Steffen Koch

Spatiotemporal anomaly detection through visual analysis of geolocated Twitter messages

Analyzing message streams from social blogging services such as Twitter is a challenging task because of the vast number of documents that are produced daily. At the same time, the availability of geolocated, realtime, and manually created status updates are an invaluable data source for situational awareness scenarios. In this work we present an approach that allows for an interactive analysis of location-based microblog messages in realtime by means of scalable aggregation and geolocated text visualization. For this purpose, we use a novel cluster analysis approach and distinguish between local event reports and global media reaction to detect spatiotemporal anomalies automatically. A workbench allows the scalable visual examination and analysis of messages featuring perspective and semantic layers on a world map representation. Our novel techniques can be used by analysts to classify the presented event candidates and examine them on a global scale.

ScatterBlogs2: Real-Time Monitoring of Microblog Messages through User-Guided Filtering

The number of microblog posts published daily has reached a level that hampers the effective retrieval of relevant messages, and the amount of information conveyed through services such as Twitter is still increasing. Analysts require new methods for monitoring their topic of interest, dealing with the data volume and its dynamic nature. It is of particular importance to provide situational awareness for decision making in time-critical tasks. Current tools for monitoring microblogs typically filter messages based on user-defined keyword queries and metadata restrictions. Used on their own, such methods can have drawbacks with respect to filter accuracy and adaptability to changes in trends and topic structure. We suggest ScatterBlogs2, a new approach to let analysts build task-tailored message filters in an interactive and visual manner based on recorded messages of well-understood previous events. These message filters include supervised classification and query creation backed by the statistical distribution of terms and their co-occurrences. The created filter methods can be orchestrated and adapted afterwards for interactive, visual real-time monitoring and analysis of microblog feeds. We demonstrate the feasibility of our approach for analyzing the Twitter stream in emergency management scenarios.

Visual Classifier Training for Text Document Retrieval

Performing exhaustive searches over a large number of text documents can be tedious, since it is very hard to formulate search queries or define filter criteria that capture an analysts information need adequately. Classification through machine learning has the potential to improve search and filter tasks encompassing either complex or very specific information needs, individually. Unfortunately, analysts who are knowledgeable in their field are typically not machine learning specialists. Most classification methods, however, require a certain expertise regarding their parametrization to achieve good results. Supervised machine learning algorithms, in contrast, rely on labeled data, which can be provided by analysts. However, the effort for labeling can be very high, which shifts the problem from composing complex queries or defining accurate filters to another laborious task, in addition to the need for judging the trained classifiers quality. We therefore compare three approaches for interactive classifier training in a user study. All of the approaches are potential candidates for the integration into a larger retrieval system. They incorporate active learning to various degrees in order to reduce the labeling effort as well as to increase effectiveness. Two of them encompass interactive visualization for letting users explore the status of the classifier in context of the labeled documents, as well as for judging the quality of the classifier in iterative feedback loops. We see our work as a step towards introducing user controlled classification methods in addition to text search and filtering for increasing recall in analytics scenarios involving large corpora.

Raman Spectroscopy: A Noninvasive Analysis Tool for the Discrimination of Human Skin Cells

Noninvasive monitoring of tissue-engineered (TE) constructs during their in vitro maturation or postimplantation in vivo is highly relevant for graft evaluation. However, traditional methods for studying cell and matrix components in engineered tissues such as histology, immunohistochemistry, or biochemistry require invasive tissue processing, resulting in the need to sacrifice of TE constructs. Raman spectroscopy offers the unique possibility to analyze living cells label-free in situ and in vivo solely based on their phenotype-specific biochemical fingerprint. In this study, we aimed to determine the applicability of Raman spectroscopy for the noninvasive identification and spectral separation of primary human skin fibroblasts, keratinocytes, and melanocytes, as well as immortalized keratinocytes (HaCaT cells). Multivariate analysis of cell-type-specific Raman spectra enabled the discrimination between living primary and immortalized keratinocytes. We further noninvasively distinguished between fibroblasts, keratinocytes, and melanocytes. Our findings are especially relevant for the engineering of in vitro skin models and for the production of artificial skin, where both the biopsy and the transplant consist of several cell types. To realize a reproducible quality of TE skin, the determination of the purity of the cell populations as well as the detection of potential molecular changes are important. We conclude therefore that Raman spectroscopy is a suitable tool for the noninvasive in situ quality control of cells used in skin tissue engineering applications.

