the predominant application arguably stems from

the inherent complexity of context, difficulties in the manage-

ment of context and context matching [10], and issues effect-

ing the processing of contextual information. These are the

issues that intelligent context processing using ontological

context modelling seeks to address.

FIGURE 1. Context process model.

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4. SMART-CONTEXT

The previous sections have introduced the study, addressed the

background, and considered the modelling of context. This

section addresses Smart-Context, which with the context

process model forms the core concept of this paper. The smart-

Context concept builds on work carried out to create an RDF

context framework for personalised mobile learning [8]. This

research focused on the development of a generic context fra-

mework generalisable to similar domains to enable the effec-

tive definition of context for a student in the domain of higher

education (HE).

Smart-Context is an intelligent context-aware system to

enable the mapping of problems (input data) to potential sol-

utions (identified individuals together with their computational

devices/device characteristics). Smart-Context can be viewed

in terms of a decision-making system to implement intelligent

context processing. The context framework and RDF class struc-

ture has been developed with a context reasoning ontology to

provide a basis for effective decision-making to support person-

alised information services and cooperative computing.

Smart-Context is event driven, activated by change. For

example, the movement of an individual (or an individual’s

mobile device) from one location to another prompts an

input into the system notifying the location change, this will

trigger context processing to update and implement an individ-

ual’s context based on context-processing rules (discussed in

Section 3.1).

This section presents the context factors used in the devel-

opment of the context framework with the identification and

evaluation processes. The context properties are reflected in

the RDF/S class structure. A semantic context reasoning ontol-

ogy is presented with consideration of implementation and

evaluation issues.

4.1. Identifying the context factors

The context factors identified are presented in Fig. 2, the list is

non-hierarchical being analogous to a menu from which appli-

cable factors suitable to a domain-specific contextual design

can be selected.

4.2. Context factor identification

The identification of the context factors was achieved using

semi-structured interviews with staff drawn from the Depart-

ment of Computing at Birmingham City University forming

the sample population. Fifteen interviews were conducted, a

breakdown shows that of the fifteen staff interviewed thirteen

were active tutors, one was involved in student support,

and one was employed in technical services. The tutorial

staff interviewed included computing (technical) and

business-oriented (soft systems) staff. A quantitative analysis

shows that 66% were from a computing or technical back-

ground with 33% drawn from soft systems disciplines.

Semi-structured interviews effectively addressed the data

elicitation requirements and the associated issues identified,

the principal issues being:

† Availability and timing constraints.

† Identification and access to the sample population.

† Ethical considerations.

The interviews were conducted by appointment on a

one-to-one basis. Prior to the interview briefing documen-

tation was provided to give the respondents an overview of

the research, its aims, objectives and motivation. The time

envisaged for each interview was 30 minutes, in actuality

the time varied from approximately 30 to 75 minutes.

A potential issue identified at an early stage was the rela-

tively small population size. The research design addressed

this issue using the purposive sampling method [31]. Purpo-

sive sampling calls for the use of populations with recognised

knowledge and experience in the domain the research is

designed to address, thus mitigating the potential for a

biased result. The selected population satisfied the require-

ments of the purposive sampling method.

4.3. Context factor evaluation

The factors were evaluated using a questionnaire, the design

being based on a variation of the Likert scale with four avail-

able options: strongly agree, agree, disagree, and unsure (see

Fig. A1). The questionnaire initially identifies the respondent

as a student or tutor. The questions are set under four sections:

personal, academic, mobile learning system, and device.

FIGURE 2. The context factors.

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Sample Sections of the questionnaire are set out in Fig. A1 in

Section 9 (the appendix).

The questions are framed using the identified context

factors, the final questionnaire being the result of a piloting

process using a different (but similar) population to that

polled in the actual survey. The questionnaire was distributed

to staff and students at Birmingham City University (UK) and

Guilin University of Technology (P.R. China) to determine the

degree to which the factors identified in the interview process

are representative of other HE domains.

