WORKSHOP REPORT

Report on the 2nd Workshop on Managing the
Evolution and Preservation of the Data Web(MEPDaW 2016)
Jeremy Debattista
University of Bonn and Fraunhofer IAIS, Bonn, Germany
debattis@cs.uni-bonn.de
Javier D. Fernández, Jürgen Umbrich
Vienna University of Economics and Business, Vienna, Austria{javier.fernandez,juergen.umbrich}@wu.ac.at
Abstract
Research on preserving evolving linked datasets is gaining increasingly attention in the Semantic Web community. The 2nd workshop on Managing the Evolution and Preservation of
the Data Web (MEPDaW 2016) aimed at addressing the numerous and diverse emerging challenges, from change discovery and scalable archive representations/indexes/infrastructures
to time-based query languages and evolution analysis. In this report, we motivate our workshop and outline the keynote by Axel Polleres and the papers (3 full papers and one industry
paper) presented at MEPDaW 2016 co-located with the ESWC 2016 conference in Anissaras,
Crete, Greece. Finally, we conclude with an outlook of future directions.

1

Introduction

There is a vast and rapidly increasing quantity of scientific, corporate, government andcrowd-sourced data published on the emerging Data Web. In fact, the last decade has seen
an impressive growth of the use of RDF (Resource Description Framework) [14] as the defacto graph-based data model to express facts about any field of knowledge on the Web. The
main driving force has been the Linked Open Data community, which promotes to publicly
share such structured data on the Web and to connect it by re-using HTTP Unique Resource
Identifiers (URIs) between different data sources [3]. Besides accessible identifiers, at themost basic level, data providers serve RDF dumps in a wide range of formats. Additionally,
data providers expose data through public query APIs with different expression power. On
the one hand, services such as Linked Data Fragments [20] and other RESTful APIs mainly
focus on traditional simple lookups. While, SQL-like structured queries can be written in
the SPARQL query language [12] and served via SPARQL endpoints.
All this offers a great potential for building innovative products and services that create
new value from already collected data. Open Data published according to the Linked Data

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Paradigm are transforming the Web into a vibrant information ecosystem. However, recent
studies [13] show that these publication schemes are subject to unnoticed changes and service
disruptions, following the same scale-free nature of the Web. In other words, the emerging
huge, heterogeneous and interconnected cloud of data-to-data hyperlinks evolves widely;
without notice, data providers often add or delete data, thus creating new versions of theserved datasets, and migrate or abandon APIs to obsolescence. As an example of this latter,
SPARQLES [4], a service that monitors 553 SPARQL endpoints, shows that more than 55%
of the endpoints are currently unavailable (as of September 2016).
In this scenario, and lacking from a scalable and queryable archive, current applications
and businesses on top of any of the aforementioned publication schemes have been left to bear
all problems associated with version management and discontinued services. A traditionalview of digitally preserving them by “pickling them and locking them away” for future use,would conflict with their evolution. There are a number of approaches and frameworks, suchas the LOD2 stack [1], that manages a full life-cycle of the Data Web. More specifically,
these techniques are expected to tackle major issues such as the synchronisation, curation,
appraisal, citation, archiving, and sustainability problems.
The goal of this workshop was to provide a forum for researchers and practitioners who
apply Linked Data technologies to discuss, exchange and disseminate their work on preserving
Linked Data. More broadly, our aim was to enable communities interested in data, knowledge
and ontology dynamics to network and cross-fertilise. The topics of interest included, butwere not limited to the following themes related to the evolution and preservation of linked
data:• Change Discovery– Change detection and computation in data and/or vocabularies;– Change traceability;– Change notifications (e.g., PubSubHubPub, DSNotify, SPARQL Push);– Visualisation of evolution patterns for datasets and vocabularies;– Prediction of changes.
• Formal models and theory– Formal representation of changes and evolution;– Change/Dynamicity characteristics tailored to graph data;– Query language for archives;– Freshness guarantee for query results;– Freshness guarantee in databases.
• Data Archiving and preservation– Scalable versioning and archiving systems/frameworks;– Query processing/engines for archives;– Efficient representation of archives (compression);– Benchmarking archives and versioning strategies.
The workshop included a keynote talk by Dr. Axel Polleres (Section 2), three research
papers (Section 3) and one industry contribution (Section 4). The workshop was closedwith a plenary discussion (Section 5) on the most relevant and open questions, collected for

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future directions of the workshop (Section 6). Furthermore, based on the review scores, thebest paper award has been given to Ruben Taelman, Ruben Verborgh, Pieter Colpaert, Erik
Mannens and Rik Van de Walle for their work “Continuously Updating Query Results over
Real-Time Linked Data”. MEPDaW proceedings are publicly available in [6].

