Data Analysis and Visualization

Information systems usually show as a particular point of failure the vagueness between user search terms and the knowledge orders of the information space in question. Some kind of guided searching therefore becomes more and more important in order to more precisely discover information without knowing the right search terms. Knowledge maps are promising tools for visualizing the structure of large-scale information spaces. However, there is no continuous knowledge exchange between the "map makers" on the one hand and the Information Retrieval (IR) specialists on the other hand. Thus, knowledge maps are still far away from being applicable for searching a digital library, due to a lack of models, explorations and user studies that properly combine insights of the two strands. The workshop aims at bringing together these two communities: experts in IR reflecting on visual enhanced search interfaces and experts in knowledge mapping reflecting on interactive visualizations of the content of a digital library collection. The focus of the workshop is to discuss the potential of interactive knowledge maps for information exploration.

In this work we present a system which visualizes the results obtained from image search engines in such a way that users can conveniently browse the retrieved images. The way in which search results are presented allows the user to grasp the composition of the set of images "at a glance". To do so, images are grouped and positioned according to their distribution in a prosemantic feature space which encodes information about their content at an abstraction level that can be placed between visual and semantic information. The compactness of the feature space allows a fast analysis of the image distribution so that all the computation can be performed in real time.

Figure 1: Closeup view of an isosurface feature in the mixing interface of two gases showing the texture mapped surface, underlying triangle mesh, and the adaptively refined tetrahedral mesh around the region of interest. Time step = 273, Isovalue = 206, Isosurface error = 1.5, 50K Triangles, rendered at 7 frames per second.

In this paper, we study the complexity of rectangular cartograms, i.e., maps where every region is a rectangle, and which should be deformed such that given area requirements are satisfied. We study the closely related problem of cartograms with orthogonal octagons, and show that this problem is NP-hard. From our proof, it also follows that rectangular cartograms are NP-hard if we allow for the existence of a "sea", i.e., a region of arbitrarily high complexity on the outside of the drawing.

Excessive edge density in graphs can cause serious readability issues, which in turn can make the graphs difficult to understand or even misleading. Recently, we introduced the idea of providing tools that offer interactive edge bending as a method by which edge congestion can be disambiguated. We extend this direction, presenting a new tool, Edge Plucking, which offers new interactive methods to clarify node-edge relationships. Edge Plucking expands the number of situations in which interactive graph exploration tools can be used to address edge congestion.

While their importance is increasingly recognized, there remain many challenges in the development of uncertainty visualizations. We introduce two uncertainty visualizations for 2D bidirectional vector fields: one based on a static glyph and the other based on animated flow. These visualizations were designed for the task of understanding and interpreting anisotropic rock property models in the domain of seismic data processing. Aspects of the implementations are discussed relating to design, interaction, and tasks.

The significance of quality assurance in universities is a top priority, with advancements in technology composed to revolutionize global educational standards. Enhanced student-teacher communication and independent learning capabilities are among the direct benefits anticipated from technological progress. This research paper investigates how Jordanian universities primarily employ cutting-edge technology to sustain educational excellence. Employing the innovation spread concept, field research was conducted at five different universities in Jordan to identify the specific technological factors that might impact quality assurance in higher education, and involving sixteen semi-structured interviews with key figures in higher education. Participants included academic leaders such as deans, department chairs, and IT managers, with interviews employing triangulation and an interpretive paradigm. In addition, student surveys and focus groups were utilized for deeper insights.