4.3.1. The analysis

A statistical analysis used aggregate response (N) values nor-

malised to a maximum value of 1 with standard deviation

(SD), correlation (C) and moving averages (MA). The

results are cross tabulated in Tables 1–3, the results obtained

from the statistical analysis were analysed visually. The

formula for calculating the (N) values is

N ¼X

R� �

=M

where N ¼ the aggregate response value, R ¼ the sum total of

the encoded responses, and M ¼ the notional maximum value

of the encoded responses. For example, given 10 strongly

agree responses to a specific question the value of R is (10 *

4) which equates 40, applying the formula for R ¼ 40:

�N ¼

XR

� �=M

�N ¼ 40=40� ��

N ¼ 1:00�

and applying the formula for R ¼ 30:

�N ¼

XR

� �=M

�N ¼ 30=40� ��

N ¼ 0:75�

It can be seen from the results obtained from applying the

formula that in the first case the result is 1.00 and in the

second case the result is 0.75. This demonstrates the overall

level of agreement with a specific context factor.

The questionnaire returns demonstrated a wide variation in

response to each question, the SD (Table 2) supporting this

observation. The wide range of questionnaire responses were

reflected in the data obtained in the interview process. To

test for relationships between the interview, student, and

tutor data sets and to quantify the strength of any relationship,

correlation coefficients (C) were calculated. The (C) results

were negative ranging from weak (interview/tutor data)

through moderate (interview/student data) to strong (student/

tutor data)(Table 3). These results suggest that statistically sig-

nificant relationships are present in the data.

The (N) values were computed for each question from the

responses returned, a summary of the results is cross tabulated

in Table 1. The (N) values represent a measure of the overall

level of agreement respondents expressed related to the use of

each factor in a student context definition. The results fell into

relatively narrow ranges for tutor and student responses, the

interview results showing a similar maximum but widely dif-

fering minimum and average result. The wide differences

identified are explained by the smaller sample used in the

interviews as compared to the larger survey sample. With

respect to the (MA), notwithstanding the widely differing

(N) values identified, significant patterns and trends in all

three data sets were observed.

The results derived from the statistical and visual analysis

support the observation that there is a level of statistical sig-

nificance in the data. The analysis supports the conclusion

that: (1) the wide variation in the identified factors noted in

the interview process (and used in the development of the fra-

mework) is reflected in the questionnaire responses and (2) the

factors identified in the interviews are representative of other

larger populations.

4.4. RDF Context Properties

Sections 4.2–4.4 have discussed the identification and evalu-

ation of the context factors. The identified factors (Fig. 2) form

the basis for the RDF context properties (Fig. 3) which define

the context framework.

The context framework [8] is defined in RDF schema

document(s) setting out the schema specific vocabularies

that describe entities based on combinations of classes

TABLE 1. Aggregate response values

Max Min Avg

Tutor responses 0.97 0.50 0.74

Student responses 0.90 0.59 0.71

Interview responses 0.93 0.07 0.31

(N) value ranges

TABLE 2. Standard deviation

Max Min Avg

Tutor responses 1.32 0.33 0.81

Student responses 1.01 0.55 0.81

SD value ranges

TABLE 3. Correlation coefficient

Data set comparison Coefficient

Interview data/Q_Tutor data 0.377069672

Student/Q_Tutor data 0.671195618

Interview data/Q_Student data 0.480650532

(C) values



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and properties. The RDF schema is non-hierarchical and when

implemented in an application provides the ability to tailor

classes and properties to suit other similar domains. Set out

in Fig. 3 are extracts from the validated RDF member

schema showing the header code with qualified namespaces,

datatypes and example class and property definitions. The

example RDF/S (Fig. 3) illustrates the definitions for the

Class:Profile with its sub-class Class:Person and an individ-

ual’s name property with the associated datatype.

4.5. The Class Structure

The class structure has been developed from the RDF context

framework discussed in [8] and is based on object-orientation

(OO) using classes and sub-classes with associated properties

and sub-properties (based on the identified context factors)

defined in the RDF Schema to represent types and classifi-

cations of entity. The class structure (Fig. 4) provides the cap-

ability to add and remove classes, vocabularies and properties

to tailor and refine context definitions to suit the domain

specific nature of context and contextual design.

The class structure provides a basis upon which context pro-

cessing (discussed in Section 3.1) can be implemented and

process-awareness, arguably the most important element in

Smart-Context, achieved.

