the information workbench – a platform for linked data applications … · undeﬁned 0 (2013)...

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The Information Workbench –A Platform for Linked Data ApplicationsAnna Gossen a, Peter Haase a, Christian Hütter a, Michael Meier a, Andriy Nikolov a,Christoph Pinkel a, Michael Schmidt a, Andreas Schwarte a and Johannes Trame a

a fluid Operations AG, Walldorf, GermanyE-mail: [email protected]

Abstract. In this system paper, we describe the Information Workbench, a platform for developing Linked Data applications.The platform features a highly customizable user interface to present the data to the users and realize different interactionmechanisms. UI development is based on Semantic Wiki technologies, enriched with a large set of widgets for data access,navigation, exploration, visualization, data authoring, analytics, as well as data mashups with external data sources. Widgetscan be easily embedded into Semantic Wiki pages in a fully declarative way using a simple wiki-based syntax. In this way, thestructure and behavior of the user interface can be easily customized to create domain- and application-specific solutions withlittle effort. Our paper describes the technical architecture of the Information Workbench platform, particularly focusing on thetight coupling between the domain-specific semantic data model and the elements of the user interface, which in turn facilitatesUI customization. Furthermore, we describe examples of applying the Information Workbench platform to build applicationswith semantic interfaces for real-world use case scenarios in different domains, including data center management and semanticcontent publishing.

Keywords: Linked Data, Visualization, User Interfaces

1. Introduction

In recent years, a large amount of Linked Open Data(LOD) has been published on the Web, comprisingover 200 data sets from domains such as media, geog-raphy, publication, government, life sciences, as wellas cross-domain data [1]. Growing in size and domaincoverage, this data becomes more and more interestingfor building innovative applications that integrate het-erogeneous data from different sources, with the goalto overcome the limitations of traditional data man-agement systems. Apart from a new era of Web ap-plications that exploit the large LOD corpus, this de-velopment offers opportunities to build novel applica-tions for the enterprise by bringing together company-internal data with external data, thereby augmentingand contextualizing internal knowledge bases [6].

The development of specific applications that bene-fit from Linked Data often remains a time-consumingand costly task. First, at the data integration and man-

agement side, developers are faced with a variety ofnew data formats and query languages (such as RDF,OWL, and SPARQL). They also struggle with hetero-geneity at the data level (facing Linked Data availablevia HTTP lookups, RDF dumps, and SPARQL end-points), which requires various new database systemsand tools to store, process, and access this data. Sec-ond, once the relevant data has been identified and in-tegrated into the system, Linked Data applications re-quire new data interaction paradigms to deal with thespecific challenges – and opportunities – of the un-derlying data formats, such as schema flexibility anddata semantics. In particular, leveraging the benefits ofLinked Open Data requires the dynamic discovery ofavailable data sources, seamless integration of LinkedData from multiple sources, provenance and infor-mation quality assessment. Third, end-user interfacesthat implement generic visualization, exploration, andinteraction paradigms for Linked Data without over-whelming the user with technical details of represen-

0000-0000/13/$00.00 c© 2013 – IOS Press and the authors. All rights reserved

2 A. Gossen et al. / The Information Workbench – A Platform for Linked Data Applications

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Fig. 1. Information Workbench Architecture

tation formats are important aspects when buildingdomain-specific applications [4].

Pursuing the goal to lower the entry barrier intothe world of Linked Data and to leverage its benefits,the Information Workbench1 is an open platform forLinked Data applications. It accelerates the develop-ment process by abstracting from the technical detailsbehind application deployment, data management, andUI customization. This paper presents the key conceptsof the Information Workbench, focusing on the follow-ing novel aspects of the platform:

– Concepts for UI development based on a semanticdata corpus

– Tight coupling between a domain-specific ontol-ogy and elements of the user interface

– Building semantic applications for real-world usecases in different domains

2. The Information Workbench

The Information Workbench aims at supporting theentire Linked Data application building process – fromdata integration to rich UIs that visualize informationand allow user interaction. Figure 1 shows the high-level architecture of the Information Workbench.

