In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
will defend his dissertation proposal
A Density-Contour Based Framework for Spatio-temporal Clustering and Event Detection
Due to the advances in remote sensors and sensor networks, different types of spatio-temporal datasets become increasingly available these days. Revealing interesting spatio-temporal patterns from such datasets is very important as it has broad applications, such as understanding climate change, identifying crime patterns, epidemics detection, and earthquake analysis. In this research, we mainly focus on developing a framework for spatio-temporal clustering, which aims to identify dense regions of spatio-temporal entities and to discover interesting spatio-temporal patterns associated with each region.
In our preliminary work, we introduced a density-contour based framework, for spatio-temporal clustering including several serial, density-contour based spatio-temporal clustering algorithms we proposed: ST-DCONTOUR, ST- DPOLY, ST-COPOT. They all rely on 3-phase clustering approach, which takes the point cloud stream as input and divides it into batches based on fixed size time windows; next, a density estimation approach and contouring algorithms are employed to obtain spatial clusters as polygon models; finally, spatio-temporal clusters are formed by identifying continuing relationships between spatial clusters in consecutive batches. The framework was successfully applied to NYC taxi trips data; the experimental results show that all the algorithms can effectively discover interesting spatio-temporal patterns in taxi pickup location streams.
Recently, Twitter, as one of the fastest-growing microblogging services, has induced a lot of research, one hot topic is event detection from Tweets. As geotagged tweets can be viewed as location streams with time tags and the content of the tweets themselves, we plan to develop a novel framework to detect events from Twitter streams by integrating an LDA-based approach and ST-COPOT. In the proposed framework, we first divide the geotagged tweet stream into temporal time windows; next, events are identified as topics in tweets using an LDA-based natural language processing step; next, each tweet is assigned an event label; at last, after all locations extracted from each event, the previously mentioned spatio-temporal approach is employed to track the temporal evolution of identified spatial events, in particular, the continuity of events.
Moreover, we aim to acquire not only the semantic aspect of the events, but also the geographic distribution of the events and their continuity along time; this information can be used to help individuals, corporations, or government organizations to stay informed of "what is happening now" and to acquire actionable knowledge.
Date: Monday, November 20, 2017
Time: 12:30 PM
Place: PGH 550
Advisors: Dr. Christoph F. Eick
Faculty, students, and the general public are invited.