Recall that context can be viewed in terms of a series of

states with transitional states. To effectively manage the inter-

actions that take place during context processing the system

must maintain an awareness of all prevailing states and the

context processing process. Class:Process has been incorpor-

ated to fulfil these functions, to receive the input data and

return the resultant output (the matched context(s)). Class:

Process can be viewed in terms of an interface to initiate,

control and manage the context processing process.

The class structure is predicated on the RDF data model and

at its root is the Class:Context, a class designed to hold the

overall context definition built from the entity contexts.

There are four sub-classes of Class:Context, Person, Spatio-

Temporal, Device, and Resource; these classes define the

entity contexts (sub-contexts). Properties that relate to

human users are defined in Class:Person and its sub-classes:

Preference, Schedule, Activity, Task, and Role. Classes

FIGURE 3. RDF/RDFS context properties.

FIGURE 4. The class structure.

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Device Resource and Spatio-Temporal relate to properties

specific to physical objects (Device), computational objects

(Resources) and places including date and time properties

(Spatio-Temporal) respectively.

The class structure is designed to accommodate the

demands imposed by the differing inputs the system must

accept, the inputs fall into three general use-cases:

† A question placed on the system by a questioner with the

objective of identifying a suitably qualified adviser or an

appropriate resource

† A resource entered on the system for distribution to

appropriate individuals

† A resource distributed dynamically based on users con-

texts [5], actions and BDI [6]

In the three use-cases identified the system will be required

to perform context matching in the context space (discussed in

Section 7.2). Context matching involves the mapping of the

problem context to potential solution contexts to identify suit-

able matches. To accommodate this process Class:Process

will enable the use of in-memory data storage to hold the

problem context (while context matching takes place) and

the resultant matched context(s).

The class structure and RDF Schema provide a basis for the

creation of context definitions and context matching. This does

not however address the issue of context reasoning, a context

reasoning ontology has been developed to address this issue.

4.6. Reasoning with Smart-Context ontology

Ontologies are introduced in Section 2.2 with consideration of

semantic issues, inference and reasoning. The Semantic Web,

predicated on the idea that data can be arranged in the form of

ontologies, is discussed in Section 2.3 with an introduction to

OWL presented in Section 2.4. Section 3 reviews and con-

siders the available approaches to context modelling, OBM

representing the optimal approach.

Using OBM a semantic context reasoning ontology based

on the class structure (see Section 4.5 and Fig. 4) has been

developed and is presented graphically in Fig. 5. The follow-

ing brief illustrative location-based on-line cooperative com-

puting scenario demonstrates the context reasoning process.

† A1: Spatio-Temporal (date ¼ “2006:27:11”) [Monday]

† A2: Spatio-Temporal (time ¼ “10:35:48”) [am]

† B1: Person (name ¼ “Tom”) IsAtSpatio-Temporal (location ¼ “library”)

† B2: Person (name ¼ “Tom”) HasAActivity (activity ¼ “on-line”)

† B3: Activity (activity ¼ “on-line”) HasATask (question (subject ¼ “Prolog”))

† B4: “Tom” is on line, is located in the library and has a

role of student

† C1: Person (name ¼ “Sue”) IsAtSpatio-Temporal (location ¼ “on-campus”)

† C2: Person (name ¼ “Sue”) HasAPreference (availability (location ¼ “on campus”))

† C3: Person (name ¼ “Sue”) HasAPreference (day ¼ “Monday - Friday”))

† C4: Person (name ¼ “Sue”) HasAPreference (time ¼ “am”))

† C5: Person (name ¼ “Sue”) HasAStatusPerson (available ¼ “yes”)

† C6: Person (name ¼ “Sue”) HasAModule (module ¼ “Programming Languages”)

† C7: Person (name ¼ “Sue”) HasAQualification (qualification ¼ “Prolog”)

† C8: “Sue” is available, is an appropriate adviser to

“Tom” and has a role of tutor

�ðB1 ^ B2 ^ B3Þ ) B4

��ððA1 ^ A2Þ ^ ðC1 ^ C2 ^ C3 ^ C4ÞÞ ) C5

��ðC5 ^ ðC6 _ C7ÞÞ ) C8

�

FIGURE 5. The semantic reasoning ontology.



