Session 1 and 2 "Challenges and Opportunities with Big Linked Data Visualization" tutorial @ISWC 2018
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"Challenges and Opportunities with Big Linked Data Visualization" tutorial @ISWC 2018 A book on the topic published by the author is "Linked Data Visualization: Techniques, Tools and Big Data" Laura Po, Nikos Bikakis, Federico Desimoni & George Papastefanatos Synthesis Lectures on Data, Semantics and Knowledge Morgan & Claypool, 2020 ISBN: 9781681737256 | 9781681737263 (ebook) DOI: 10.2200/S00967ED1V01Y201911WBE019 Morgan & Claypool: https://www.morganclaypool.com/doi/abs/10.2200/S00967ED1V01Y201911WBE019 Homepage: http://www.linkeddatavisualization.com
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Session 1 and 2 "Challenges and Opportunities with Big Linked Data Visualization" tutorial @ISWC 2018
- 1. CHALLENGES AND OPPORTUNITIES WITH BIG LINKED DATA VISUALIZATION Laura Po ‘‘Enzo Ferrari’’ Engineering Department University of Modena and Reggio Emilia ITALY laura.po@unimore.it Download the slides available at https://sites.google.com/view/tu torial-iswc-2018/materials
- 2. INTRO • Staggering growth in the production/consumption of Linked Open Data (LOD) • Increasingly large dimension of the datasets • Datasets get continuously updated with newer versions • Exploring, visualizing and analysing BLD is a core task for a variety of users in numerous scenarios.
- 3. VISUALIZATION AS APOWERFUL TOOL Visualization for… • visually presenting the internal structure in the data • showing the relationship between the data • allowing the users to identify any unreasonable, incorrect or duplicate data and links in the Linked Data
- 4. THE LOD CLOUD The LOD CLOUD: • Linked Open Data (LOD) are public available RDF Data in the Web, identifiable via URI and accessable via HTTP, contain more than 1000 triples 1,224 datasets [lod-cloud.net 2018] > 28 billion unique triples [ISWC 2017] http://lod-cloud.net/
- 5. THE LOD CLOUD The LOD CLOUD: • Linked Open Data (LOD) are public available RDF Data in the Web, identifiable via URI and accessable via HTTP, contain more than 1000 triples 1,224 datasets [lod-cloud.net 2018] > 28 billion unique triples [ISWC 2017] http://lod-cloud.net/
- 6. PRE-REQUISITES • Some basic knowledge of Linked Data • Uniform Resource Identifiers (URIs) • the Hypertext Transfer Protocol (HTTP) • the Resource Description Framework (RDF) • RDF Schema. • Knowledge of the SPARQL Protocol, SPARQL Query Language not mandatory
- 7. AT THE END … You will be able • to get started with your own experiments on the LOD Cloud • to select the most appropriate tool for a defined type of analysis … be aware • of the open issues and challenging problems that remain unsolved in the scenario of the exploration of Big Linked Data
- 8. WHAT WILL NOT BE COVERED • Data Visualization is a broader topic • dataviz.tools and datavizcatalogue list a large number of visualization tools, libraries and resources Data Visualization BOLD Visualization
- 9. SCHEDULE OF THE TUTORIAL • Session 1: The exploration of Big Linked Data (15 min) • Session 2: Big Linked Data tools for visualization, exploration and navigation (25 min) • Session 3: Hands-on-session on exploration of Linked Data by using online tools (30 min) ** COFFEE BREAK 15.20-16.00 ** • Session 3: Hands-on-session on exploration of Linked Data by using online tools (40 min) • Session 4: Closing and Free Discussion (20 Min) All slides and references are available at the tutorial website
- 10. SESSION 1: THE EXPLORATION OF BIG LINKED DATA
- 11. Exploring LOD is not exploring your own dataset You do not know the dataset You do not know if the dataset is relevant for you