Purpose – Innovative technologies pave the way to address alarming global climate issues. Among these technologies is the expansion of renewable and clean energy in farming, which aims to meet the global cheap energy demand and, at the same time, replace fossil fuels. In pursuant to this, agrivoltaic technology is an innovation that provides sustainable and low-cost production solutions to diminish the adversities associated with climate change and global warming. However, farmers from developing nations remain unacquainted or unenthusiastic about adopting such sustainable technologies. Therefore, in response to these key challenges related to climate change, this study aims to provide the utility of communication resources to inspire climatefriendly
behaviors among farmers.
Design/methodology/approach – This study used a cross-sectional field survey method for data collection from992 farmers.
Findings – The results verified that using data visualization storytelling in communication campaigns could significantly enhance farmers’ public understanding of adopting renewable technologies.
Research limitations/implications – Theoretically, results highlighted the importance of communication strategies in a downward spiral of ongoing challenges of optimal climate protection, counteracting rebound
effects and reducing carbon emissions.
Practical implications – The novel contribution of this research by examining the data visualization storytelling in climate and energy communication campaigns paved the way for social marketers to develop a straightforward and user-friendly platform for implementing innovative renewable technologies.
Originality/value – This research underpinned a novel approach that remains understudied to understand how data visualization storytelling supports renewable technology adoption. Furthermore, it addressed the
timely call for research on how data visualization storytelling can assist in achieving UNSD goals 12 and 13 by promoting renewable technologies among the farmers fromthe neglected area of the Global South.

Dramatic technological advances in the field of genomics have made it possible to sequence the complete genomes of many different organisms. With this overwhelming amount of data at hand, biologists are now confronted with the challenge of understanding the function of the many different elements of the genome. One of the best places to start gaining insight on the mechanisms by which the genome controls an organism is the study of embryogenesis. There are multiple and inter-related layers of information that must be established in order to understand how the genome controls the formation of an organism. One is cell lineage which describes how patterns of cell division give rise to different parts of an organism. Another is gene expression which describes when and where different genes are turned on. Both of these data types can now be acquired using fluorescent laser-scanning (confocal or 2-photon) microscopy of embryos tagged with fluorescent proteins to generate 3D movies of developing embryos. However, analyzing the wealth of resulting images requires tools capable of interactively visualizing several different types of information as well as being scalable to terabytes of data. This paper describes how the combination of existing large data volume visualization and the new Titan information visualization framework of the Visualization Toolkit (VTK) can be applied to the problem of studying the cell lineage of an organism. In particular, by linking the visualization of spatial and temporal gene expression data with novel ways of visualizing cell lineage data, users can study how the genome regulates different aspects of embryonic development.

Abstract: Many of the current dynamic network visualisations methods or techniques rely on node-link force-based models that were originally developed for visualising static network snapshots. In this study, we diverge from this traditional layout approach and develop a layout for ego networks that places the time dimension in the foreground, by turning time into an element of shape. In addition to this we develop an interactive system that enables the visualisation of multiple networks simultaneously by employing small multiples. Using the ...

Although historical network research has been part of historical research for decades, the rise of the internet has raised awareness of the digital and quantitative possibilities of network analysis. The data and methods supplied and applied in the essays collected in this special issue show that these promises are now starting to be fulfilled. However, current research still struggles to move beyond case studies into large-scale assessments. Burdensome datafication will allow us to link data and critically assess our archives in descriptive modes, but is also a condition for the application of more analytical tools and artificial intelligence.

Visual analysis of 3D diffusion tensor fields has become an important topic especially in medical imaging for understanding microscopic structures and physical properties of biological tissues. However, it is still difficult to continuously track the underlying features from discrete tensor samples, due to the absence of appropriate interpolation schemes in the sense that we are able to handle possible degeneracy while fully respecting the smooth transition of tensor anisotropic features. This is because the degeneracy may cause rotational inconsistency of tensor anisotropy. This paper presents such an approach to interpolating 3D diffusion tensor fields. The primary idea behind our approach is to resolve the possible degeneracy through optimizing the rotational transformation between a pair of neighboring tensors by analyzing their associated eigenstructure, while the degeneracy can be identified by applying a minimum spanning tree-based clustering algorithm to the original tensor samples. Comparisons with existing interpolation schemes will be provided to demonstrate the advantages of our scheme, together with several results of tracking white matter fiber bundles in a human brain.

This report provides insights into the challenges, emerging topics, and opportunities related to human–data interaction and visual analytics in the AI era.
The BigVis 2024 organizing committee conducted a survey among experts in the field. They invite the Program Committee members and the authors of accepted papers to share their views. Thirty-two scientists from diverse research communities, including Databases, Information Visualization, and Human–Computer Interaction, participated in the study. These scientists, representing both industry and academia, provided valuable insights into the current and future landscape of the field.