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A wireless device or RFID tag locates and identifies the user

and initiates event-driven (location change) context updating

and input (task/problem) tagging. The scenario sets out a

typical cooperative interaction with an example of context

reasoning. In the scenario spatio-temporal, activity, task, pre-

ference, status, qualification, module, and role context com-

ponents are identified.

Context reasoning with inference using the RDF property

values enables the availability of “Sue” and her suitability as

an adviser to the questioner “Tom” to be inferred based on

their situated roles. The ontology identifies the semantic

relationships based on the semantic links defined by context

properties and their associated values (e.g. location ¼

“library”) enabling reasoned decisions including disambigua-

tion and partial validation to be achieved.

The reasoning process demonstrates conjunctions (AND)

and disjunctions (OR). Implementation of ontologies has

been discussed in Section 7, decision trees (discussed in

Section 7.1) being proposed as a potential solution to enable

effective implementation in decision-centric context-aware

pervasive systems, decision tree induction needing both con-

junction(s) and disjunction(s).

5. RELATED RESEARCH

This paper has introduced Smart-Context and considered con-

textual computing, and context modelling. A goal of Smart-

Context is the ability to generalise to diverse domains,

systems and technologies. Achieving these aims requires an

effective and tractablesolution to

(i) Create and update context definitions

(ii) Enable adaptability

(iii) Enable intelligent context processing

(iv) Implement context in intelligent decision-centric

context-aware systems

To address (i), the class structure is encoded in RDF (dis-

cussed in Sections 4.4 and 4.5). RDF provides an adaptable,

portable and lightweight cross platform solution to context

definition in computer readable form [8] [32] [33] [34]. RDF

has been shown to function effectively in diverse domains,

systems and technologies developed using for example: (1)

Java technologies, (2) ASP, and (3) PHP [32]. This enables

RDF solutions to be used in diverse Internet and distributed

networked applications including [33]:

† Resource Description

† Site Maps

† Collaborative Services

† Content Rating

† Privacy Preferences

In addressing (ii), there exists considerable documented

research in the area of adaptability in context-aware mobile

systems, examples include:

† Mobile Adaptation with Multiple Representation

Approach as Educational Pedagogy [35]: addresses the

issue of web page level content adaptation providing

guidelines for content adaptation in e-learning and

mobile learning environments.

† The Cognitive Trait Model (CTM) for Persistent Student

Modelling [36]: provides a model to supplement

performance-based modelling of students enabling the

transference of relevant information (e.g. cognitive

resources) to similar domains.

† Supporting Learning in Context: Extending Learner-

Centered Design to the Development of Handheld Edu-

cational Software [37]: challenges to the development

of educational software for handheld devices are con-

sidered with the design of Pocket PiCoMap — a learner-

centred tool to support concept mapping activities on

handheld devices.

The research documented in [35] [37] principally targets the

provision of content, the CTM [36] focusing on an adaptable

approach to student modelling; the research considered in

Section 3.2 (generally) focuses on the use of location-based

context. The research identified while addressing contextual

computing issues is limited in scope and fails to address the

requirements of intelligent context and the specific demands

of the Smart-Context concept.

5.1. Intelligent context

To address (iii), RDF in facilitating the description of web

resources in computer readable form has been shown to

enable automated decisions using processing rules [33], a

context definition is an example of a set of processing rules

[5] [8]. Semantic rules form an important element in intelli-

gent systems employing inference and reasoning. The Seman-

tic Web enables the creation of rules with inference and

reasoning, the issue that requires addressing is the implemen-

tation of intelligent context.

In considering (iv) (implementation issues are discussed in

Section 7), research addressing context modelling in intelli-

gent systems generally employs a machine learning approach

using either supervised or unsupervised learning. Flanagan

[16] in addressing personalisation and ontology proposes

unsupervised learning as an alternative to ontologies.

Flanagan [16] recognises the obvious advantages of ontology

however it is argued that ontology is a major barrier to

personalisation. A further potential issue identified is the

provision of a stored context, a system prerequisite for the

Smart-Context system (discussed in Section 3.1).

An abstract context model for an intelligent environment is

proposed in [24], a supervised learning approach using the ID3

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decision tree algorithm being used. The environment, its

occupants and their activities are represented by a model (in

the form of a situation network) consisting of situations and

roles played by entities and their relationships. System ser-

vices (associated to the modelled situations) are adapted to

the changing needs of the user with supervisor feedback cor-

recting inappropriate system services. The proposed approach

is however semi-autonomous being essentially location-based,

issues for Smart-Context; the location-based focus is an issue

shared by other research considered.

