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We introduce a new research area in visual analytics (VA) aiming to bridge existing gaps between methods of interactive machine learning (ML) and eXplainable Artificial Intelligence (XAI), on one side, and human minds, on the other side. The gaps are, first, a conceptual mismatch between ML/XAI outputs and human mental models and ways of reasoning, and second, a mismatch between the information quantity and level of detail and human capabilities to perceive and understand. A grand challenge is to adapt ML and XAI to human goals, concepts, values, and ways of thinking. Complementing the current efforts in XAI towards solving this challenge, VA can contribute by exploiting the potential of visualization as an effective way of communicating information to humans and a strong trigger of human abstractive perception and thinking. We propose a cross-disciplinary research framework and formulate research directions for VA.

We propose an approach to underpin interactive visual exploration of large data volumes by training Learned Visualization Index (LVI). Knowing in advance the data, the aggregation functions that are used for visualization, the visual encoding, and available interactive operations for data selection, LVI allows to avoid time-consuming data retrieval and processing of raw data in response to user's interactions. Instead, LVI directly predicts aggregates of interest for the user's data selection. We demonstrate the efficiency of the proposed approach in application to two use cases of spatio-temporal data at different scales.

As the number of moving objects increases, the challenges for achieving operational goals w.r.t. the mobility in many domains that are critical to economy and safety emerge dramatically. In domains such as air traffic management, this dictates a shift of operations’ paradigm from location based, as it is today, to trajectory based, where trajectories are turned into “first-class citizens”. Additionally, the increasing amount of data from heterogenous and disparate data sources implies the need for advanced analysis methods that require exploiting spatio-temporal mobility data in appropriate forms and at varying levels of abstraction. All these call for an in-principle way for organising integrated views of mobility data, with trajectories playing the main role. In this paper, we propose an ontology for modelling semantic trajectories, integrating spatio-temporal information regarding mobility of objects, at multiple, interlinked levels of abstraction. Our work builds upon a comprehensive framework that identifies fundamental spatio-temporal data types and specific conversions among these types. We validate the ontological specifications towards satisfying the needs of visual analysis tasks in the complex air traffic management domain, using real-world data.

For understanding the circumstances, causes, and consequences of events that may happen during movement (e.g., harsh brake, sharp turn), it is necessary to analyze event context. The context includes dynamic attributes of the moving objects before and after the event and external context elements such as other moving objects, weather, terrain, etc. To explore events in context, we propose an analytical workflow including event contextualization, context pattern detection, and exploration of the spatio-temporal distribution of the detected patterns. The approach involves clustering of events based on the similarity of their contexts and interactive visual techniques for exploration of the distribution of the clusters in time, geographic space, and multidimensional attribute space. In close collaboration with domain experts, we apply our method to real-world vehicle trajectories with the purpose of identifying and investigating potentially dangerous driving behaviors., EuroVis Workshop on Visual Analytics (EuroVA), Analyzing Movement and Events, 49, 53, Siming Chen, Gennady Andrienko, Natalia Andrienko, Christos Doulkeridis, and Athanasios Koumparos

We propose an approach to interactive visual exploration of trajectories of moving objects in which trajectories are mapped onto different coordinate systems enabling the analyst to look at different aspects of the movement. Geographic visualization techniques can be applied to these coordinate systems in the same way as in usual geographic map displays.

In air traffic management and control, movement data describing actual and planned flights are used for planning, monitoring and post-operation analysis purposes with the goal of increased efficient utilization of air space capacities (in terms of delay reduction or flight efficiency), without compromising the safety of passengers and cargo, nor timeliness of flights. From flight data, it is possible to extract valuable information concerning preferences and decision making of airlines (e.g. route choice) and air traffic managers and controllers (e.g. flight rerouting or optimizing flight times), features whose understanding is intended as a key driver for bringing operational performance benefits. In this paper, we propose a suite of visual analytics techniques for supporting assessment of flight data quality and data analysis workflows centred on revealing decision making preferences.

The correlated exploitation of heterogeneous data sources offering very large archival and streaming data is important to increase the accuracy of computations when analysing and predicting future states of moving entities. Aiming to significantly advance the capacities of systems to improve safety and effectiveness of critical operations involving a large number of moving entities in large geographical areas, this paper describes progress achieved towards time critical big data analytics solutions to user-defined challenges in the air-traffic management and maritime domains. Besides, this paper presents further research challenges concerning data integration and management, predictive analytics for trajectory and events forecasting, and visual analytics.

