two types of context

As noted, context-aware computing aims to simplify and enhance life experience through systems that take over simplified tasks for humans. The formal defini-tion for context-aware systems is rather broad; the term refers to any system that uses a relevant context to adapt to users’ needs [1]. Thus, to understand context awareness, we must first understand what context is and how different fields, especially computer science, has approached it.

As a research field, context-aware computing has existed since the early 1990s, to extend ubiquitous computing. However, even computer science has several definitions for context (see Perera, Zaslavsky, Christen, and Georgako-poulos, 2014, for a list of definitions [5]), which are then used to describe context-aware services. In this work, we are motivated by Abowd et al.’s (1999) proposed definition, which is widely accepted in the literature [1]:

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. (emphasis ours)

The definition is indeed broad; one might argue that this is a nondefinition, in a way. It states that context is any (relevant) information that characterizes the situation. This highlights the scale and variety of different applications already envisioned in 1999. However, many context-aware services focus on specific context information only, such as location (criticism of this, see Schmidt, Beigl, and Gellersen, 1999 [6]). In this article, we see a continuum between the

objec-tive and the subjecobjec-tive context. Subjecobjec-tive context extends current attempts to explore context awareness as social constructions. Naturally, authors such as Dourish (2004) [7] pose context as an interactional problem in which the main focus is to explore how and why people reach mutual understandings.

In our view, the subjective context is a product of interaction too; however, it also takes into consideration overarching concepts, philosophies, and values that make people reach certain understandings—or not—and the way they do so (e.g., values such as honesty, nature, health, or respect). The former implies that something that can be detected from sensors directly is considered objec-tive context, whereas subjecobjec-tive context links to a person’s values, perceptions of the situation, and understanding of relationships. In the following sections, we further elaborate the notion of context in detail, exploring first the history of (objective) context in computer science and then discussing its criticisms, in the form of the new notion of (subjective) context.

2.1 Objective Context

In the early 1990s, the idea of context-aware computing was first introduced by Schilit et al. (1994) as an extended form of mobile computing [3]. The basic idea was that because computations can span over several locations and situations (e.g., office, home, etc.), there is a need for new types of services that are aware of these contexts. Although the idea of using contextual information in applications was quite fascinating, the instances of using this idea in practice did not exceed the lab experiments based on location context. It was not until late 1990s that new trends in hardware manufacturing started to prepare a good environment for context-aware applications. At that time, smartphones started to become cheaper and more powerful, and sensors started to become more a common addition [2].

Nowadays, context-aware computing has proven to be successful in under-standing sensor data. Context-aware applications have been successfully used in navigation, advertisement, recommender systems [8], [9], monitoring of patients, and other situations. At the same time, the number of sensors around the world is rapidly growing, and the Internet of Things (IoT) has provided the necessary infrastructure for connecting these billions of sensors through the Internet [5].

However, the remaining question is: Are these massive amounts of measurable contexts enough to understand and accommodate human inner needs? Our argu-ment in this work is that context-aware computing cannot accommodate human needs based solely on directly measurable contexts (i.e., objective context). Ma-chines will only be able to fully complement human lifestyles when they are able to comprehend the subjective contexts.

2.2 Subjective Context

As mentioned in the previous section, sensing mechanisms used to infer loca-tion and other objective data are common nowadays. Because these sensors can encompass so many locations, the collection of contextual data is possible given a stable environment where context is independent of our actions and interactions.

However, to become truly responsive and integrated—that is, for computational systems to be fully integrated and invisible to human interactions, to understand our intentions and the reasons for our behavior—they must address how our values and perspectives influence our actions and thoughts and how we interpret our shared experiences and reach common understandings.

In other words, how do people agree on what is relevant while interacting, and how does this contribute to the flow and construction of a conversation? For ex-ample, in an interaction, meanings are constructed by utterances and ideas that precede one another.

In this work, we introduce the term subjective context as the context gener-ated through our experiences and interactions with one another or an object. The objective context has been criticized by Dourish (2004), who stated that context is also “a relational property that holds between objects or activities” [7]. Our approach to the subjective is closer to the human values; in other words, it is of a more intimate nature and closely related to what we judge important in life and influences how we relate to others. We therefore define subjective context as any information that can be used to characterize the user’s personal accounts, feelings, and emotions about an entity.

Disciplines such as phenomenology and ethnomethodology—which draw from social sciences fields—study the settings in which actions unfold, offering insights into how context can be studied through interactions and concentrate on understanding how people use practical reasoning instead of formal logics to account for their experience of the world [10], [11], [12]. This is how they come to understand their world.

The aforementioned terms are relevant to understanding the subjective con-text because numerous interpretations and reinterpretations may occur during an interaction or phenomena—such as a conversation between two people, which requires a system to process the utterances made by the individuals involved (natural language processing), but also to understand references to the physical world and social conventions under which these conversations take place. Sub-jective context also accounts for the inner thoughts and feelings that a situation or interaction may provoke and thus presents difficulties for data collection. It may seem that for a computational system to recognize the data encrypted in

subjective context, it would require cognitive processes in which systems can infer different courses of action.

Figure 1 is an attempt to illustrate a new suggested typology for subjective context in which there is a direct correlation between the objective and what we consider to be the first- (values) and second-order of subjective context. The new typology arranges data through values, intentions, and feelings. The typology is produced by extending the original dimensions of objective data into broader lev-els of understanding and deeper levlev-els of intimacy. In other words, the typology reflects the type of data that would be understood through a phenomenological point of view, the phenomena accounting for my values, my intentions, and the meaning of my actions (e.g., the use of a space by someone carries with it cer-tain types of associated meanings and feelings; a conversation that takes place between my employer and myself in which we reached a certain understanding).

Fig. 1. Typology of subjective and objective context awareness.

However, after introducing the notion of subjective context, we must address how to computationally detect it. One implication of subjective context is that for each participant, the subjective context is variable even if the objective context is constant. Is it possible to get more from the objective—and easy-to-detect—con-text than from the subjective and individual subjective coneasy-to-detect—con-text? What types of techniques are required to collect and analyze the data to be able to work with the

information? The following section presents an overview on how new types of technologies approach data and provide insights of people’s state of mind that go beyond the objective. It also discusses the probable lines of service for the future and their respective technical challenges.