Verfügbarkeit: Linked data management

Linked data management:

"With the growing popularity of the Semantic Web, more and more semantic data and data sources become available and accessible for everyone. By establishing semantic links between the data, answers to (complex) queries can be evaluated based on the data on multiple providers instead of just one...

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Bibliographische Detailangaben
Format:	Buch
Sprache:	English
Veröffentlicht:	Boca Raton [u.a.] CRC Press 2014
Schriftenreihe:	Emerging directions in database systems and applications
Schlagworte:	COMPUTERS / Database Management / General COMPUTERS / Database Management / Data Mining COMPUTERS / Internet / General Linked data Database management Data structures (Computer science) Semantic Web Heterogeneous computing Linked Data Datenmanagement Aufsatzsammlung
Online-Zugang:	Cover image Inhaltsverzeichnis
Zusammenfassung:	"With the growing popularity of the Semantic Web, more and more semantic data and data sources become available and accessible for everyone. By establishing semantic links between the data, answers to (complex) queries can be evaluated based on the data on multiple providers instead of just one. This book motivates, introduces, and details techniques for processing heterogeneous structured data on the Web by providing a comprehensive overview for database researchers and practitioners about this new publishing paradigm on the web, and show how the abundance of data published as Linked Data can serve as a fertile ground for database research and experimentation"..
Beschreibung:	Includes bibliographical references and index
Beschreibung:	XXVII, 547 S. Ill., graph. Darst.
ISBN:	9781466582408

Internformat

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Datensatz im Suchindex

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adam_text	Contents I Introduction 1 1 Linked Data &c the Semantic Web Standards 3 Aidnn Hogan 1.1 Introduction ........................... 3 1.2 Semantic Web .......................... 6 1.3 Resource Description Framework (RDF) ........... 10 1.3.1 RDF Terms ........................ 10 1.3.2 RDF Triples and Graphs ................ 12 1.3.3 RDF Vocabulary ..................... 16 1.3.4 RDF Syntaxes ...................... 18 1.4 RDF Semantics, Schemata and Ontologies .......... 20 1.4.1 RDF Semantics ...................... 20 1.4.2 RDF Schema (RDFS) .................. 23 1.4.3 Web Ontology Language (OWL) ............ 25 1.5 Querying RDF with SPARQL ................. 31 1.5.1 Query Types ....................... 33 1.5.2 Dataset Clause and Named Graphs ........... 34 1.5.3 Query Clause ....................... 35 1.5.4 Solution Modifiers .................... 39 1.5.5 Towards SPARQL 1.1.................. 40 1.6 Linked Data ........................... 41 1.6.1 The Early Semantic Web on the Web ......... 42 1.6.2 Linked Data Principles and Best Practices ....... 43 1.6.3 Linking Open Data .................... 44 2 Aligning Ontologies of Linked Data 49 Rahul Parundekar, Craig A. Knoblock, and José Luis Ambite 2.1 Introduction ........................... 49 2.2 Linked Data Sources with Heterogeneous Ontologies ..... 51 2.3 Finding Alignments Across Ontologies ............. 51 2.3.1 Source Preprocessing ................... 51 2.3.2 Aligning Atomic Restriction Classes .......... 52 2.3.3 Aligning Conjunctive Restriction Classes ........ 53 2.3.4 Eliminating Implied Alignments ............ 55 2.3.5 Finding Concept Coverings ............... 57 Contents VI 2.3.6 Curating Linked Data................... · ....... (»О 2.4 Results ............... . /,. , ,-,ι 2 4 1 Representative Examples of Atomii Alignments . . . fill 2.4.2 Representative Examples of Conjunctive Alignments . lil 2.4.3 Representative Examples of Concepì Coverings .... (¥2 2.4.4 Outliers .......................... Ul 2.4.5 Precision and Recall of Country Λ lignin« -ні s ...... Vùi 2.4.6 Representative Alignments from О! her Domains ... (ІЗ 2.5 Related Work .......................... ( 2.6 Conclusion ............................ 