VA 2 : A Visual Analytics Approach for // Evaluating Visual Analytics Applications

Evaluation has become a fundamental part of visualization research and researchers have employed many approaches from the field of human-computer interaction like measures of task performance, thinking aloud protocols, and analysis of interaction logs. Recently, eye tracking has also become popular to analyze visual strategies of users in this context. This has added another modality and more data, which requires special visualization techniques to analyze this data. However, only few approaches exist that aim at an integrated analysis of multiple concurrent evaluation procedures. The variety, complexity, and sheer amount of such coupled multi-source data streams require a visual analytics approach. Our approach provides a highly interactive visualization environment to display and analyze thinking aloud, interaction, and eye movement data in close relation. Automatic pattern finding algorithms allow an efficient exploratory search and support the reasoning process to derive common eye-interaction-thinking patterns between participants. In addition, our tool equips researchers with mechanisms for searching and verifying expected usage patterns. We apply our approach to a user study involving a visual analytics application and we discuss insights gained from this joint analysis. We anticipate our approach to be applicable to other combinations of evaluation techniques and a broad class of visualization applications.

Iterative integration of visual insights during patent search and analysis

Patents are an important economic factor in todays globalized markets. Therefore, the analysis of patent information has become an inevitable task for a variety of interest groups. The retrieval of relevant patent information is an integral part of almost every patent analysis scenario. Unfortunately, the complexity of patent material inhibits a straightforward retrieval of all relevant patent documents and leads to iterative, time-consuming approaches in practice. With ‘PatViz’, a new system for interactive analysis of patent information has been developed to leverage iterative query refinement. PatViz supports users in building complex queries visually and in exploring patent result sets interactively. Thereby, the visual query module introduces an abstraction layer that provides uniform access to different retrieval systems and relieves users of the burden to learn different complex query languages. By establishing an integrated environment it allows for interactive reintegration of insights gained from visual result set exploration into the visual query representation. We expect that the approach we have taken is also suitable to improve iterative query refinement in other Visual Analytics systems.

CiteRivers: Visual Analytics of Citation Patterns

The exploration and analysis of scientific literature collections is an important task for effective knowledge management. Past interest in such document sets has spurred the development of numerous visualization approaches for their interactive analysis. They either focus on the textual content of publications, or on document metadata including authors and citations. Previously presented approaches for citation analysis aim primarily at the visualization of the structure of citation networks and their exploration. We extend the state-of-the-art by presenting an approach for the interactive visual analysis of the contents of scientific documents, and combine it with a new and flexible technique to analyze their citations. This technique facilitates user-steered aggregation of citations which are linked to the content of the citing publications using a highly interactive visualization approach. Through enriching the approach with additional interactive views of other important aspects of the data, we support the exploration of the dataset over time and enable users to analyze citation patterns, spot trends, and track long-term developments. We demonstrate the strengths of our approach through a use case and discuss it based on expert user feedback.

ScatterBlogs: Geo-spatial document analysis

We presented Scatterblogs, a system for microblog analysis that seamlessly integrates search backend and visual frontend. It provides powerful, automatic algorithms for detecting spatio-temporal ‘anomalies’ within blog entries as well as corresponding visual representations and interaction facilities for inspecting anomalies or exploiting them in further analytic steps. Apart from that, we consider the systems combinatoric facilities for building complex hypotheses from temporal, spatial, and content-related aspects an important feature. This was the key for creating a cross-checked analysis for MC1.

Two-stage framework for a topology-based projection and visualization of classified document collections

During the last decades, electronic textual information has become the worlds largest and most important information source. Daily newspapers, books, scientific and governmental publications, blogs and private messages have grown into a wellspring of endless information and knowledge. Since neither existing nor new information can be read in its entirety, we rely increasingly on computers to extract and visualize meaningful or interesting topics and documents from this huge information reservoir. In this paper, we extend, improve and combine existing individual approaches into an overall framework that supports topologi-cal analysis of high dimensional document point clouds given by the well-known tf-idf document-term weighting method. We show that traditional distance-based approaches fail in very high dimensional spaces, and we describe an improved two-stage method for topology-based projections from the original high dimensional information space to both two dimensional (2-D) and three dimensional (3-D) visualizations. To demonstrate the accuracy and usability of this framework, we compare it to methods introduced recently and apply it to complex document and patent collections.

Preliminary study into query translation for patent retrieval

Patent retrieval is a branch of Information Retrieval (IR) aiming to support patent professionals in retrieving patents that satisfy their information needs. Often, patent granting bodies require patents to be partially translated into one or more major foreign languages, so that language boundaries do not hinder their accessibility. This multilinguality of patent collections offers opportunities for improving patent retrieval. In this work we exploit these opportunities by applying query translation to patent retrieval. We expand monolingual patent queries with their translations, using both a domain-specific patent dictionary that we extract from the patent collection, and a general domain-free dictionary. Experimental evaluation on a standard CLEF-IP dataset shows that using either translation dictionary fetches similar results: query translation can help patent retrieval, but not always, and without great improvement compared to standard statistical monolingual query expansion (Rocchio). The improvement is greater when the source language is English, as opposed to French or German, a finding partly due to the effect of the complex French and German morphology upon translation accuracy, but also partly due to the prevalence of English in the collection. A thorough per-query analysis reveals that cases where standard query expansion fails (e.g. zero recall) can benefit from query translation.