Ayush Shrestha

Storygraph: extracting patterns from spatio-temporal data

Analysis of spatio-temporal data often involves correlating different events in time and location to uncover relationships between them. It is also desirable to identify different patterns in the data. Visualizing time and space in the same chart is not trivial. Common methods includes plotting the latitude, longitude and time as three dimensions of a 3D chart. Drawbacks of these 3D charts include not being able to scale well due to cluttering, occlusion and difficulty to track time in case of clustered events. In this paper we present a novel 2D visualization technique called Storygraph which provides an integrated view of time and location to address these issues. We also present storylines based on Storygraph which show movement of the actors over time. Lastly, we present case studies to show the applications of Storygraph.

Storygraph: Telling Stories from Spatio-temporal Data

A major task of spatio-temporal data analysis is to discover relationships and patterns among spatially and temporally scattered events. A most common analytic method is to plot them on a 3D chart with latitude, longitude and time being the three dimensions. The first drawback of this technique is that it fails to scale well when there are thousands of concentrated events since they suffer from cluttering, occlusion and other limitations of 3D plots. Second, it is hard to track the time component if the events are clustered in a region. To overcome these, we present a novel 2D visualization technique called Storygraph that provides an integrated view of location and time. Based on Storygraph, we also present storylines which show the movement of the characters over time. Finally, we present two case studies to demonstrate the effectiveness of the Storygraph.

Digging into human rights violations: Data modelling and collective memory

Archives of human rights violations reports, by virtue of their poor metadata, basis in natural language, and scale, obscure fine grain analyses of violation event patterns. Cross-document coreference of victim or perpetrator occurrences from across a corpus is challenging, particularly when those mentions relate to different events. These challenges are emblematic of the transition from small scale to big data analysis in the humanities. This paper discusses these issues and proposes a framework to address these challenges so as to explore narrative construction and the formation of collective memory. Though our framework is based on processing human rights violation reports, it can be readily extended to support other big data problems in the humanities.

Cross-Document Non-Fiction Narrative Alignment

This paper describes a new method for narrative frame alignment that extends and supplements models reliant on graph theory from the domain of fiction to the domain of nonfiction news articles. Preliminary tests of this method against a corpus of 24 articles related to private security firms operating in Iraq and the Blackwater shooting of 2007 show that prior methods utilizing a graph similarity approach can work but require a narrower entity set than commonly occurs in non-fiction texts. They also show that alignment procedures sensitive to abstracted event sequences can accurately highlight similar narratological moments across documents despite syntactic and lexical differences. Evaluation against LDA for both the event sequence lists and source sentences is provided for performance comparison. Next steps include merging these semantic and graph analytic approaches and expanding the test corpus.

NetTimeView: Applying Spatio-temporal Data Visualization Techniques to DDoS Attack Analysis

Distributed Denial-Of-Service (DDoS) is a common network attack where multiple computers attempt to disable a single system with overwhelming network traffic. Various data visualization methods have been developed to help explain, analyze, and deal with DDoS attacks. However, most of the existing visualization methods do not effectively present the temporal aspect of the DDoS attack data. In this paper, we present a novel DDoS visualization technique, NetTimeView, that applies spatio-temporal data visualization to DDoS data. This technique integrates network traffic data and temporal data in a single view. Its multi-layered visualization technique is able to handle very large data sets with efficient use of visualization space. This tool is particularly useful for system administrators and network security analysts to conduct network forensic analysis. We demonstrate our method with a case study of a large DDoS data set.

Visualizing computational, transversal narratives from the World Trade Towers.

Semi-automated extraction of details corresponding to narratological fabula from a corpus of narrative interviews on a single event provides decontextualized building blocks for transversal, or cross-document, narratives. With information extracted from 503 World Trade Center Task Force Interviews comprising 12,000 pages of testimony and novel visualization techniques, this article proposes a computational method for the emergence of narratives that cross beyond the boundaries of one interview. These assembled narratives, in cases like that of Chief Ganci, can document those who did not survive to tell their own story.

A method for cross-document narrative alignment of a two-hundred-sixty-million word corpus

Identifying similar narrative sections across longer documents would help identify key events within a corpus, enrich understanding of those events, provide a mechanism for organizing corpora according to their event content, and allow for bottom-up testing of theories of narrative. This paper proposes an automated method for narrative alignment across large textual corpora using techniques from natural language processing and similarity-based image segmentation. This method proceeds by segmenting each document into a series of events, constructs sequences of abstracted representations of those events, compares pairs of sequences to generate image matrices, segments the images, identifies similar segments to discover commonly occurring narrative units, and, finally, returns the source sentences to make the clusters of narrative similarity readable. Preliminary tests of elements of this method were conducted on a small heterogeneous corpus (<; 100 documents) and a moderate heterogeneous corpus (10k documents). Further implementation as described in this position paper is necessary to scale to the full 251k document corpus from which the moderate corpus was drawn.