spontaneous task composition in urban computing environments based on social, spatial, and temporal...

Engineering Applications of Artificial Intelligence 24 (2011) 1446–1460

Contents lists available at ScienceDirect

Engineering Applications of Artificial Intelligence

0952-19

doi:10.1

n Corr

E-m

iko@kai

journal homepage: www.elsevier.com/locate/engappai

Spontaneous task composition in urban computing environments based onsocial, spatial, and temporal aspects

Angel Jimenez-Molina n, In-Young Ko

Department of Computer Science, KAIST, Korea Advanced Institute of Science and Technology, 335 Gwahangno (373-1 Guseong-dong), Yuseong-gu, Daejeon 305-701,

Republic of Korea

a r t i c l e i n f o

Available online 1 July 2011

Keywords:

Task-oriented computing

Spontaneous task composition

Semantic interoperability

Urban computing

Ubiquitous computing

76/$ - see front matter & 2011 Elsevier Ltd. A

016/j.engappai.2011.05.006

esponding author.

ail addresses: [email protected] (A. Jimen

st.ac.kr (I.-Y. Ko).

a b s t r a c t

Ubiquitous and urban computing share the goal of enabling users to access networked services and

resources anytime, anywhere. The intermesh of planned and situational activities is a distinguishable

characteristic of urban computing environments. This produces a diversity of service requirements that

need to be tackled by opportunistically suggesting appropriate services to users or social groups,

without having a previous definition of applications in templates or any other descriptions in advance.

This paper leverages the approach of task-oriented computing to represent user goals in tasks. A task is

composed of unit-tasks: user centric configurations of abstract service coordinations. The focus of this

paper is on the provision of a mechanism to cover the spontaneous unit-task composition cycle, based

on social, spatial, and temporal aspects. This is realized by identifying the essential semantic elements

that describe unit-tasks, UrbComp environments, and social groups. We have extended a unit-task

selection mechanism from our previous work. In addition, this paper contributes a set of composability

metrics based on social, spatial, and temporal aspects. These metrics concern the measurement of

semantic interoperability and potential conflicts between unit-tasks or unit-task composites. These

metrics are used to join unit-tasks together in sequences. Experimental results for a real dataset of tasks

were obtained. These results show a suitable time-overhead for the unit-task selection mechanism. In

addition, a simulation of arrivals at a crowded space was utilized to measure the performance,

throughput, and efficacy ratio of the composition mechanism.

& 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Urban computing (UrbComp) is an extension of ubiquitouscomputing (Ubicomp). Similar to Ubicomp, the main goal ofUrbComp is to enable users to access services embedded in theinfrastructure or in the Web anytime, anywhere (Satyanarayanan,2001; Weiser, 2003). However, UrbComp environments are den-ser in terms of the number of users, larger regarding physicalsettings, and more diverse in relation to the type of users andsocial groups (Paulos and Goodman, 2004; Shklovski and Chang(2006)). In such an environment, situational and planned users’activities intermesh with each other (Tamminen et al., 2004).These situational activities consist of opportunities and draw-backs that alter the normal proceeding of users’ activities. Forinstance, the normal proceeding of a commuting task can beinterrupted if a person misses the bus. This situation may triggerthe opportunity of realizing sidestepping activities constrained to

ll rights reserved.

ez-Molina),

the time-window of the next bus. Among multiple other potentialactivities supported by the space, the user may: spontaneouslysee an advertisement for some product of her/his interest locatedin a nearby shopping area; receive a recommendation to meet afriend who is in the area; or receive a recommendation to play avideo game, watch a TV stream or use a social network applica-tion on her/his smartphone. That is, diverse service requirementsneed to be tackled by opportunistically suggesting appropriateservices to users to enable them to select or create their own lightapplications (Balasubramaniam et al., 2008).

Nowadays, this vision is being made possible by an increasingnumber of enablers for quick rollout of situated software. Forinstance, end-users toolkits and languages like Marmite, Mash-Maker, and Anthracite; infrastructure to support reusability likeYahoo Pipes and the Google Mashup Editor; and establishedstandards for Web based and service oriented environments—

XML, SOAP, REST, RSS, etc.However, the diversity of service requirements in UrbComp

environments hinders the creation of these new composites fromapplications fully established in predefined descriptions (Paulosand Goodman, 2004). New, situated software needs to be createdbased on particular contexts to support ad hoc interactions of

www.elsevier.com/locate/engappai

dx.doi.org/10.1016/j.engappai.2011.05.006

mailto:[email protected]

mailto:[email protected]

dx.doi.org/10.1016/j.engappai.2011.05.006

A. Jimenez-Molina, I.-Y. Ko / Engineering Applications of Artificial Intelligence 24 (2011) 1446–1460 1447

users in short-lived and transient ways. This requires appropriatecomposite services spontaneously configured and recommendedduring runtime. Nevertheless, providing users with reliable,meaningful, and understandable service functionality duringruntime to create their desired applications is a challenging issue.

The sight of situated software in UrbComp environments lacksa previous step that would lie in first preparing service function-ality to reflect situational social and spatial context in a sponta-neous fashion. In addition, such recommended functionalityneeds to hide low-level system concerns from users. Therefore,spontaneous composite service coordination and recommenda-tion to end-users requires user-centricity and context awareness.That is, (1) abstracting ubiquitous services to be prepared anddelivered from the perspective of users’ goals rather than accord-ing to system elements, and (2) enriching these abstract servicedescriptions with the contextual information of ad hoc situationsoccurring in UrbComp environments, as well as the spacesemantics.

The approach of task-oriented computing (Wang and Garlan,2000) is a way to realize these requirements. In our previous work(Huerta-Canepa et al., 2008; Jimenez-Molina et al., 2009, 2010)we defined unit-tasks as the essential elements which, composedtogether, define a larger task. Each unit-task is a basic configura-tion of abstract services. In addition, unit-tasks are arranged by asubsuming of relationships in a hierarchical ontology. As generalexamples of unit-tasks we consider varieties of functionality likealarming specific locations, controlling appliances, controlling envir-

onmental conditions, guiding and monitoring user activities, recom-

mending social encounters, sharing audio/video contents, etc.Considerable research has been devoted to demonstrating the

influence of space and social behaviors in the deployment ofubiquitous services in urban environments. Particularly interest-ing is the work of Kostakos et al. (2009), which defines threedimensions of the urban ubiquitous infrastructure – the social

structure of users’ routines, the spatial structure of the infrastruc-ture, and the temporal rhythms of users’ behaviors. In addition,nowadays it is feasible to conduct spontaneous social groupingson the large scale allowed by public places (Quentin and Grandhi,2005; Gupta et al., 2009; Bojic et al., 2010). Also, recently we havewitnessed the rise of technologies to recognize activities byubiquitous intelligence, which aggregates increasingly rich sen-sing information extracted from smartphones (Ganti et al., 2010)and sensors embedded in the infrastructure, as well as from spacesemantics and personal schedules. These current technologiesmake it easier to measure the factors and properties derived from

Fig. 1. Generation of un

the above dimensions. This new state of technology provides arationale for the definition of the three objects involved in thespontaneous generation of composite services: (1) social groups,(2) UrbComp environments, and (3) unit-tasks. The characteristicsand interrelations of these objects are described by three essentialaspects: social, spatial, and temporal. These aspects are a way ofembedding the semantics of these objects.

Current technologies of placeness based context managers(Lee, 2004) allow us to realize the detection of spontaneous,disruptive situations that affect the normal proceeding of tasks.Also, the identification of the context information attached to theplace – called the placeness context – , and the identification ofpotential social groups that may be formed with the users thathave co-presence in the UrbComp environment (Paulos andGoodman, 2004) are possible (see Fig. 1).

The focus of this paper (see the dotted box in Fig. 1) is theprovision of a unit-task selection mechanism and a unit-taskcomposition mechanism. The former is realized by identifying theessential semantic elements that describe unit-tasks, UrbCompenvironments, and social groups, as well as their interrelations. Inorder to meet this aim, we have developed a semantic descriptionmodel for each object based on the social, spatial, and temporalaspects. We have also extended a unit-task selection mechanismfrom our previous work. This mechanism systematically arrangesthe semantic elements of each object model to select appropriateunit-tasks. On the other hand, the composition mechanism isrealized by a set of composability metrics based on the social,spatial, and temporal aspects. These metrics concern the mea-surement of the semantic interoperability and potential conflictsbetween unit-tasks or unit-task composites. This mechanismjoins unit-tasks together in sequences, which are represented inBPeL (The Business Process Execution Language, 2001), in accor-dance with their semantic information.