In this report, we analyze the survey responses and compare them to the findings of a similar study conducted four years ago. The results reveal some interesting insights. First, many of the critical challenges identified in the previous survey remain highly relevant today, despite being unrelated to AI. Meanwhile, the field’s landscape has significantly evolved, with most of today’s vital challenges not even being mentioned in the earlier survey, underscoring the profound impact of AI-related advancements.

NEES at UC Davis capitalized on the size of the centrifuge and innovations in instrumentation and information technology to enable generation of higher resolution information (control, sensors, and images) and to create more realistic physical models. With NEES the Center for Geotechnical Modeling is working to implement advanced instrumentation, digital video, robotics, and geophysical testing to increase the quality and quantity of data that can be collected; to increase the capacity of the centrifuge; and to develop a biaxial shaking capability. These improvements will allow us to extract more detailed and more accurate information from experiments and simulations, maximizing the information and knowledge that can be gained from these experiments. The Center for Geotechnical Modeling facilities are intended for use by researchers from academia and industry, from both the US and abroad. The centrifuge at UC Davis will be a shared-use facility under NEES, with experiments being performed by teams of researchers. New hardware and software developments are currently being implemented as part of the NEESgrid System Integration project. These new resources will allow teams to collaborate regardless of their physical location. Remote researchers will be able to control their experiments at UC Davis as easily as local researchers. Further information about the UC Davis NEES site can be obtained at , or by email to

To achieve extremely high-resolution severe weather simulation and visualization in the area of O’hare International Airport (ORD) as well as other domains of interest, Raytheon Company collaborates with Texas Advanced Computing Center (TACC) and National Center for Atmospheric Research (NCAR) to operate the simulation with Weather Research and Forecasting Model (WRF) (WRF Development Teams, 2015). WRF is a state-of-the-art parallel mesoscale numerical weather prediction system. It serves a large worldwide community of over 25, 000 users in over 130 countries for both atmospheric research and operational forecasting needs. It is traditionally suitable for mesoscale meteorological simulations, but is applied to high-resolution simulations as well in recent days.

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.

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.

Monitoring and analysis of streaming data, such as social media, sensors, and news feeds, has become increasingly important for business and government. Volume and velocity of incoming data are key challenges. To effectively support monitoring and analysis, statistical and visual analytics techniques need to be seamlessly integrated; analytic techniques for a variety of data types (e.g. text, numerical) and scope (e.g. incremental, rolling-window, and global) must be properly accommodated; interaction and coordination among several visualizations must be supported in an efficient manner; and the system should support the use of different analytics techniques in a pluggable and collaborative manner. Especially in web based environments, these requirements pose restrictions on the basic architecture for such systems. In this paper we report on our experience of building a reference web architecture for real-time visual analytics of streaming data, identify and discuss architectural patterns that address these challenges, and report on applying the reference architecture for real-time Twitter monitoring.

Maps of the world are common in classroom settings. They are used to teach the juxtaposition of natural and political functions, mineral resources, political, cultural and geographical boundaries; occurrences of processes such as tectonic drift; spreading of epidemics; and weather forecasts, among others. Recent work in scientometrics aims to create a map of science encompassing our collective scholarly knowledge. Maps of science can be used to see disciplinary boundaries; the origin of ideas, expertise, techniques, or tools; the birth, evolution, merging, splitting, and death of scientific disciplines; the spreading of ideas and technology; emerging research frontiers and bursts of activity; etc. Just like the first maps of our planet, the first maps of science are neither perfect nor correct. Today's science maps are predominantly generated based on English scholarly data: Techniques and procedures to achieve local and global accuracy of these maps are still being refined, and a visual language to communicate something as abstract and complex as science is still being developed. Yet, the maps are successfully used by institutions or individuals who can afford them to guide science policy decision making, economic decision making, or as visual interfaces to digital libraries. This paper presents the process and results of creating hands-on science maps for kids that teaches children ages 4-14 about the structure of scientific disciplines. The maps were tested in both formal and informal science education environments. The results show that children can easily transfer their (world) map and concept map reading skills to utilize maps of science in interesting ways.

