The semantic similarity between documents of a text corpus can be visualized using map-like metaphors based on two-dimensional scatterplot layouts. These layouts result from a dimensionality reduction on the document-term matrix or a representation within a latent embedding, including topic models. Thereby, the resulting layout depends on the input data and hyperparameters of the dimensionality reduction and is therefore affected by changes in them. Furthermore, the resulting layout is affected by changes in the input data and hyperparameters of the dimensionality reduction. However, such changes to the layout require additional cognitive efforts from the user. In this work, we present a sensitivity study that analyzes the stability of these layouts concerning (1) changes in the text corpora, (2) changes in the hyperparameter, and (3) randomness in the initialization. Our approach has two stages: data measurement and data analysis. First, we derived layouts for the combination of three text corpora and six text embeddings and a grid-search-inspired hyperparameter selection of the dimensionality reductions. Afterward, we quantified the similarity of the layouts through ten metrics, concerning local and global structures and class separation. Second, we analyzed the resulting 42 817 tabular data points in a descriptive statistical analysis. From this, we derived guidelines for informed decisions on the layout algorithm and highlight specific hyperparameter settings. We provide our implementation as a Git repository at hpicgs/Topic-Models-and-Dimensionality-Reduction-Sensitivity-Study and results as Zenodo archive at DOI:10.5281/zenodo.12772898.

Point clouds are widely used as a versatile representation of 3D entities and scenes for all scale domains and in a variety of application areas, serving as a fundamental data category to directly convey spatial features. However, due to point sparsity, lack of structure, irregular distribution, and acquisition-related inaccuracies, results of point cloud visualization are often subject to visual complexity and ambiguity. In this regard, non-photorealistic rendering can improve visual communication by reducing the cognitive effort required to understand an image or scene and by directing attention to important features. In the last 20 years, this has been demonstrated by various non-photorealistic rendering approaches that were proposed to target point clouds specifically. However, they do not use a common language or structure for assessment which complicates comparison and selection. Further, recent developments regarding point cloud characteristics and processing, such as massive data size or web-based rendering are rarely considered. To address these issues, we present a survey on non-photorealistic rendering approaches for point cloud visualization, providing an overview of the current state of research. We derive a structure for the assessment of approaches, proposing seven primary dimensions for the categorization regarding intended goals, data requirements, used techniques, and mode of operation. We then systematically assess corresponding approaches and utilize this classification to identify trends and research gaps, motivating future research in the development of effective non-photorealistic point cloud rendering methods.

In 2016, BitMEX introduced a novel type of crypto derivates – Perpetual Swaps, i.e., futures with an infinite term. Perpetual swaps provide a new strategic risk management tool for cryptocurrencies due to their custody-free nature, high leverage, and funding mechanism, but there has been little quantitative analysis on the their benefits. In this paper, we introduce a trading strategy that combines a Quanto Perpetual Swap with a spot position to benefit from the funding mechanism. We compare our strategy with a long-only investment in the underlying cryptocurrency and a similar strategy based on Linear Perpetual Swaps to evaluate their performances in a large-scale backtest covering the years 2021 and 2022. Our analysis shows that our strategy generates positive returns in bullish market phases of the underlying with lower volatility.

Topic models are a class of unsupervised learning algorithms for detecting the semantic structure within a text corpus. Together with a subsequent dimensionality reduction algorithm, topic models can be used for deriving spatializations for text corpora as two-dimensional scatter plots, reflecting semantic similarity between the documents and supporting corpus analysis. Although the choice of the topic model, the dimensionality reduction, and their underlying hyperparameters significantly impact the resulting layout, it is unknown which particular combinations result in high-quality layouts with respect to accuracy and perception metrics. To investigate the effectiveness of topic models and dimensionality reduction methods for the spatialization of corpora as two-dimensional scatter plots (or basis for landscape-type visualizations), we present a large-scale, benchmark-based computational evaluation. Our evaluation consists of (1) a set of corpora, (2) a set of layout algorithms that are combinations of topic models and dimensionality reductions, and (3) quality metrics for quantifying the resulting layout. The corpora are given as document-term matrices, and each document is assigned to a thematic class. The chosen metrics quantify the preservation of local and global properties and the perceptual effectiveness of the two-dimensional scatter plots. By evaluating the benchmark on a computing cluster, we derived a multivariate dataset with over 45 000 individual layouts and corresponding quality metrics. Based on the results, we propose guidelines for the effective design of text spatializations that are based on topic models and dimensionality reductions. As a main result, we show that interpretable topic models are beneficial for capturing the structure of text corpora. We furthermore recommend the use of t-SNE as a subsequent dimensionality reduction.