5.2. Summary

The accepted advantages of ontology and OBM show that it is

a viable solution to provide an effective basis upon which (i),

(ii), and (iii) can be addressed, the issues relating less to the

concept of ontology than the approach adopted to enable

implementation. Considering (iv), decision trees (discussed

in Section 7.1) in a rule-based system are proposed as a poten-

tial solution to the implementation of context in intelligent

context-aware systems, the reported results achieved using

decision trees to create an abstract context model in an intel-

ligent environment [24] indicates that this approach represents

a potentially viable solution.

6. SIMULATION AND EVALUATION

The twin goals of Smart-Context are (1) to enable personalised

service provision and (2) to support cooperative computing.

To demonstrate these goals a simulation of the proposed

system based on two illustrative scenarios is presented. In

both scenarios location (in this case the library) forms an

important context component.

Section 6.1 presents the simulation, the evaluation process

is discussed in Section 6.2.

6.1. Simulation of Smart-Context

The following scenarios demonstrate the Smart-Context

process, the two scenarios reflecting typical system usage

based on users current context(s). The first scenario (Section

6.1.1) demonstrates automated resource provision. The

second scenario (Section 6.1.2) shows a cooperative inter-

action, the identification of an appropriately qualified individ-

ual user able to assist in the resolution of a problem is shown.

6.1.1. A personalised resource provision scenario

The first scenario describes a situation where the Smart-

Context system allocates a system input (the resource/

problem) to automatonomously identified appropriate recipi-

ents (the solution(s)) based on their context definition. The

identified users are contacted based on their situated role

and related preferences.

† Resources (in this case books on Java Programming) are

entered on the Smart-Context system with tags identify-

ing potential users. [Resource(type ¼ “book”), Module

(module ¼ “Java Programming”)]

† “Bob” is currently enrolled on the Java Programming

Module. [Person(“Bob”) HasA Module(module ¼

“Java Programming”)]

† “Bob” enters the library and a location-change-event is

triggered, the context processing (discussed in Section

3.1) updates the context to reflect the new location and

situated role. [Person(name ¼ “Bob”) IsAt Spatio-

Temporal(location ¼ “library”)] [Person(“Bob”) HasAModule(module ¼ “Java Programming”)]

† The agent in his PDA performs a handshakes with the

agent in context tag at the library. [Device(type ¼

“PDA”) IsAt Spatio-Temporal((location ¼ “library”),

(date ¼ “2006:27:11”), (time ¼ “11:47:39”))]

† After negotiation about trust, the agent in context tag pre-

sents some notices to him based on his registered

modules and sends Bob recommended books on Java

programming. [Resource(type ¼ “book”) IsA Preference

(preference ¼ “textMessage”)]

† “Bob” identifies a book (Java How To Program) and the

agent in the context tag sends the booking message to the

library information system after checking his library ID.

† The context tag receives the book ready message and

transfers to Bob. [Resource(type ¼ “book”) IsAPreference(preference ¼ “textMessage”)].

† “Bob” collects the book at the identified shelfmark.

Fig 6–8 show typical system output(s). Figure 6 shows the

welcome screen setting out the user name and location, the

news field notifies the user that there are messages (book rec-

ommendations). Figure 7 details the book recommendations.

FIGURE 6. Initial screen.



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Figure 8 notifies the user “Bob” of the shelfmark where the

selected book can be found.

6.1.2. A cooperative computing scenario

The second scenario demonstrates a cooperative computing

example in which a question entered by the questioner (the

input) is tagged to enable the Smart-Context system to auton-

omously identify potential qualified adviser(s) (the output)

based on their context and situated role.

† “Bob” is working in the library investigating Java

programming. [Person(name ¼ “Bob”) IsAt Spatio-

Temporal(location ¼ “library”)] [Person(“Bob”) HasAModule(module ¼ “Java Programming”)].

† “Bob” is logged onto the Intranet. [Person(name ¼

“Bob”) HasA Activity(activity ¼ “on-line”)].