Generating effective visual embedding of high-dimensional data is difficult-the analyst expects to see the structure of the data in the visualization, as well as patterns and relations. Given the high dimensionality, noise and imperfect embedding techniques, it is hard to come up with a satisfactory embedding that preserves the data structure well, whilst highlighting patterns and avoiding visual clutters at the same time. In this paper, we introduce a generic framework for improving the quality of an existing embedding in terms of both structural preservation and class separation by feature space transformations. A compound quality measure based on structural preservation and visual clutter avoidance is proposed to access the quality of embeddings. We evaluate the effectiveness of our approach by applying it to several widely used embedding techniques using a set of benchmark data sets and the result looks promising.

Fuzzy sets refer to the datasets which do not have separate, distinct clusters, and they contain data elements whose membership values are between 0 and 1. Often each element belongs to several groups. We design a disk diagram with an augmented frequency graph to visualize elements in a fuzzy set. It shows the distribution of the elements within the set with respect to the membership function value. Fuzzy set operations-like intersection-are used to find the elements of interests with certain degree of confidence. However, few techniques exist for visualization of fuzzy set operations whose process and result provide both the relationship and the distribution of individual data point between sets. With the proposed disk diagram representing a fuzzy set, we suggest an interactive visualization system to analyze fuzzy data and grasp the relationship among the sets. By interactively overlapping the disk diagrams, a user can not only bring out the individual elements of interests, but also overview the whole data elements associated with the sets. Furthermore, we investigated two different visualization scenarios for intuitive interpretation of the visualization results. We demonstrate the proposed visualization system with a terrorist analysis output dataset.

MobiVis is a visual analytics tools to aid in the process of processing and understanding complex relational data, such as social networks. At the core of these tools is the ability to filter complex networks structurally and semantically, which helps us discover clusters and patterns in the organization of social networks. Semantic filtering is obtained via an ontology graph, based on another visual analytics tool, called OntoVis. In this summary, we describe how these tools where used to analyze one of the mini-challenges of the 2008 VAST challenge.

Graphic design is a creative process that includes art and technology to convey thoughts, particularly if it is
accompanied with creative skills based on strong academic knowledge. It can be used to reflect ideas, trends, and tendencies
and this helpstouching their reality. This research is mainly aiming at studying how critical presentation of scientific findings,
data and applications with graphic and creative designs using an expressive visual language would help enhancing data
dissemination and simplifying difficult scientific data and phenomenon making them more convenient for a wide range of
audiences and better understood by various levels of background and professionality.

Popular tools for constructing user screens use the drag-drop-set-property principle. The developer drops components (buttons, text boxes, etc.) on the screen and defines their properties, e.g. position, color and text. Then the screen looks right, but it has little functionality. If you want real functionality or a custommade visualization, you have to switch to tools that are more like programming. Many domain experts are familiar with the popular tools and would like to provide functionality and visualize data, but they are uncomfortable with programming. Could we improve the drag-drop-set-property principle and cover their needs without asking them to program? This paper presents a tool (uVis) that takes a long step in this direction. The principle is to allow each property to be a formula that computes position, color, etc. A formula can combine data from several database tables with data about components and data entered by the enduser. Although aimed at non-programmers, the t...

Modern imaging and simulation techniques have enhanced system-level understanding of neural function. In this article, we present an application of interactive visualization to understanding neuronal dynamics causing locomotion of a single hip joint, based on pattern generator output of the spinal cord. Our earlier work visualized cell-level responses of multiple neuronal populations. However, the spatial relationships were abstract, making communication with colleagues difficult. We propose two approaches to overcome this: (1) building a 3D anatomical model of the spinal cord with neurons distributed inside, animated by the simulation and (2) adding limb movements predicted by neuronal activity. The new system was tested using a cat walking central pattern generator driving a pair of opposed spinal motoneuron pools. Output of opposing motoneuron pools was combined into a single metric, called Net Neural Drive, which generated angular limb movement in proportion to its magnitude. Net neural drive constitutes a new description of limb movement control. The combination of spatial and temporal information in the visualizations elegantly conveys the neural activity of the output elements (motoneurons), as well as the resulting movement. The new system encompasses five biological levels of organization from ion channels to observed behavior. The system is easily scalable, and provides an efficient interactive platform for rapid hypothesis testing.

