DYNAMIC CONTEXT MODELING BASED FCA

IN CONTEXT-AWARE MIDDLEWARE

Zhou Zhong, Xin Lin and Junzhong Gu

Institute of Computer Applications, East China Normal University, Shanghai, China

Keywords: Dynamic Context Modeling, Context-aware Middleware, Formal Concept Analysis.

Abstract: This paper presents a proposal that aims to process dynamic Context modeling for mobile objects in the

Generalized Adaptive Context-Aware Middleware (GaCAM). Context changes closely related to difference

sensors in mobile environment, so the common data modeling in which data structures were fixed after

models designed is inadaptable. The principal task is: dynamic Context modeling when the mobile entities

holding Context models meet each other or meet a new environment. It is important to consider the

differences between dynamic Context modeling and common data modeling. The proposal is Context

modeling by applying Formal Concept Analysis (FCA) merging. Our approach creates a merged

specialization/generalization hierarchy which captures the knowledge of Context source in mobile

environment. The hierarchy can not only describe both Context concepts and related sensors, but also can

help higher Context reasoning and activating potential Context event.

1 INTRODUCTION

With the Internet of Things developing, A new

dimension has been added to the world of

information and communication technologies

(ICTs): from anytime, anyplace connectivity for

anyone, we will now have connectivity for anything.

Accordingly, Context-aware applications are

becoming more and more popular, because they can

dynamically adapt to specific user and thing

situations to provide smarter services and reduce the

frequency of required manual inputs. In this context,

we are developing a new Context-Aware

Middleware infrastructure called Generalized

Adaptive Context-Aware Middleware (GaCAM), to

support Context-aware application developing

distributed, platform-independent and self-adaptive.

What is Context? Context is defined by Abowd et

al. (A.K.Dey, 2000): “Context is any information

that can be used to characterize the situation of an

entity. An entity is a person, place, or object that is

considered relevant to the interaction between a user

and an application, including the user and

applications themselves.” The benefits of having

Context models include being able to abstract and

represent relevant contextual information used in

Context-aware systems. As well, this approach

enables future use of model-driven approaches and

reduces implementation effort.

The one of GaCAM’s issues is self-adaptive

about Context source access, Context modeling,

Context storage, Context reasoning, and Context

query/subscription.

Among the issues, self-adaptive Context models

are essential to mobile environments. Because our

final users with mobile devices or clothing sensors,

RFID things and intelligent agents are movable,

Context models carried with them should

dynamically adapt to their situations. Another reason

for dynamic Context modeling is that global Context

model is too large to storage in local environment

and high frequency of model learning for mobile and

local users will increase event process delay.

As mentioned above, dynamic Context modeling

is helpful in mobile environment, and we will

implement it by applying a mathematical technique

of information organization, Formal Concept

Analysis (FCA). FCA algorithms are machine

learning techniques that enable the creation of a

common structure, which may reveal some

associations between elements of the two original

structures. The rest of the paper is organized as

follows: Section 2 presents the backgrounds. Section

3 introduces the FCA concept Analysis. Section 4

introduces dynamic Context modeling strategy, and

103

Zhong Z., Lin X. and Gu J..

DYNAMIC CONTEXT MODELING BASED FCA IN CONTEXT-AWARE MIDDLEWARE.

DOI: 10.5220/0003433101030110

In Proceedings of the 13th International Conference on Enterprise Information Systems (ICEIS-2011), pages 103-110

ISBN: 978-989-8425-56-0

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

the proposal is explained by an example of what can

be achieved with GaCAM. Advantages of the

method are discussed in Section 5. Finally, Section 6

gives the conclusions and identifies future lines of

work.

2 BACKGROUND

2.1 GaCAM

Generalized Adaptive Context-Aware Middleware

(GaCAM) is a rich middleware that makes

effectively Context fusion, Context modeling,

Context storage and Context reasoning in running

time, and provide upper application with

development toolkits and service interfaces. It

decouples the Context processing with high level

application developing and low level development

complicated physical sensor programming and

common virtual sensor programming, reducing the

developers’ burden.

GaCAM’s characteristics are: 1) Reflective.