† “Bob” places a request on the Smart-Context system

asking for assistance. [Activity(activity ¼ “on-line”)

HasA Task(question(subject ¼ “Java”))].

† The system identifies “Lisa” (amongst other suitable

individuals) as a suitable contact (based on the values

in her context definition including enrolled course,

project, modules, interests and availability for consul-

tation). [Person(name ¼ “Lisa”) HasA Role(function ¼

“adviser”)].

† “Bob” is advised of the potential contacts with their

contact details.

† “Bob” selects Lisa and contacts her using a text message.

[Preference(preference ¼ “textMessage”)].

† “Bob” opens an on-line cooperative interaction with Lisa

to request assistance to resolve his query.

Figures 9 and 10 demonstrate typical system output(s).

Figure 9 shows the status of the user (“on-line”) and the

subject of the question entered onto the Smart-Context

system (the input/problem). Figure 10 shows the opening

contact made by “Bob” to “Lisa”(an output/solution). “Lisa”

is identified as a suitable qualified advisor based on her

context.

6.1.3. Observations

The two scenarios are related to the context reasoning scenario

discussed in Section 4.6. In actuality both scenarios require

that the context matching process in the context space as dis-

cussed in Section 7.2 (matching the resource/problem context

to potential solution context(s)) is implemented. The two scen-

arios demonstrate the use of spatio-temporal, preference and

qualifying (e.g. module) properties to enable context matching

and autonomous decision making in the context matching

process.

FIGURE 8. Selected book details.

FIGURE 7. Book recommendations.

FIGURE 9. User state/question.

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6.2. Evaluation of smart-context

Evaluation of Smart-Context involves the use of a prototype

personalised mobile learning system using scenario-based

metrics based on predefined test data (investigation having

failed to identify a suitable existing corpus). Evaluating the

effectiveness entails comparing the results achieved during

the testing process (using a prototype personalised mobile

learning system) with the predefined test data.

Personalised service provision mapped to user-defined need

introduces the concept of relevance [11]. There is a correlation

between the concept of relevance and issues in search and

information retrieval. The recognised measures of the efficacy

of search and retrieval strategies are precision and recall, a

further measure is fall-out.

† Precision: is a measure of the number of relevant docu-

ments retrieved as measured against the relevant and

retrieved documents received.

† Recall: is a measure of the number of relevant documents

retrieved as measured against the total number of rel-

evant documents available.

† Fall-Out: is a measure of the probability of an irrelevant

document being retrieved as measured against actual

documents retrieved.

The results are expressed as a ratio (normalised to a value

of 1) or a percentage. To effectively evaluate Smart-Context,

evaluation metrics will be developed using the three identified

measures to enable quantitative evaluation upon which the

efficacy of system can be assessed.

Evaluating cooperative computing performance employs a

similar process, predefined test data (matched questioners

and adviser(s) contexts) compared to actual results define

the efficacy of the system.

7. IMPLEMENTATION ISSUES

Ontologies and OBM have been identified as an effective

approach to enable intelligent context. RDF/S with Jena [38]

provide a basis upon which data structures can be created

and updated [5] [32] [34], this however fails to enable

support for ontologies, inference and context reasoning. It

has been shown that the limitations of RDF and RDF

Schema [17] when extended using the OWL vocabulary can

address this issue and provide a basis for intelligent context

implementation. Creating an ontology does not however

address the issue of implementation, this requires the develop-

ment of a strategy which requires autonomous decision

making based on current (updated) context definitions. Ontol-

ogy implementation therefore reduces to a decision problem.

A decision problem is characterised as one of selecting from

a number of potential options based on the relationship

between the values that describe the input (resource or

query) and the solution (identified users). The modelling

school of decision analysis attempts to construct an explicit

model of such relationships, usually in the form of decision

trees [39].

7.1. Decision trees

Decision trees are types of directional graph employing

IF-THEN statements [40]. The inputs to a decision tree are a

set of attribute values (in this research context properties),

the output being Boolean (or multi-valued) decisions. Each

internal node in a decision tree tests the value of one of the

properties, the branches from the node being labelled with

the possible values resulting from the test. Each leaf node in

the tree specifies the value to be returned if the leaf is

reached [39] [40]. The output of the decision tree is derived

by testing attributes sequentially commencing at the root

node and following the branch labelled with the appropriate

value.

