iDiary

Abstract

This article describes iDiary, a system that takes as input GPS data streams generated by users’ phones and turns them into textual descriptions of the trajectories. The system features a user interface similar to Google Search that allows users to type text queries on their activities (e.g., “Where did I buy books?”) and receive textual answers based on their GPS signals. iDiary uses novel algorithms for semantic compression and trajectory clustering of massive GPS signals in parallel to compute the critical locations of a user. We encode these problems as follows. The k-segment mean is a k -piecewise linear function that minimizes the regression distance to the signal. The ( k,m )- segment mean has an additional constraint that the projection of the k segments on R d consists of only m ≤ k segments. A coreset for this problem is a smart compression of the input signal that allows computation of a (1+ε)-approximation to its k -segment or ( k,m )-segment mean in O ( n log n ) time for arbitrary constants ε, k , and m . We use coresets to obtain a parallel algorithm that scans the signal in one pass, using space and update time per point that is polynomial in log n . Using an external database, we then map these locations to textual descriptions and activities so that we can apply text mining techniques on the resulting data (e.g., LSA or transportation mode recognition). We provide experimental results for both the system and algorithms and compare them to existing commercial and academic state of the art. This is the first GPS system that enables text-searchable activities from GPS data.

Finding Patterns in Signals Using Lossy Text Compression

“…The algorithms in the previous sections are optimal but take polynomial time in the input (length of string). However, their running time can be easily reduced to be linear in the input, by running them on core-sets for segmentation [29,30]. Roughly speaking, core-set is a problem-dependent reduction of the input, such that running the existing algorithm for solving the problem on the core-set, would yield a provable approximation compared to the result of running the algorithm on the complete data.…”

“…The sum of distances from the original set to every signal that consists of a constant number of k linear segments, is approximated by C, up to (1 + ε) multiplicative factor, where ε ∈ (0, 1) is constant. More generally, the core-set time has roughly quadratic dependency on k and 1/ ; see [29,30] for details. Unlike many solutions in machine or PAC-learning, in this and most core-sets there are no special assumptions on the size of input or its distribution (i.e., worse case input is assumed).…”

An Optimized Iterative Semantic Compression Algorithm And Parallel Processing for Large Scale Data

“…However, their work only supports categorical attributes. Other effective algorithms are proposed [10][11][12][13][14][15][16][17][18][19][20][21], but all have the shortcoming that can not handle a large scale data effectively.…”