Experimental results for a real dataset of tasks (ATUS, TheAmerican Time-use Study, 2007) were obtained. These resultsshow a suitable performance for the unit-task selection mechan-ism. In addition, a simulation of arrivals at a crowded space wasutilized to measure the performance, throughput, and efficacyratio of the composition mechanism.

The roadmap of this paper is as follows. Section 2 describesrelated work. Section 3 describes three aspects of identifyingcomposability in UrbComp environments, coming out with a setof essential semantic elements. Section 4 describes the composa-bility metrics. Section 5 describes the spontaneous task composi-tion cycle, explaining the unit-task selection mechanism and the

it-task composites.

A. Jimenez-Molina, I.-Y. Ko / Engineering Applications of Artificial Intelligence 24 (2011) 1446–14601448

way the composability metrics are applied. Section 6 describesthe mechanism implementation and the experimental results.Section 7 concludes the paper.

2. Related work

Three major categories of related work are meaningful for thispaper: (1) analysis of frameworks that make use of task-orientedcomputing to support users with available services in ubiquitouscomputing environments, (2) approaches from the serviceoriented computing area focused on the composition of servicesbased on semantic modeling of services, and (3) approaches thatconsider the semantics attached to places as a way to supportmultiple users in urban environments. To the best of our knowl-edge, a spontaneous service composition leveraging specific factsof UrbComp environments has not yet been developed, and this isthe major focus of our paper.

2.1. Task oriented frameworks

Research on task-oriented computing has tended to focus onfine-grained tasks that do not properly realize user-centricity, oneof the requirements of Ubicomp and UrbComp. That is the case ofthe ABC framework, which provides a task-centered collection ofapplications, services, and data to dynamically adapt tasks to theavailable resources on heterogeneous computing devices(Bardram, 2007); however, tasks are defined in a fine-grainedway. In addition, there has been research focusing on staticallybinding user’s information against predefined applications. Anexample is the Gaia project, which created a user-centric, context-aware, location-aware and event-driven framework. Gaia focuseson interaction between users and active spaces. Users shouldmake their data available even when they move across differentactive spaces. Therefore, a new application is selected by match-ing user’s data against a predefined application ontology (mobilepolymorphic applications). Additionally, when users switch toanother active space, a semantically similar application is selected(Roman et al., 2002; Ranganathan et al., 2004). Additionally, therehas been research focusing on providing solutions that directlyselect and then integrate services that are equivalent to the user’stask. An example in the ambient systems domain, which sharescommon characteristics with Ubicomp, is the IST AMIGO project.This solution consists of a ‘‘QoS-aware dynamic composition ofusers’ tasks from networked services’’ in the environment(Mokhtar et al., 2005). It uses predefined applications, describedas workflows, which represent users’ goals and could even becarried by the user in a mobile device. However, workflows aredirectly matched against services, without any mediating processor abstract layers, which produces a lack of flexibility andreusability of composition patterns. A similar example is thetask-driven Aura project, which realized user-centricity by repre-senting users’ goals in coarse-grained tasks. These tasks aredynamically mapped to virtual services. However, there is still alack intermediate layers that would allow improvements offlexibility and reusability (Garlan et al., 2002).

2.2. Semantic services

Literature on dynamic service composition has tended to focuson leveraging available service templates (Mennie and Pagurek,2000; Garlan et al., 2002; Sheng et al., 2002; Sirin et al., 2002;Minami et al., 2003). The general idea of these approaches is to filla template by dynamically selecting services that are equivalentto the components of the template defined on an abstract level.

The major limitation of these approaches is the need to havetemplates in advance that embed service requirements.

Another category of work is based on combining input, output,preconditions, and effects (IOPE) with planning techniques(Ponnekanti and Fox, 2002; Fujii and Suda, 2004), in order togenerate an execution path of composites. However, thisapproach requires the explicit definition of a composition goal,which in a majority of cases needs to be defined by users.

In general, a huge challenge to the realization of automaticservice composition is the necessity of bridging the gap betweenuser-centric semantics and machine interpreted data. SemanticWeb technologies are a way to overcome this challenge; thesetechnologies are a major effort as part of the OWL-S, which willenable automated service discovery, interoperation, composition,execution, and monitoring (McGuinness and van Harmelen,2004). OWL-S consists of a service ontology that provides aservice description model by means of three perspectives: (1) aservice profile that models the required inputs and generatedoutputs, (2) a service model that describes the behavior of theservice operations, and (3) a service grounding that states the dataand ways to actually invoke a service. Another work along thesame lines is the Web Service Modeling Ontology (WSMO), whichprovides a conceptual model to describe services in a language-independent fashion. On top of the WSMO is the Web ServiceModeling Language (WSML) (Brujin et al., 2008), which is aconcrete way of realizing WSMO ontologies. WSML allows therepresentation of descriptions using either XML or RDF. The latteris specifically used to represent automated exchanges of descrip-tions. WSML is composed of three languages that describe theessential semantic elements of a service. The first language is a setof ontologies of terms and knowledge to be used in the servicedescription. The second is concerned with the functional descrip-tion of services, while the third language is concerned withdescribing the behavioral perspective of services. That is, thebehavior of a service is understood as the interactions amongservice interfaces.

2.3. Place based semantic approaches

P3 (Quentin and Grandhi, 2005) is a location-aware informa-tion system that links social networks with geographical places(people-to-people-to-geographical-places). The aim of this sys-tem is to strengthen the relationships among places and socialgroups. That is, it enables users to leverage location informationinto engagement in new social groups, as well as reinforcingexisting social ties within certain communities.

MobiSoc (Gupta et al., 2009) is a middleware used to developMobile Social Computing Applications (MSCAs). Its goal is toenhance the conformation of new social opportunities in physicalenvironments. The process leverages information about people,social relations, and physical spaces. MSCAs developed fromMobiSoc maintain the social state of physical communities inrelation to a place. These applications also try to augment thesocial state of communities by applying geo-social patternsextracted from users’ historical location information.

Gupta et al. (2007) developed a clustering algorithm thatidentifies semi-permanent social groups of people and the placeswhere they commonly meet. For instance, this algorithm identi-fies places where students spend time. This information may beused to recommend new users, allowing them to enhance theirgeo-social experiences. This clustering algorithm uses co-pre-sence historical information.

A major drawback of MobiSoc, P3, and the work of Gupta et al.(2007), is that although these systems are able to identify typicalactivities performed in a place, they do not enhance spontaneous


interaction of users, and do not suggest a selection of appropriatetasks.

Kang et al. (2005) propose an algorithm to identify meaningfulplaces to users by analyzing a trace of coordinates. The contribu-tion of this work is to extend the traditional concepts ofcoordinates or landmarks attached to a place to the meaningsthat individual users associate with those locations. For instance,this algorithm attempts to extract user-level notions of a placelike ‘‘my place of work’’, ‘‘the place we live’’, or ‘‘my favorite lunchspot’’. Although such a concept enriches our idea of placenesswith meanings attached to a place, it does not use this informa-tion to identify the most appropriate user-centric tasks, and doesnot allow users to dynamically bridge the gap with availableservices.

3. Three aspects of identifying composability in urbancomputing environments

3.1. Semantic elements rationale

The rationale that guides the identification of the essentialsemantic elements required to describe unit-tasks is taken fromtwo sources: first, from a real dataset of users’ activities in urbanenvironments known as Time-use Studies (ATUS, The AmericanTime-use Study, 2007); and second, from the essential variablesthat affect and govern spontaneous social interaction amongusers. Those variables have been taken from existing empiricalethnographic studies about users’ behavior in urban environ-ments (Granovetter, 1973; Oldenburg, 1999; Knox and Ozolins,2000; McCullough, 2001; Banerjee, 2001; Carmona et al., 2003;Tamminen et al., 2004; O’Neill et al., 2006; Paay et al., 2009;Kostakos et al., 2009).