- 12. ISSUES 1. Large size and the dynamic nature of data 2. Exploratory search 3. Variety of tasks and users
- 13. LARGE SIZE & DYNAMIC DATASETS Examples • Dbpedia - 6 million triples in English - 7 billion RDF triples in total • BBC Music - 27 billion triple (http://lod.openlinksw.com) • Linked Geo Data - 400 million geographic elements - 20 billion triples (http://linkedgeodata.org) • PubMed - 186 million concepts - 1.3 billion triples (http://pubmed.bio2rdf.org) • and many others…
- 14. LARGE SIZE DATASETS • Problems with • Load /Memory • Navigation • Visualization
- 16. users do not know what exactly they are searching for
- 17. EXPLORATION-DRIVEN SETTING ≠ Lookup search - focused searches where the user has a specific goal in mind and an idea of the expected result Exploratory search (ES) is performed whenever a user wants to discover a domain, increase his knowledge, learn about new topics, etc.” [Marie 2014 bis] ES is open-ended, with an unclear information need, a search with multiple targets
- 18. VARIATY OF USERS • An increasingly large number of diverse users • politicians, citizens, researchers, decision makers, practitioners • Different preferences and skills • A plethora of different scenarios A tool, that does not require technical skills, can also be useful for domain or technology experts
- 19. IMPACT High potential value of OPEN DATA • the economic impact of open data has a value of € 140 billion a year between direct and indirect effects [EU Commission 2011] • the social impact of open data: increasing transparency, and enhancing public services, creating new opportunities for citizens and organizations [http://odimpact.org ] • Big Data can introduce innovative solutions through the development of data driven infrastructures and applications. OPEN + LINKED + BIG
- 20. WHAT WE NEED TO EXPLORE BOLD? • Provide a glimpse of the dataset • Implement the exploratory search • Encourage user comprehension • offer customization capabilities to different user-defined scenarios • Deal with large datasets • Highlight the evolution over time of the dataset • Provide multiple visual perspectives (foster discovery of patterns using different views) • Allow a panoramic and specific view on demand over the data • Provide real-time response and progressive results - partial and preferably representative results, as soon as possible • …
- 21. SESSION 2: BIG LINKED DATA TOOLS FOR VISUALIZATION, EXPLORATION AND NAVIGATION
- 22. Disco Linked Data browsers VizBoard Rhizomer SemLens Linked Data Exploration Systems LOD Viewer Payola Linked Data Graph Tools Definition of Linked Data Aesthetics in Interface Design for Linked Data [Mazumdar] SynopsisViz H-BOLD Lodlive LODWheel Balloon synopsis LDVizWiz Aemoo Fenfire Gephi graphVizdb LODeX Vis Wizard RelFinder ViziQuer Ontology Visualization Systems CropCircles FlexViz GLOW OntoGraf OntoTrix OWLViz VOWL 2 Explorator Marbles Tabulator gFacet EVOLUTION OVER TIME Dbpedia first version (September) Big linked data visualization tool survey [Bikakis] Surveys on visualising Linked Data [Dadzie] Exploratory search surveys [Marie 2014, Palagi 2017]
- 23. IN THE BEGINNING WAS… LINKED DATA BROWSERS • Linked Data provide the functionality for link navigation and representation of WoD resources and their properties; browsers such as Disco, Tabulator or Explorator allow users to navigate the graph structures and display property-value pairs in tables. • They provide a view of a subject, or a set of subjects and their properties, but not any additional support getting a broader view of the dataset being explored.