3 Architecture of Linked Data Applications Benjamin Heitmann, Bichará Cyganiak. Conor flat/is, ami Stefan Decker 3.1 Introduction ........................... (1!) 3.2 An Empirical Survey of RDF-based Applications ....... 71 3.2.1 Research Method of the Survey ............. I 3.2.2 Findings .......................... 7 Л 3.2.3 Discussion of Threats to Validity ............ 7(i 3.3 Empirically-grounded Conceptual Architect in«· ........ 77 3.3.1 Architectural Style of the Conceptual Architecture . . 77 3.3.2 Decomposing the Surveyed Applications into Components ....................... 7M 3.3.3 Description of Components ............... 7!) 3.4 Implementation Challenges ................... S I 3.4.1 Integrating Noisy and Heterogeneous Data ...... SI 3.4.2 Mismatch of Data Models between Coi u poi HMit. s ... Hł 3.4.3 Distribution of Application Logic across Components . 8.1 3.4.4 Missing Guidelines and Patterns ............ Kľi 3.5 Analysis of an Example Application .............. SI» 3.6 Future Approaches for Simplifying Linked Data Application Development .......... SS 3.6.1 More Guidelines, Best Practices, and Design Patterns SS 3.6.2 More Software Libraries ...... SS 3.6.3 Software Factories ..... S!) 3.7 Related Work ......... s<, 3.7.1 Empirical Surveys ..... ÎM) 3.7.2 Architectures for Semantic Web Applications .... !) 3.» Conclusions ....... II Centralized Query Processi g 93 Contents vii 4 Mapping Relational Databases to Linked Data 95 Juan F. Sequeda and Daniel P. Miranker 4.1 Introduction ........................... 95 4.2 Scenarios ............................. 96 4.2.1 Direct Mapping and ETL ................ 96 4.2.2 Manual R2RML Mapping and ETL .......... 97 4.2.3 Direct Mapping as Ontology with Ontology Alignment 98 4.2.4 Automatic Mapping with Wrapper ........... 99 4.3 W3C RDB2RDF Standards ................... 100 4.3.1 Running Example .................... 100 4.3.2 Direct Mapping ...................... 100 4.3.3 R2RML: RDB to RDF Mapping Language ...... 103 4.4 Semantic Integration Architecture ............... 106 4.4.1 Τ>ΛΛ(>ηΙ: Direct Mapping as Ontology ......... 108 4.4.2 Wrapper Systems ..................... Ill 4.4.2.1 information Preservation ........... Ill 4.4.2.2 Query Preservation .............. Ill 4.4.2.3 Ultrawrap .................... 112 4.Г) Conclusions ............................ 114 5 Efficient Query Processing in RDF Databases 117 Andrey Gubichev and Thomas Neumann 5.1 Introduction ........................... 117 5.2 Storing and Indexing RDF Data ................ 118 5.2.1 Relational-based Approach ............... 119 5.2.2 Graph-based Approach ................. 123 5.3 Query Processing and Query Optimization of SPARQL Queries 124 5.3.1 lYanslating SPARQL Queries .............. 124 5.3.2 Join Ordering ....................... 126 5.3.3 Cardinality Estimation .................. 130 5.3.4 Physical Operators: Joins, Path Traversals ....... 134 5.4 Open Issues and Conclusion .................. 135 6 Evaluating SPARQL Queries over Linked Data Streams 137 Jean-Paul Calbimonte and Oscar Corcho 6.1 Data Stream and Event Query Processing ........... 139 6.1.1 Data Stream and Event Models ............. 139 6.1.2 Continuous Query Processing and Windows ...... 142 6.1.3 Streaming Data & Event Processing Engines ..... 143 6.2 SPARQL Extensions for Querying RDF Streams ....... 145 6.2.1 RDF Stream Model ................... 145 6.2.2 SPARQL Extensions for Event and Data Streams ... 146 6.2.3 SPARQL Extensions Semantics ............. 149 6.3 RDF Stream Query Processing ................. 151 6.3.1 Query Processing Strategies ............... 151 Contents VIU íiľj 6 3.2 Mappings from Streaming Data to RDF ..... 6.3.3 Query Rewriting over Diverse Streaming Data Technologies ....................... 