Time-use Studies are publically available datasets with recordsof humans’ daily activities in urban and private environments,reported by hundreds of thousands of participants over decades.Partridge and Golle (2008) discovered that there are two essentialcombinations of variables that are effective for use in recognizinguser activities. The first combination is composed of the character-

istics of the urban environment itself, in conjunction with the phase

of the day when the activity is carried out. Another combination iscomposed of the age range of the user or social group performingthe activity, in addition to the day of the week. Filtering by thesetwo pairs of variables allowed recognition of activities with up to80% accuracy on the basis of the ATUS records.

Regarding the social interactions of users, of particular interestare some empirical studies carried out either by analyzing users’feedback data, logs about co-presence history, or by engaging infield observations. It has been determined that social ties amongusers in an urban environment are in a great majority of casespreferably governed by three types of social contexts: familiarity

type, profile similarity, and favorability type among users.

3.2. Semantic elements of UrbComp environments

�
Spatial aspect of UrbComp environments—two major factorsare used to describe the spatial aspect of an UrbCompenvironment object: spatial availability and environmental con-
straints. The former describes the UrbComp in terms of its place

type and absolute location. Place type is an ontology arranged bysubsumption relationships. One property of this ontology isthe place potential. This property is concerned with the mostlikely user behaviors or tasks determined by urban designerswhile manipulating environment settings (Carmona et al.,2003; Paay et al., 2009). Values used in this paper to definethe domain of the place potential property are borrowed from

McCullough (2001). On the other hand, the place type ontologyis also described by the place category. This property isconcerned with the symbolic representation of the place basedon commonly used terms – bar, restaurant, museum, park,plaza, cafe, and subway – among several other categories.Finally, the place type ontology is also described by the geo-

containment type property. This property refers to the physicalgranularity of space in urban settings. It is divided into outdoor

type and indoor type. The latter in its turn is made up ofsubcategories arranged by partOf relationships, like building,floor, corridor, stairs, room, etc. The absolute location propertyis represented by GPS longitude and latitude. On the other hand,the environmental constraints factor is represented by ranges –lower and higher – of temperature, humidity and brightness,defined for each place (see Fig. 2).
� Social aspect of UrbComp environments—the social aspect
of the Urbcomp object is described by its level of publicness.This refers to the publicness spectrum of the public realm(Banerjee, 2001). A general categorization of this publicnessspectrum may consider places ranging from absolutely public

external spaces; to internal public spaces; to quasi-public places(also known as nominally public places), where someone retainsrights of access control, like University campuses; to private

places (Carmona et al., 2003; see Fig. 2).
� Temporal aspect of UrbComp environments—the temporal
aspect of an UrbComp environment is described by the temporal

pattern of use factor. This factor comprises the dynamic seman-tic information that reflects the multiple potentials that a placemay have at different times (Kostakos et al., 2009). It refers tothe dynamicity of UrbComp environments, in the sense thatthey have their own daily, weekly, and even seasonal rhythms.The property that characterizes the temporal pattern of use

factor is a tuple composed of season, day of the week, phase of

the day, and place potential (Carmona et al., 2003; see Fig. 2).

3.3. Semantic elements of social groups

�
Social aspect of social groups—a first factor for this aspect isthe set of social relationship types – familiarity type, favor-
ability type, profile similarity – among users that may partici-pate in a potential social group. Second, this aspect isdescribed by a characterization of the social group type. Asfor the familiarity type, it is first described by the level of

familiarity property. This denotes the strength of the social tiebetween two users. Two users may be familiar if they haveprevious social ties and know each other very well. In contrast,two users may be perfect strangers, or people who have neverhad any contact with each other in the past. Moreover, acertain degree of knowledge among users in a specific Urb-Comp environment, like a bus stop or the corridors of a school,converts them into familiar strangers (Paulos and Goodman,2004). As for profile similarity, this is concerned with thedegree of similitude between every property of users’ profiles,and is made up of, among other properties: (1) preferences,which we describe as an independent ontology, (2) age range,(3) belonging to a place, insider or outsider to a place, since notall places are open to everyone, (4) gender; (5) interest

similarity, computed through the semantic distance amongindividual interests in a preference ontology, and (6) occupa-

tion. Favorability between two users may be obtained fromusers’ feedback information about the performed activities. Inthis sense, a social group is comprised of users of a specificfamiliarity level, with similar profiles and high favorability.On the other hand, the social group type factor is describedby two properties: spontaneity level and social encounter type.

Fig. 2. Representation of unit-tasks semantics (excerpt).


The former is concerned with characterizing the social groupas ad hoc (spontaneous) or pre-existent. The latter is concernedwith describing the type of shared interaction users potentiallywould carry out based on their personal and social character-istics, and the above factors that govern social encounters (seeFig. 2).
� Temporal aspect of social groups—this aspect is described by
the encountering frequency factor. This aspect is concernedwith the frequency that best characterizes the realization ofsome type of encounter for a determined type of social group.Values can vary from first time, rarely, and occasionally, toregularly. This frequency is not associated with an actual socialgroup. On the contrary, it is general enough to cover differentsocial groups that fit into a specific type (see Fig. 2).
� Spatial aspect of social groups—in this aspect it is necessary
to identify the usage of a place for a specific type of socialgroup. This information is embedded in the place usage factor,which is described by two properties: place usage intensity, andbelonging to the place. As a specific type of social group may usea place with a certain intensity, the property place usage

intensity takes values like first time, rarely, occasionally, andregularly. As for the factor of belonging to the place, since inpractice not all places are open to everyone, the values for thisproperty are defined as insider ownership, insider, or outsider

(Carmona et al., 2003; see Fig. 2).

3.4. Semantic elements of unit-tasks

�
Spatial aspect of unit-tasks—the spatial aspect of a unit-taskis described by two factors: environmental effects and favorable
place. The former is concerned with the physical effects that aunit-task may generate in an UrbComp environment if it is

executed. Consistent with the environmental constraints

attached to an UrbComp environment, these effects consistof variations of the place context caused by changes oftemperature, brightness, and humidity. These properties aredefined as positive or negative in accordance with the incre-ment or decrement of each effect. Another type of environ-mental effect is that of the type of delivered content. This isactually an independent ontology that is concerned withmodifications of the environmental context due to the deliveryof acoustic, visual, or other types of contents that affect thehuman senses. As for the favorable place factor, it is concernedwith the most appropriate UrbComp environment – or multi-ple environments – for a specific unit-task (see Fig. 2).
� Social aspect of unit-tasks—this aspect is described by two
factors: the social effect and the favorable type of social

encounter. The former comprises the output publicness level

effect, the social formality effect, the input publicness authoriza-

tion level, and the output publicness authorization level. Therationale is that it is necessary to attach a publicness level to theinput information of a unit-task. That is, users may find certaintypes of private information sensitive. Therefore, when suchprivate information is manipulated, there are authorizationlevels to respect. On the other hand, it is also appropriate tohighlight the social formality effects that outputs may producein the environment. In certain cases, the delivery of contentmay violate the social formality attached to a place. In regardsto the factor favorable type of social encounter, a unit-task maybe more appropriate to support either a physical or a socialencounter (see Fig. 2).
� Temporal aspect of unit-tasks—in regards to this aspect,
a unit-task object is described by two factors: favorable time

of execution and temporal availability. The former indicatesthe times when it is more convenient to execute a unit-task.

Fig. 3. Structural types of a unit-task composition.


This factor is composed of the properties that indicate theseason, day of the week, and phase of the day. As explained inthe beginning of this section, the rationale for including thesetemporal properties was obtained from Partridge and Golle(2008), an analysis of the Time-Use Studies dataset (ATUS, TheAmerican Time-use Study, 2007). The temporal availability

factor is composed of the following properties: (1) availability

of delivered content, (2) execution time of delivered content,(3) input processing frequency, and (4) output availability fre-

quency. The first property indicates the duration of contentdelivery by a specific unit-task. The second property measuresthe time required from the point at which a unit-task is invokeduntil it starts delivering content (Ardagna and Pernici, 2007). Inturn, the input processing frequency refers to the number ofprocessed inputs during an execution of a specific unit-task,while the output availability frequency is the number of generatedoutputs in the same time-frame (see Fig. 2).