It is proposed that the implementation process will involve

the development of an intelligent context middleware to

implement the decision-making process using decision trees

to model the input and potential solution(s) and enable

context matching to arrive at a set of identified solutions

(users).

From a design perspective a potential issue when the use of

decision trees in a rule-based system is considered is the temp-

tation to apply an inappropriate solution to a problem. This

requires that the following question: Is a rule-based solution

implemented using decision trees a suitable option? be

addressed. The following parameters to test the suitability of

decision trees in a KBS as a potential approach have been

identified:

(i) Is human problem-solving knowledge being repli-

cated in the solution?

(ii) Is the problem-solving knowledge heuristic in nature?

FIGURE 10. Contact message.



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(iii) Does the knowledge (or expertise) periodically

change?

(iv) If expertise is involved, is the expertise fairly well

understood and accepted?

(v) Is the problem well understood?

(vi) Are the input data always complete and correct?

(vii) Can the problem be (better) solved by conventional

database or programming solutions?

(viii) Does it pass the telephone test? (this test asks whether an

expert, by speaking with someone over the telephone,

can gather sufficient information to solve the problem)

(ix) Is the decision making process merely the matching of

data? (or are rules applied in the decision making

process)

(x) Is speed of operation a prime requisite for the system?

(a rule-based KBS is arguably slower at runtime than

conventional programming solutions)

(xi) Are partial solutions acceptable solutions to the identi-

fication of individuals?

The design parameters i to viii are derived from [40], par-

ameters ix, x and xi represent three further design consider-

ations relevant to the problem under investigation. A

comprehensive discussion on the design and engineering of

knowledge-based systems can be found in [40], however, in

summary a positive response to parameters [i, ii, iii, iv, v,

viii and xi] indicates that a rule-based system does potentially

provide an effective solution. A positive response to par-

ameters [vi, vii, ix and x] however implies that a rule-based

KBS is not potentially a suitable solution.

Applying the design parameters to the problem under inves-

tigation indicates that a rule-based system implemented using

decision trees does potentially provide an effective basis for

constructing a context-aware decision-making system. Such

a system requires sufficient computational intelligence (dis-

cussed in Section 2), to achieve this it is proposed to use a

semantic context ontology (see Sections 2.2 and 4.6)

implemented in an intelligent context-aware system.

Intelligent systems can be viewed as a cooperative of intel-

ligent agents that perceive and act in an environment [41].

Agents can be considered intelligent relative to the degree to

which their actions are successful.

Ontologies are constructed using a number of entity con-

texts (sub-contexts). To effectively process the information

contained in each entity context, it is envisaged that a

number of agents will be employed working in concert in a

context-aware multi-agent system, each agent (sub-system)

being responsible for a specific function such as location

sensing, resource formatting and so on.

7.2. Context middleware

The Context middleware is responsible for the creation,

storage, updating, retrieval and implementation of context(s).

The context middleware must enable a number of key

functions but before we consider the functions the context

middleware is designed to fulfil the notion of context space

[10] must be introduced.

Context space defines the complete set of entity contexts

that make up the overall stored context. The context space

can be viewed in terms of a database made up of past,

current and future contexts (as discussed in Section 3.1) pro-

viding the capability to manage context(s). Based on the

context space, the context middleware must enable the follow-

ing functionalities:

† basic operations (e.g. create, update, delete, search, get

and implement)

† advanced functions (e.g. context matching [10] with

support for context reasoning)

An example of context matching is a typical trouble-

shooting scenario in which a context that defines the ques-

tioner’s situated role (the problem) is matched to that of a

suitably qualified adviser (a solution). In a matching procedure

the questioner and adviser contexts are presented as inputs into

a context space, the context matching agent analysing context

pair(s) based on a set of rules in a rule-based system.

The result will be the production of a signal by the context

matching agent to show the potential suitability of a matched

context pair. This is an example of the decision-making

system in operation. In practice the context middleware is a

Java package based on the Jena RDF API which can be

called by other layers within the application.

8. CONCLUSIONS AND FUTURE WORK

This paper has introduced the Smart-Context concept; context

modelling has been considered with issues related to intelli-

gent context in decision-centric context-aware systems.

Decision trees in rule-based systems have been discussed

with ontologies, the Semantic Web, inference and reasoning.