6.4 Discussion and Lessons Learned ................. \| 6.4.1 Lessons Learned ..................... (liJ 6.4.2 Future Directions ..................... l(l(i III Parallel Query Processing 163 SPARQL Query Processing in the Cloud Francesca Bugiotti, Jesús Camacho- Rodríguez, Francois Gmsduuê. Zoi Kaoudi, Ioana Manolescu, and Starnati* Zampettikts 7.1 Introduction ....................... ... Hifi 7.2 Classification of RDF Data Management Platforms in the Cloud ............................... 1(iT 7.2.1 Cloud-based RDF Storage ................ Hi7 7.2.2 Cloud-based SPARQL Query Processing ........ HW 7.2.3 RDF Reasoning in the Cloud .............. 7.3 AMADA: RDF Data Repositories in tlu· Amu/on ( loud . . . 7.3.1 Amazon Web Services 7.3.2 General Architecture . 7.4 RDF Storing and SPARQL Querying within AMADA 7.4.1 Data Model and Running Example ....... 7.4.2 Answering Queries from the Index 7.4.3 Selective Indexing Strategics . . . 70 70 A 7Г) I I 8-І 7.5 Implementation Details ..................... 7.6 Experimental Evaluation .................... lNfi 7.6.1 Experimental Setup and Datasets ............ 1НГ> 7.6.2 Indexing Time and Costs ................ 186 7.6.3 Querying Time and Costs ................ 7.6.4 Scalability ......................... 7.6.5 Experiments Conclusion ................. H)l 7.7 Summary and Perspectives ................... H)\| 8 The Bigdata® RDP Graph Database 193 Bryan Thompson, Mike Personick, and Martyn Catcher 8.1 Introduction ............. \| i>:ţ. 8.2 Bigdata Database Architecture ........ ц 8.2.1 Deployment Models ........... 194 8.2.2 Scale-Up Architecture ........ 199 8.2.3 Scale-Out Architecture ..... oof, 8.3 RDF Database Architecture ..... 214 8.3.1 Resource Description Framework ...... 213 8.3.2 Database Schema for RDF 914 8.3.3 SPARQL Query Processing . . . ¡I« Contents ix 8.3.4 Reification Done Right .................. 224 8.4 Related Work .......................... 227 8.4.1 Clustered Graph Databases ............... 227 8.4.2 Key-Value and Map/Reduce Systems .......... 230 8.4.3 Graph Mining Platforms ................. 234 8.4.4 Analysis .......................... 235 8.5 Conclusion ............................ 236 9 Experiences with Virtuoso Cluster RDF Column Store 239 Peter Boncz, Orri Erling, and Minh-Duc Pham 9.1 Virtuoso Cluster ......................... 239 9.1.1 Architecture of a Server Process ............ 240 9.1.2 Differences with Virtuoso 6............... 242 9.2 Query Evaluation ........................ 244 9.2.1 Vectoring ......................... 245 9.2.2 Cluster Tuning Parameters ............... 245 9.2.3 Micro Benchmarks .................... 246 9.3 Experimental Evaluation .................... 250 9.3.1 BSBM Results to Date .................. 250 9.3.2 Cluster Configuration .................. 251 9.3.3 Bulk Loading RDF .................... 251 9.3.4 Notes on the BI Workload ................ 253 9.3.5 BSBM Benchmark Results ............... 254 9.4 Conclusion ............................ 258 IV Distributed Query Processing 261 10 Linked Data Query Processing Based on Link Traversal 263 Olaf Hartig 10.1 Introduction ........................... 263 10.2 Theoretical Foundations ..................... 265 10.2.1 Data Model ........................ 266 10.2.2 Full-Web Semantics for SPARQL ............ 267 10.3 Principles and Characteristics of LTBQE ........... 268 10.3.1 Live Exploration ..................... 269 10.3.2 Integrated Execution ................... 271 10.3.3 Combining Live Exploration and Integrated Execution 271 10.4 An Iterator Based Implementation ............... 272 10.4.1 Introduction to the Implementation Approach ..... 273 10.4.2 Characteristics of the Implementation Approach . . . 276 10.4.3 Selecting Query Plans for the Approach ........ 278 10.4.4 Improving Performance of the Approach ........ 281 10.4.5 Alternative Implementation Approaches ........ 282 10.5 Summary ............................. 282 Contents 11 Semantic Navigation on the Web of Data 285 Valeria Fionda, Claudio Gutierrez, and Giuseppe I tiro , . ........ Jo.) 11.1 Introduction .................. 11.2 The NaUTiLOD Navigational Language ........... ƒ* 11.2.1 Preliminaries ....................... 11.2.2 Syntax of NautiLOD .................. 