Starting with the Resources layer, the platform of-fers built-in capabilities for integrating and convert-

1http://www.fluidops.com/information-workbench/

ing data into semantic formats. Depending on the ac-tual use case, this process may involve enterprise datasources or Open Data available on the Web. One wayto integrate such data is via so-called data providers- configurable plug-in components, which periodicallygather and refresh information from the source sys-tems, convert it into RDF, and materialize the outputin a central triple store following a data warehouse ap-proach. The platform ships with standard providers forintegrating RDF data from dumps or SPARQL end-points, and also offers generic providers for commonother formats and data sources (including RDBMS,XML and Web Services). Another option for import-ing data into the platform are predefined UIs and APIs.Data coming from the same source (e.g., gathered bya specific provider or entered in a single user edit ses-sion) are stored in a dedicated named graph in the cen-tral repository, which is annotated with relevant prove-nance metadata. This helps to address several datamaintenance challenges: particularly, keeping the dataup-to-date (new data gathered by a provider replace itsold data) and realizing access control.

As an alternative to the data warehouse approach,the platform supports virtualized integration of localand public Linked Data sources using a federationlayer provided by the FedX federated SPARQL queryengine [9]. The advantage of this approach is that datadoes not need to be materialized in a central store –thus allowing on-demand access to up to date dataas well as scaleout. The FedX federation layer doesnot rely on a pre-computed local metadata index de-scribing federation member repositories, but is able toretrieve necessary information at query time, whichguarantees complete and up-to-date query results. Thecosts of this approach in comparison with the datawarehouse one include the time delays caused by net-work latency and federated query planning. By meansof virtual mappings2, federation techniques can ap-plied to also integrate relational data sources [3].

On top of the (physically or virtually) integratedresource layer, the core Platform provides out-of-the-box modules and functionalities covering genericneeds of Linked Data applications. Central in the Infor-mation Workbench architecture is the Semantic Wiki &Widget Engine, which builds upon Semantic Wiki [8]technologies: In the platform each resource in thedata graph is automatically associated with a Seman-tic Wiki page, which enables visualization of and in-

2http://www.w3.org/TR/r2rml/

A. Gossen et al. / The Information Workbench – A Platform for Linked Data Applications 3

teraction with the resource. Built-in mechanisms makeit possible to access, extract and display informationfrom the underlying data graph (e.g., by means ofSPARQL queries) as well as to write resource-relateddata back to the store. Particularly the widgets – smallUI components for visualization, exploration and in-teraction that can be declaratively embedded – are akey element for composing domain specific UIs andapplications. Standard Semantic Wiki principles thusenable out-of-the-box functionality for managing andinterlinking structured and unstructured content frommultiple sources. To provide a homogenous view overresources of the same type, template wiki pages can bedefined and associated with ontological classes. Sucha template will then be applied to all resources belong-ing to the associated class. In this way, the structureof the underlying RDF data defines the composition ofthe user interface.

Complementing the Semantic Wiki & Widget En-gine, the platform further provides customizable mod-ules for User Management & Access Control, a Search& Analytics Engine and a Workflow Engine. While thelatter enables declarative and data-driven collabora-tive workflows, the search engine supports the user inconstructing expressive search queries, ranging fromkeyword search to complex graph-pattern based ex-ploration. With these modules the core platform out-of-the-box provides the key technologies required forimplementing most standard use-cases of enterprise-ready applications.