Reflection refers to the function that the system can

describe internal structure themselves, can represent

the own behaviour, and can dynamically reconfigure

their operation modes according to the operation

environment changes. The advantage of Reflective

middleware relative to traditional middleware lies in

loose coupling in modules or components, namely

the reflective middleware can be easily by

expanding and reallocation.2) Adaptive. Adaptive

demands middleware can adapt according to the

changes of the environment automatically, so it

requires that some Context middleware components

such as sensors and services information should also

be Context that can be sensed and processed in the

mobile and changeable environments. In this paper,

we mainly discuss how to dynamically building

Context model for adaptive requirement. 3) Meta-

data-descript. Metadata is the data about data. By the

system describing the members and method in

Context middleware components by metadata, it can

help to realize the reflection mechanism. 4) Agent.

As a part on the system, agents are behaviour

entities that stay in certain space or follow some

objective Context and identify object situations and

solve the problems associated with the current

situation. This ﬂexibility is a great beneﬁt when new

application agents are implemented into the system.

The ﬂexibility lies primarily in the idea that the

implementation of the agent’s behaviour is hidden

for other parties. Agent need to interact with the

environment. Agent model in our middleware is like

BDI (Belief-Desire-Intention) Model, and please

refer to (Rao A., 1995) for details of BDI.

Aiming at the characteristics of Context-aware

middleware above, multi-Agent based system

architecture is founded. GaCAM agents are divided

into two categories: management Agents and

function Agents. Management Agents primarily are

responsible for managing function Agent, and

Management transactions include life cycle,

Naming, Register and Security etc. Function Agents

are to realize functions Agent program, and

according to different function they are mainly

divided into: Sensor Agent, Context evolutionary

Agent, Context reasoning Agent, Context storage

/access Agent, and Service planning Agent. Figure 1

shows GaCAM architecture with Context sources

and applications.

2.2 Context Models

In previous work, researchers have proposed many

Context model modalities such as key-value, XML,

object, UML-ER, Ontology and so on (T. Strang,

2004 ), and fused Contexts formally or informally.

MIcontext (N. Savio, 2007) considers various

kinds of contextual elements for mobile application

design. But it does not consider the associations

between contextual elements. SOUPA (H. Chen,

2004) is an ontology designed to support pervasive

applications, and it models intelligent agents and

other relevant information. SOUPA includes user

Context such as beliefs, desires, intentions, and

background information. Even though SOUPA is

one of the most comprehensive Context models, it

does not model dynamic Context changes.

CoDAMoS (D. Preuveneers, 2004) is an ontology

for creating Context-aware computing

infrastructures, and it models user preferences and

roles. However, it does not model the dynamic

interactions between these elements. The approach

in this thesis focuses on three user Contexts, namely

user preferences, roles, and social relationships as

well as the dynamic interactions between these

contextual elements. SOCAM (T. Gu, 2005)

proposes a dual-layer ontology inspired by CONON,

and the required Context knowledge is reduced to

the upper ontology, and the domain-specific onto

logy can be dynamically bound. An MDE approach

(C. TACONET, 2010) is proposed to deﬁne context-

aware application models by UML meta-models.

The advantage is that models may be applied for

different platforms and technologies especially

different context management technologies. But the

work focuses on how to use context view Meta

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Figure 1: GaCAM architecture.

models in the development phrase for global design.

All of these works give us very integrity global

Context models or global Context model designs

which refer to their Context system and domain

ontology is designed beforehand. However, how to

process the dynamic modeling when Context relates

with specific situations is not mentioned clearly. In

the next section, we will discuss our mathematics

basis of our dynamic Context modeling.

3 INTRODUCING OF FCA

Formal Concept Analysis (FCA) was introduced by

(R. Wille, 1982) and is completely developed in (B.

Ganter, 1999). FCA is the process of abstracting

conceptual descriptions from a set of objects

described by attributes (R. Wille, 1982). The FCA

has been used in works related to symbolic data

analysis and knowledge representation (R.Godin,

1995). Olivier Cur´e in (Curé Olivier, 2009) extends

existing FCA-based systems for ontology merging.

In our modeling, we also use FCA-based Merging

for dynamically Context modeling, but we consider

Context-aware characteristics so the merging

method is easier than the FCA merging method that

mainly processes texts in(Curé Olivier, 2009) , and

we find that FCA merging enables the creation of a

common lattice structure, which may reveal some

associations between elements of the two original

structures and the Context model using the lattice is

also helpful to retrieving, so it is very appropriate for

local Context modeling. We shall begin by

introducing the basic notions defined by Wille.

Definition. A formal context

is defined as a

triple of sets, (G, M, I), where G is a set of objects,

M is a set of attributes, and

I is a binary relation

between G and M (i.e.