11.2.3 Semantics of NautiLOD ................ 11.2.4 Evaluation Cost and Complexity of Νλι Ί IbOD · . ■ 2!« 11.3 NautiLOD: Algorithms and Implementation ......... &M 11.3.1 Evaluating NautiLOD Expressions .......... 291 11.3.2 swget Scripts ....................... 11.3.3 swget Architecture .................... 11.4 Some Application Scenarios ................... 11.4.1 The swget API and Command Lint* .......... - .»íl 11.4.2 The swget GUI ...................... · «»» 11.4.3 The swget Web Portal .................. 402 11.5 Related Research ........................ 303 11.6 Concluding Remarks ...................... 3<Иі 12 Index-Based Source Selection and Optimization 311 Jürgen Umbrich, Marcel Karnstedt, Axel I ollcrfs. and hai- ( in Sattler 12.1 Introduction ........................... . ill 12.2 Source Selection Approaches .................. M A 12.2.1 Generic Query Processing Model λ- Assumption for Source Selection ..................... 313 12.2.2 Source-Selection Approaches .............. 31 I 12.3 Data Summaries ......................... ;;i(i 12.3.1 Multidimensional Histograms .............. 317 12.3.2 QTree ........................... л w 12.3.3 Construction and Maintenance ............. 322 12.4 Importance of Hashing .............. .423 12.4.1 Hash Functions ................ ;{21 12.4.2 Comparison of Hash Functions ............. ;V2(i 12.5 Source Selection ......... ^т 12.5.1 Triple Pattern Source Selection ............. 327 12.5.2 Join Source Selection ....... ;j2H 12.5.3 Result Cardinality Estimation and Source Ranking . . 332 12.6 Practicability ......... ..... 12.7 Conclusion .......... !.L. 13 ľľP ?tSed QUery Processinë «ver Linked Data 339 Mareei Karnstedt, Каг -Uwe Sattler, and Manfred Hanswirth 13.1 Introduction ....... 13.2 General Architecture ............ .. ,!, ..................... ЛАЈ. Contents xi 13.3 A Distributed Index ....................... 343 13.3.1 DHTs for Index Management .............. 343 13.3.2 Indexing RDF Data ................... 346 13.4 Query Processing ........................ 348 13.4.1 Logical Algebra & Operators .............. 348 13.4.2 Physical Operators & Query Execution ........ 350 13.4.3 Query Execution ..................... 356 13.4.4 Query Planning ...................... 360 13.5 Other Existing Approaches ................... 363 13.6 Summary ............................. 367 14 Federated Query Processing over Linked Data 369 Peter Haase, Katja Hose, Ralf Schenkel, Michael Schmidt, and Andreas Schwarte 14.1 Introduction ........................... 369 14.2 State of the Art ......................... 371 14.3 Optimization Techniques .................... 375 14.3.1 Federated Query Processing Model ........... 376 14.3.2 Source Selection ..................... 377 14.3.3 Join Ordering ....................... 378 14.3.4 Exclusive Groups ..................... 379 14.3.5 Bind Joins ........................ 379 14.3.6 Alternative Join Techniques ............... 381 14.4 Benchmarking Federation Techniques ............. 381 14.4.1 The FedBench Benchmark Suite ............ 383 14.4.2 SPLODGE ........................ 385 14.4.3 Additional Work on Benchmarking ........... 386 14.5 Summary and Outlook ..................... 387 V Reasoning over RDF Data 389 15 On the Use of Abstract Models for RDF/S Provenance 391 Irini Fundulaki, Giorgos Flouris, and Vassilis Papakonstantinou 15.1 Introduction ........................... 391 15.2 Representation Models for Provenance ............. 392 15.2.1 Concrete Representation Models ............ 393 15.2.2 Abstract Representation Models ............ 395 15.2.3 Handling Changes in Abstract Models ......... 397 15.2.4 Alternative Abstract Models .............. 397 15.3 Abstract Provenance Models: Formalities ........... 398 15.3.1 Basic Concepts ...................... 398 15.3.2 Defining Abstract Provenance Models ......... 399 15.4 Algorithms ............................ 401 15.4.1 Annotation ........................ 401 15.4.2 Evaluation ........................ 402 ·■ Contents Л.Ј.