Advances in size, power, and ubiquity of computing technology, sensors, and communication technology made possible the development of so-called mobile or nomadic information systems. A mobile information system has the potential to autonomously change its behaviour according to different location settings. Variability of location and system behaviour is a central feature of such new generation of information systems. This paper stresses the importance of modeling and analyzing variability of location as a basis for variability of software. We describe graphical and formal techniques to model location information, show their usage in conjunction with the goal-oriented framework i*/Tropos, and propose three analysis techniques on location-based goal models

Ali, Raian

Dalpiaz , Fabiano

Giorgini, Paolo

English

J. Krogstie

P. Bresciani

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Location-Based Variability for Mobile Information Systems

Bournemouth University Research Online

Location-based Variability forMobile Information SystemsRaian Ali, Fabiano Dalpiaz, and Paolo GiorginiUniversity of Trento - DISI, 38100, Povo, Trento, Italy.{raian.ali, fabiano.dalpiaz, paolo.giorgini}@disi.unitn.itAbstract. Advances in size, power, and ubiquity of computing, sensors,and communication technology make possible the development of mobileor nomadic information systems. Variability of location and system be-havior is a central issue in mobile information systems, where softwarebehavior has to change and re-adapt to the diﬀerent location settings.In this paper, we motivate the need for integration of variable locationand variable software behavior. We adapt the goal-oriented frameworki*/Tropos to model and analyze the alternative goal satisfaction strate-gies and the location where each alternative can be adopted. We intro-duce analysis techniques for the proposed location-based models.1 IntroductionAdvances in computing and communication technology have recently led to thegrowth of interest in Mobile Information Systems (hereafter MobIS). MobISs em-phasize mobility concerns (space, time, personality, society, environment, and soon) often not considered by traditional desktop systems [1]. Technology advancesdo not necessarily imply the easiness of exploiting it, rather more challenges areintroduced. Nomadic user expects smarter information systems, able to adapttheir behavior without human intervention. MobIS has to reason about the sur-rounding location, including user itself, and adapt autonomously their behaviorto location settings. Consequently, we need to model and analyze the variablelocation and the variable behavior and deﬁne how location inﬂuences behavior.Behavioral and location variability are complementary. Supporting two al-ternative behaviors, without specifying when to adopt each of them, arises thequestion “why do we support two alternatives and not just one?”. Conversely,considering location variability without supporting alternative behaviors arisesthe question “what can we do if location changes?”. We use i*/Tropos [2, 3] goal-oriented framework to model alternative strategies for MobIS to satisfy a goal,and specify location properties that apply to each alternative. This allows usto support the decision making process when deriving a location-tailored MobISinstance and make possible diﬀerent kinds of reasoning. The intended automatedreasoning allows to answer questions like: “are all MobIS objectives achievablein a given location?”, “what is the optimal alternative to achieve an objective ina given location?”, and “what is the optimal modification that is needed in onelocation to satisfy some MobIS objectives?”.2 Location-based Goal ModelsFig. 1. A location-based goal model.In i*/Tropos, the system is modeled as a set of inter-dependent actors havinggoals, and that can commit to strategies to satisfy their goals. Autonomousselection among goal satisfaction strategies requires criteria an actor builds itsdecision upon. One alternative can be recommended in a certain location, whileit can be even unapplicable in others. The criteria to select among alternativesis not explicitly modeled in the current i*/Tropos goal model. Fig.1 shows apartial goal model of a PDA MobIS intended for a client in a shopping mall.As a step to support location-based variability, i*/Tropos can attach locationproperties to its following variability points:1. Location-based Or-decomposition: Or-decomposition is the basic variabilityconstruct; in current i*/Tropos the choice of a speciﬁc Or-alternative isleft to actor intention, without considering location properties that can in-hibit some alternatives. E.g. (from Fig. 1): goal Establish connection can beachieved using Wireless Connection only if the mall has a wireless networkand client is authorized to access it, and client’s PDA supports WiFi (L1).2. Location-based contribution to soft-goals : the value of contributions to soft-goals can vary from one location to another. E.g. the contribution from goalWireless Connection to soft-goal Reliable Connection changes depending onthe level of received signal: if user is in a location where the signal comingfrom the WiFi access point is high (L2), the contribution will be positive,while if the client is far from the WiFi access point and the signal level ispoor (L3), the contribution will be negative.3. Location-based dependency: in certain locations, an actor is unable to satisfya goal using its own strategies. In such case, the actor might delegate thisgoal to another actor that is able to satisfy it. E.g. the MobIS can satisfygoal Provide Answer by fulﬁlling Query Mall DB ; while if the database isoﬄine and a mall website exists and has a mobile devices version (L4), theMobIS can delegate the goal to Mall Website browsing that website.4. Location-based goal activation: an actor, when location settings change, mightﬁnd necessary or possible triggering (or stopping) the desire of satisfying agoal. E.g. if the MobIS has adopted the alternative Wired Connection toestablish a connection, and while the client is getting to one cable-based ter-minal, the PDA detects a wireless signal (L5), the goal Wireless Connectioncould be triggered to better satisfy the soft-goals.5. Location-based And-decomposition: a sub-goal might (or might not) be neededin certain location, that is some sub-goals are not always mandatory to ful-ﬁll the top-level goal in And-decomposition. E.g. to satisfy the goal WiredConnection, the MobIS has ﬁrst to show a demo to client only if the clientis using the system for the ﬁrst time (L6).3 Defining, Eliciting and Modeling LocationWe refer by “location” to an environment with high degree of commonality, likeshopping malls, museums, or airports. The commonality concerns location con-structs: resources (physical and informational); actors having responsibilities,objectives, and relations with resources and other actors; and rules that coor-dinate the interaction among actors and the use of resources. Using i*/Troposconcepts, we deﬁne location from the perspective of an actor as: “the set ofavailable actors and resources that can be employed to achieve actor goals”.Goal analysis will capture location properties that are needed at each variabilitypoint, and this in turn will enable us to construct location model. In our broadvision, location will be the input that guides MobIS derivation process: MobISwill be instantiated according to the location model instance as shown in Fig. 2.Fig. 2. The process of instantiating a location-tailored MobIS instance.4 Analysing Location-based ModelsThe proposed location-based goal model has two components: (1) the goal modelthat describes how a goal can be satisﬁed and (2) the location properties thatconstrain each alternative. Location properties are predicates speciﬁed over a lo-cation model, whose truth values can be either true or false at a certain location.By formalizing location model and location-based goal model, we can do severalanalysis. We outline now three types of such automated analysis:1. Location-based goal satisfiability (LGS): it veriﬁes whether a goal is achiev-able through one alternative in a speciﬁc location.2. Location properties satisfiability (LPS): this analysis checks if the currentlocation structure is compliant with the MobIS goals. It is exploited to iden-tify what is missing in a particular location where some top-level goals havebeen identiﬁed as unsatisﬁable by LGS. When a goal cannot be satisﬁed,LPS will identify the denying conditions and ﬁnd ways to solve the problem.3. Preferences analysis (PA): this type of analysis requires the speciﬁcation ofpreferences over alternatives. Preferences can be speciﬁed using soft-goalsas in [4]. We need this analysis in two cases: 1) when there are several al-ternatives to satisfy a goal: the selection will be based on the contributionsto preferred soft-goals. 2) when there is no applicable alternative: in thiscase, LPS might provide several proposals about the needed location mod-iﬁcations. The adopted modiﬁcations are those leading to better satisfyingpreferences expressed over soft-goals.5 Discussion and Future WorkWe have brieﬂy shown how to integrate goal satisfaction strategies with theconcept of location, and what kind of analysis we can do over the location-basedmodels. More details and a concrete example can be found in our technicalreport ([5]). For the future, we need to deﬁne a modeling language for location,and to study how to capture location model and integrate it with system behaviorvariability at diﬀerent levels (goal satisfaction is one of them). Formalization isa basic need, since location is perceived and needed to perform reasoning. Wewill look for an appropriate formalism to automate the analysis techniques.6 AcknowledgementThis work has been partially funded by EU Commission, through the SEREN-ITY project, by MIUR, through the MEnSA project (PRIN 2006), and by theProvincial Authority of Trentino, through the STAMPS project.References1. Krogstie, J., Lyytinen, K., Opdahl, A., Pernici, B., Siau, K., Smolander, K.: Re-search areas and challenges for mobile information systems. International Journalof Mobile Communications 2(3) (2004) 220–2342. Yu, E.: Modelling strategic relationships for process reengineering. Ph.D. Thesis,University of Toronto (1995)3. Bresciani, P., Perini, A., Giorgini, P., Giunchiglia, F., Mylopoulos, J.: Tropos: Anagent-oriented software development methodology. Autonomous Agents and Multi-Agent Systems 8(3) (2004) 203–2364. Liaskos, S., McIlraith, S., Mylopoulos, J.: Representing and reasoning with prefer-ence requirements using goals. Technical report, Dept. of Computer Science, Uni-versity of Toronto (2006) ftp://ftp.cs.toronto.edu/pub/reports/csrg/542.5. Ali, R., Dalpiaz, F., Giorgini, P.: Location-based variability for mobile in-formation systems. Technical Report DISI-08-008, DISI, University of Trento,http://eprints.biblio.unitn.it/archive/00001351/

Location-based variability for mobile information systems.

Modelling strategic relationships for process reengineering.

Representing and reasoning with preference requirements using goals.

Research areas and challenges for mobile information systems.

Tropos: An agent-oriented software development methodology. Autonomous Agents and MultiAgent Systems 8(3)

Location-based variability for mobile information systems

Dalpiaz, Fabiano

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Location-based Variability for Mobile Information Systems

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Location-based variability for mobile information systems

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