Chromatography is a technique used to separate and quantify the components in a complex chemical mixture. We have created a 3D visualization system capable of comparing the chemical properties of chromatographic systems. The visualization system combines scatter plots, parallel coordinates, and specialized glyphs to assist in the analysis of chromatographic data and comparisons of multiple systems. Using this tool, numerous separation systems can be readily compared simultaneously-greatly facilitating the ability to select systems that are likely to produce desired separations during method development.

Timely data analysis on a wide variety and a large volume of data unveil valuable information or new insights. The analysis results could be used to innovate new avenues in health care service, business and e-service, etc. However, releasing, storing and reusing sensitive data to third parties results in breaching the data privacy of the individual. To combat privacy breach invasion, privacy-preserving techniques such as suppression, generalization and encryption-based privacy models have been proposed in the literature. The widely used privacy preservation model k-anonymity model prevents record-linkage invasions but fails to satisfy monotonicity property. It has more data distortion and fails to defend semantic-similarity, closeness, nearest-neighborhood data privacy breaches. Moreover, existing approaches are not scalable for the large-scale data set. The paper proposes a semantic similarity two-phase cluster based privacy preservation model. The proposed model considers both numerical and categorical attribute values for data anonymization. Two-phase clustering contains two phases. In the first phase, the t-centroid clustering algorithm is designed and used to partition a set of transaction records of data set D into a set of t-centroids based on the Euclidean distance between transaction records. In the second phase, the neighborhood-aware hierarchical clustering algorithm is designed. It is used to split a set of transaction records within clusters based on neighborhood aware attribute values. Two-phase clustering operations are carried out in parallel and scalable for Big Data sets. The proposed privacy model relies on cell generalization to combat records linkage and semantic-similarity, closeness, nearest-neighborhood privacy breach invasion. All experiments are carried out on two different datasets: Income-Census (KDD) and Bank Credit Card dataset. The experimental results demonstrate that the proposed privacy model can combat privacy breach invasion with cell generalization principles. The proposed privacy model is scalable and time efficient for large-scale data sets.

We describe an approach to measure visualization fidelity for encoding of data to visual attributes based on the number of unique levels that can be perceived; and a summarization across multiple attributes to compare relative lossiness across visualization alternatives. These metrics can be assessed at design time in order to compare the lossiness of different visualizations to aid in the selection between design alternatives. Examples are provided showing the application of these metrics to two different visualization design scenarios. Limitations and dependencies are noted along with recommendations for other metrics that can be used in conjunction with fidelity and lossiness to gauge effectiveness at design-time.

Moment tensors derived from seismic measurements during earthquakes are related to stress tensors and keep important information about surface displacement in the earth's mantle. We present methods facilitating an interactive visualization of scattered moment data to support earthquake and displacement analysis. For this goal, we combine and link visualizations of spatial location and orientation information derived from moment tensor decompositions. Furthermore, we contribute new tensor glyphs highlighting the indefinite character of moment tensors as well as novel tensor clustering and averaging techniques to aid interactive visual analysis and ease the challenges of interpreting moment tensor data.

The paper deals with visualization of multiple datasets by exploiting data coherency among the datasets under consideration to address the issues related to storage and computation. The proposed data coherency approach utilizes the similarities among datasets by using the already generated polygon data for one or more reference datasets to approximate the isosurfaces of similar regions in other datasets. For finding the similarity among the multiple datasets, we use the octree data structure and compare datasets block by block. Blocks of a nonreference dataset, whose difference from the corresponding blocks of the reference dataset is within the user defined tolerance level, we use the already extracted polygon data for the reference data blocks to approximate the isosurfaces. Thus, only those non-reference data blocks, which differ from the reference blocks, are processed. Thereby, we reduce the number of polygons to represent the isosurfaces. To overcome the problem of cracks in thus extracted isosurfaces, we use a simple approach of overlapping at the interface between the directly processed and approximated portions of the isosurfaces. We have explored the effects of various factors including the data coherency condition, tolerance level and block size on the performance. Our results show that the proposed data coherency technique considerably improves the overall performance.