Real estate is the largest asset class and is equally popular with professional and retail investors. However, this asset class has the disadvantage that it is very illiquid, and investments have a high entry barrier in terms of equity. The adoption of the Electronic Securities Act in 2021 by the German Bundestag has created the legal framework for tokenizing real estate assets and their management using digital ledger technology in Germany. In this paper we describe a business concept for managing ownership and business transactions for real estate in Germany using blockchain technology. Besides its possibilities, we present a market analysis that comprises existing approaches and discusses legal limitations specific to the country.

Offers to buy and sell cryptocurrencies on exchanges are collected in an order book as pairs of amount and price provided with a timestamp. Contrary to tick data, which only reflects t he l ast t ransaction p rice o n a n e xchange, t he order book reflects t he m arket's a ctual p rice i nformation a nd the available volume. Until now, no system has been presented that can capture many different order books across several markets. This paper presents Hephaistos, a system for processing, harmonizing, and storing massive spot order book data from 22 centralized crypto exchanges and 55 currency pairs. After collecting the data, Hephaistos aggregates several order books in a so-called Unified O rder B ook, w hich i s t he f oundation f or a S mart Order Routing algorithm. As a result an order is splitted across several exchanges, which results in a better execution price. As component of a high-frequency trading system, Hephaistos captures 32 % of the total daily spot trading volume. We provide examples with data from two exchanges that show that the Smart Order Routing algorithm significantly r educes t he slippage.

Trading for a currency pair on centralized crypto exchanges is organized via an order book, which collects all open buy and sell orders at any given time and thus forms the basis for price formation. Usually, the exchanges provide basic visualizations, which show the accumulated buy and sell volume in an animated 2D representation. However, this visualization does not allow the user to compare different order books, e.g., several order book snapshots. In this work, we present OrderBookVis, a 2.5D representation that shows a discrete set of order books comparatively. For this purpose, the individual snapshots are displayed as a 2D representation as usual and placed one after the other on a 2D reference plane. As possible use cases, we discuss the analysis of the temporal evolution of the order book for a fixed market and the comparison of different order books across multiple markets.

Most cryptocurrencies are bought and sold on centralized exchanges that manage supply and demand via an order book. Besides trading fees, the high liquidity of a market is the most relevant reason for choosing one exchange over the other. However, as the different liquidity measures rely on the order book, external events that cause people to sell or buy a cryptocurrency can significantly impact a market's liquidity. To investigate the effect of external events on liquidity, we measure various liquidity measures for nine different order books comprising three currency pairs across three exchanges covering the entire year 2022. The resulting multivariate time series is then analyzed using different correlations. From the results, we can infer that as a cryptocurrency's market capitalization and the exchange's trading volume increases, so does its liquidity. At the same time, only a moderate correlation of liquidity between exchanges can be observed. Furthermore, our statistical observations show that external events, particularly the events around FTX and the Terra Luna crash, caused significant changes in liquidity. However, depending on the exchange's size and the cryptocurrency's market cap, the liquidity took a shorter or longer time to recover.

Software projects under version control grow with each commit,accumulating up to hundreds of thousands of commits per repository. Especially for such large projects, the traversal of a repository and data extraction for static source code analysis poses a trade-off between granularity and speed.

The number of software projects developed collaboratively on social coding platforms is steadily increasing. One of the motivations for developers to participate in open-source software development is to make their development activities easier accessible to potential employers, e.g., in the form of a resume for their interests and skills. However, manual review of source code activities is time-consuming and requires detailed knowledge of the technologies used. Existing approaches are limited to a small subset of actual source code activity and metadata and do not provide explanations for their results. In this work, we present CodeCV, an approach to analyzing the commit activities of a GitHub user concerning the use of programming languages, software libraries, and higher-level concepts, e.g., Machine Learning or Cryptocurrency. Skills in using software libraries and programming languages are analyzed based on syntactic structures in the source code. Based on Labeled Latent Dirichlet Allocation, an automatically generated corpus of GitHub projects is used to learn the concept-specific vocabulary in identifier names and comments. This enables the capture of expertise on abstract concepts from a user's commit history. CodeCV further explains the results through links to the relevant commits in an interactive web dashboard. We tested our system on selected GitHub users who mainly contribute to popular projects to demonstrate that our approach is able to capture developers' expertise effectively.