Х 15.4.3 Adding Quadruples .................... jjj-j 15.4.4 Deleting Quadruples ................... 15.4.5 Changing the Semantics ................. ^ 15.5 Implementation of an Abstract Provenance Model ...... 405 15.5.1 Basic Schema ....................... 10Γι 15.5.2 Partitioned Schema ................... l0(J 15.6 Experimental Evaluation .................... ^ 15.6.1 Experimental Setting ................... 1(l/ 15.6.2 Experimental Results .................. KIN 15.7 Conclusions ............................ 16 Incremental Reasoning on RDF Streams 413 Daniele Dell Aglio and Emanuele Della Valle 16.1 Introduction ........................ 16.2 Basic Concepts ....................... 16.2.1 RDF Stream .................... 16.2.2 RDFS+ ....................... 16.3 Running Example ..................... 16.4 Incremental Materialization Algorithm for RDI·1 Streams 16.4.1 Assumptions and Preliminary Definitions .... I!) 16.4.2 Maintenance Program Generation ........... -122 16.4.3 Execution of the Maintenance Program in the IMaRS Window .......................... 127 16.5 Related Works .......................... 42« 16.6 Evaluation ............................ 4.42 16.7 Conclusions and Future Works ................. , VI Linked Data Interfaces 437 17 Linked Data Services 439 Sebastian Speiser, Martin Junghans, and Armin Haller 17.1 Introduction ................. 43t) 17.2 Scenario ................. 440 17.3 Information Services .......... 441 17.4 Linked Data Services (LIDS) ............ 44.4 17.5 Describing Linked Data Services ...... 44Γ) 17.5.1 Relation to Source Descriptions in Information Integration Systems ............. 44O 17.5.2 Describing LIDS Using RDF and SPARQL Graph Patterns ......... · ,- 17.6 Interlinking Data with LIDS л ля 17.7 Related Work . ............. irn 1-OT , ....................... 4.1O 17.8 Implementation and Evaluation .... 451 17.8.1 Realization of Scenario . ... 45] 17.8.2 Implementing and Interlinking Linked Data Services . 454 Contents xiii 17.8.2.1 Implementing LIDS Services......... 455 17.8.2.2 Interlinking Existing Data Sets with LIDS . 456 17.9 Conclusion ............................ 456 18 Using read-write Linked Data for Application Integration 459 Arnaud J Le Hors and Steve Speicher 18.1 Introduction ........................... 459 18.2 The Integration Challenge ................... 460 18.3 What Would Success Look Like? ................ 461 18.4 The Solution ........................... 462 18.5 Linked Data as an Application Integration Framework .... 463 18.6 The Linked Data Platform (LDP) ............... 463 18.6.1 Introduction to the Linked Data Platform (LDP) ... 465 18.6.2 Linked Data Platform Resources (LDPR) ....... 465 18.6.3 Linked Data Platform Container (LDPC) ....... 469 18.6.4 Retrieving the Non-member Properties of an LDPC . 471 18.6.5 Creating and Adding a Resource to an LDPC ..... 471 18.6.6 Inlined Member Resources ................ 472 18.6.7 Updating an LDPR ................... 473 18.6.8 Deleting an LDPR ..................... 473 18.6.9 Creating an LDPC .................... 473 18.6.10 Updating an LDPC ................... 474 18.6.11 Deleting an LDPC .................... 474 18.6.12 Adding a Binary Resource to an LDPC ........ 475 18.6.13 Membership Subject and Predicate of an LDPC ... 477 18.6.14 Paging ........................... 478 18.6.15 Ordering ......................... 480 18.7 Observations ........................... 481 18.8 LDP Limitations ......................... 481 18.9 Conclusion ............................ 482 Bibliography 485 Index 537
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genre	(DE-588)4143413-4 Aufsatzsammlung gnd-content
genre_facet	Aufsatzsammlung
id	DE-604.BV041902250
illustrated	Illustrated
indexdate	2024-07-10T01:07:51Z
institution	BVB
isbn	9781466582408
language	English
lccn	014003727
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-027345966
oclc_num	884936702
open_access_boolean
owner	DE-12 DE-473 DE-BY-UBG DE-11 DE-739
owner_facet	DE-12 DE-473 DE-BY-UBG DE-11 DE-739
physical	XXVII, 547 S. Ill., graph. Darst.