On top of the core platform, the Information Work-bench provides support for developing, maintaining,and deploying specific applications. A comprehensiveSDK (Solution Development Kit) allows developersto build customized applications targeting specific do-mains or use cases. As shown in Figure 1, the SDKcomprises various components serving as customiza-tion and extension points in different dimensions. First,there are the data related components, including ex-tensible data providers for data integration as well asgeneral data management facilities for import, exportand querying. These facilities allow solutions to inte-grate RDF data into the application’s triple store, aswell as to manage and modify ontologies. Second, theSDK provides components to build and customize theuser interface of the application. On top of the Se-mantic Wiki & Widget Engine mentioned before, tem-plates can be used to define the UI in a declarativemanner. Here, developers are particularly supported byan extensive set of common purpose widgets for visu-alization, exploration and authoring. In case some spe-

cial functionality is required in the target application,it is possible to implement custom widgets using theplatform’s AJAX framework. Finally, the SDK offerswell-defined APIs and an extensive system configura-tion to automate processes and access the core func-tionality of the platform. By means of these APIs it iseasily possible to define rules (for reacting on eventsor changes in the data), as well as collaborative work-flows.

The solution development process is supported bythe concept of packaged and reusable Solution arti-facts that can be deployed by the Information Work-bench through a RESTful API or by placing the zippedfile into a dedicated deployment directory on disk. Thedeployment process then automatically installs the so-lution components on top of the core platform, con-verting it into a domain-specific and end user orientedapplication. This concept can be seamlessly used toinstall a combination of multiple solutions, assumingthat the artifacts do not contain contradicting elements.

A solution may consist of the required system con-figuration (e.g., triple store configuration, ACL setup,or label and preferred language configuration), datafiles, ontologies, and semantic wiki pages and tem-plates, which may contain widgets allowing the appli-cation end users to interact with the underlying datacorpus. Advanced solutions may even ship own wid-gets and data providers and thus extend the platformwith custom functionality, implemented on top of theSDK APIs through Java or Groovy code.

The data files are typically provided as serializedRDF file(s) (e.g., as NTriples) and used to bootstrapthe system’s repository. To organize the data and tomake knowledge available as structured information, itis recommended to also provide an ontology describingthe concepts and classes of the application scenario aswell as the relationships between them. In the Informa-tion Workbench this information is for instance used toimprove the user experience, e.g., to support the userwith suitable auto-suggestions in data input forms.

From a developer’s perspective, the solution mech-anism is backed by a variety of platform features andbest practices. Beyond mechanisms for versioning andupgrading solutions, there are best practices and guide-lines for collaborative development, maintenance, andstaging during the development process. The platformfor instance offers a dedicated UI facility for exportingwiki pages from a running system, as well as an au-tomated backup process for restoring old solution ver-sions on demand.


Fig. 2. Sample widgets available in the Information Workbench.

As a running example for a solution built on topof the Information Workbench, we present a publiclyavailable explorer for the MusicBrainz3 database ac-cessible at http://musicbrainz.fluidops.net.The system allows for browsing through the avail-able database entities such as music artists, releases,or tracks. A user can start by searching for a band,say U2, using one of the pre-configured search wid-gets on the start page. On the band’s page, the systemvisualizes various pieces of information related to theband specified by means of widget definitions. All U2releases are, for instance, shown in a a timeline wid-get, which is constructed from the result of a simpleSPARQL query. The user can then choose a specificU2 release and look, e.g., for information about sometrack on the release.

Technically, the underlying solution artifact consistsof the Linked Data version of the MusicBrainz dataset4

in combination with the Music Ontology5 (provided bythe system as RDF dumps), some system configurationsettings (e.g. the login schema and access rights), plusa set of templates and widgets for defining the userinterface.

3. Self-Service User Interface

The user interface of the Information Workbenchcomes as an extensible AJAX-based Web applicationthat supports mashups on both data and UI level.This makes it possible to interlink heterogeneous data

3The system was developed as a Linked Data training system forthe project EUCLID, as a continuation of the work in the UK JISC-funded project LinkedBrainz.

4http://www.musicbrainz.org/5http://musicontology.com/

from multiple sources using different visualization andexploration techniques in a homogeneous Living UI.Knowing what, when, and where to show informationis controlled by the data and realized through a flexibleconfiguration mechanism.