2Keynote Talk

Dr. Axel Polleres gave an inspiring talk1 on Archiving Linked and Open Data. Dr. Polleresis a full professor in the area of ”Data and Knowledge Engineering“ at the Institute ofInformation Business of Vienna University of Economics and Business (WU Wien). He
has published more than 100 articles, most of them in top journals and conferences, andhe actively contributed to World Wide Web Consortium (W3C) standardisation efforts, cochairing the W3C SPARQL working group. He was invited as a renowned expert on querying
and reasoning about ontologies, rules languages, Semantic Web technologies, Linked Open
Data and knowledge management.
During his talk, Dr. Polleres first motivated the need of monitoring evolution and archiving as a necessary step to understand the evolution (e.g. climate change, social and topic
trends, etc.), the validity of the information (e.g. continuous revisions in Wikipedia articles)
and its consequences (e.g. predicting data in the Open City Data Pipeline [2]). He briefly
reviewed existing preservation projects in the Web, such as the Common Crawl2 , Internet
Memory3 , Internet Archive4 , and time-based access such as the HTTP memento protocol(RFC 7089). As stated by Dr. Polleres, most of current archiving approaches present a poor
granularity (access to “some” snapshots), offer aggregated data instead of raw data, sufferfrom scalability problems and, in general, disregard a large-scale structured access (e.g. withtemporal query languages). Similar problems arise in the particular field of Linked Dataarchiving, with even fewer and more limited approaches, such as the dynamic linked data
observatory5 [13]. Dr. Polleres established important open research challenges on evolving
structured interlinked data, summarised as follows:• How can we represent archives of continuously evolving linked datasets? (efficiency vs.
compact representation).
• How can we improve completeness of archiving?
• How can emerging retrieval demands in archiving (e.g. time-traversing and traceability)
be satisfied?
• How can certain time-specific queries over archives be answered? Can we re-use existing
technologies (e.g. SPARQL or temporal extensions)? What is the right query language
for such queries?
Then, Dr. Polleres focused on three general archiving challenges: the synchronisation
problem, i.e. how we can monitor changes, the appraisal problem, how we can assess
the quality of a dataset (via archiving) and the archiving and query problem, i.e. how
1Slides of the talk: https://aic.ai.wu.ac.at/ polleres/presentations/20160530Keynote-MEPDaW2016.pdf
http://commoncrawl.org/
3
http://internetmemory.org/
4
https://archive.org/index.php
5
http://swse.deri.org/dyldo/
2

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we can efficiently archive and perform time-based retrieval queries of a dataset. As forsynchronisation, he distinguished between pull changes (crawl) vs. push changes (notify), andsummarised one of his previous work on adaptive archiving [18] to accurately capture content
change, and the DBpedia Wayback Machine [9] as an example of recreating the versions fromthe original sources (in this particular case, re-apply mappings on the Wikipedia revisionhistory). Regarding the appraisal problem, he presented a recent work on archiving andanalysing the history of datasets in open data portals [19]. Finally, Dr. Polleres addressed
the archiving and query problem reviewing current RDF archiving techniques and structured
query languages managing time [8], presenting an initial blueprint on benchmarking archivesof semantic data [10]. To conclude, Dr. Polleres summarised some open questions, such as
the compact representation of archives, the missing query language for archives and the needof a large-scale archiving indexing/infrastructure, and discuss with the audience if there isan actual and urgent need of archiving in the community.