IGM⊆×

). (g, m) ∈ I is

read “object g has attribute m”.

Definition. For

G⊆

, we define

': { |AmM=∈

:( , ) }

Agm I∀∈ ∈

and, for

M⊆

, we define

:{ | :(, ) }

GmBgmI=∈∀∈ ∈

A formal concept of a formal context

(, ,)GM I

defined as a pair

(,)

with

GB M⊆⊆,'

and

. The sets

and

are called the extent

and the intent of the formal concept

(,)AB

. On

partial order relation ≤ can be defined through the

following formula where

(, ),( ',AB A

') :

K∈

( , ) ( ', ') '( ')AB AB AA BB≤⇔⊆⇔⊇

. This relation

is a generalization/specialization hierarchy

Application

Context Evolution Agent

Filter

Convert

Fuse

Storage and Access Agent

Global Context Model

Ontology

Rules and Algorithms

Context Reasoning Agent

Knowledge Base

Meta-Reasoning

Rule-based Reasoning

Context-based Query

Language

Algorithm-based

Reasoning

Service Planning

Agent

Service Publish

Service Discovery

Service

Composition

Service

Subscription

Service Registry

Context Resource

Security

Mobile

DYNAMIC CONTEXT MODELING BASED FCA IN CONTEXT-AWARE MIDDLEWARE

105

relationship. The set of all formal concepts of

context

with the partial order ≤ is always a

complete lattice, call the concept lattice (or Galois

lattice).

A possible confusion might arise from the

double use of the word ‘context’ in FCA and in

Context model. This comes from the fact that FCA

and context model are two models for the concept of

‘context’ which arose independently. In order to

distinguish both notions, we will always refer to the

FCA context as ‘formal context’. The Contexts in

Context model are referred to just as ‘Context’.

There is no direct counter-part of formal concepts in

Context model. Context classes /Context objects are

best compared to FCA objects, and sensors / sensor

Types are compared to FCA attributes.

4 DYNAMIC CONTEXT

MODELING

4.1 Prerequisite

As mentioned above, relative agents will follow

moving objects or stay in local environments. Sensor

agents are responsible for transforming the raw data

collected from real sensors into an identifiable

Context classes/objects. Dynamic Context modeling

happens when moving objects come into the new

environment. Different from common modeling, it is

important to consider sensor information into

modeling, because Contexts and sensors are

changing in mobile environment. Moreover, like

previous works, it is necessary for global

consistency to design a global Context model.

Therefore, before introducing the modeling process,

we firstly discuss sensor agents and global Context

Model.

Sensor agent in our middleware named Virtual

Sensor. Virtual sensors abstract similar sensor type

from logic sensors and physical sensors. Each virtual

sensor consists of two elements: Self Description

Profile and Output Format template (Output). For

example, Virtual Sensor about location includes

GPS sensor, WIFI sensor, etc. Dynamic Context

modeling process not accesses the real sensors

directly, but gets information from virtual sensors. In

order to simplify the expression, virtual sensor is

referred to as ‘sensor’ in the following. Table 1

shows the example of Virtual Sensors designed in

our middleware.

Table 1: Example of Virtual Sensors.

Sensor Type Example

Location

GPS, Wireless positioning

device (WIFI),FID (from GIS

Map)

Acoustic Sound detector, Microphone

Thermal Thermometers

Computational Environment

Device detector, Network

monitor

Profile

RFID(item profile), User profile,

Society Relation, Group profile

Log

System logger, User behaviour

logger

Document Search engine, Spider

Time

Time sensor, Date sensor, Zone

sensor

Event Record

Outlook, Anti-virus monitor,

Firewall

Global Context model is also essential to tell

what can be known. The global Context models of

many Context-Aware Systems are quite similar

because of the similar domain researched such as

smart space and location based application etc. After

some work of summary, the fundamental element

classification framework in middleware categorizes

the elements in the Context models into two main

categories: Physical Context and Logic Context.

Physical Context includes kinds of Context from

physical sensors, and Logic Context includes kinds

of Context from Logic sensors. Its sub Contexts are

Social Relation Context, Activity Context,

Computational Context and Information Context.

Social Relation Context represents the people and

their information changed with little frequency.

Activity Context represents activities that people and

things involved in. Computational Context

represents software and hardware information from

virtual sensors. Figure 2 summarizes the

fundamental element classification framework. The

global Context model is described as a full ontology

with Context relations (relations not mentioned in

this paper).