The living UI can be composed in two differentways: First, widgets can be embedded into wiki pagesproviding ‘inline’ interaction components that enrichthe unstructured content. Using wiki-based syntax thiscan be done in a fully declarative way. Second, theplatform offers so-called views, basically tabbed UIslinked and configured for particular resources andclasses. Consider, for example, a map widget thatshows up automatically whenever geolocations aremodeled in the underlying data. By default, there is atabular view showing the underlying RDF triples asso-ciated to the current resource, a graph view visualizingthe neighborhood of the resource, and a Pivot view forvisual exploration.

Out of the box, the Information Workbench providesa rich set of UI components – the widgets – supportingdifferent interaction paradigms with the data (see Fig-ure 2). In the following we present the most relevantwidgets targeting different dimensions.

Navigation and exploration. The user can exploreunderlying data in several ways. Standard table andgraph views over resource properties realize LinkedData browsing functionality and allow users to navi-gate through the data graph by following references tolinked semantic entities. To find relevant data in boththe RDF triple store and the associated wiki pages key-word search is the natural paradigm. Here, customiz-able search widgets can be used to pre-configure pa-rameterized SPARQL query patterns, allowing usersto access structured data without specifying SPARQLqueries (compare the configuration a) and the renderedresult b) in Figure 3). Finally, SPARQL queries pro-vide an expressive way to retrieve results for specificinformation needs in a structured form.

Data visualization. In the Information WorkbenchSPARQL queries serve as the basis for accessing struc-tured RDF data from the triple store. Provided aspart of the widget configuration, these define the datawhich are then presented to the user in the selectedwidget’s form (e.g., table, bar chart, stock chart, map,timeline, etc.). These widgets rely on common librariesand APIs such as amCharts6 and Google Maps API7 to

6http://www.amcharts.com/7https://developers.google.com/maps/


generate the corresponding UI controls which are theninserted into the corresponding wiki pages.

The second very important UI concept are wiki tem-plates which describe how instances of ontology con-cepts are presented. A wiki template includes textualcontent as well as parameterized widget configura-tions, in which queries can use the actual instanceas a parameter. These wiki templates are automati-cally instantiated for a particular instance of a class.In this way, in accordance with the visual analyticsparadigm [7] (“intial analysis and overview first, de-tails on demand”), the conceptual views of a class canprovide a data overview and initial analysis results,while the instantiated templates provide details for theinstances on demand.

The wiki template concept allows the user interfaceto be customized and to develop domain-specific solu-tions: customized templates are packaged as a part ofthe solution bundle and extend or override the defaultviews. In the following we illustrate this concept forour running MusicBrainz example. The solution pro-vides a wiki template for the type mo:MusicArtistthat specifies how musicians are displayed. The tem-plate contains the name of the artist, his country of ori-gin, an image, as well as a list of band members:

= {{#show: {{this}}| foaf:name}} =

* From: {{#show: {{this}}| foaf:based_near}}

{{#show: {{this}}| foaf:img | valueResolver=IMAGE}}

=== Band members ===

{{#widget: TableResult |

query = ’SELECT ?Member WHERE { ?? mo:member ?Member }’ }}

We use the #show parser function defined inthe Semantic MediaWiki to display the values forfoaf:name, foaf:based_near, and foaf:imgof the current resource. Also, the widgets are accessi-ble as parser functions where configuration parameters(such as a SPARQL query) can be specified as namedarguments. To access the URI of the current resourcewithin parser functions, the magic word {{this}}can be used, while in SPARQL queries the syntacticplaceholder ?? is applicable. When the user navigatesto a particular music artist, say the band U2, the wikipage is instantiated automatically from the templateand presents the desired information:

Following the self-service paradigm, the Informa-tion Workbench provides wizards that support the con-figuration of widgets, allowing non-expert users tobuild UIs without the need to specify wiki syntax.Consider for instance the configuration of the before-

mentioned search widget in Figure 3. This widget de-fines a parameterized query pattern that allows the userto search for music artists in a given country. To im-prove the user-experience an autoSuggest querycan be provided which automatically populates the listof countries.

a) Wizard based configuration of a search widget

b) Rendered UI element

Fig. 3. Example of a search widget configuration: the configurationdefines a search interface for music artists, offering the artist nameand the country to be used as parameters.

