JST Vol. 21 (1) Jan. 2013 - Pertanika Journal - Universiti Putra ...

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Az Azrinudin Alidin and Fabio Crestani mobile information retrieval (mobile IR). Besides, the characteristics of mobile IR are quite different from the traditional IR frameworks. One notable characteristic of mobile IR is the physical limitations of mobile devices. Mobile devices are built physically small for ease of portability. This makes it challenging for mobile users to operate with the mobile device when they are on the move. Besides, adopting desktop browser to fit the size of a mobile devices screen forces them to spend more time selecting and reading the right information in small font and crowded text (Church et al., 2002). Mobile users also need to put extra attention when using mobile devices because they can easily get distracted by other things going on around them (Sohn et al., 2008). On the other hand, the evolving smart-phone technologies have enabled researchers and developers to exploit the embedded sensors in smartphones to sense users’ surrounding information (Lane et al., 2010). The user’s surrounding information is also known as context. Context, in general, can be used to overcome the problem of IR systems retrieving too much information that may not be relevant in the user’s current situation and also to minimize their involvement in interaction particularly on a mobile device. The embedded sensors on the smartphones make them ideal devices to capture users’ context because these users always carry the devices. The inclusion of context in the IR frameworks has become increasingly important particularly for the implementation of mobile IR. The problem with the current IR systems is that these systems retrieve all relevant information in response to a query without considering the representation of the query. These systems do not have any knowledge about user’s preferences, or why the user submits a particular query or what is the user’s intention when submitting that particular query. As a result, the relevant information retrieved by the IR systems is too generic and is not within the right user’s context, and the user needs to spend time finding the desired information. In order to improve the retrieval results, the implementation of context in the IR frameworks is employed in one of the following retrieval phases: (a) query reformulation, (b) document re-ranking, and (c) document retrieval (Tamine-Lechani et al., 2010). However, in mobile IR, apart from improving the retrieval results, minimizing the user interaction is also a key to increase the user’s experience when using the mobile IR. Smartphones can react and adapt to the context to minimize user’s interaction and use context as information trigger to pro-actively present information to the user. This is also known as Just-In-Time Information Access system (Leake et al., 1999). For this research, the system is named as Just-In-Time Mobile Information Retrieval (JIT-MobIR). JIT-MobIR works by monitoring context, predicting user’s information needs in any given context and proactively providing the user with relevant information with the aim to reduce user’s interaction. Thus, the effectiveness of JIT-MobIR is measured both in the capability of the system to understand the context and in the ability of the system to retrieve information that satisfies user’s information needs in that particular context. The motivation of this research is to construct, understand and apply context for developing JIT-MobIR. However, very little work has been done on utilizing context in mobile IR because of the complexity of constructing and generalizing context. Context, by nature, is always changing particularly when a user is on the move. This makes context extremely complex to be identified (Schmidt et al., 1999). How context can be identified and captured by using a smartphone is explained in this paper. In specific, the paper is organized as follows: In the next section, work related to the current study is presented. The context modelling in JIT-MobIR is 228 Pertanika J. Sci. & Technol. 21 (1): 283 - 298 (2013)
Context Modelling for Just-In-Time Mobile Information Retrieval (JIT-MobIR) introduced subsequently. Then, the implementation of JIT-MobIR is described in detail, with a discussion on the results, followed by conclusion and future work. DEFINITION OF CONTEXT According to the Free Online Dictionary of Computing (FOLDOC), context is defined as “that which surrounds, and gives meaning to, something else”. However, the definition of FOLDOC is too broad and vague, and for this reason, many researchers defined the context according to their own understanding that is influenced by their research observations (Dey & Abowd, 2000; Schilit et al., 1994; Schmidt et al., 1999). Schilit et al. (1994) proposed the definition of context based on location awareness. However, Schmidt et al. (1999) disagree when context is treated merely as a location as past researchers always do. They describe that context has many aspects in three dimensions: Environment, Self and Activity. Nevertheless, Dey and Abowd (2000) argue that those two definitions of context are experimental oriented and difficult to be applied. They also argue that one aspect of context is not important from others since these aspects will change from one situation to another. Thus, context according to Dey and Abowd (2000) emphasizes that any information will be considered as context when this information helps a user to interact with an application. However, if the information is no longer relevant to the user’s interaction, then it is no longer a part of the context. This definition does not restrict where the context is coming from as long as that context helps the user’s interaction. Dey and Abowd’s (2000) definition of 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”. This definition is widely recognized as a formal definition of context by current researchers. The definition also emphasizes that context has several dimensions which can clarify the situation of an entity. DIMENSIONS OF CONTEXT Previous works on context awareness usually used location as an approximation of context. Although location is one of the dimensions of context, relying solely on it does not explain the entire context of a user. Schilit et al. (1994) explained that context should exploit the changing aspects of the environment. They further summarized the environments as: (a) Computing environment, such as network connectivity, communication costs, and nearby resources such as printers and workstations; (b) User environment, such as location and user’s profile; and (c) Physical environment, such as lighting and noise level. However, according to Schmidt et al. (1999), the computer environment and the physical environment overlap with each other. They proposed a hierarchical context model where context is divided into general categories of human environment and physical environment. Beyond these general categories, each of them is further divided into three sub-categories where a set of relevant features is identified. The value of these features will determine the overall user’s context. Based on those two works, researchers began to propose their own dimensions of context by expanding, reducing the dimensions or proposing new general models of context according to their own works (Bierig & Göker, 2006; Brown & Jones, 2002; Korpipaa et al., 2003; Raento et al., 2005; Razzaque et Pertanika J. Sci. & Technol. 21 (1): 283 - 298 (2013) 229
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Journal of Science & Technology Jou
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International Advisory Board Adarsh
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ut bovine milk allergy by far is th
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Selected Articles from UPM-Malaysia
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ISSN: 0128-7680 © 2013 Universiti
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Aspect of Fatigue Analysis of Compo
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Application of Anthropometric Dimen
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Different Media Formulation on Bioc
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Heng, S. C., Ibrahim, Z. B., Suleim
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CONCLUSION Heng, S. C., Ibrahim, Z.
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Y. Yusuf, J. M. Juoi, Z. M. Rosli,
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Surface Roughness Y. Yusuf, J. M. J
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DISCUSSION Y. Yusuf, J. M. Juoi, Z.
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Modelling of Motion Resistance Rati
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The Empirical Method Modelling of M
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Assessment of Heavy Metal Pollution
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Physico-Chemical and Electrical Pro
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TABLE 1: (continued) Physico-Chemic
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Norhashila Hashim et al. effectiven
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Statistical Analysis Norhashila Has
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Norhashila Hashim et al. Post-hoc a
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Norhashila Hashim et al. Cubero, S.
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Lemma 1.1 The equation S. Ismail an
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INTRODUCTION Tajau, R. et al. A hig
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Aji, I. S., Zinudin, E. S., Khairul
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Aji, I. S., Zinudin, E. S., Khairul
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CONCLUSION Aji, I. S., Zinudin, E.
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TABLE 1: (continue) 4 D. Bachtiar,
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N. Maizatul, I. Norazowa, W. M. Z.
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REFERENCES N. Maizatul, I. Norazowa
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CBIR Using Colour and Shape Fused F
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Toward Automatic Semantic Annotatin
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table should be prepared on a separ
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Usability of Educational Computer G
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