Another set of properties that does not fit into the categoriessupposed by the three aspects are the inputs and outputs of a unit-task, as well as its preconditions and effects, and some primitiveproperties like unit-task name and unit-task URI. Inputs and outputs

are described in an independent ontology. Nevertheless, theseproperties are directly linked to unit-tasks, and also to someproperties of the spatial, social, and temporal aspects of unit-tasks. In addition, a unit-task is also described by a propertyknown as its structural type (Jimenez-Molina and Ko, 2010; seeFig. 3). This property indicates if a unit-task is an encountering

method recommender, a cognitive information delivery, or an envir-

onmental effecter. The first type is concerned with the requiredfunctionality for recommending a social encountering methodamong users of a social group, and the general purpose of carryingout that interaction. The second type is concerned with deliveringto users useful cognitive information associated with the purposeof the encounter. The third type considers the functionality thatgenerates physical effects in the UrbComp environment, like audiocontent that produces noise, an adjustment of brightness ortemperature, or movement of objects in the space – opening/closing of windows or doors, etc. Fig. 4 shows multiple examplesof unit-task hierarchically arranged by subsumption relationship.

4. Composability metrics

The aim of having composability metrics is to measure thesuitability of two unit-tasks – or two unit-tasks composites – tooperate conjunctly on the basis of the three aspects. Compositesconsist of a coordination of composable unit-tasks.

4.1. Interoperability based on social effects

Let CA and CB be two unit-task composites, with (IA,OA), and(IB,OB) denoting their inputs and outputs (IO), respectively. In the

case of outputs of CB semantically similar to inputs or outputs ofCA, the social effect of CB may violate the publicness authorization

level associated with the IO of CA. In addition, the publicness level isspecific for each IO in each unit-task or composite. Therefore, inorder to check the interoperability of CA and CB in terms of socialeffects, it is necessary to identify and analyze the publicness

associated with the IO of the composites involved in the composi-tion. The rationale is that the new composite generated byinteroperating CA and CB may not fulfill the publicness authoriza-

tion level given to the information exposed to the UrbCompenvironment.

Let IA ¼ iA1,. . .,iAn

� �, OA ¼ oA1

,. . .,oAm

� �, and OB ¼ oB1

,. . .,oBp

� �.

In addition, let sm IA,OBð Þ ¼ InA,OnB

� �be the computation of the

semantic similarity among the inputs of CA and the outputs ofCB. This similarity is calculated as an ontological semanticdistance (Jimenez-Molina et al., 2009). In this case, it is calculatedamong the nodes of the IO ontology.

The semantically similar inputs of CA and outputs of CB are

represented by IA+ InA ¼ fin

A1,. . .,inAn1

g, andOB+OnB ¼ fo

nB1

,. . .,onBn1g,

ðn1rnÞ4ðn1rpÞ, with inAjAon

Bjð8j¼ 1,. . .,n1Þ denoting the similar-

ity. In an analogous way, it is possible to computesm OA,OBð Þ ¼

Onn

A ,OnnB

� �, such thatOA+Onn

A ¼ fonn

A1,. . .,onn

An2g; OB+Onn

B ¼

fonnB1

,. . .,onnBn2g, ðn2rmÞ4ðn2rpÞ, and onn

AjAonn

Bjð8j¼ 1,. . .,n2Þ.

A unit-task’s IO with a private publicness authorization level canbe exposed in the UrbComp environment only by outputs of aninteroperable unit-task with a private publicness level effect.Otherwise, the social effects would violate the IO authorization

level of the first composite. Another example is that a quasi-public

publicness authorization level cannot be exposed in a public way,and so on.

On the other hand, CA and CB need to have the same values fortheir social formality effect property because a place may supportdifferent types of social formalities for different configurations ofplaceness context. In this way, there will be different sets ofinteroperable composites – in terms of their social effects –consistent with each type of social formality. Therefore, the taskcandidates that may emerge from the joining of these compositeswill be coherent with each of the social formalities supported bythe UrbComp environment.

4.2. Interoperability based on temporal availability

This interoperability measurement consists of checkingthe consistency among the temporal availabilities between theoutputs of CA and the inputs of CB. That consistency is obtainedfor each pair ðOn

A,IBÞ. That is, outputs of CA that are semanti-cally similar to all the inputs of CB – and that will eventuallybe interoperable in terms of the social effect factor. Thisassumes that all the inputs of CB are mandatory for its execution.However, the analysis may be extended to the case of optionalinputs as well. Therefore, this assessing of interoperability needs

Fig. 4. Unit-task hierarchy (excerpt).


to be carried out by analyzing the output availability frequency andthe input processing frequency of CA and CB, respectively. There areconcrete cases of interoperability between different values ofavailability and processing frequency of CA outputs and CB inputs,respectively. In fact, an input with a continuous processingfrequency may process outputs that are generated either con-tinuously, in a single way, or in an iterative way. However, aninput with a single processing frequency cannot process outputsgenerated in a continuous or iterative way. In addition, an inputwith an iterative processing frequency is not able to processoutputs generated in a continuous or single way. However, suchan input may interoperate with an output that is generated inan iterative way as well, but it will be constrained to the actualvalues of the frequencies. That is, the number of generatedoutputs during one execution of the first composite needs to beequal to the number of processed inputs during one execution ofthe second composite.

This interoperability measurement based on the temporalavailability factor may be extended to cases of asynchronousinput processing. In such a case, generated outputs, for instance,may be stored in a buffer.

4.3. Interoperability based on environmental effects

The three aspects, and in particular the environmental effect

factor, allow the selecting of unit-tasks that individually do notviolate the environmental constraints of the place. However, theaggregated environmental effects of the composed unit-tasks mayconjunctly violate the constraints anyway, for instance, con-straints regarding temperature based on an energy saving policy.This is the case for additive effects like heat, brightness, andhumidity, for which the interoperability analysis between the twocomposites CA and CB needs to make use of physical magnitudes tomeasure each of those effects – calories, candelas, and percentageof humidity, respectively.

This interoperability measurement may be extended for cases ofco-existence of tasks in the same place – currently executing tasksor newly composed ones that would co-exist. Thus, the analysisshould be conducted considering the aggregated environmentaleffects of co-existent tasks. In addition, the description of unit-tasksmay be enhanced by considering the availability frequency ofenvironmental effects. This would allow the creation of compositesin which non-interoperable composites with the same type of


effects would interleave, respecting therefore the constraints. Insuch a case, an analysis similar to that conducted for interoperabilitybased on temporal availability may be integrated with this inter-operability measurement based on environmental effects.

4.4. Conflict analysis based on delivered content and environmental

effects

This analysis of possible conflicts is conducted for environ-mental effects and the type of delivered content. The former isconcerned with unit-tasks or composites that have a structural

type that matches the environmental effecter. Clearly, in theprevious analysis – interoperability based on environmentaleffects – the composition ensures that the constraints attachedto the place are not violated. Nevertheless, this could be due tounit-tasks that have effects that are opposite. For instance, on theone hand, CA may affect the environment by making a roomwarmer. On the other hand, CB may make it cooler. If theaggregate effect respects the constraint related to temperature,such interoperation would be valid in accordance with theprevious metric. However, these conflicting situations need tobe avoided and reduced from the composite candidates. There-fore, the analysis of conflict based on environmental effects isconcerned with checking that the effect types are not actuallyopposite. For that reason, the effect properties were defined aspositive or negative (see Section 3).

On the other hand, conflicts between delivered contents areconcerned with unit-tasks or composites in which the structural

type matches the cognitive information deliverer. The co-existenceof delivered contents, in general, compromises users’ level ofattention. It does not allow users to focus properly on thedelivered content. However, these potential conflicts are not strictfor all users or situations. For instance, there are users who mightbe willing to receive two TV streamings at the same time.Moreover, they might like to partition the screen in differentproportions in accordance with their level of interest in thecontents. In addition, some users might be willing to receivevisual content like a computer game while receiving acousticcontent like background music. The implementation of thismetric requires interaction with the user in order to obtain his/her preferences.