Network or a Graph: A mathematical structure to represent objects and their interactions. Objects are represented by Nodes or Vertices (often denoted by a set V) and interactions are represented by Links or Edges (often denoted by a set E). Mathematically, a graph G is defined as a tuple G(V, E). Mathematicians use the 2 term Graph whereas scientists from other disciplines usually use the term Network to refer to the same concept. Throughout this text, we use these terms interchangeably. Social Network: A network where objects represent people and their interactions represent some sort of relationship among people. For example, two individuals may be connected to each other if they have studied at the same school, or play for the same football team. Clusters: A group of nodes (representing objects) that are densely connected to each other and sparsely connected to other nodes in the network. Formally, a clustering of a static graph G = (V, E) is defined by a set C of subsets of V : C = {c 1 , c 2 , ..., c l } such that V = c 1 ∪ c 2 ∪ ... ∪ c l. Small World Network: A graph with two characteristic properties. The average path length i.e. the number of nodes needed to traverse from one node to another on average is low, as compared to an equivalent size random graph. The second characteristic is the high transitivity among nodes i.e. many sets of three nodes are connected to each other with three vertices. Scale Free Network: A graph whose degree distribution follows a power law where the power law coefficient is usually between [2,3]. In other words, this means that most nodes nodes have only a few connections (low degree) and few nodes have many connections (high degree) in the network. Definition Network Science has emerged as an interdisciplinary field of study to model many physical and real world systems. A network, although consists of only a set of nodes and edges, but is a very powerful structure to represent a wide variety of systems such as people related through social relations, airports related through flights and Formally, we can define a dynamic network as a network which undergoes structural changes over time. The analysis and visualization of these networks is the study of algorithms, methods, tools and techniques which help us understand these networks and extract applicable knowledge from them. The study of Dynamic Networks forms a new and cross disciplinary area of study with research opportunities and applications in many diverse fields.

• Accelerate query using indexing technology without storing data into DBMS. • Develop a generalized data access infrastructure for scientific data analysis. • Improve system I/O performance and efficiency. Challenges: • Miss-matching between the array model used by scientific data and the relational model used by database technology. • Arbitrary hierarchical file layout • Diverse scientific data format, such as HDF5[4], NetCDF[5]. • Heavy index building overhead. Approaches: • Develop FastQuery[1], a generalized parallel index and query system for scientific data. • Use FastBit[6,7], a start-of-the-art index and query technology. • Allow array specification for querying and indexing data, such as buildIndex("data[0,:]"), getNumHits("data[0,:]>0"). • Deploy a flexible yet simple variable naming scheme based on regular expression. • Define a unified array I/O interface. • Parallel I/O strategy and system design.

Modern scientific datasets present numerous data management and analysis challenges. State-of-theart index and query technologies such as FastBit can significantly improve accesses to these datasets by augmenting the user data with indexes and other secondary information. However, a challenge is that the indexes assume the relational data model but the scientific data generally follows the array data model. To match the two data models, we design a generic mapping mechanism and implement an efficient input and output interface for reading and writing the data and their corresponding indexes. To take advantage of the emerging many-core architectures, we also develop a parallel strategy for indexing using threading technology. This approach complements our ongoing MPI-based parallelization efforts. We demonstrate the flexibility of our software by applying it to two of the most commonly used scientific data formats, HDF5 and NetCDF. We present two case studies using data from a particle accelerator model and a global climate model. We also conducted a detailed performance study using these scientific datasets. The results show that FastQuery speeds up the query time by a factor of 2.5x to 50x, and it reduces the indexing time by a factor of 16 on 24 cores.

Modern scientific datasets present numerous data management and analysis challenges. State-of-the-art index and query technologies are critical for facilitating interactive exploration of large datasets, but numerous challenges remain in terms of designing a system for processing general scientific datasets. The system needs to be able to run on distributed multi-core platforms, efficiently utilize underlying I/O infrastructure, and scale to massive datasets. We present FastQuery, a novel software framework that address these challenges. FastQuery utilizes a state-of-theart index and query technology (FastBit) and is designed to process massive datasets on modern supercomputing platforms. We apply FastQuery to processing of a massive 50TB dataset generated by a large scale accelerator modeling code. We demonstrate the scalability of the tool to 11,520 cores. Motivated by the scientific need to search for interesting particles in this dataset, we use our framework to reduce search time from hours to tens of seconds.

