Franz Wanner

State-of-the-Art Report of Visual Analysis for Event Detection in Text Data Streams

Event detection from text data streams has been a popular research area in the past decade. Recently, the evolution of microblogging and social network services opens up great opportunities for various kinds of knowledge-based intelligence activities which require tracking of real-time events. In a sense, visualizations in combination with analytical processes could be a viable method for such tasks because it can be used to analyze the sheer amounts of text streams. However, data analysts and visualization experts often face grand challenges stemming out of the ill-defined concept of event and various kinds of textual data. As a result, we have few guidelines on how to build successful visual analysis tools that can handle specific event types and diverse textual data sources. Our goal is to take the first step towards answering the question by organizing insights from prior research studies on event detection and visual analysis. In the scope of this report, we summarize the evolution of event detection in combination with visual analysis over the past 14 years and provide an overview of the state-of-the-art methods. Our investigation sheds light on various kinds of research areas that can be the most beneficial to the field of visual text event analytics.

Event identification for local areas using social media streaming data

Unprecedented success and active usage of social media services result in massive amounts of user-generated data. An increasing interest in the contained information from social media data leads to more and more sophisticated analysis and visualization applications. Because of the fast pace and distribution of news in social media data it is an appropriate source to identify events in the data and directly display their occurrence to analysts or other users. This paper presents a method for event identification in local areas using the Twitter data stream. We implement and use a combined log-likelihood ratio approach for the geographic and time dimension of real-life Twitter data in predefined areas of the world to detect events occurring in the message contents. We present a case study with two interesting scenarios to show the usefulness of our approach.

ForAVis : explorative user forum analysis

User generated textual content on the internet has become increasingly valueable during the past few years. Forums, blogs, twitter and other social media websites are accessible for a huge amount of people all over the world. Hence, methods and tools are needed to handle this vast bulk of textual data. In this paper we present an explorative forum analysis system helping various stakeholders to cope with the task analyzing user generated content in online forums. The used mobile communication forums picture an example of user generated content in online discussion forums. Central to our system is a flexible visualization, which supports the analysis and exploration visually. Flexible means, that the ordering and the mapping of colors can be interactively changed by the analyst and the visualization is also capable to show the different structural levels of a user forum. The filter area offers beside well-known features many interesting features with respect to forum analysis, which we introduce in this paper. A detailed view of the particularly selected thread in the main visualization is presented in a third area. For a convenient manipulation and interaction we implemented intuitive mechanism. We describe the system and present various fictive user scenarios of different typical stakeholder tasks to illustrate the benefit of the system.

Visual pattern discovery in timed event data

Publisher’s Note: This paper, originally published on 24 January 2011, was replaced with a corrected/revised version on 9 April 2015. If you downloaded the original PDF but are unable to access the revision, please contact SPIE Digital Library Customer Service for assistance. Business processes have tremendously changed the way large companies conduct their business: The integration of information systems into the workflows of their employees ensures a high service level and thus high customer satisfaction. One core aspect of business process engineering are events that steer the workflows and trigger internal processes. Strict requirements on interval-scaled temporal patterns, which are common in time series, are thereby released through the ordinal character of such events. It is this additional degree of freedom that opens unexplored possibilities for visualizing event data. In this paper, we present a flexible and novel system to find significant events, event clusters and event patterns. Each event is represented as a small rectangle, which is colored according to categorical, ordinal or intervalscaled metadata. Depending on the analysis task, different layout functions are used to highlight either the ordinal character of the data or temporal correlations. The system has built-in features for ordering customers or event groups according to the similarity of their event sequences, temporal gap alignment and stacking of co-occurring events. Two characteristically different case studies dealing with business process events and news articles demonstrate the capabilities of our system to explore event data.

Integrated visual analysis of patterns in time series and text data - Workflow and application to financial data analysis

In this article, we describe a workflow and tool that allows a flexible formation of hypotheses about text features and their combinations, which are significantly connected in time to quantitative phenomena observed in stock data. To support such an analysis, we combine the analysis steps of frequent quantitative and text-oriented data using an existing a priori method. First, based on heuristics, we extract interesting intervals and patterns in large time series data. The visual analysis supports the analyst in exploring parameter combinations and their results. The identified time series patterns are then input for the second analysis step, in which all identified intervals of interest are analyzed for frequent patterns co-occurring with financial news. An a priori method supports the discovery of such sequential temporal patterns. Then, various text features such as the degree of sentence nesting, noun phrase complexity, and the vocabulary richness, are extracted from the news items to obtain meta-patterns. Meta-patterns are defined by a specific combination of text features which significantly differ from the text features of the remaining news data. Our approach combines a portfolio of visualization and analysis techniques, including time, cluster, and sequence visualization and analysis functionality. We provide a case study and an evaluation on financial data where we identify important future work. The workflow could be generalized to other application domains such as data analysis of smart grids, cyber physical systems, or the security of critical infrastructure, where the data consist of a combination of quantitative and textual time series data.

Visual exploration of local interest points in sets of time series

Visual analysis of time series data is an important, yet challenging task with many application examples in fields such as financial or news stream data analysis. Many visual time series analysis approaches consider a global perspective on the time series. Fewer approaches consider visual analysis of local patterns in time series, and often rely on interactive specification of the local area of interest. We present initial results of an approach that is based on automatic detection of local interest points. We follow an overview-first approach to find useful parameters for the interest point detection, and details-on-demand to relate the found patterns. We present initial results and detail possible extensions of the approach.

Relating interesting quantitative time series patterns with text events and text features

In many application areas, the key to successful data analysis is the integrated analysis of heterogeneous data. One example is the financial domain, where time-dependent and highly frequent quantitative data (e.g., trading volume and price information) and textual data (e.g., economic and political news reports) need to be considered jointly. Data analysis tools need to support an integrated analysis, which allows studying the relationships between textual news documents and quantitative properties of the stock market price series. In this paper, we describe a workflow and tool that allows a flexible formation of hypotheses about text features and their combinations, which reflect quantitative phenomena observed in stock data. To support such an analysis, we combine the analysis steps of frequent quantitative and text-oriented data using an existing a-priori method. First, based on heuristics we extract interesting intervals and patterns in large time series data. The visual analysis supports the analyst in exploring parameter combinations and their results. The identified time series patterns are then input for the second analysis step, in which all identified intervals of interest are analyzed for frequent patterns co-occurring with financial news. An a-priori method supports the discovery of such sequential temporal patterns. Then, various text features like the degree of sentence nesting, noun phrase complexity, the vocabulary richness, etc. are extracted from the news to obtain meta patterns. Meta patterns are defined by a specific combination of text features which significantly differ from the text features of the remaining news data. Our approach combines a portfolio of visualization and analysis techniques, including time-, cluster- and sequence visualization and analysis functionality. We provide two case studies, showing the effectiveness of our combined quantitative and textual analysis work flow. The workflow can also be generalized to other application domains such as data analysis of smart grids, cyber physical systems or the security of critical infrastructure, where the data consists of a combination of quantitative and textual time series data.

DYNEVI : DYnamic News Entity VIsualization

Dynamic news entity visualization shows an implementation of visualizing news entity data to give an overview as well as to display emerging and vanishing news topics. We present a robust and dynamic visualization system with case studies that show its benefits and high functionality.