A semantic context reasoning ontology has been presented

with a simulation and evaluation using personalised service

provision and cooperative computing scenarios.

Context is made up of a number of context types including

personal, task, role, spatio-temporal, and awareness. The

awareness context includes activity, state, social and process

awareness [10]. Process-awareness has been identified as a

pivotal component in intelligent context-aware systems. An

overall context is made up of sub-contexts representing enti-

ties to enable the creation of (static and dynamic) contexts.

It has been shown that context definitions can be effectively

created using the Semantic Web technologies, ontology-based

context modelling (OBM) providing a basis upon which effec-

tive implementation can be realised.

Context modelling (discussed in Section 3) is pivotal to effec-

tive context matching in the context space (discussed in Section

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7.2), demanding the modelling of both the solution and problem

spaces within the overall context space. Given that perfect

matches are highly unlikely, an approach enabling partial vali-

dation is important. Context modelling, whilst providing a

basis upon which intelligent context can be realised fails to

address the issue of implementation in intelligent systems.

A review of current contextual computing research identi-

fied has concluded that the demands of intelligent context

implementation are not addressed. To address this issue

decision trees in a rule-based approach are proposed in this

paper, the parametric analysis set out in Section 7.1 showing

that decision trees do represent a good potential solution in a

decision-centric context-aware systems. The work of Brdiczka

et al. [24] has demonstrated that decision tree induction using

the ID3 algorithm is an effective potential solution however

the limited scope of their system does not fully address the

needs of intelligent context implementation as envisaged for

the Smart-Context concept.

There are a number of approaches in the literature that

address the issue of decision tree induction including purely

statistical approaches and evolutionary systems. These

approaches also fail to meet the demands of the Smart-Context

concept. These observations raise a number of significant

research questions:

† There is an apparent synergy between the tree structure

that is a feature of both the Semantic Web technologies

and decision trees. Can this apparent synergy be har-

nessed to enable effective implementation?

† What contribution can decision tree induction using

machine learning (supervised and unsupervised) make

to realising intelligent context implementation?

† Do decision trees in rule-based systems (implemented in

intelligent multi-agent context middleware) represent an

effective approach?

† What contribution can logic-based context modelling

make to the implementation of intelligent context?

† Context is complex and often ambiguous, can fuzzy

logic, fuzzy sets, or rough sets contribute to resolution

of such issues in intelligent context-aware systems?

† “Neural network [learning] methods provide a robust

approach to approximating real-valued, discrete-valued,

and vector-valued target functions . . . it [ANN learning]

is also applicable to problems for which more symbolic

representations are often used, such as decision tree

learning” [42]. Given the observations of Mitchell

[42], do artificial neural networks provide an effective

basis upon which implementation of intelligent context

can be achieved?

† Given that IF-THEN statements exist in all high-level

programming languages, can the use of such approaches

including database solutions using relational database

systems (a capability enabled in Jena) provide an effec-

tive solution?

† Will evolutionary systems (ES) provide a better approach

than rule-based systems or traditional high-level pro-

gramming approaches?

† Will the optimum solution be a hybrid approach [43] that

exploits the global perspective of ES with the conver-

gence of traditional problem-specific search techniques?

A principal aim of the research is the development of a

generic system incorporating interoperability, the system

being capable of implementing intelligent context in diverse

domains, systems and technologies. Addressing the research

questions and resolving the issues identified with the develop-

ment of an implementation strategy to implement the Smart-

Context concept discussed in this paper to realise the design

goals forms the basis of future work.

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9. APPENDIX

Figure A1 presents a screen shot showing sample sections of

the (4 page) questionnaire used in the evaluation process.

The Questionnaire initially identified the respondent as

either a student or tutor, the data being analysed using the

data derived from the questionnaire for the overall sample

with comparisons drawn for student and tutor groups of

respondents.

The evaluation compared the results obtained from the

questionnaire data with the data obtained from the semi-

structured interviews. The context factor identification

206 P. MOORE et al.


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(the interviews) is addressed in Section 4.2 with the evaluation

process (the questionnaire) and the analysis of the data being

discussed in Section 4.3.

FIGURE A1. Questionaire – example questions.



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smart-context: a context ontology for pervasive mobile computing

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