title: Compressing GPS data on mobile devices

authors: Lever, Ryan

subjects: computer science

abstract: In context-aware mobile systems, data on past user behaviour or use of a device can give critical information. The scale of this data may be large, and it must be quickly searched and retrieved. Compression is a powerful tool for both storing and indexing data. For text documents powerful algorithms using structured storage achieve high compression and rapid search and retrieval. Byte-stream techniques provide higher compression, but lack indexation and have slow retrieval. 
Location is a common form of context frequently used in research prototypes of tourist guide systems, location-aware searching and adaptive hypermedia. In this paper, we present an exploration of record-based compression of Global Positioning System (GPS) data that reveals significant technical limitations on what can be achieved on mobile devices, and a discussion of the benefits of different compression techniques on GPS data.

text: Compressing GPS data on mobile devices In context-aware mobile systems, data on past user behaviour or use of a device can give critical information. The scale of this data may be large, and it must be quickly searched and retrieved. Compression is a powerful tool for both storing and indexing data. For text documents powerful algorithms using structured storage achieve high compression and rapid search and retrieval. Byte-stream techniques provide higher compression, but lack indexation and have slow retrieval. 
Location is a common form of context frequently used in research prototypes of tourist guide systems, location-aware searching and adaptive hypermedia. In this paper, we present an exploration of record-based compression of Global Positioning System (GPS) data that reveals significant technical limitations on what can be achieved on mobile devices, and a discussion of the benefits of different compression techniques on GPS data.

year: 2006

title: Clustering mixed data

authors: Hunt, Lynette Anne

subjects: mixture model clustering

abstract: Mixture model clustering proceeds by fitting a finite mixture of multivariate distributions to data, the fitted mixture density then being used to allocate the data to one of the components. Common model formulations assume that either all the attributes are continuous or all the attributes are categorical. In this paper, we consider options for model formulation in the more practical case of mixed data: multivariate data sets that contain both continuous and categorical attributes.

text: Clustering mixed data Mixture model clustering proceeds by fitting a finite mixture of multivariate distributions to data, the fitted mixture density then being used to allocate the data to one of the components. Common model formulations assume that either all the attributes are continuous or all the attributes are categorical. In this paper, we consider options for model formulation in the more practical case of mixed data: multivariate data sets that contain both continuous and categorical attributes.

year: 2011

title: Sociocultural realities: exploring new horizons by Angus Macfarlane, Sonja Macfarlane and Melinda Webber [Book Review]

authors: McNae, Rachel Elizabeth

abstract: This article reviews the book "Sociocultural realities: exploring new horizons", by Angus Macfarlane, Sonja Macfarlane and Melinda Webber

text: Sociocultural realities: exploring new horizons by Angus Macfarlane, Sonja Macfarlane and Melinda Webber [Book Review] This article reviews the book "Sociocultural realities: exploring new horizons", by Angus Macfarlane, Sonja Macfarlane and Melinda Webber

year: 2017