Context environments and mobile objects have

their own knowledge about Contexts that are wanted

to be shared for enriching Context each other. When

dynamically modeling happens, the merging is

necessary.

Meanwhile, Context environment changes when

new certain Context or new certain sensor comes in.

Therefore, we should consider remodeling the

Context model to integrate new Context and

estimate new sensor ability.

For the above prerequisite, we define the

modeling process as follows:

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Context

Physical Context Logic Context

Information

Context

Computational

Context

Social Relation

Context

Activity

Context

Information Security

Document

Log

Network

Hardware

Software

Person

Group

Organization

Meeting

Shopping

Chatting

Assembly

Electromagnetic

Location

Visual

Movement

Optical and Radiation

Temperature

Sound

Chemical

Figure 2: Global Context model classifications.

4.2 Initial

Firstly, the space range of the local environment or

mobile objects is defined as Context set and sensor

set available, where G= Context set, M=sensor set,

formal context K= space. I can be illustrated with an

example about a local environment in Table 2. In the

example, the G includes three Context classes (A:

Computer, B: Room, C: Person) and four objects

(A1, A2, B1, B2) in which (A1, A2) are class A’s

objects and (B1, B2) are class B’s objects. The M

includes three types (α: RFID sensor, β: Location

sensor, γ: Profile sensor) and four sensors (α1, α2,

β1, β2) in which (α1, α2)’s type is α and (β1, β2)’s

type is β. Because the modeling process is not

involved Context name and sensor name, letters are

instead of full names in order to simplify the

expression.

Context class C and sensor type γ have no

instances, it means that the space knows what are C

and γ, but instances of C and γ don’t appear

currently. The situations of this kind are always

widespread, when the local environment has known

the class of certain Context of which the objects

have not appeared and the type of certain sensor of

which the sensors have not appeared. In the Table 2,

if Context class has objects, then we will not list the

class to avoid the lattice nodes too large, but it is no

problem for presenting sensor types because the

types presented can be helpful to abstraction of

Context classes.

Table 2: The example of the local environment.

α α1 α2 β β1 β2 γ

3 3

Secondly, we can get Galois connection lattice

from this table using FCA. The Galois connection

lattice is showed by Figure 3. The lattice is a

specialization/generalization hierarchy that presents

the Context model of Contexts and relative sensors

in the example, and all nodes are almost meaningful

because of using sensor as the scale of attributes.

Figure 3: The Galois connection lattice of the example.

The reason why we use lattices as the formal

presentation of the dynamic Context model is that

only aiming at the class description of global model

is not appropriate for the dynamic environment,

where individual-relations between objects and

sensors are also very important. The lattice can also

contain and present these individual relations.

4.3 Merging

The merging schematic is showed by Figure 4. To

complete the example above for explaining merging

DYNAMIC CONTEXT MODELING BASED FCA IN CONTEXT-AWARE MIDDLEWARE

107

process, we assume that a moving object with its

sensors also carry Contexts showed by Table 3. Its

space can be set as the formal context K’ (G’, M’, I’).

G’ includes three Context classes (C: Person, E:

Device) and four objects (C1, C2, E1, E2) in which

(C1, C2) are class C’s objects and (E1, E2) are class

E’s objects. M’ includes three types (α: RFID sensor,

β: Location sensor, γ: Profile sensor) and four

sensors (γ1, β3, β4) in which (β3, β4)’s type is β and

γ1’s type is γ. When the moving object meets local

environment, contexts and sensor agents will be

computed by management agent in the local

environment. Contexts and sensors may be

complementary, so extra Context information can be

got because of adaptive agent characteristic of

sensors. In the example, (A1, A2) get the γ1 attribute

and (E1, E2) get the α2 attribute. Table 4 is the

whole table for Context modeling, and then we can

get a merging model K’’ dynamically from FCA, the

result Lattice is showed by Figure 5.

5 ADVANTAGES OF THIS

METHOD

In addition to solve dynamic Context modeling in

mobile environment, the method has the following

advantages:

Figure 4: The merging schematic.

Table 3: Contexts of another mobile object.

β β3 β4 γ γ1 α

3 3

3 3 3

3 3

Table 4: Whole model after modeling.

α α1 α2 β β1 β2 β3 β4 γ γ1

3 3

3 5

3 3

3 3 3

5 3

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Figure 5: The result lattice of the whole model after merging.