publishDate	2014
publishDateSearch	2014
publishDateSort	2014
publisher	CRC Press
record_format	marc
series2	Emerging directions in database systems and applications
spelling	Linked data management Andreas Harth ; Katja Hose ; Ralf Schenkel Boca Raton [u.a.] CRC Press 2014 XXVII, 547 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Emerging directions in database systems and applications Includes bibliographical references and index "With the growing popularity of the Semantic Web, more and more semantic data and data sources become available and accessible for everyone. By establishing semantic links between the data, answers to (complex) queries can be evaluated based on the data on multiple providers instead of just one. This book motivates, introduces, and details techniques for processing heterogeneous structured data on the Web by providing a comprehensive overview for database researchers and practitioners about this new publishing paradigm on the web, and show how the abundance of data published as Linked Data can serve as a fertile ground for database research and experimentation".. COMPUTERS / Database Management / General bisacsh COMPUTERS / Database Management / Data Mining bisacsh COMPUTERS / Internet / General bisacsh Linked data Database management Data structures (Computer science) Semantic Web Heterogeneous computing COMPUTERS / Database Management / General COMPUTERS / Database Management / Data Mining COMPUTERS / Internet / General Linked Data (DE-588)7863462-3 gnd rswk-swf Datenmanagement (DE-588)4213132-7 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Linked Data (DE-588)7863462-3 s Datenmanagement (DE-588)4213132-7 s DE-604 Harth, Andreas 1976- Sonstige (DE-588)143354639 oth Hose, Katja Sonstige (DE-588)139260285 oth Schenkel, Ralf Sonstige (DE-588)1054050813 oth http://images.tandf.co.uk/common/jackets/websmall/978146658/9781466582408.jpg Cover image Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027345966&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis
spellingShingle	Linked data management COMPUTERS / Database Management / General bisacsh COMPUTERS / Database Management / Data Mining bisacsh COMPUTERS / Internet / General bisacsh Linked data Database management Data structures (Computer science) Semantic Web Heterogeneous computing COMPUTERS / Database Management / General COMPUTERS / Database Management / Data Mining COMPUTERS / Internet / General Linked Data (DE-588)7863462-3 gnd Datenmanagement (DE-588)4213132-7 gnd
subject_GND	(DE-588)7863462-3 (DE-588)4213132-7 (DE-588)4143413-4
title	Linked data management
title_auth	Linked data management
title_exact_search	Linked data management
title_full	Linked data management Andreas Harth ; Katja Hose ; Ralf Schenkel
title_fullStr	Linked data management Andreas Harth ; Katja Hose ; Ralf Schenkel
title_full_unstemmed	Linked data management Andreas Harth ; Katja Hose ; Ralf Schenkel
title_short	Linked data management
title_sort	linked data management
topic	COMPUTERS / Database Management / General bisacsh COMPUTERS / Database Management / Data Mining bisacsh COMPUTERS / Internet / General bisacsh Linked data Database management Data structures (Computer science) Semantic Web Heterogeneous computing COMPUTERS / Database Management / General COMPUTERS / Database Management / Data Mining COMPUTERS / Internet / General Linked Data (DE-588)7863462-3 gnd Datenmanagement (DE-588)4213132-7 gnd
topic_facet	COMPUTERS / Database Management / General COMPUTERS / Database Management / Data Mining COMPUTERS / Internet / General Linked data Database management Data structures (Computer science) Semantic Web Heterogeneous computing Linked Data Datenmanagement Aufsatzsammlung
url	http://images.tandf.co.uk/common/jackets/websmall/978146658/9781466582408.jpg http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027345966&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT harthandreas linkeddatamanagement AT hosekatja linkeddatamanagement AT schenkelralf linkeddatamanagement

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