As stated in Section 3, content may also be described byproperties in the temporal aspect. The following properties fordelivered content are included in this aspect: (1) availability

of delivered content – which refers to the duration of the contentdelivery, (2) delivered content frequency, and (3) execution time of

delivered content – which is a metric about the expected timerequired from the point at which a request is sent to the unit-taskor composite until the content is actually delivered (Ardagna andPernici, 2007). In accordance with these properties, CA and CB maynot conflict in some cases, even though they cannot co-exist. Thereason is that the two types of content, despite the impossibilityof their co-existence, may not conflict if they are deliveredasynchronously. If two types of content are delivered in acontinuous way, they will not have a chance to interleaveasynchronously. This case is extended to any situation in whichat least one continuous content delivery is involved because whencontent is delivered in a continuous way by a composite, when-ever another composite delivers content in a single or iterativeway, there will be a conflict. On the other hand, situations thatinvolve either single or iterative content deliveries need to bechecked individually. This checking is conducted by using theavailability of delivered content and the execution time of delivered

content properties. The analysis consists of checking that contentsfrom both composites are able to interleave, i.e., there are notime-windows of co-existence.

5. Unit-tasks composites generation cycle

This section describes the unit-task selection mechanism andthe composition mechanism fed by these selected unit-tasks. Thewhole cycle to generate unit-task composites is depicted in Fig. 5.

5.1. Unit-tasks selection mechanism

The input of this mechanism consists of the placeness contextassociated with the situation (factors and properties of the spatial,social, and temporal aspects, as stated in Section 3). The input alsoincludes a set of potential social groups or individual usersidentified by the place-aware context manager.

On the other hand, the output of the mechanism consists of aset of available unit-tasks that reflects the placeness context anduser or social groups’ requirements and information – like theappropriate type of social encounter, users’ familiarity and favor-ability, and users’ profiles. These selected unit-tasks are suitablecandidates for conducting the composition of a major task. Such aselection is realized by semantically matching the placenesscontext and the users’ requirements for the unit-task ontology.We introduced this semantic, priority based matchmakingmechanism in our previous work (Jimenez-Molina et al., 2010).

As stated in Section 3, this ontology is structured as ahierarchy of nodes (unit-tasks) arranged by subsumption rela-tionships. The upper part of this ontology – the generalized level –consists of coarse-grained unit-tasks. Unit-tasks that are morespecialized, in terms of their functionality, are allocated in a layercalled the specialized level. These specialized unit-tasks are finer-grained. In accordance with these levels, the unit-task selectionmechanism is further divided into two phases (the generalizedunit-task selection and the specialized unit-task selection).

5.1.1. Generalized unit-tasks candidates

This phase is concerned with identifying a subset of general-ized unit-tasks from the upper level of the unit-task ontology. It isdone by semantically matching a set of primary variables fromthe placeness context that are the best in predicting users’activities—see Section 3 for a rationale. These variables consistof: (1) the place type property of the spatial availability factor inthe spatial aspect of the UrbComp environment object, (2) thephase of the day, and the day of the week of the favorable time of

execution factor in the temporal aspect of the unit-task object, and(3) the age range of the similarity profile factor in the social aspectof the social group object. Let Q¼(q1,q2) be these primaryvariables, with q1¼( place type, phase of the day) and q2¼(day of

the week, age range). Let N be the set of generalized unit-tasks inthe ontology. The primary variables in q1 are first semanticallymatched against the set N. Resulting nodes are further matchedagainst the primary variables in q2. The semantic matchingfunction, denoted by Match(Q,N), is defined and described inJimenez-Molina et al. (2010). In particular, it is selected from asubset of totally matched unit-tasks for each age range ai. Thesesubsets are further constrained by the properties of the environ-

mental constraint factor Cu associated with the UrbComp environ-ment object. This creates a family T ¼ ta1

,ta2,. . .,tan

� �of new

subsets of unit-task candidates.The next step consists of expanding the generalized unit-tasks

by running the specialized unit-task selection mechanism.

5.1.2. Specialized unit-tasks candidates

As for the specialized unit-task selection phase, it is done byreflecting the identified user or social groups’ requirements andinformation. These requirements consist of factors such as thesocial aspect in the social group object—users’ familiarity and

Fig. 5. Overall vision of the composition cycle.


favorability types, and users’ similarity profile. In particular, adifferent version of the unit-task selection mechanism is run foreach type of social group.

The place-aware context manager generates three differenttypes of social groups in accordance with the familiarity, favor-

ability, and similarity profile factors. It starts grouping familiarusers into several subgroups Gfj. Then, it arranges subgroups Gfsj

of familiar stranger users, while remaining users are allocatedinto subgroups Gsj of strangers. Each subgroup in its turn isdefined in a way such that users who belong to it share socialfactors of similarity and favorability. Further details about usergrouping are beyond the scope of this paper.

�
Support to social groups of familiar users—social groups do notnecessarily need to conform to a specific age range. Thus, theidentification of generalized unit-task candidates is started byanalyzing the age homogeneity of each social group Gfj. In the caseof homogeneous users in terms of their age ai, the subset tai
AT ofthe generalized unit-tasks is selected for specialization. On thecontrary, in the case of heterogeneous ages, the whole set T isselected for specialization. Paulos and Goodman (2004) haveempirically observed users’ social behavior in UrbComp environ-ments. Their findings suggest that ‘‘familiar users who are awareof their co-presence in an urban environment will be most likelyto prefer an identity-awareness physical encounter’’ (Jimenez-Molina et al., 2010). Let R¼(r1,r2) be the variables of specialization,

where r1 is the inferred social encounter type variable in accordancewith the familiarity factor. In addition, let r2 be the preferences

property in the similarity profile factor in the social aspect of thesocial group object. Moreover, let M be the set of specialized nodesin the unit-task ontology. Therefore, into the subsumed nodes of

taiAT the semantic matching Matchtai

ðr1,MÞ is checked against

the social encounter type variable. The inferred value of thisvariable for familiar users corresponds to the identity-awareness

physical encounter. Only unit-tasks with exact matches areselected, and these are denoted WDM. These unit-tasks (1) recom-mend that users carry out an identity-awareness physical encounter,and (2) are able to support such a physical encounter. LetP¼ ðp1,p2,. . .,pf Þ be the values of the preferences property. The

semantic similarity sm(P,V) of this value is computed against thepreference ontology FO, which generates the nodes VDFO. Thissimilarity represents the closet semantic distance between each pi

and the preference ontology FO. Therefore, the subset W isrestricted by selecting the nodes that match with the preferences

V by applying MatchW ðr2,VÞ. This process generates the subsetSDW of specialized unit-task candidates. The rationale of match-ing against semantically similar preferences is that it allows theexpanding of the set of specialized unit-task candidates (seeTable 1).

�
Support to social groups of familiar strangers—in the case ofa social group Gfsj of familiar strangers, a similar unit-task

Table 1Unit-task selection mechanism.

� Input: subgroups; Gfj; a set T ¼ ta1,ta2

,. . .tan

� �of generalized unit-task

candidates; user preferences P¼ p1 ,p2 ,. . .,pf

� �.

� Output: a set S of specialized unit-task candidates.

0: for each subgroup Gfj do

1: VGfjað Þ; //a: age

2: if VGfjað Þoa* do

3: Get range ai of users’ age;

4: Select the subset taiAT of generalized unit-tasks;

5: Matchtaiðr1 ,MÞ; //ðr1 ARÞ, M: specialized nodes.

6: Define the set WDM of total matched nodes;

7: Determine set fV DFO=similarityðP,VgÞ; //closet distance.

8: MatchW r2 ,Vð Þ; // ðr2 ARÞ.

9: Define the set SDW of total matched nodes; end if

10: else if VGfjað ÞZa* do

11: Select the whole set Tof generalized unit-tasks;

12: MatchT r1 ,Mð Þ;

13: 6 to 9; end else14: return S ; end for

Table 2Unit-task composition mechanism.

� Input: A set S of feasible unit-tasks

� Output: A set of valid unit-tasks composites.