Mashups with external services. In addition to pre-senting data contained in the RDF repository, specialmashup widgets can be used to incorporate related in-formation from external sources: e.g., to embed relatedvideos from Youtube, to show up-to-date news items,or to present Twitter feeds. A mashup widget receivesits input by selecting the relevant property of a datainstance (e.g., the name of a music artist to search on


Youtube) and generates the HTML elements that dis-play the corresponding external content.

Collaborative data editing. Finally, the platform pro-vides customizable data input forms for collaborativeediting with the triple editor. This widget provides arich interface for curating, authoring, or adding infor-mation. It uses information from the schema ontologyto select relevant editable properties of an instance andoffers appropriate form elements for them (e.g., textinputs, drop-down boxes, multi-line text areas, or date-time pickers). The triple editor provides many config-uration options to customize the form in the desiredway. As an example, autosuggestion queries for prop-erties can be provided to assist users in modifying datawith suitable suggestions.

In this way, the Information Workbench provides arich UI toolbox which can be customized for specificuse case needs with a minimal effort.

4. The Information Workbench-basedApplications

The Information Workbench platform serves as abasis for several productized application solutions cov-ering diverse domain areas. The main benefits of build-ing a domain specific solution with the InformationWorkbench are twofold. The developers can focus ondomain-specific functionalities and custom configura-tions instead of building the application from scratch,while the application-specific data and ontology de-fine the structure for the initial interface. The end usersgain the opportunity to interact with the data fromthe start and tailor the UI to their specific needs atany time. In this section, we will provide an overviewof some real-life solutions based on the platform andshow the added value in each of them. Live exam-ples of the Information Workbench are accessible athttp://www.fluidops.com/demonstrators/.

4.1. Data Center Management

The eCloudManager [5] is a software solution builton top of the Information Workbench platform focus-ing on the task of enterprise cloud environments man-agement. It provides users with the possibility to man-age heterogeneous data center resources in an inte-grated way. These resources comprise the various lay-ers of the IT stack: the Hardware layer (physical stor-age, network, and computing infrastructure), the Vir-

tualization layer (virtual clusters, live migration, andother management capabilities), and the Applicationlayer (applications and landscapes running on of vir-tualized resources). Information concerning all theseresources is gathered by data providers, which makeuse of specific low-level APIs of each resource type,and expressed in RDF by means of the fluidOps Enter-prise Cloud Ontology8. Using semantic technologiesit is automatically integrated with other business re-sources, including, for instance, data about customersor system users from the Active Directory.

The user interface of the eCloudManager serves twomain purposes: (1) presenting combined informationcollected from different sources concerning managedresources and (2) allowing the user to perform vari-ous control tasks (e.g., cloning a virtual machine froma template). Organization of the user interface is cen-tered around the concepts of the eCloudManager on-tology and helps to achieve both these goals intu-itively. Semantic wiki pages corresponding to ontolog-ical classes contain dashboards providing overview in-formation about all entities of a specific class: for in-stance, the wiki page associated to the concept Hostcontains a table showing all virtual and physical hostsin the data center, a pie chart showing the distributionof different VM types, and a menu with links to relatedconcepts (e.g., servers and virtual machines). The wikipage template for particular instances of class Hostcombines detailed information about the specific ob-ject (e.g. a list of data stores linked to this physical hostor a timeline chart showing the utilization of the hostover time). In addition, it provides a control interfaceallowing the user to perform certain operations for theinstance (e.g., start/stop/delete a virtual machine or awhole virtual landscape). The templates are composedof separate wiki modules encapsulating atomic UI sub-units, thus allowing common interface elements to bereused across different pages.