- 24. GENERIC EXPLORATION SYSTEMS • support different types of data • provide different types of visualization • Tree Maps, Graphs, Diagrams … • visual scalability, most systems do not adopt approximation techniques such as sampling, filtering or aggregation. • exceptions are SynopsViz and VizBoard which exploit external memory at runtime Payola
- 25. GRAPH BASED TOOLS • A large number of systems visualize LOD adopting a graph-based (a.k.a., node-link) approach. • Some systems provide keyword search functionality or mechanisms for data filtering. H-BOLD
- 26. ONTOLOGY VISUALIZATION SYSTEMS • The problems of ontology visualization and exploration have been extensively studied in several research areas (e.g., biology, chemistry • Some graph-based ontology visualization systems have been developed in the LOD context VOWL2
- 27. DOMAIN / DEVICE SPECIFIC VISUALIZATION SYSTEMS • Several systems focus on visualizing and exploring geo-spatial data. • For example the LinkedGeoData Browser [Auer 2009, Stadler 2012] is a faceted browser and editor derived from Open Street Map. • DBpedia Atlas [Valsecchi 2015] offers exploration over the DBpedia dataset by exploiting the dataset’s spatial data. Dbpedia Atlas
- 28. DOMAIN SPECIFIC LOD VISUALIZER • A visualization system for the linked biomedical data to exhibit the relationships among targets, compounds, and diseases. • Repository of biomedical data: Open PHACTS
- 29. SCALABILITY ISSUE In order to handle large graphs • hierarchical aggregation approaches - the graph is recursively decomposed into smaller subgroups [Archambault 2007, Auber 2004, Tong 2013, Li 2015]; • Clustering/Partitioning techniques/Hierarchy of levels of abstraction • edge grouping techniques – aggregate the edges of the graph into bundles [Cui 2008, Gansner 2011] In order to show on-the-fly results as soon as possible • progressive techniques - The results/visual elements are computed/constructed incrementally based on user interaction or as time progresses [Bikakis 2017], also using incremental and approximate techniques
- 30. BIG DATA VISUALIZATION TOOLS Modern visualization and exploration systems should effectively and efficiently handle the following aspects • Real-time Interaction. Efficient and scalable techniques should support the interaction with billion objects datasets, while maintaining the system response in the range of a few milliseconds. • On-the-fly Processing. Support of on-the-fly visualizations over large and dynamic sets of volatile raw (i.e., not preprocessed) data is required. • Visual Scalability. Provision of effective data abstraction mechanisms is necessary for addressing problemsrelated to visual information overloading (a.k.a. overplotting). • User Assistance and Personalization. Encouraging user comprehension and offering customization capabilities to different user-defined exploration scenarios and preferences according to the analysis needs are important [Bikakis 2018]
Editor's Notes
- Today, we are assisting at a staggering growth in the production and consumption of Linked Open Data (LOD) and the generation of increasingly large datasets. In this scenario, it is crucial to provide intuitive tools for researchers, domain experts, but also businessmen and citizens to view and interact with LOD resources. Linked Data already spans a wide range of application areas, a strong indication that its potential value is already largely acknowledged Representing, querying, and visualizing linked data is crucial. High potential of LOD WHO The lack of development environments for interdisciplinary research conducted on large-scale datasets hampers research at every stage. Projects incur large startup costs as disparate infrastructure is assembled; experimentation slows when software components and environment are mismatched for specific research tasks; and findings are disseminated in forms that are hard to examine, learn from, and reuse. Behind these problems is a common cause — the lack of good tools. When large, heterogeneous and distributed data is added to the equation, further frustration, at the least, ensues. As a result using existing platforms, the programmers of 21st century interactive visualizations are reduced to working in the same fashion with the same tools as 20th century database programmers. Our contribution is to bring the tools of digital artists to bear on the aforementioned data analysis and visualization challenges. Here we report on the current state of progress in adapting Field for large-scale, web-based scientific data analysis and visualization with an emphasis on
- visualization can be a reasonable way to visually present the internal structure in the data and the relationship between the data; friendly visualization interfaces allow the users to identify any unreasonable, incorrect or duplicate data and links in the Linked Data
- Only dataset with >1000 triples and
- Only dataset with >1000 triples and
- We will not take into consideration visual exploration tool that are not specialized from LOD – Tableau, Qlink …
- What remains unsolved ---- 7 min senza approfondimenti
- 10 min
- temporal dimension of linked data is crucial
- DBpedia is a leading project for publishing LD started by individuals at the Free University of Berlin and Leipzig University in cooperation with OpenLink Software The dynamic nature of nowadays data (e.g., stream data), hinders the application of a preprocessing phase, such as traditional database loading and indexing. Hence, systems should provide on-the-fly processing over large sets of raw data.
- with limited computational and memory resources (e.g., laptops).