Adaptive representations are increasingly indispensable for reducing the in-memory and on-disk footprints of large-scale data. Usual solutions are designed broadly along two themes: reducing data precision, e.g., through compression, or adapting data resolution, e.g., using spatial hierarchies. Recent research suggests that combining the two approaches, i.e., adapting both resolution and precision simultaneously, can offer significant gains over using them individually. However, there currently exist no practical solutions to creating and evaluating such representations at scale. In this work, we present a new resolution-precision-adaptive representation to support hybrid data reduction schemes and offer an interface to existing tools and algorithms. Through novelties in spatial hierarchy, our representation, Adaptive Multilinear Meshes (AMM), provides considerable reduction in the mesh size. AMM creates a piecewise multilinear representation of uniformly sampled scalar data and can selectively relax or enforce constraints on conformity, continuity, and coverage, delivering a flexible adaptive representation. AMM also supports representing the function using mixed-precision values to further the achievable gains in data reduction. We describe a practical approach to creating AMM incrementally using arbitrary orderings of data and demonstrate AMM on six types of resolution and precision datastreams. By interfacing with state-of-the-art rendering tools through VTK, we demonstrate the practical and computational advantages of our representation for visualization techniques. With an open-source release of our tool to create AMM, we make such evaluation of data reduction accessible to the community, which we hope will foster new opportunities and future data reduction schemes.

A toolkit facilitates the visualization development process. The process can be further enhanced by integrating the toolkits in development environments. This paper describes how the uVis toolkit, a formula-based visualization toolkit, has been extended with a development environment, called uVis Studio. Instead of programming, developers apply a Drag-Drop-Set-View-Interact approach. Developers bind controls to data, and the Studio gives immediate visual feedback in the Design Panel. This is a novel feature, called What-You-Bind-Is-What-You-Get. The Studio also provides Modes that allow developers to interact and view the visualization from the end-user's perspective without switching workspace, and Auto-Completion; a feature of the Property Grid that provides suggestions not only for the formula language syntax but also for the tables, the table fields and the relationships in the database. We conducted a usability study with six developers to evaluate if the Studio and its features enhance cognition and facilitate the visualization development. The results show that developers appreciated the Drag-Drop-Set-View-Interact approach, the What-You-Bind-Is-What-You-Get, the Auto-Completion and the Modes. Several usability problems were identified, and some suggestions for improvement include: new panels, better presentation of the Modes, and better error messages.

Visual analytics systems continue to integrate new technologies and leverage modern environments for exploration and collaboration, making tools and techniques available to a wide audience through web browsers. Many of these systems have been developed with rich interactions, offering users the opportunity to examine details and explore hypotheses that have not been directly encoded by a designer. Understanding is enhanced when users can replay and revisit the steps in the sensemaking process, and in collaborative settings, it is especially important to be able to review not only the current state but also what decisions were made along the way. Unfortunately, many web-based systems lack the ability to capture such reasoning, and the path to a result is transient, forgotten when a user moves to a new view. This paper explores the requirements to augment existing client-side web applications with support for capturing, reviewing, sharing, and reusing steps in the reasoning process. Furthermore, it considers situations where decisions are made with streaming data, and the insights gained from revisiting those choices when more data is available. It presents a proof of concept, the Shareable Interactive Manipulation Provenance framework (SIMProv.js), that addresses these requirements in a modern, client-side JavaScript library, and describes how it can be integrated with existing frameworks.

This work is interdisciplinary in nature. This work tries to apply latest discoveries in Artificial Intelligence to classic testing methodologies. Machine Learning which is the field of Artificial Intelligence is explored in this work. The work demonstrates that provided the test team maintains the required data, Machine Learning Algorithms can aid in deciphering patterns from the test data. Patterns of interest are the relation between testers experience in the project and bugs uncovered, relations between the testers experience and the efficiency of test case with respect to code coverage and test execution time. Relation between testers experience and efficiency of test case with respect to code coverage and execution time, relation between testers experience and bugs uncovered are explored using classic statistical techniques and clustering Machine Learning Algorithms. This clustering can be of immense help in test selection, prioritization, pruning and Regression test execution time reduction.