While the eCloudManager itself represents a domain-specific application built on top of the InformationWorkbench platform, it needs to be easily adjustablewith respect to additional customer requirements, i.e.,to display additional related data or hide non-relevantfunctionality parts. The UI based on semantic wikitemplates makes this possible with minimal effort:wiki pages and templates can be simply customized bythe users at runtime. Using the concepts for applicationdevelopment sketched in the previous section, main-

8http://www.fluidops.com/ontologies/ecloud


taining these customizations becomes straightforward:modified wiki pages and templates are exported andencapsulated into a customer-specific solution bundle,which at the installation time extends or overrides thedefault resources.

4.2. Semantic Authoring and Publishing

In the context of Dynamic Semantic Authoring andPublishing, the Information Workbench supports con-tent producers and content consumers to author, cu-rate and publish ontology and instance data follow-ing an editorial workflow. For example, for the 2012Olympics we have developed a solution to support theeditorial process of creating, editing, and publishingdata around sports competitions and athletes9. The so-lution is built around the BBC Sports Ontology10, cov-ering concepts such as athletes, teams, events, com-petitions, and disciplines. For each of these concepts,wiki page templates have been composed that visualizeavailable information and also allow for easy curation,editing and interlinking using intuitive data forms. Thetemplate for the concept athlete for instance shows anathlete’s profile with the help of visualization widgetsand provides a form to edit all or selected data.

The semantic authoring and publishing process issupported by an editorial workflow. By means of dif-ferent user roles, the editorial workflow allows tomodel multi-level internal approval processes, suchthat, e.g., data journalists may consume the data, sube-ditors can see and edit data, and data architects can de-cide if and when the data are published on the website.An important part of this solution thus is the definitionof the wiki page templates and the editorial workflowwith the respective access control mechanisms that re-alize logical views for users depending on their as-signed role.

In the editorial process, end users have differentroles with specified permission levels assigned (e.g.,data journalists who consume the data, subeditors whocan see and edit data, and data architects who decide ifand when the data is published on the website). Usinga review and approval process, data are only publishedafter a responsible data architect performs the respec-tive action.

9http://www.fluidops.com/solutions/semantic-authoring-publishing/

10http://www.bbc.co.uk/ontologies/sport/

4.3. Conference Explorer

The Conference Explorer solution is a one-stop-shop for conference visitors, providing informationabout conference events, presenters, publications, anddiscussions. It integrates publicly available conferenceinformation, metadata about publications from DBLP,as well as data from social networks such as LinkedIn,Twitter, and Facebook. The solution is built around ex-isting ontologies for the conference domain (Seman-tic Web Conference Ontology11 and Semantic Web forResearch Communities Ontology12) as well as otherstandard ontologies (FOAF, Dublin Core, and ICAL).

Many conference series (including ISWC, ESWC,WWW, and Semtech) already publish their metadataaccording to these ontologies. With this solution, cre-ating a Linked Data frontend for a new conference sim-ply requires to point the data provider to the respectiveendpoint (or RDF dump) of the conference metadata,as well as to set up providers that augment the con-ference data with social network data (e.g., from thepublic profiles of the presenters or the event itself).

Fig. 4. A conference page of the Conference Explorer solution

The conference explorer UI comes with a set of wikipages and templates that allow for intuitive browsingthrough the data. As illustrated in Figure 4, it embeds arich set of different widgets, including event timelines,tag clouds depicting the conference topics, graph andpivot views for visual exploration, and mashups withexternal data sources. In addition, it provides detailedstatistical information about the conference on specialdashboards, showing, for instance, the demographicsof presenters.