3

Research Papers

The first paper presented in this workshop, Continuously Updating Query Results over RealTime Linked Data 6 , describes how Triple Pattern Fragments [20] can be extended in order toallow clients to execute real-time SPARQL queries whose results are “continuously” updated
on the client-side. In their presentation, the authors discuss various experiments showing
how the server’s costs are lowered, however at the expense of execution time per query on
the client. This paper was chosen by the program committee as the best paper and was
extended in [16].
Meimaris and Papastefanatos presented The EvoGen Benchmark Suite for Evolving RDF
Data [15]. In this talk, the authors describe a benchmark framework that extends the Lehigh
University Benchmark (LUBM) [11] in order to cover the need for creating synthetic evolving
RDF data, that will then be used to benchmark versioning and change detection techniquesin dynamic datasets. In this talk, the authors discuss the various parameters required togenerate this kind of evolving RDF data.
The final research paper of this workshop was Toward Semantic Sensor Data Archives
on the Web 7 , authored by Calbimonte and Aberer [5]. In this presentation, the authors
talk about the current barriers of archiving semantic sensor data and possible solutions.
The authors presented their visionary semantic-based architecture using the well known
DCAT8 and SSN9 ontologies, together with a use case based on their air quality monitoring
deployment and preliminary results comparing them with an optimised ERI [7] interchange
format for RDF.
6

Slides are available at: http://www.slideshare.net/RubenTaelman/continuously-updating-queryresults-over-realtime-linked-data by Taelman et al. [17]. Last Accessed on 01/09/2016
7
Slides are available at: http://www.slideshare.net/jpcik/toward-semantic-sensor-data-archives-onthe-web. Last Accessed on 01/09/20168
http://www.w3.org/TR/vocab-dcat/
9
http://www.w3.org/TR/vocab-ssn/

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4

Industry Paper

In this workshop we had one industry talk from the founders of Dydra (http://dydra.com).
Dydra is a cloud graph database that can be accessed and updated via SPARQL. In their
talk Transaction-Time Queries in Dydra, Anderson and Bendiken describe a taxonomy oftransaction-time queries for enabling versioning of RDF graphs in Dydra. The authors
present some examples showing how the Dydra architecture combined with extensions to thelanguage supports stakeholders to work with versioned RDF data.

5

Closing Discussion

During the workshop, there was an active discussion between all presentations, discussing
future directions, possible improvements, and raising controversial yet interesting questions
to all our presenters. The plenary was closed by a round table discussion, with Dr. Pollares
raising the following question:“Is there an actual and urgent need in the (Semantic Web) community for handlingthe dynamicity of the Data Web?”
This debatable questions led to many other insights towards the need of having techniques
to handle the evolution and the subsequent preservation of such expansion. The audience
asked and debated whether a killer-app is needed in this regard. Mixed reactions were
given regarding the killer-app, where the creators of Dydra argued that whilst this evolution
and preservation of the Data Web is of utmost importance for their clients, they just needan infrastructure on top of technologies such as SPARQL rather than an extra “complex”
application.

6

Conclusion and Future Directions

The MEPDaW organisers were pleased of the quality of the papers and their presentationin the workshop, the lively discussing of the participants and the emerging involvement ofindustry partners. All this showed the timeliness of the topic and the lack of well-establishedtechniques to cope with most of the challenges when managing preserving the evolving Data
Web. In particular, we envision four important topics under discussion:• Compact representation of RDF and Linked Data archives.
• Query languages for archives satisfying these requirements for evolving interlinked data.
• Index archives at large scale (and keeping up with evolution rate) to process time-based
queries efficiently.
• Query optimization for archives, e.g. enabling query rewriting for querying archives ofstructured non-RDF sources.
The organisers are planning a follow-up workshop next year, expecting that the works canaddress this topics as well as other related areas such as evolution studies, adaptive changemonitoring and practical archiving applications.

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Acknowledgements
We would like to thank the authors for their high-quality contributions and their active
participation in the workshop. We express our gratitude to all program committee members
for reviewing the submissions for the workshop. We are also grateful to the organisers of the
ESWC 2016 conference for their support, and our keynote speaker, “Axel Polleres” from theVienna University of Economics and Business. This workshop was co-organised by members
funded by the Austrian Science Fund (FWF): M1720-G11.

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