Mining user moving patterns for personal data allocation in a mobile computing system

2002 ◽  
Author(s):  
Wen-Chih Peng ◽  
Ming-Syan Chen
Keyword(s):  
Mobile Computing ◽  
Personal Data ◽  
Computing System ◽  
Data Allocation ◽  
Moving Patterns

Design of Wearable Computing Systems for Future Industrial Environments

2011 ◽  
pp. 1226-1245
Author(s):  
Pierre Kirisci ◽  
Ernesto Morales Kluge ◽  
Emanuel Angelescu ◽  
Klaus-Dieter Thoben
Keyword(s):  
Mobile Computing ◽  
Design Process ◽  
Reference Model ◽  
Wearable Computing ◽  
Computing System ◽  
Context Aware ◽  
Context Model ◽  
Computing Systems ◽  
Model Driven ◽  
Industrial Environments

During the last two decades a lot of methodology research has been conducted for the design of software user interfaces (Kirisci, Thoben 2009). Despite the numerous contributions in this area, comparatively few efforts have been dedicated to the advancement of methods for the design of context-aware mobile platforms, such as wearable computing systems. This chapter investigates the role of context, particularly in future industrial environments, and elaborates how context can be incorporated in a design method in order to support the design process of wearable computing systems. The chapter is initiated by an overview of basic research in the area of context-aware mobile computing. The aim is to identify the main context elements which have an impact upon the technical properties of a wearable computing system. Therefore, we describe a systematic and quantitative study of the advantages of context recognition, specifically task tracking, for a wearable maintenance assistance system. Based upon the experiences from this study, a context reference model is proposed, which can be considered supportive for the design of wearable computing systems in industrial settings, thus goes beyond existing context models, e.g. for context-aware mobile computing. The final part of this chapter discusses the benefits of applying model-based approaches during the early design stages of wearable computing systems. Existing design methods in the area of wearable computing are critically examined and their shortcomings highlighted. Based upon the context reference model, a design approach is proposed through the realization of a model-driven software tool which supports the design process of a wearable computing system while taking advantage of concise experience manifested in a well-defined context model.

scholarly journals Structural Holes in the Multi-Sided Market: A Market Allocation Structure Analysis of China’s Car-Hailing Platform in the Context of Open Innovation

2019 ◽  
Vol 11 (20) ◽  
pp. 5813 ◽  
Author(s):  
Lei Huang ◽  
Yandong Zhao ◽  
Liang Mei ◽  
Peiyi Wu ◽  
Zhihua Zhao ◽  
...  
Keyword(s):  
Open Innovation ◽  
Personal Data ◽  
Data Driven ◽  
Edge Weight ◽  
Data Allocation ◽  
Structural Holes ◽  
Data Resource ◽  
Platform Governance ◽  
Sustainable Innovation ◽  
Market Allocation

Car-hailing platform governance is an emerging topic of research and practice. The governance of the data-driven platform economy is challenging the research paradigm of competition regulation in the context of open innovation. This research is trying to reveal the market allocation structure of China’s online car-hailing industry from the perspective of personal data allocation by the study of Application Programming Interface (API) of sample platforms. On the basis of the networked nature of personal data allocation via APIs, this research constructs a mathematical model of the edge weight of data resource connections between platforms. Furthermore, this research optimises the structural hole analysis of complex networks to discuss the state of personal data resource allocation in China’s car-hailing industry. Results reveal that there are obvious structural holes within the sample network. When compared with related indicators, we found that accessing personal data resources is an essential component of the sample network competition capability and sustainable innovation. Social media platforms and online payment platforms more greatly impact car-hailing platform competition than other types of platforms within the multi-sided market context. This research offers a research perspective of personal data allocation for further study of competition, regulation and sustainable innovation of data-driven platform economies.

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