Context Aware Academic Planner Design

Current issues

Current need of

Common elements

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students and lecturers

FRAMEWORK

Element/Feature Application

Figure 1: Research mission.

Figure 1 represent the methods used to determine the features before design the application

Literature review

A literature review need to be done in order to continue the study on this topic. A literature survey was conducted to investigate the current issues and common element features of developing a context aware. Table 2 is a draft of element functions involving the academic planner system.

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TABLE 2: DRAFT FROM LITERATURE SURVEY

1. The existing other Planner

A literature review need to be done in order to continue the study on this topic

Context awareness

Ubiquitous computing (pervasive systems) was first proposed by Weiser (1991). Context-aware systems are a type of pervasive system and are viewed by computer scientists as a mature technology [1, 2]. A definition for context is given by Day in [3]: “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 application themselves“. Context-aware systems are able to gather contextual information from a variety of sources without explicit user interaction and adapt their operation accordingly [4]. Context-aware systems have the ability to integrate easily with any service domain, such as healthcare, commerce, learning and transport.

A context-aware system must include three essential elements: sensors, processing and action. Three types of sensors are defined: physical, virtual and logical [5]. A physical sensor, such as a camera or thermometer, captures information about its local environment [6]. In contrast, virtual sensors extract information from virtual space, which is defined as the set of data, applications and tools created and deployed by the user. Logical sensors combine physical and virtual sensors to extract context information. For example, a company can infer that an employee is working from home using login information (a virtual sensor) and a camera (physical sensor) [1].

Context-aware user interfaces facilitate the user interaction by suggesting or prefilling data derived from the user’s current context. This raises the problem of determining which context information can be used as input for which interaction element in the user interface. This task is especially challenging as the texts that describe the elements, e.g. their labels, often differ in the terminology used. To facilitate the interaction with an application, we need user interfaces (UIs) that provide proactive assistance, for example by suggesting which values to enter in a form.

Melanie is his paper present a novel mapping process for that purpose which combines the advantages of string-based and semantic similarity measures to bridge the vocabulary gap between context and UI element, and which is able to automatically extend its vocabulary by observing the user’s interactions. Their research show that these two features dramatically increase the quality of the resulting mapping. Unlike previous approaches, the proposed mapping process does not require any training or manually tagged data. Further, it does not only use the label to describe the context and UI elements, but additional texts like their tooltips.

Context-aware applications are expected to become a remarkable application area within future mobile computing. As mobile phones form a natural tool for interaction between people, the influence of the current context on collaboration is desirable to take into account to enhance the efficiency and quality of the interaction [1].

Context-aware mobile devices have so far been investigated mainly from the technological point of view, examining context-recognition and sensor technologies inferring logic, system architectures or infrastructure. There have also been examples where contextual information has been used to facilitate co-operation between mobile users. User’s personal information, such as reminders, phonebook contacts or calendar notes, can be used as an information source which is used when creating location-sensitive messages, as done with CybreMinder [2]. Schmidt et al. [3] introduced a context-aware phonebook, which indicates the availability of a contact the user wants to call to. Location is probably the most commonly used context attribute, and it has been used to develop numerous location-aware mobile systems, such as GUIDE tour guide in Lancaster [4] or visitor’s guide at Tate Gallery, London [5].

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Cloud Application

A cloud application (or cloud app) is an application program that functions in the cloud, with some characteristics of a pure desktop app and some characteristics of a pure Web app. A desktop app resides entirely on a single device at the user’s location (it doesn’t necessarily have to be a desktop computer). A Web app is stored entirely on a remote server and is delivered over the Internet through a browser interface.

Like desktop apps, cloud apps can provide fast responsiveness and can work offline. Like web apps, cloud apps need not permanently reside on the local device, but they can be easily updated online. Cloud apps are therefore under the user’s constant control, yet they need not always consume storage space on the user’s computer or communications device. Assuming that the user has a reasonably fast Internet connection, a well-written cloud app offers all the interactivity of a desktop app along with the portability of a Web app. If you have a cloud app, it can be used by anyone with a Web browser and a communications device that can connect to the Internet. While tools exist and can be modified in the cloud, the actual user interface exists on the local device. The user can cache data locally, enabling full offline mode when desired. A cloud app, unlike a Web app, can be used on board an aircraft or in any other sensitive situation where wireless devices are not allowed, because the app will function even when the Internet connection is disabled. In addition, cloud apps can provide some functionality even when no Internet connection is available for extended periods (while camping in a remote wilderness, for example).

Cloud apps have become popular among people who share content on the Internet. Linebreak S.L., based in Spain, offers a cloud app named (appropriately enough) “CloudApp,” which allows subscribers to share files, images, links, music, and videos. Amazon Web Services offers an “AppStore” that facilitates quick and easy deployment of programs and applications stored in the cloud. Google offers a solution called “AppEngine” that allows users to develop and run their own applications on Google’s infrastructure. Google also offers a popular calendar (scheduling) cloud app.

FINDINGS

Questionnaires Analysis

Proposed Feature In Academic Planner

After several study in traditional planner and existing planner that related to Academic Planner, reviewing literature and questionnaire, the new features introduced to improve the academic planner

Optimizing class scheduling in collaborative mobile systems through distributed voting

Decision making through distributed voting can help automate routine-like collaborative class schedule, appointment and Event. In this paper author concentrate on how distributed voting strategies can be used for scheduling meetings in mobile and pervasive environments. Their work focuses on optimizing the meeting scheduling result for each participant in a mobile team by using user-specific preferences and information available on their devices. This negotiation is done in a distributed manner directly between the peers. In this paper author describe different approaches for the decision making strategy involving voting theory to balance out the different user preferences and availabilities. The weight of the votes from each participant can also be adjusted according to their importance or necessity in the given meeting. We also introduce briefly an approach to support distributed decision making strategies pervasively using a lightweight Web-based platform. To conclude the paper, we give our views on the future development directions and evaluation plans as well as extend the approach for other related domains [1].

Categorizing Task Occurrence Pattern

When we make a future plan of our work, we can predict or forecast the upcoming tasks, because we know that fair amount of our tasks are to be occurred as were occurred in the last year/month repeatedly. In addition, we know we have many dependent tasks; for example, there will be a series of regular meetings with the office staff for which various auxiliary tasks need to be completed, for example, Announcement, Setting up Room, and Sending Minutes tasks. These related tasks are approximately on the same time grid with other corresponding tasks. This type of regularity is called a Task Occurrence Pattern, which arises from the repetition of tasks and the alignment of related tasks [4]. To confirm how much the real tasks are on the Task Occurrence Pattern, all tasks of a year of a user, who is a graduate student, are gathered and inspected from the view point of dependence and recurrence.

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