Recent data mining techniques exploit patterns of statistical independence in multivariate data to make conjectures about cause/effect relationships. These relationships can be used to construct causal graphs, which are sometimes represented by weighted node-link diagrams, with nodes representing variables and combinations of weighted links and/or nodes showing the strength of causal relationships. We present an interactive visualization for causal graphs (ICGs), inspired in part by the Influence Explorer. The key principles of this visualization are as follows: Variables are represented with vertical bars attached to nodes in a graph. Direct manipulation of variables is achieved by sliding a variable value up and down, which reveals causality by producing instantaneous change in causally and/or probabilistically linked variables. This direct manipulation technique gives users the impression they are causally influencing the variables linked to the one they are manipulating. In this context, we demonstrate the subtle distinction between seeing and setting of variable values, and in an extended example, show how this visualization can help a user understand the relationships in a large variable set, and with some intuitions about the domain and a few basic concepts, quickly detect bugs in causal models constructed from these data mining techniques.

The aim of this study is to carry out a bibliometric analysis of literature on work engagement based on the Web of science Core Collection. References to work engagement literature published between 1980 and 2020 have been included. Biblioshiny application was used to analyze the annual scientific productivity, top contributing authors and their impact, top contributing countries and institutions, most relevant sources of publication, most cited documents, and most frequently used keywords and collaboration among countries in work engagement research. The results of study show that Netherlands is the most productive country and that the Erasmus University is the most productive institution in this area. Bakker AB is the leading author of the maximum citation category. "Journal of Vocational Behavior" is the most widely cited journal in work engagement filed. The paper of Schaufeli WB (2006) is a popular and symbolic reference with the maximum citations (697). The five most frequent keywords used in work engagement research have also been revealed, namely: (1) work engagement, (2) engagement, (3) work, (4) job satisfaction, and (5) burnout. Furthermore, it was found that Netherlands, Finland, United States, Spain, and Peoples Republic China have engaged in the most significant collaborations. These analyzes would provide the reader with an overview of the study commitment and developments over these years.

In today's dynamic business landscape, the ability to harness the full potential of data is a critical differentiator. As organizations navigate the complexities of data-driven decision-making, the role of advanced analytics becomes increasingly pivotal. Tableau, a frontrunner in the business intelligence arena, emerged as a robust platform for advanced analytics.
The white paper explores the evolution of Tableau from a renowned data visualization tool to a comprehensive platform for advanced analytics which has adapted to meet the demand for sophisticated analytics capabilities. The paper highlights key features that position Tableau as a powerful solution for advanced analytics, including seamless integration with Python, predictive analytics, trend analysis, clustering, geo-analytics.
The use case demonstrates the practical applications of Tableau's advanced Predictive analytics using python for Time series dataset. The document also addresses the challenges organizations may face in implementing advanced analytics, emphasizing the importance of data quality, skilled analysts, and ethical considerations in predictive modelling.
The white paper concludes by reaffirming Tableau's crucial role in empowering organizations to turn data into strategic assets. It envisions Tableau as a steadfast ally for businesses navigating the complexities of the modern digital landscape, providing actionable insights, and fostering a competitive edge. As the business landscape continues to evolve, Tableau remains a dynamic force in enabling informed decision-making and innovation through advanced analytics.

We introduce a novel, automatic streamline seeding algorithm for vector fields defined on surfaces in 3D space. The algorithm generates evenly-spaced streamlines fast, simply, and efficiently for any general surface-based vector field. It is general because it handles large, complex, unstructured, adaptive resolution grids with holes and discontinuities, does not require a parameterization, and can generate both sparse and dense representations of the flow. It is efficient because streamlines are only integrated for visible portions of the surface. It is simple because the image-based approach removes the need to perform streamline tracing on a triangular mesh, a process which is complicated at best. And it is fast because it makes effective, balanced use of both the CPU and the GPU. The key to the algorithm's speed, simplicity, and efficiency is its image-based seeding strategy. We demonstrate our algorithm on complex, real-world simulation data sets from computational fluid dynamics and compare it with object-space streamline visualizations.