0: Group by place potentials: S1, S2,y,Sn, iAptð Þ;

1: for each subset Si do

2: Filter by structural type: Ai, Bi, and Ci, iAptð Þ;

3: Filter by place category: Bij, ðiAptÞ, ðjApcÞ, and Cij, ðiAptÞ, ðjApcÞ;

4: For each Bij do5: Compute interoperability based on social effects;

6: If (interoperable) do7: Compute interoperability based on temporal availability;

8: If (interoperable) do9: Compute conflict analysis based on delivered content;

10: 5 to 9; end for11: For each Cij do12: Compute conflict analysis based on environmental effects;

13: Compute conflict analysis based on environmental constraints

14: 12 to 13; end for15: return composites;


selection mechanism is applied. The only difference is that thesocial encounter type may be multiple depending on thelocation of the users. As stated in Section 3, ethnographystudies have identified that familiar strangers have their ownparticular social behavior in UrbComp environments. Basically,they behave as familiars if a social encounter eventually occursin a place different from the place where they usually have co-presence. Therefore, in such a case it is meaningful to define anidentity-awareness social encounter. However, familiar stran-gers always keep a social distance in the place where theyusually have co-presence—like the bus stop, or subway plat-form every morning. In this last case, it is meaningful to definean anonymous physical encounter or an anonymous virtual

encounter—like sharing music files or playing on-line gamesconjunctly on their mobile devices.
� Support for social groups of strangers—in the case of a social
group Gsj of strangers, it is unlikely to get such users engagedin a physical encounter. Therefore, the specialization is donebased on anonymous virtual encounters.

5.2. Unit-tasks composition mechanism

5.2.1. Reduction of unit-task composable space

Let S be the set of available, feasible unit-tasks of the placeidentified by the specialized unit-task selection algorithm. Asstated in Section 3, a place may have multiple potentials. Inaddition, according to the favorable place factor in the spatialaspect, a unit-task may be appropriate to support multiplepotentials of a place. This appropriateness reflects on the place

potential (pt) property of a unit-task object. Therefore, appropri-ateness is meaningful to group unit-tasks that are semanticallycloser in terms of their place potential property. For instance, oneof these groups may be formed of unit-tasks appropriate tosupport business, commercial, and shopping place potentials. Onthe other hand, another group may consider entertainment,leisure, or eating place potentials. For each of these groups theplace potentials are of the same semantic nature in accordancewith the place type ontology. Additionally, the place potentials

listed in these examples may belong to the same UrbComp place.Let S1, S2,y,Sn, (iApt), be the subsets of unit-tasks belonging tothis grouping. Since unit-tasks support multiple potentials of aplace, these subsets are non-exclusive. That is, a unit-task maybelong to more than one Si, i.e., [n

i ¼ 1Si ¼ S, and \ni ¼ 1Sia|. Each of

these Si subsets is used to generate its own unit-task composites

(configurations of interoperable and non-conflicting unit-tasks).Eventually, these composites may be aggregated to create differ-ent task candidates.

On the other hand, the generic structure of a task is composedof three parts (see Section 3): (1) encountering method recommen-

ders, (2) cognitive information deliverers, and (3) environmental

effecters. These categories are embedded in the structural type

property of a unit-task object. Therefore, in accordance with thesecategories, each subset Si is further partitioned into three exclu-sive subsets of unit-tasks – Ai, Bi, and Ci, (iApt). These subsets arefiltered out by matching the values of the structural type property.In order to make the cognitive information deliverers morefocused, subsets are further divided on the basis of the place

category (pc) that they support. For instance, unit-tasks thatdeliver information regarding restaurants, bars, museums,libraries, or hair shops are focused on specific place categories. Inthis way, Bi is divided into non-exclusive subsets Bij, (iApt), (jApc).These subsets are obtained by matching the values of the place

category property. The subsets Cij (environmental effecters),(iApt), (jApc), are determined in an analogous way (Table 2).

5.2.2. Composability and conflict analysis for cognitive information

deliverers

This subprocess performs an analysis of composabilityfor unit-tasks that belong to the subsets Bij, (iApt), (jApc). For

each IO interoperable pair of unit-tasks ½ðtkBijABijÞ,ðt

lBijABijÞ�,

k,lr Bij

�� Þ,ðka lÞ�

, the interoperability based on the social effects

factor is analyzed (see Section 4.1). Pairs that are not able tointeroperate under this social composability check are rejected asinvalid composites. When there is a successful social interoper-

ability between tkBij

and tlBij

, the ability to interoperate in terms of

the temporal availability factor is analyzed (see Section 4.2). Again,pairs that fail to meet this temporal analysis are rejected as validcomposites. For resulting interoperable unit-tasks, a conflictanalysis in terms of the content that the pairs deliver is conducted(see Section 4.4).

This analysis of composability and conflicts results in a set ofnon-conflictable, interoperable unit-task composites. These com-posites are conformed by composable, cognitive informationdeliverer pairs of unit-tasks. This set is further refined by recur-sively applying interoperability and conflict measurement for theresulting composites. The refinement stops when it is no longerpossible to get composable composites (Table 2).

Fig. 6. A unit-task composite.


5.2.3. Composability and conflict analysis for environmental

effecters

This subprocess makes use of unit-tasks that belong to the

subset Cij, ðiAptÞ, (jApc). That is, unit-tasks defined to produceenvironmental effects in the place. Therefore, for each IO inter-

operable pair of unit-tasks ½ðtkCijACijÞ,ðt

lCijACijÞ�, k,lr Cij

�� , ðka lÞ,

an analysis of conflicts in terms of environmental effects andenvironmental constraints is conducted (see Section 4.3). If a caseof effects conflict or constraints violation is identified, the pair isrejected as a composite candidate. The analysis is applied until nomore composites can be generated (Table 2).

5.2.4. Unit-task composite example

Let’s consider a user has a plenty of time for waiting in a busstop. Let’s suppose the context manager has identified a socialgroup of strangers, while the inferred social encounter type wouldcorrespond to a virtual encounter. In such a situation, Fig. 6illustrates an appropriate unit-task composite.

In this example, matchMusicList&Files, and PlayMusicOnSmart-

phone are interoperable in terms of publicness effects. The reasonis that (1) the publicness authorization level of the first unit-taskinput is set to private, since the music list can only be sharedamong the participants of the social group of strangers, (2) analo-gously, the publicness effect of the first unit-task output is set toprivate by the same reason, and (3) the publicness effect of thesecond unit-task output also is set to private, because the musicstream is played on the smartphone, a personal mobile device.

In addition, executeGameOnSmartPhone, and PlayMusicOnS-

martphone have non-conflictable delivered content. In fact, eventhough the availability of the delivered content is set to contin-uous for both unit-tasks, their types are different, that is, theyconsist of an audio content co-existing with a visual content. Therest of the unit-tasks of the composite are interoperable in termsof their input and outputs. Finally, Fig. 7 shows the compositedefinition in the Business Process Execution Language (BPeL). Thisdefinition represents the coordination of the unit-tasks usingdifferent control-flows and conditions.

6. Implementation and experimental results

6.1. Implementation

The task selection algorithm and the composition metrics wereimplemented in two modules called the task selector and the task

composer, respectively. Both modules were contained in a majormodule called the task manager. These modules were embedded

into the framework for service provision for Ubiquitous andUrbComp environments, which framework is described inJimenez-Molina et al. (2007, 2009, 2010). This framework is apart of the Ubiquitous and UrbComp Middleware shown in Lee(2004) and Huerta-Canepa et al. (2008). The algorithm and theframework were implemented in Java. The unit-task ontology andother ontologies were edited using Protege (2011). The semanticreasoning to select unit-tasks was developed using Jess (2011).The ontology data was represented in The OWL Web OntologyLanguage (2011).

6.2. Experimental results

The aim of this section is to report a set of experimental resultsobtained from both the unit-task selection mechanism and theunit-task composites generation mechanism. Results were eval-uated separately by running them on a Windows XP platformwith an Intel Pentium 4 (3.33 GHz) and 896 MB of memory.

In the first set of experiments, the performance of the unit-taskselection mechanism was determined. It can easily be observed inFig. 8 that this performance depends on the number of nodes inthe unit-task ontology. The mechanism was tested with differentnumbers of simulated nodes: 200, 300, 400, 500, 600, and 700nodes. The input data was randomly chosen from 65,635 recordsof users’ tasks found in the 2007 ATUS Time-use Study (ATUS, TheAmerican Time-use Study, 2007).