Modern cities generate a flood of rich and varied data. New information sources like public transport and wearable devices provide opportunities for novel applications that will improve citizens׳ quality of life by reducing transportation time, enhancing city planning, and improving air quality to name a few applications. From a data science perspective, data emerging from smart cities give rise to a lot of challenges that constitute a new interdisciplinary field of research. This article introduces the third part of a special issue on the topic ‘Mining Urban Data’ published in the journal Information Systems. © 2016 Elsevier Ltd

The correlated exploitation of heterogeneous data sources offering very large historical as well as streaming data is important to increasing the accuracy of computations when analysing and predicting future states of moving entities. This is particularly critical in the maritime domain, where online tracking, early recognition of events, and real-time forecast of anticipated trajectories of vessels are crucial to safety and operations at sea. The objective of this paper is to review current research challenges and trends tied to the integration, management, analysis, and visualization of objects moving at sea as well as a few suggestions for a successful development of maritime forecasting and decision-support systems

Analysis of action sequence data provides new opportunities to understand and model user behaviour. Such data are often in the form of timestamped and labelled series of atomic user actions. Cyber security is one of the domains that show the value of the analysis of these data. Elaborate and specialised models of user-behaviour are desired for effective decision making during investigation of cyber threats. However, due to their complex nature, activity sequences are not yet well-exploited within cyber security systems. In this paper, we describe the initial phases of a visual analytics approach that aims to enable a rich understanding of user behaviour through the analysis of user activity sequences. First, we discuss a motivating case study and discuss a number of high level requirements as derived from a series of workshops within an ongoing research project. We then present the components of a visual analytics approach that constitutes a novel combination of ``action space'' analysis, pattern mining, and the interactive visual analysis of multiple sequences to take the initial steps towards a comprehensive understanding of user behaviour.

Current technologies allow movements of the players and the ball in football matches to be tracked and recorded with high accuracy and temporal frequency. We demonstrate an approach to analyzing football data with the aim to find typical patterns of spatial arrangement of the field players. It involves transformation of original coordinates to relative positions of the players and the ball with respect to the center and attack vector of each team. From these relative positions, we derive features for characterizing spatial configurations in different time steps during a football game. We apply clustering to these features, which groups the spatial configurations by similarity. By summarizing groups of similar configurations, we obtain representation of spatial arrangement patterns practiced by each team. The patterns are represented visually by density maps built in the teams’ relative coordinate systems. Using additional displays, we can investigate under what conditions each pattern was applied.

Modern cities are flooded with data. New information sources like public transport and wearable devices provide opportunities for novel applications that will improve citizens' quality of life. From a data science perspective, data emerging from smart cities give rise to a lot of challenges that constitute a new interdisciplinary field of research. This article introduces the second part of a special issue on the topic 'Mining Urban Data' published in the journal Information Systems. © 2016 Published by Elsevier Ltd.

In this demo we present INSIGHT, a system that provides traffic event detection in Dublin by exploiting Big Data and Crowdsourcing techniques. Our system is able to process and analyze input from multiple heterogeneous urban data sources. © Springer International Publishing AG 2016.

People using mobile devices for making phone calls, accessing the internet, or posting georeferenced contents in social media create episodic digital traces of their presence in various places. Availability of personal traces over a long time period makes it possible to detect repeatedly visited places and identify them as home, work, place of social activities, etc. based on temporal patterns of the person’s presence. Such analysis, however, can compromise personal privacy. We propose a visual analytics approach to semantic analysis of mobility data in which traces of a large number of people are processed simultaneously without accessing individual-level data. After extracting personal places and identifying their meanings in this privacy-respectful manner, the original georeferenced data are transformed to trajectories in an abstract semantic space. The semantically abstracted data can be further analyzed without the risk of re-identifying people based on the specific places they attend.

We demonstrate interactive visual embedding of partition-based clustering of multidimensional data using methods from the open-source machine learning library Weka. According to the visual analytics paradigm, knowledge is gradually built and refined by a human analyst through iterative application of clustering with different parameter settings and to different data subsets. To show clustering results to the analyst, cluster membership is typically represented by color coding. Our tools support the color consistency between different steps of the process. We shall demonstrate two-way clustering of spatial time series, in which clustering will be applied to places and to time steps.

Movement data are sensitive, because people's whereabouts may allow re- identification of individuals in a de-identified database and thus can potentially reveal intimate personal traits, such as religious or sexual preferences. In this paper, we focus on a distributed setting in which movement data from individual vehicles are collected and aggregated by a centralized station. We propose a novel approach to privacy-preserving analytical processing within such a distributed setting, and tackle the problem of obtaining aggregated traffic information while preventing privacy leakage from data collection and aggregation. We study and analyze three different solutions based on the differential privacy model and on sketching techniques for efficient data compression. Each solution achieves different trade-off between privacy protection and utility of the transformed data. Using real-life data, we demonstrate the effectiveness of our approaches in terms of data utility preserved by the data transformation, thus bringing empirical evidence to the fact that the "privacy-by-design" paradigm in big data analytics has the potential of delivering high data protection combined with high quality even in massively distributed techno-social systems.