5.1 Decouple between Class

and Attribute

Context classes and Context classes’ attributes are

decoupling. The method is different from normal

FCA modeling in which FCA’s attributes are simple

attribute names. Dynamically, the Context objects of

the same class are discrepantly described from

discrepant ability sensors of the same class, and also

could be different because of information security

and environment interference factor. So when

Context objects move, it is well designed that

Context classes and Context classes’ attributes are

decouple. Context objects/classes should have

composite description from output template of

different sensors/sensor types’ dynamically. Using

sensors/sensor types as the scale of attributes in

modeling is also avoiding such meaningless

abstraction that the results still need optimizing after

merging. For example, the two classes, book and

person, which both have an attribute named “name”,

are generalized into an abstract class. Obviously, this

case is meaningless.

From the lattice, we can find it easy to check

replaceable sensors of Contexts and easy to change

more powerful sensor when some sensor does not

work.

5.2 Assistance in Reasoning

The lattice can be helpful to get higher level relation

and activate semantic event. While Context class

relations are predefined, individual relations are

dynamically ascertained by sensors. Like human

beings’ five senses, the Context-aware middleware

can use sensors to ascertain relations. For example,

using location sensors to ascertain the distance of

two objects is similar to using eyes of human being.

Therefore, relation reasoning is dependent on

sensors to some extent. In our modeling, sensors’

types and objects are attributes of FCA. Contexts

which have the same sensor type may have relations

dependent on the sensor type. Comprehensive

sensing by human beings’ five senses with

consciousness can tell human beings higher level

relation or semantic event, such as having a meeting

or listening to the lecture. Like profile sensors,

certain sensors can realize similar functions of

consciousness about society and culture knowledge.

For example, we can know an object with its

location is a person by combination of location

sensor and profile sensor. To some extent, the more

different sensor attributes an object has in FCA, the

concreter it is. Meanwhile, it may have higher level

relations and involved events. So to one FCA

context which has determinate sensor types, the

objects of it may have relations with each other. We

can ascertain current relations by retrieving the

lattice and getting the sensors output.

For example, the node 3 from Figure 5 has only

one attribute, β: Location sensor type. We assume

that it can ascertain two relation {spatial part of,

near} derived from β: Location sensor type. Its

objects {B1: Room1, B2: Room2, C1: Person1, C2:

Person2, E1:Device1, E2:Device2} may have these

relations to be ascertained. We not only want to

know spatial relations, but also higher relations than

spatial relations. Similarly, we assume that it can

DYNAMIC CONTEXT MODELING BASED FCA IN CONTEXT-AWARE MIDDLEWARE

109

ascertain the higher level event that Person using

computer in derived from {β: Location sensor}

combined respectively with {α: RFID sensor, γ:

Profile sensor}. The node 7 has the attributes {α:

RFID sensor, β: Location sensor} and the node 8 has

the attributes {β: Location sensor, γ: Profile sensor,

γ1}, so the objects of the two nodes are helpful for

reasoning. Further, reasoning agent can traverse

perceptual nodes from top to bottom and check

whether these objects have relative sensors sensing

and get the values of them to ascertain current

interrelations of objects.

MES (H. Chaker, 2010) also tries to assist a user

in his business tasks requiring information. MES

takes contextual factors (user, environment, business

tasks) into account. Compared with MES, our

solution takes sensor factor extra and every mobile

or sensing body can be an abstract user. Also, we

prefer discovering to tasking when coping with

context, and tasks are carried by BDI-style Agent.

MES’s reasoning is based on task process. Our

reasoning is based on sensing ability. Therefore, our

reasoning is appropriate to recommendation.

6 CONCLUSIONS AND FUTURE

WORK

Our study in this paper shows that our dynamic

Context modeling in the Context-aware middleware

is feasible and necessary for providing Context-

aware applications. We’ve implemented a Context

modeling agent as an infrastructure to support

Context-aware applications, decoupling and

dynamically modeling between sensors and

Contexts. The work of this paper is part of our

ongoing research project – Generalized Adaptive

Context-Aware Middleware (GaCAM). Because

FCA is not appropriate for large data, our next step

is finding the threshold value of Context quantity to

define the scale of local storage space. Meanwhile, it

is helpful to explore novel approaches to both

improve the assist ability in reasoning and reduce

the time cost.

ACKNOWLEDGEMENTS

This work is partly supported by Science and

Technology Commission of Shanghai Municipality

(STCSM) project (No. 09510703000 and No.

08511500303).

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