Based on the assumption that semantic relatedness between documents is reflected in the distribution of the vocabulary, topic models are a widely used class of techniques for text analysis tasks. The application of topic models results in concepts, the so-called topics, and a high-dimensional description of the documents. For visualization tasks, they can be projected onto a lower-dimensional space using dimensionality reduction techniques. Though the quality of the resulting point layout mainly depends on the chosen topic model and dimensionality reduction technique, it is unclear which particular combinations are suitable for displaying the semantic relatedness between the documents. In this work, we propose a benchmark comprising various datasets, layout algorithms and their hyperparameters, and quality metrics for conducting an empirical study.

Software visualization uses metaphors to depict software system and software development data that usually has no inherent gestalt. The choice of a fitting metaphor for visual display is researched broadly, but deriving a layout based on similarity is still challenging. We present a novel approach to 3D software visualization called Software Galaxy. Our layout is based on applying Latent Dirichlet Allocation on source code documents. We utilize a metaphor inspired from astronomy for depicting software metrics for single documents and clusters of documents. Our first experiments indicate that a 3D visualization capturing semantic relatedness can be beneficial for standard program comprehension tasks.

Personal, long-term investment products, especially ones for retirement savings, require thorough understanding to use them profitably. Even simple savings plans based on exchange-traded funds (ETFs) are subject to many variables and uncertainties to be considered for expected and planned-upon returns. We present an interactive simulation of an ETF-based savings plan that combines forecasts, risk awareness, taxes and costs, inflation, and dynamic inflows and outflows into a single visualization. The visualization consists of four parts: a form-fill interface for configuration, a savings and payout simulation, a cash flow chart, and a savings chart. Based on a specific use case, we discuss how private investors can benefit from using our visualization after a short training period.

This work investigates the extent to which animated procedural texture patterns can be used to support the representation of changes in 2.5D treemaps. Changes in height, color, and area of individual nodes can easily be visualized using animated transitions. Especially for changes in the color attribute, plain animated transitions are not able to directly communicate the direction of change itself. We show how procedural texture patterns can be superimposed to the color mapping and support transitions. To this end, we discuss qualitative properties of each pattern, demonstrate their ability to communicate change direction both with and without animation, and conclude which of the patterns are more likely to increase effectiveness and correctness of the change mapping in 2.5D treemaps.

Treemaps are a commonly used tool for the visual display and communication of tree-structured, multi-variate data. In order to confidently know when and how treemaps can best be applied, the research community uses usability studies and controlled experiments to "understand the potential and limitations of our tools" (Plaisant, 2004). To support the communities' understanding and usage of treemaps, this survey provides a comprehensive review and detailed overview of 69 user studies related to treemaps. However, due to pitfalls and shortcomings in design, conduct, and reporting of the user studies, there is little that can be reliably derived or accepted as a generalized statement. Fundamental open questions include configuration, compatible tasks, use cases, and perceptional characteristics of treemaps. The reliability of findings and statements is discussed and common pitfalls of treemap user studies are identified.

This paper provides an overview of published treemap layout algorithms from 1991 to 2019 that were used for information visualization and computational geometry. First, a terminology is outlined for the precise communication of tree-structured data and layouting processes. Second, an overview and classification of layout algorithms is presented and application areas are discussed. Third, the use-case-specific adaption process is outlined and discussed. This overview targets practitioners and researchers by providing a starting point for own research, visualization design, and applications.

Software maps provide a general-purpose interactive user interface and information display for software analytics tools. This paper systematically introduces and classifies software maps as a treemap-based technique for software cartography. It provides an overview of advanced visual metaphors and techniques, each suitable for interactive visual analytics tasks, that can be used to enhance the expressiveness of software maps. Thereto, the metaphors and techniques are briefly described, located within a visualization pipeline model, and considered within the software map design space. Consequent applications and use cases w.r.t. different types of software system data and software engineering data are discussed, arguing for a versatile use of software maps in visual software analytics.

This paper presents a novel technique for combining 2D and 2.5D treemaps using multi-perspective views to leverage the advantages of both treemap types. It enables a new form of overview+detail visualization for tree-structured data and contributes new concepts for real-time rendering of and interaction with treemaps. The technique operates by tilting the graphical elements representing inner nodes using affine transformations and animated state transitions. We explain how to mix orthogonal and perspective projections within a single treemap. Finally, we show application examples that benefit from the reduced interaction overhead.