The algorithm was compared with that used in the casewithout discrimination between primary and secondary proper-ties (see Sections 5.1.1 and 5.1.2 for the rationale). Fig. 8 showsthat the execution time is proportional to the number of nodesin the unit-task ontology. In addition, this figure shows the effec-tiveness of prioritizing the properties of tasks. The result indicatesthat the prioritization of the properties contributes to a significantdecrease of the execution time of the algorithm because thenumber of candidate nodes of that the unit-task ontology mustconsider can be narrowed down quickly by using the primaryproperties, matching the secondary variables with a smaller set ofcandidates.

The remaining set of experiments was conducted through asimulation of a crowded space, in order to prove the effectivenessof the algorithm under realistic settings of UrbComp environ-ments. First a set of events was simulated that representedindividual users and social groups that arrived at a specific place.This set of arrivals was represented as an exponential probabilitydistribution, with 170 events emerging each minute (Yamadaet al., 2005). The simulation was done to cover a peak period of3 h, generating 30,600 logs. The values of the contextual informa-tion for each event were randomly assigned from the records of

Fig. 7. A unit-task composite coordination logic in BPeL.


the 2007 ATUS Time-use Study in a subway station location. Thelogs generated by this initial simulation were utilized to test thecomposition mechanism under intense concurrency of events. Inparallel, a set of 100 unit-tasks were simulated by randomlyassigning values to their properties, respecting the domainsstated in Section 3. A traditional Input/Output checking mechan-ism was selected as the baseline method to compare the compo-sition mechanism.

Having generated the required data, the second set of experi-ments consisted of a measurement of the performance of thecomposability algorithm. In that sense, the execution time for thegeneration of composites was evaluated with an increased

number of execution threads. This metric is relevant to thequalifying of the composition algorithm’s scalability performancewhen all the threads work concurrently to fully utilize the systemresources. Such a case is reasonable in urban environmentsettings with hundreds of users. This was done by groupingevents with time between arrivals of less than 500 ms, in whicheach event represents a thread. Such a time period ensures thatthere will be a pool of threads containing events that are closeenough in time to each other. Then, the average time for eachgroup of threads was computed. Fig. 9 shows these results for thebaseline method with a dotted line, and the composition algo-rithm with a solid line. It can be observed that, with the increase

Fig. 8. Unit-task selection mechanism performance.

Fig. 9. Execution time with increased number of application threads.

Fig. 10. Efficacy ratio.


in threads, the performance in most cases goes up for bothmethods. However, the tendencies reveal that the compositionalgorithm is not particularly sensitive to the thread number. Itsexecution time remained steady for different numbers of applica-tion threads, with a reasonable execution time of less than 1 s in amajority of cases. In contrast, the baseline method shows perfor-mance degradation with an upward trend. This clearly is due tothe fact that the baseline method does not reduce the composablespace at the beginning with the place potential and structuraltype properties. Moreover, the baseline method performancedecreases steeply after the pool with seven threads, but then itincreases sharply to a normal value. This anomaly can probably beattributed to the increased overhead in thread synchronizationand scheduling (see Fig. 9).

The third set of experiments evaluated the throughput of thecomposition mechanism measured as the number of transactionsper second. That is, the intention was to inspect the evolution ofthe number of threads processed in concurrency against thesimulation time, in order to verify the stability of the mechanism.Fig. 11 clearly shows the superior throughput of the composa-bility mechanism, which remained steady during the entiresimulation time. It is necessary to report that during the simula-tion one event was deleted from the graph data since the programexperienced an out of memory event. However, this mishap

occurred only one time, which shows the robustness of themechanism to work under intense concurrency.

In the fourth set of experiments, we analyzed the efficacy ratioof the composability mechanism. This ratio is defined as thenumber of valid operations resulting for the total set of operationsperformed while checking the interoperability and conflicts ofeach generated composite. An operation is defined as any of thecomposability metrics. The composition algorithm shows anappropriate efficacy ratio due to the checking of all the compo-sability metrics. This ratio reaches 85% on average. However, thisratio cannot reach 100% since not all of the selected unit-tasks canbe considered composable, which discrepancy generates a set ofunit-tasks that needs to be rejected or suggested as partially validto the users (see Fig. 10).

7. Conclusion

This paper leverages task-oriented computing to realizing thespontaneous composition of service sequences required by usersor social groups in UrbComp environments. This task was accom-plished by describing the essential semantic elements of unit-tasks, social groups, and UrbComp environments in accordancewith social, spatial, and temporal aspects. In addition, this paperdescribes a set of composability metrics to measure interoper-ability or to detect conflicts between unit-tasks or unit-taskscomposites. These metrics are based on the three aspects men-tioned above, and correspond to: (1) interoperability based onsocial effects of unit-tasks, (2) interoperability based on temporalavailability of unit-tasks, (3) interoperability based on environ-mental effects of unit-tasks, and (4) conflict analysis based ondelivered content and environmental effects of unit-tasks. Thispaper also describes the spontaneous task composition mechan-ism, highlighting its major steps. First, this paper extends thesemantically based unit-task selection mechanism that wasproposed in our previous work. This mechanism is composed oftwo major phases in the selecting of unit-tasks, and makes use ofthe placeness context and the characteristics of social groups.Then a reduction of the unit-task composable space based onplace potentials and the structural types of unit-tasks was con-ducted. Having defined smaller and more focused sets of the unit-task, an analysis to check composability and potential conflictsamong unit-tasks or unit-task composites was conducted.

A performance analysis for the unit-task selection showed anappropriate time-overhead for this mechanism. This analysis also

Fig. 11. Throughput of threads processing.


confirmed the suitability of prioritizing the filtering by theprimary properties that are best in predicting users’ activities(place type, phase day, day of week, and age range). Also, asimulation of arrivals at a crowded place showed a suitableperformance for the unit-task composition mechanism in com-parison to the baseline method of traditional Input/Outputchecking, as well as the evolution of its throughput, and theefficacy ratio measured as the number of valid operations result-ing for the total set of operations.

This research is currently being extended to a task predictionmechanism based on users’ activities recorded on the ATUS Time-use Study (ATUS, The American Time-use Study, 2007). Theobjective of this mechanism will be to extract users’ behavioralpatterns, which may improve the effectiveness of the sponta-neous task composition mechanism. In addition, we are applyinga cognitive resource aware approach to reconfigure and optimizethe unit-task coordinations obtained by the unit-task compositionmechanism reported in this paper.

Acknowledgments

This work was supported by the IT R&D program of MKE/KEITunder Grant KI001877 (Locational/Societal Relation Aware SocialMedia Service Technology). This research was supported by theNational IT Industry Promotion Agency (NIPA) under the programof Software Engineering Technologies Development. Multiplediscussions with Gonzalo Huerta-Canepa have benefitted thisresearch. We thank Juan Pablo Duarte and Gun-Woo Park.

References

Ardagna, D., Pernici, B., 2007. Adaptive service composition in flexible processes.IEEE Transactions on Software Engineering 33 (6), 369–384.

ATUS, The American Time-use Study, 2007. /http://www.bls.gov/tus/S.Balasubramaniam, S., Lewis, G.A., Simanta, S., Smith, D.B., 2008. Situated software:

concepts, motivation, technology, and the future. IEEE Software 25 (6), 50–55.Banerjee, T., 2001. The future of public space: beyond invented streets and

reinvented places. APA Journal 67, 9–24.Bardram, J.E., 2007. From Desktop Task Management to Ubiquitous Activity based

Computing, Integrated Digital Work Environments. MIT Press, pp. 49–78.Bojic, I., Podobnik, V., Kusek, M., 2010. Agent-enabled collaborative downloading:

towards energy-efficient provisioning of group-oriented services. Agent andMulti-Agent Systems: Technologies and Applications, LNCS 6071, 62–71.

Brujin, J., Fensel, D., Kerrigan, M., Keller, U., Lausen, H., Scicluna, J., 2008. ModelingSemantic Web Services. The Web Service Modeling Language. Spinger-Verlag,Berlin, Heidelberg.

Carmona, M., Heath, T., Hoc, T., Tiesdell, S., 2003. Public Places—Urban Spaces, TheDimensions of Urban Design. Architectural Press.

Fujii, K., Suda, T., 2004. Dynamic service composition using semantic information.In: Proceedings of the Second International Conference on Service Oriented,pp. 39–48.

Ganti, R.K., Srinivasan, S., Gacic, A., 2010. Multisensor fusion in smartphones forlifestyle monitoring. In: Proceedings of the IEEE International Conference onBody Sensor Networks, pp. 36–43.