11http://data.semanticweb.org/ns/swc/ontology

12http://ontoware.org/swrc/


5. Lessons Learned

Having presented some example scenarios in theprevious section, we now want to summarize sometechnical insights and our experiences. When buildingLinked Data applications, it is important to know abouttechnical limitations. One limitation is the reliabilityand serviceability of public SPARQL endpoints. Weempirically found that most of today’s endpoints havedeficiencies in one or more of the following dimen-sions [2]: conformance to the exchange protocol, ser-vice uptime, query execution performance, complete-ness of result sets, and latency. To overcome these lim-itations, we recommend using SPARQL endpoints incontrolled environments (e.g., in a local intranet) thatcan guarantee reliability for interactive applicationswith a strong coupling between UI and database.

A second technical limitation becomes relevantwhen dealing with large amounts of highly dynamicdata. In these scenarios the warehousing approach doesnot scale, and alternative options such as virtual inte-gration with federation technologies are still in theirinfancy. Here it is a challenge for the research commu-nity to advance technologies over the next years.

With the Information Workbench we offer a plat-form that enables advanced end users (such as data sci-entists) without deep programming skills to create cus-tom applications. E.g., in the dynamic semantic pub-lishing use case the Information Workbench is used bydata journalists and data architects as a semantic back-end for authoring and publishing data in an editorialworkflow. The data center management scenario is tai-lored towards enterprise clouds environment manage-ment and as such is primarily used by system adminis-trators.

In summary, the benefits for using the InformationWorkbench as a platform for building Linked Data ap-plications are:

– The comprehensive SDK (components, tools, de-velopment process)

– Very fast setup and guided application building(which does not necessarily require programmingskills)

– Immediate view on any RDF data through defaulttemplate pages

– Customization and extensibility at various points

6. Conclusions and Outlook

We have presented the Information Workbench as aplatform for Linked Data applications. It is available

as a Community Edition under an Open Source licenseas well as an Enterprise Edition with a commercial li-cense. The platform provides a generic frontend for vi-sualizing and interacting with Linked Data, which canbe further customized and extended for domain spe-cific applications through an SDK. On the UI side, thecustomization is done in a completely declarative way,resorting to a rich pool of widgets and creating tem-plate pages in wiki-syntax, which are associated withelements of domain ontologies. In our experience, thissignificantly simplifies and speeds up the applicationdevelopment. Following a wiki-based approach, eventhe end user is enabled to customize the UI, to createpersonal dashboards, etc.

So far we – as well as numerous partners and cus-tomers – have developed Linked Data application ina variety of domains, including media, sports, events,data center management, pharma, and others. Wher-ever possible, these applications are based on estab-lished and standardized ontologies. As of today, wehave templates available for dozens of ontologies withhundreds of concepts. The solutions developed withthe SDK are highly reusable artifacts that can be eas-ily combined and extended for new applications. Theultimate goal is that components for ontology-basedLinked Data applications can be shared and reused justas the ontologies and the data. Towards this goal, weplan to provide an open repository of reusable solu-tions as a Linked Data App Store.

Further, in a number of projects we are extendingthe platform in various other directions. For example,in the project Optique13 the Information Workbench isused as the basis for developing a platform for scalableend user access to Big Data, integrating techniques forOntology-based Data Access (OBDA) [3] over highlydistributed and heterogeneous relational data sources.One of the focus areas are user interface techniquesthat enable the end user to pose on-demand, ad hoc an-alytical queries. In the project Durchblick14, we are ex-tending the platform towards Augmented Reality ap-plications and supporting new end-user devices suchas Google Glass and similar Head Mounted Displays.In the scenario of the conference explorer, we will thusbe able to proactively deliver information based on thecurrent context of the conference visitors via GoogleGlass.

13http://www.optique-project.eu/14http://www.fluidops.com/projects/

durchblick/


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[7] D. A. Keim, F. Mansmann, J. Schneidewind, J. Thomas, andH. Ziegler. Visual analytics: Scope and challenges. In S. J.Simoff, M. H. Böhlen, and A. Mazeika, editors, Visual DataMining, volume 4404 of Lecture Notes in Computer Science,pages 76–90. Springer, 2008.

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