- The mapping activity of the ontology enrichment process along with the editing of the ten most active mapping language communities is depicted in Figure 6. It is interesting to notice that the high mapping activity peaks coincide wi
- users attempt to find something interesting without knowing what exactly they are searching for Searching within a LOD dataset is not just about finding an answer to a specific question Progressiveness can significantly improve efficiency in exploration scenarios, users perform a sequence of operations (e.g., queries), where the result of each operation determines the formulation of the next operation in each operation, after inspecting the already produced results, the user is able to interrupt the execution and define the next operation, without waiting the exact result to be computed.
- ES is a particular information seeking activity. It is a loosely defined concept as its definition is not stable and continues to evolve every time new systems are being developed. Many papers use this dichotomy to define ES is described as open-ended, with an unclear information need, an ill-structured. This search activity is evolving and can occur over time. For example, a user wants to know more about Senegal, she doesn’t really know what kind of information she wants or what she will discover in this search session; she only knows she wants to learn more about that topic. “learning in exploratory search is not only about memorization of salient facts, but rather the development of higher-level intellectual capabilities” [White 2016] The main goal in ES is learning.
- with the exponential increase in data sets we can only assume that the variety of users and profiles will increase
- Data plays a fundamental role in all aspects of human activity and social interest. DATA on a socio-economic level the impact of open data and technology-enabled transparency does not lie solely in the economic sphere. Government openness produces tremendous other benefits for our societies through increasing state or institutional responsiveness, reducing levels of corruption, building new democratic spaces for citizens, empowering local and disadvantaged voices or enhancing service delivery and effective service utilization. Big Linked Data can introduce innovative solutions in the public and private sectors, through the development of data driven infrastructures and applications.
- - differences from version to version Take into account the human cognition model Provide a direct interaction (interaction to be provided without interfering with the user’s train of thought);
- the best known systems in each category are shown In this second session, we describe the state-of-the-art of Linked Data visualization systems with particular attention to those tools able to navigate vast amount of data [Dadzie 2011, Marie 2014]. We start describing generic systems and then focus on graph-oriented systems; in the end, we pay attention on the scalability issues.
- WoD browsers have been the first systems developed for WoD utilization and analysis [35, 4]. Similarly to the traditional ones, WoD browsers provide the functionality for link navigation and representation of WoD resources and their properties; thus enabling browsing and exploration of WoD in a most intuitive way. WoD browsers mainly use tabular views and links to provide navigation over the WoD resources. Disco 2007 renders all information related to a particular RDF resource as HTML table with property-value pairs. Explorator 2009 is a WoD exploratory tool that allows users to browse a dataset by combining search and facets. Tabulator {Berners-Lee2006} another WoD browser, additionally provides maps and timeline visualizations.
- there is a large number of generic visualization frameworks, that offer a wide range of vi- sualization types and operations types of data (for example, numbers, temporal, graphical, spatial) provide a graphical representation of the data, using bubbles, circles, charts or graphs. This tool are the most interesting when talking about big linked open data visualization, and that is why tables with some the principal characteristics of this tool are inserted in this section. In particular the aspect taken into consideration are Some offer recommendation mechanisms suggesting the most suitable form of visualization depending on the input data With regard to Existing approaches assume that all objects can be presented on the screen and managed through traditional visualization techniques, thus limiting their applicability to data sets of limited size.
- Thematic map shows the depth of the classes in the DBpedia ontology hierarchy (the darker, the deeper).
- An interface for biological scientists to consume large amounts of biomedical data A tree-like structure to show the query results Iterative query approach The width of the lines reflects the degrees of relationships between compounds and diseases The first layer is the query input, the second layer is the compounds that are related to the input, and the third layer is the diseases that are related to these compounds The width of the lines in the uppermost layer is the sum of all the degrees of reactions on the paths to the diseases that are contained in the subtrees of the rooted tree. This design is to better reflect the degrees of relationships between compounds and diseases, thus reducing the impact of the intermediate variables, to make the relations between two entities more clear.
- , modern systems should adopt more sophisticated techniques such as and deepen disk-based implementations Scalability and performance should be considered as key requirements [Tong 2006, Sundara 2010].