WHAT'S NEW?
Loading...

Collecting information about GPS navigation using android


When we Collecting information about GPS navigation using android, first of all we study the research papers that published already. We have describe following research paper.

Navigation Application Using Android -Harshal Kolhe, Saurabh Upankar, Mukesh Atone, Sanjay kamade. Student, (Department of IT), RGCER, Nagpur




Index Terms: Android, GPS, Navigation, Position, Statistical data, Database

In here trying to implement a device tracking system. They used GPS to track the device. Why they used GPS??
Because the GPS is increasingly being used for a wide range of application. When getting user’s co-ordinate location can track him according to where his android device has been to.

When we studying about navigation we can study about process of monitoring and controlling the moment of craft or vehicle from one place to another and also can determination of position and direction.

What we can do with navigation, we can detect users/any of that device attached location, travelling patterns, location patterns, measure distance to the object, etc.……

I wish to add some facilities for our developing app in feature:
-        Sharing interesting place through social media and collect that sharing info we can gather details about pattern of the customers most interested places…

When we developing such as system:
-        Need IDE such as Android Studio or ECLIPSE
-        Java programming language
-        Database handling such as SQL
-        Design UI
-        Getting user input we can use XML

According to this research paper we have to break whole app to several parts. That mean there is several processes. Each process has its own virtual machine. So, an application’s code runs in isolation from other applications.

Context-based Personalized Settings for Mobile Location Sharing



Location Based Services are popular but the usage of the same privacy settings for

all users is not appropriate.

LBSes are coupled with smartphone services and these services cause 84%

potential privacy problems and 49% are more comfortable with their own location

information.

Improving User Topic Interest Profiles by Behavior Factorization




        It is true that one of the significant aims of building content recommendation is the construction

of person ahead user profiles. In this case the two problems of feature engineering and the proper

utilization of the signals using data mining and machine learning approaches are faced.

Predicting User Interests from Contextual Information


            This paper present a systematic study of the effectiveness of five variant sources of contextual information for user interest modeling. The five contextual information sources used are: social, historic, task, collection, and user interaction. This study focus on website recommendations rather than search results. This research evaluate the utility of these five sources, and overlaps between them, based on how effectively they predict users’ future interests. The results demonstrate that the sources perform differently depending on the duration of the time window used for future prediction, and that context overlap outperforms any isolated source.