Detecting Suspected Epidemic Cases Using Trajectory Big Data

Detecting Hotspots from Taxi Trajectory Data Using Spatial Cluster Analysis

A method of trajectory clustering based on decision graph and data field is proposed in this paper. The method utilizes data field to describe spatial distribution of trajectory points, and uses decision graph to discover cluster centres. It can automatically determine cluster parameters and is suitable to trajectory clustering. The method is applied to trajectory clustering on taxi trajectory ...

Sometimes Too Big: Compressing trajectory Data

In the regime of “Big Data”, data compression techniques take crucial part in preparation phase of data analysis. It is challenging because statistical properties and other characteristics need to be preserved while the size of data need to be reduced. In particular, to compress trajectory data, movement status (such as position, direction, and speed etc.) need to be retained. In this paper, we...

Big Data Cases in Banking and Securities

Detecting Concept Drift in Data Stream Using Semi-Supervised Classification

Data stream is a sequence of data generated from various information sources at a high speed and high volume. Classifying data streams faces the three challenges of unlimited length, online processing, and concept drift. In related research, to meet the challenge of unlimited stream length, commonly the stream is divided into fixed size windows or gradual forgetting is used. Concept drift refer...

Big Trajectory Data Analysis for Clustering and Anomaly Detection

We’ve been developing a sensor that can acquire positional data. Recently, a position-based big data creation is easy task and trajectory analysis is the highest priority for ”position-based service”. Traffic congestion, marketing mining, and pattern analysis are the one of the examples in trajectory analysis field. In this paper, we propose the trajectory analysis approach for clustering and a...