ABSTRACT
Analysis of complex data sets to infer/discover meaningful information/knowledge involves (after data collection and cleaning): (i) Modeling the data – an approach for deriving a suitable representation of data for analysis, (ii) translating analysis objectives into computations on the generated model instance;these computations can be as simple as a query or a complex computation (e.g., community detection over multiple layers), (iii) computation of expressions generated – considering efficiency and scalability, and (iv) drill-down of results to understand them clearly. Beyond this, it is also useful to visualize results for easier understanding. Covid-19 visualization dashboard presented in this paper is an example of this. This paper covers the above steps of data analysis life cycle using a representation (or model) that is gaining importance. With complex data sets containing multiple entity types and relationships, an appropriate model to represent the data is important. For these data sets, we first establish the advantages of Multilayer Networks (or MLNs) as a data model. Then we use an entity-relationship based approach to convert the data set into MLNs for a precise representation of the data set. After that, we outline how expected analysis objectives can be translated using keyword-mapping to aggregate analysis expressions. Finally, we demonstrate, through a set of example data sets and objectives, how the expressions corresponding to objectives are evaluated using an efficient decoupling-based approach. Results are further drilled down to obtain actionable knowledge from the data set. Using the widely popular Enhanced Entity Relationship (EER) approach for requirements representation, we demonstrate how to generate EER diagrams for data sets and further generate, algorithmically, MLNs as well as Relational schema for analysis and drill down, respectively. Using communities and centrality for aggregate analysis, we demonstrate the flexibility of the chosen model to support diverse set of objectives. We also show that compared to current analysis approaches, a “decoupling-based” approach using MLNs is more appropriate as it preserves structure as well as semantics of the results and is very efficient. For this computation, we need to derive expressions for each analysis objective using the MLN model. We provide guidelines to translate English queries into analysis expressions based on keywords. Finally, we use several data sets to establish the effectiveness of modeling using MLNs and their analysis using the decoupling approach that has been proposed recently. For coverage, we use different types of MLNs for modeling, and community and centrality computations for analysis. The data sets used are from US commercial airlines, IMDb (a large international movie data set), the familiar DBLP (or bibliography database), and the Covid-19 data set. Our experimental analyses using the identified steps validate modeling, breadth of objectives that can be computed, and overall versatility of the life cycle approach. Correctness of results is verified, where possible, using independently available ground truth. Furthermore, we demonstrate drill-down that is afforded by this approach (due to structure and semantics preservation) for a better understanding and visualization of results.
ABSTRACT
Analysis of data sets that may be changing often or in real-time, consists of at least three important synchronized components: i) figuring out what to infer (objectives), ii) analysis or computation of those objectives, and iii) understanding of the results which may require drill-down and/or visualization. There is considerable research on the first two of the above components whereas understanding actionable inferences through visualization has not been addressed properly. Visualization is an important step towards both understanding (especially by non-experts) and inferring the actions that need to be taken. As an example, for Covid-19, knowing regions (say, at the county or state level) that have seen a spike or are prone to a spike in the near future may warrant additional actions with respect to gatherings, business opening hours, etc. This paper focuses on a modular and extensible architecture for visualization of base as well as analyzed data. This paper proposes a modular architecture of a dashboard for user interaction, visualization management, and support for complex analysis of base data. The contributions of this paper are: i) extensibility of the architecture providing flexibility to add additional analysis, visualizations, and user interactions without changing the workflow, ii) decoupling of the functional modules to ease and speed up development by different groups, and iii) supporting concurrent users and addressing efficiency issues for display response time. This paper uses Multilayer Networks (or MLNs) for analysis. To showcase the above, we present the architecture of a visualization dashboard, termed CoWiz++ (for Covid Wizard), and elaborate on how web-based user interaction and display components are interfaced seamlessly with the back-end modules. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The Covid-19 pandemic disrupted the world as businesses and schools shifted to work-from-home (WFH), and comprehensive maps have helped visualize how those policies changed over time and in different places. We recently developed algorithms that infer the onset of WFH based on changes in observed Internet usage. Measurements of WFH are important to evaluate how effectively policies are implemented and followed, or to confirm policies in countries with less transparent journalism. This paper describes a web-based visualization system for measurements of Covid-19-induced WFH. We build on a web-based world map, showing a geographic grid of observations about WFH. We extend typical map interaction (zoom and pan, plus animation over time) with two new forms of pop-up information that allow users to drill-down to investigate our underlying data. We use sparklines to show changes over the first 6 months of 2020 for a given location, supporting identification and navigation to hot spots. Alternatively, users can report particular networks (Internet Service Providers) that show WFH on a given day. We show that these tools help us relate our observations to news reports of Covid-19-induced changes and, in some cases, lockdowns due to other causes. Our visualization is publicly available at https://covid.ant.isi.edu, as is our underlying data. © 2021 IEEE.