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|Titel:||Caching concept for mobile engineering apps|
|Zusammenfassung:||Mobile apps in the engineering domain, have to deal with data coming from Product Data Management (PDM)-Systems. This data contains details about the products that are very large. Geometry data like 2D or 3D Computer Aided Design (CAD) representations are included. To get the data, the apps use wireless mediums like WiFi or mobile data networks (LTE, 3G, etc.). Transferring large size data over these mediums take a lot of time and can be aborted through intermittent connectivity. Also the energy consumption increases through the long-lasting transfers. In this master thesis a concept is created that overcomes these problems. A cache on the client is used that stores the relevant data for a fast access. As the disk space on mobile devices is limited, the data that is cached has to be chosen well. Only the data that is currently needed should be stored in the cache and provided to the app. To reduce the waiting times these data should be there before it is explicitly requested. To make this possible the concept of this thesis provides preemptive caching (hoarding). Thereby, the data is cached that will probably be needed next. To decide what data is needed, context is used. The information coming from the environment of the client is used, to derive situations. With the help of these situations the data is determined that gets cached. Besides this context-aware strategy, a traditional way of caching where all requested data gets cached is used in the concept. Furthermore, this thesis addresses the caching mechanism in its entirety. It determines a policy for the replacement of not needed data to free space. Also a strategy for invalidating obsolete data in the cache is determined. Finally, a prototypical implementation of the concept within an existing mobile engineering app is presented. With the help of this prototype the concept is evaluated.|
|Enthalten in den Sammlungen:||05 Fakultät Informatik, Elektrotechnik und Informationstechnik|
Dateien zu dieser Ressource:
|Masterarbeit Michael Steffl.pdf||4,51 MB||Adobe PDF||Öffnen/Anzeigen|
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