Name
Title | ||
|---|---|---|
Understanding the Impact of Data Sparsity and Duration for Location Prediction Applications. |
Abstract | ||
|---|---|---|
As mobile devices capable of sensing location have become pervasive, the collection and transmission of location data has become commonplace, enabling the creation of models of behaviour that support location prediction. With such devices often heavily resource-constrained, the nature of data used in location prediction must be understood in order to optimise storage and processing requirements. This paper specifically explores data sparsity and collection duration. The results presented provide insight which suggest: (i) a relationship of diminishing returns in predictive accuracy when collecting user location data at increased rates over a fixed period, and (ii) the duration over which a fixed size sample of location data is collected has a greater impact on predicative accuracy than data sparsity. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-19743-2_29 | Lecture Notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering |
Keywords | Field | DocType |
Collection,Data,Duration,Location prediction,Sparsity | Collection duration,Data mining,Computer science,Location data,Mobile device,Diminishing returns,Location prediction,Predicative expression | Conference |
Volume | ISSN | Citations |
151 | 1867-8211 | 1 |
PageRank | References | Authors |
0.34 | 13 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alasdair Thomason | 1 | 15 | 3.58 |
| Matthew Leeke | 2 | 75 | 10.26 |
| Nathan Griffiths | 3 | 115 | 15.49 |