Garlan, D., Siewiorek, D.P., Smailagic, A., Steenkiste, P., 2002. Project aura: towarddistraction-free pervasive computing. IEEE Pervasive Computing 1 (2), 22–31.

Granovetter, M.S., 1973. The strength of weak ties. The American Journal ofSociology 78 (6), 1360–1380.

Gupta, A., Paul, S., Jones, Q., Borcea, C., 2007. Automatic identification of informalsocial groups and places for geo-social recommendations. The InternationalJournal of Mobile Network Design and Innovation 2 (3/4).

Gupta, A., Kalra, A., Boston, D., Borcea, C., 2009. MobiSoC: a middleware for mobilesocial computing applications. Mobile Networks and Applications 14 (1),35–52.

Huerta-Canepa, G.F., Jimenez-Molina, A.A., Ko, I.Y., Lee, D., 2008. Adaptive activitybased middleware. IEEE Pervasive Computing 7 (2), 58–61.

Jess, The Rule Engine for the Java Platform, 2011. /http://www.jessrules.com/S.Jimenez-Molina, A.A., Koo, H., Ko, I.Y., 2007. A template-based mechanism for

dynamic service composition based on context prediction in ubicomp envir-onments. In: Proceedings of the International Workshop on Intelligent WebBased Tools, IEEE ICTAI-07.

Jimenez-Molina, A.A., Kim, J., Koo, H., Kang, B., Ko, I.Y., 2009. A semantically-basedtask model and selection mechanism in ubiquitous computing environments.In: Proceedings of the Thirteenth International Conference on Knowledge-Based Intelligent Information and Engineering Systems (KES 09), LNCS, 5712,pp. 829–837.

Jimenez-Molina, A.A., Kang, B., Kim, J., Ko, I.Y., 2010. A task-oriented approach tosupport spontaneous interactions among users in urban computing environ-ments. In: Proceedings of the IEEE International Conference on PervasiveComputing and Communications (PERCOM). The 7th IEEE PerCom Workshopon Managing Ubiquitous Communications and Services (MUCS), pp. 176–181.

Jimenez-Molina, A.A., Ko, I.Y., 2010. A historically-based task compositionmechanism to support spontaneous interactions among users in urbancomputing environments. In: Proceedings of the 14th International Confer-ence on Knowledge-Based Intelligent Information and Engineering Systems(KES 2010), LNCS, 6277, pp. 54–63.

Kang, J., Welbourne, W., Stewart, B., Borriello, G., 2005. Extracting places fromtraces of locations. ACM SIGMOBILE Mobile Computing and CommunicationsReview 9 (3), 58–68.

Knox, P., Ozolins, P., 2000. Urban Social Geography: An Introduction. Prentice Hall,Harlow.

Kostakos, V., Nicolai, T., Yoneki, E., O’Neill, E., Kenn, H., Crowcroft, J., 2009.Understanding and measuring the urban pervasive infrastructure. Personaland Ubiquitous Computing 13, 5.

Lee, D., 2004. Active surroundings: a group-aware middleware for embeddedapplication systems. In: Proceedings of the 28th International Conference onComputer Software and Applications (COMPSAC 04), pp. 404–405.

McCullough, M., 2001. On typologies of situated interaction. Human–ComputerInteraction 16, 337–349.

McGuinness, D.L., van Harmelen, F., 2004. OWLWeb Ontology Language. /http://www.w3.org/TR/owl-featuresS.

Mennie, D., Pagurek, B., 2000. An architecture to support dynamic composition ofservice components. In: Proceedings of the 5th International Workshop onComponent-Oriented Programming (WCOP 2000), Sophia Antipolis, France.

Minami, M., Morikawa, H., Aoyama, T., 2003. The design and evaluation of aninterface-based naming system for supporting service synthesis in ubiquitous

http://www.bls.gov/tus/

http://www.jessrules.com/

http://www.w3.org/TR/owl-features

http://www.w3.org/TR/owl-features


computing environment. Transactions of The Institute of Electronics, Informa-tion and Communication Engineers J86-B (5), 777–789.

Mokhtar, S.B., Liu, J., Georgantas, N., Issarny, V., 2005. QoS-aware dynamic servicecomposition in ambient intelligence environments. In: Proceedings of the 20thIEEE/ACM International Conference on Automated Software Engineering,pp. 317–320.

Oldenburg, R., 1999. The Great Good Place: Cafes, Coffee Shops, Bookstores, Bars,Hair Salons and the Other Hangouts at the Heart of a Community. Marlowe &Company, New York.

O’Neill, E., Kostakos, V., Kindberg, T., Fatah, G., Schieck, A., Penn, A., Stanton Fraser, D.,Jones, T., 2006. Instrumenting the city: developing methods for observing andunderstanding the digital cityscape. In: Proceedings of the Ubicomp 2006: EighthInternational Conference on Ubiquitous Computing, LNCS, 4206, pp. 315–332.

Paay, J., Kjeldskov, J., Howard, S., Dave, B., 2009. Out on the town: a socio-physicalapproach to the design of a context-aware urban guide. ACM Transactions onComputer–Human Interaction 16, 2.

Partridge, K., Golle, P., 2008. On using existing time-use study data for ubiquitouscomputing applications. In: Proceedings of the 10th International Conferenceon Ubiquitous Computing (Ubicomp 08), pp. 144–153.

Paulos, E., Goodman, E., 2004. The familiar stranger: anxiety, comfort, and play inpublic places. In: Proceedings of the Conference on Human Factors inComputing Systems, pp. 223–230.

Ponnekanti, S.R., Fox, A., 2002. SWORD: a developer toolkit for web servicecomposition. In: Proceedings of the Eleventh World Wide Web Conference(Web Engineering Track), Honolulu, Hawaii.

Protege, The Protege Ontology Editor, 2011. /http://protege.stanford.edu/S.Quentin, J., Grandhi, S., 2005. P3 systems: putting the place back into social

networks. IEEE Internet Computing 9 (5), 38–46.

Ranganathan, A., Chetan, S., Campbell, R., 2004. Mobile polymorphic applicationsin ubiquitous computing environments. Mobile and Ubiquitous Systems:Networking and Services.

Roman, M., Hess, C.K., Cerqueira, R., Ranganathan, A., Campbell, R.H., Nahrstedt, K.,2002. Gaia: a middleware infrastructure to enable active spaces. IEEE Perva-sive Computing, 74–83.

Satyanarayanan, M., 2001. Pervasive computing: vision and challenges. PersonalCommunications, IEEE 8, 4.

Sheng, Q.Z., Benatallah, B., Dumas, M., Mak, E., 2002. SELFSERV: a platform for rapidcomposition of web services in a peer-to-peer environment. In: Proceedings ofthe 28th Very Large DataBase Conference (VLDB’2002), Hong Kong, China.

Shklovski, I., Chang, M.F., 2006. Urban computing: navigating space and context.IEEE Computer 39 (9), 36–37.

Sirin, E., Hendler, J., Parsia, B., 2002. Semi-automatic composition of web servicesusing semantic descriptions. In: Proceedings of the Workshop on WebServices: Modeling, Architecture and Infrastructure, in conjunction withICEIS2003.

The Business Process Execution Language, BPeL, 2001. /http://bpel.xml.org/S.Tamminen, S., Oulasvirta, A., Toiskallio, K., Kankainen, A., 2004. Understanding

mobile contexts. Personal and Ubiquitous Computing 8 (2), 135–143.The OWL Web Ontology Language, 2011. /http://www.w3.org/TR/owl-features/S.Wang, Z., Garlan, D., 2000. Task-Driven Computing. Technical Report, CMU-CS-00-

154.Weiser, M., 2003. The computer for the 21st century. Scientific American 94–104,

19–25 (Reprinted in IEEE Pervasive Computing).Yamada, T., Kaneko, M., Katou, K., 2005. A mobile communication simulation

system for urban space with user behavior scenarios. In: Yang, L.T. (Ed.), HPCC2005, LNCS, 3726; 2005, pp. 979–990.

http://protege.stanford.edu/

http://bpel.xml.org/

http://www.w3.org/TR/owl-features/

spontaneous task composition in urban computing environments based on social, spatial, and temporal...

Documents