A Feature-based Model to Analyze Mobile Location Services

2007 ◽  
Author(s):  
Ana M. Bernardos ◽  
Jose R. Casar ◽  
Paula Tarrio
Keyword(s):  
Location Services ◽  
Mobile Location ◽  
Feature Based

Towards a Classification Framework for Mobile Location Based Services

2011 ◽  
pp. 67-85 ◽  
Author(s):  
George M. Giaglis ◽  
Panos Kourouthanassis ◽  
Argiros Tsamakos
Keyword(s):  
Business Models ◽  
New Technologies ◽  
Geographical Location ◽  
Mobile User ◽  
Location Based Services ◽  
Location Services ◽  
Mobile Location ◽  
Classification Framework ◽  
Future Potential ◽  
Pricing Schemes

The emerging world of mobile commerce is characterized by a multiplicity of exciting new technologies, applications, and services. Among the most promising ones will be the ability to identify the exact geographical location of a mobile user at any time. This ability opens the door to a new world of innovative services, which are commonly referred to as Mobile Location Services (MLS). This chapter aims at exploring the fascinating world of MLS, identifying the most pertinent issues that will determine its future potential, and laying down the foundation of a new field of research and practice. The contribution of our analysis is encapsulated into a novel classification of mobile location services that can serve both as an analytical toolkit and an actionable framework that systemizes our understanding of MLS applications, underlying technologies, business models, and pricing schemes.

Mobile Location Services

2005 ◽  
pp. 1973-1977 ◽  
Author(s):  
George M. Giaglis
Keyword(s):  
Mobile Phone ◽  
Mobile Phones ◽  
21St Century ◽  
Mobile Applications ◽  
Location Services ◽  
Mobile Location ◽  
Critical Attribute ◽  
History Of ◽  
Location Identification

The term “mobile era” as a characterization of the 21st century can hardly be considered an exaggeration (Kalakota & Robinson, 2001). Mobile phones are the fastest penetrating technology in the history of mankind, and global mobile phone ownership has surpassed even the ownership of fixed phones. Mobile applications, despite potentially being very different in nature from each other, all share a common characteristic that distinguishes them from their wire-line counterparts: they allow their users to move around while remaining capable of accessing the network and its services. In the mobility era, location identification has naturally become a critical attribute, as it opens the door to a world of applications and services that were unthinkable only a few years ago (May, 2001).

scholarly journals Visual Emotion-Aware Cloud Localization User Experience Framework Based on Mobile Location Services

2021 ◽  
Vol 15 (14) ◽  
pp. 140
Author(s):  
Aiman Mamdouh Ayyal Awwad
Keyword(s):  
User Experience ◽  
Mobile Apps ◽  
Mobile App ◽  
Immediate Feedback ◽  
Location Services ◽  
Emotional Recognition ◽  
Mobile Location ◽  
User Experience Design ◽  
Interaction Field ◽  
High Recognition Accuracy

<p>Recently, the study of emotional recognition models has increased in the human-computer interaction field. With high recognition accuracy of emotions’ data, we could get immediate feedback from mobile users, get a better perception of human behavior while interacting with mobile apps, and thus make the user experience design more adaptable and intelligent. The harnessing of emotional recognition in mobile apps can dramatically enhance users’ experience. Therefore, in this paper, we propose a visual emotion-aware cloud localization user experience framework based on mobile location services. An important feature of our proposed framework is to provide a personalized mobile app based on the user’s visual emotional changes. The framework captures the emotion-aware data, process them in the cloud server, and analyze them for an immediate localization process. The first stage in the framework builds a correlation between the application’s default language and the user’s visual emotional feedback. In the second stage, the localization model loads the appropriate application’s resources and adjusts the screen features based on the real-time user’s emotion obtained in the first stage and according to the location data that the app collected from the mobile device. Our experiments demonstrate the effectiveness of the proposed framework. The results show that our proposed framework can provide a high-quality application experience in terms of a user’s emotional levels and deliver an excellent level of usability that was before not possible.</p>

The Business of Maps

2019 ◽  
pp. 45-65
Author(s):  
Rowan Wilken
Keyword(s):  
Interesting Case ◽  
Location Based Services ◽  
The Political ◽  
Prior Work ◽  
Location Services ◽  
Mobile Location ◽  
Major Player ◽  
Rival Firms ◽  
Revenue Models ◽  
Mobile Maps

This chapter builds on prior work on the political economy of location-based services to examine the business of mobile maps, asking the following questions: Who controls maps data? What are these data? Where do these data come from? What is their quality? What does it take to build new mobile maps? What are the motivations for wanting to build new maps? And what are the business and revenue models associated with these maps? The focus of this chapter is an examination of the efforts of one of Google’s key rival firms—Apple—and its struggles to build mapping capacity of its own at sufficient quality to be able to lessen (if not entirely break from) its reliance on Google. Apple presents an interesting case in that, as is well known, it is a major player in other areas of the mobile location services ecosystem, yet took industry pundits by surprise when it announced Apple Maps in 2012.

Location Services Ecosystems

2019 ◽  
pp. 25-44
Author(s):  
Rowan Wilken
Keyword(s):  
Location Based Services ◽  
Location Services ◽  
Mobile Location ◽  
Locative Media ◽  
Constituent Species ◽  
Different Parts

What precisely is meant by location-based services (as opposed to locative media, more narrowly defined)? And, how might one give shape to and begin to discuss location-based services as an industry? Taking an ecosystems approach, the aims of this chapter are to highlight the diversity of the location-based services ecosystem; give form and shape to this ecosystem; describe some of the constituent “species” (the key corporate players that occupy this ecosystem); detail the ways that the different parts of this ecosystem work together; and detail how the mobile location ecosystem intersects and interacts with a range of other (often much larger) interconnected ecosystems.

Research on OpenLS-Based Mobile Location Service Platform

2010 ◽  
Vol 121-122 ◽  
pp. 722-727
Author(s):  
Chi Jun Zhang ◽  
Zheng Xuan Wang ◽  
Yong Jian Yang
Keyword(s):  
Large Scale ◽  
Location Based Services ◽  
Service Platform ◽  
Location Services ◽  
Domestic Industry ◽  
Mobile Location ◽  
Location Service ◽  
Navigation Algorithm ◽  
Location Algorithm ◽  
Path Navigation

Currently, LBS (Location-based Services) as a new emerging business which is based on mobile communication network is becoming more and more popular. However domestic industry is lack of perfect location service platform and standards because of the complexity and large scale of LBS. Aimed at the cases, the architecture of mobile location service (MLS) platform based on OpenLS (OpenGIS® Location Services) standards is constructed in the paper, and makes it accord with the international standard. Moreover GIS (Geographic Information System) middleware model is also proposed in the paper. We encapsulate the secondary location algorithm and path navigation algorithm into GIS middleware and present four standard interfaces, which could support distributed management and improves the portability of the platform.

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