Daniel Seebacher

Director's Cut: Analysis and Annotation of Soccer Matches

For development and alignment of tactics and strategies, professional soccer analysts spend up to three working days manually analyzing and annotating professional soccer matches. In an effort to improve soccer player and match analysis, a visual-interactive and data-analysis support system focuses on key situations by using rule-based filtering and automatically annotating key types of soccer match elements. The authors evaluate the proposed approach by analyzing real-world soccer matches and several expert studies. Quantitative measures show the proposed methods can significantly outperform naive solutions.

Patent retrieval: a multi-modal visual analytics approach

Claiming intellectual property for an invention by patents is a common way to protect ideas and technological advancements. However, patents allow only the protection of new ideas. Assessing the novelty of filed patent applications is a very time-consuming, yet crucial manual task. Current patent retrieval systems do not make use of all available data and do not explain the similarity between patents. We support patent officials by an enhanced Visual Analytics multi-modal patent retrieval system. Including various similarity measurements and incorporating user feedback, we are able to achieve significantly better query results than state-of-the-art methods.

An Adaptive Image-based Plagiarism Detection Approach

Identifying plagiarized content is a crucial task for educational and research institutions, funding agencies, and academic publishers. Plagiarism detection systems available for productive use reliably identify copied text, or near-copies of text, but often fail to detect disguised forms of academic plagiarism, such as paraphrases, translations, and idea plagiarism. To improve the detection capabilities for disguised forms of academic plagiarism, we analyze the images in academic documents as text-independent features. We propose an adaptive, scalable, and extensible image-based plagiarism detection approach suitable for analyzing a wide range of image similarities that we observed in academic documents. The proposed detection approach integrates established image analysis methods, such as perceptual hashing, with newly developed similarity assessments for images, such as ratio hashing and position-aware OCR text matching. We evaluate our approach using 15 image pairs that are representative of the spectrum of image similarity we observed in alleged and confirmed cases of academic plagiarism. We embed the test cases in a collection of 4,500 related images from academic texts. Our detection approach achieved a recall of 0.73 and a precision of 1. These results indicate that our image-based approach can complement other content-based feature analysis approaches to retrieve potential source documents for suspiciously similar content from large collections. We provide our code as open source to facilitate future research on image-based plagiarism detection.

BigGIS: a continuous refinement approach to master heterogeneity and uncertainty in spatio-temporal big data (vision paper)

Geographic information systems (GIS) are important for decision support based on spatial data. Due to technical and economical progress an ever increasing number of data sources are available leading to a rapidly growing fast and unreliable amount of data that can be beneficial (1) in the approximation of multivariate and causal predictions of future values as well as (2) in robust and proactive decision-making processes. However, todays GIS are not designed for such big data demands and require new methodologies to effectively model uncertainty and generate meaningful knowledge. As a consequence, we introduce BigGIS, a predictive and prescriptive spatio-temporal analytics platform, that symbiotically combines big data analytics, semantic web technologies and visual analytics methodologies. We present a novel continuous refinement model and show future challenges as an intermediate result of a collaborative research project into big data methodologies for spatio-temporal analysis and design for a big data enabled GIS.

From game events to team tactics: Visual analysis of dangerous situations in multi-match data

Sport analytics in general and soccer analytics in particular constitute quickly growing markets when it comes to professional analyses and visualizations. From a data analysis research perspective, soccer is a rich source of geospatial and temporal movement data, with high details and a controlled environment. However, soccer movement is complex as its compounds are actions and reactions of two opposing teams with inverse goals. Common analyses performed today are typically oriented towards statistical analysis and considering aggregate measurements. In this work, we propose a set of effective visual-interactive methods for investigating set plays as a first step towards semi-automated analysis of tactic behavior. In our analytic design, we follow the so-called Information-seeking Mantra by Ben Shneiderman by providing overview visualizations, interactive refinements, and detailed analysis views. We take an applied approach in showing case studies that give evidence for the applicability and merits of our proposed techniques.

Commercial Visual Analytics Systems-Advances in the Big Data Analytics Field

Five years after the first state-of-the-art report on Commercial Visual Analytics Systems we present a reevaluation of the Big Data Analytics field. We build on the success of the 2012 survey, which was influential even beyond the boundaries of the InfoVis and Visual Analytics (VA) community. While the field has matured significantly since the original survey, we find that innovation and research-driven development are increasingly sacrificed to satisfy a wide range of user groups. We evaluate new product versions on established evaluation criteria, such as available features, performance, and usability, to extend on and assure comparability with the previous survey. We also investigate previously unavailable products to paint a more complete picture of the commercial VA landscape. Furthermore, we introduce novel measures, like suitability for specific user groups and the ability to handle complex data types, and undertake a new case study to highlight innovative features. We explore the achievements in the commercial sector in addressing VA challenges and propose novel developments that should be on systems’ roadmaps in the coming years.

Quality Metrics for Information Visualization

The visualization community has developed to date many intuitions and understandings of how to judge the quality of views in visualizing data. The computation of a visualizations quality and usefulness ranges from measuring clutter and overlap, up to the existence and perception of specific (visual) patterns. This survey attempts to report, categorize and unify the diverse understandings and aims to establish a common vocabulary that will enable a wide audience to understand their differences and subtleties. For this purpose, we present a commonly applicable quality metric formalization that should detail and relate all constituting parts of a quality metric. We organize our corpus of reviewed research papers along the data types established in the information visualization community: multi‐ and high‐dimensional, relational, sequential, geospatial and text data. For each data type, we select the visualization subdomains in which quality metrics are an active research field and report their findings, reason on the underlying concepts, describe goals and outline the constraints and requirements. One central goal of this survey is to provide guidance on future research opportunities for the field and outline how different visualization communities could benefit from each other by applying or transferring knowledge to their respective subdomain. Additionally, we aim to motivate the visualization community to compare computed measures to the perception of humans.

Visual Analytics and Similarity Search: Concepts and Challenges for Effective Retrieval Considering Users, Tasks, and Data

A major challenge of the contemporary information age is the overwhelming and increasing data amount, especially when looking for specific information. Searching for relevant information is no longer manually possible, but has to rely on automatic methods, specifically, similarity search. From a formal perspective, similarity search can be seen as the problem of finding entities, which are considered to be similar to a query with respect to certain describing features. The question which features or which weighted combination of features to use for a given query creates a need for semi-automatic methods to address the needs of diverse users. Furthermore, the quality of the results of a similarity search is more than effectiveness, measured by precision and recall. The user ideally needs to trust the results and understand how they were computed. We propose to apply Visual Analytics methodologies, for synergistic cooperation of user and algorithms, to integrate three key dimensions of similarity search: users, tasks, and data for effective search. However, there exists a gap in knowledge how user, task as well as the available data influence each other and the similarity search. In this concept paper, we envision how Visual Analytics can be used to tackle current challenges of similarity search.