Interest-Based RDF Update Propagation

Endris, Kemele M.; Faisal, Sidra; Orlandi, Fabrizio; Auer, Sören; Scerri, Simon

doi:10.1007/978-3-319-25007-6_30

Cited by 10 publications

(14 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further, assume that the endpoint C2 is selected to process this query instead of the authoritative endpoints, DBpedia and LinkedMDB. Because the execution of the join can be delegated to C2, the number of transferred tuples can be reduced whenever not all the triples with predicate 6 The fragment synchronization problem has been studied in [11,17]. dbo:director in DBpedia have a joinable triple with predicate owl:sameAs in LinkedMDB.…”

Section: Definitionsmentioning

confidence: 99%

“…SPARQL construct queries are executed to populate these fragments; data is collected using the LDF client 10 against a local LDF server that hosts the corresponding datasets. Implementations: Source selection and query decomposition components of FedX 3.1 11 and ANAPSID 12 have been modified. First, Fedra and DAW [28] replaced FedX and ANAPSID source selection strategies; we name these new engines, Fedra + FedX, DAW + FedX, Fedra + ANAPSID, and DAW + ANAPSID, respectively.…”

Section: Experimental Studymentioning

confidence: 99%

See 1 more Smart Citation

Decomposing federated queries in presence of replicated fragments

Montoya

Skaf-Molli

Molli

et al. 2017

Journal of Web Semantics

View full text Add to dashboard Cite

Federated query engines allow for linked data consumption using SPARQL endpoints. Replicating data fragments from different sources enables data re-organization and provides the basis for more effective and efficient federated query processing. However, existing federated query engines are not designed to support replication. In this paper, we propose a replication-aware framework named LILAC, sparqL query decomposItion against federations of repLicAted data sourCes, that relies on replicated fragment descriptions to accurately identify sources that provide replicated data. We defined the query decomposition problem with fragment replication (QDP-FR). QDP-FR corresponds to the problem of finding the sub-queries to be sent to the endpoints that allows the federated query engine to compute the query answer, while the number of tuples to be transferred from endpoints to the federated query engine is minimized. An approximation of QDP-FR is implemented by the LILAC replication-aware query decomposition algorithm. Further, LILAC techniques have been included in the state-of-the-art federated query engines FedX and ANAPSID to evaluate the benefits of the proposed source selection and query decomposition techniques in different engines. Experimental results suggest that LILAC efficiently solves QDP-FR and is able to reduce the number of transferred tuples and the execution time of the studied engines.

show abstract

Section: Definitionsmentioning

confidence: 99%

Section: Experimental Studymentioning

confidence: 99%

Decomposing federated queries in presence of replicated fragments

Montoya

Skaf-Molli

Molli

et al. 2017

Journal of Web Semantics

View full text Add to dashboard Cite

show abstract

“…In this section, we formalize the main concepts required for realizing co-evolution of RDF datasets. The Resource Description Framework (RDF) 7 is widely used to represent information on the Web. A resource can be any thing (either physical or conceptual).…”

Section: Motivating Examplementioning

confidence: 99%

Co-evolution of RDF Datasets

Faisal

Endris

Shekarpour

et al. 2016

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Abstract. Linking Data initiatives have fostered the publication of large number of RDF datasets in the Linked Open Data (LOD) cloud, as well as the development of query processing infrastructures to access these data in a federated fashion. However, different experimental studies have shown that availability of LOD datasets cannot be always ensured, being RDF data replication required for envisioning reliable federated query frameworks. Albeit enhancing data availability, RDF data replication requires synchronization and conflict resolution when replicas and source datasets are allowed to change data over time, i.e., co-evolution management needs to be provided to ensure consistency. In this paper, we tackle the problem of RDF data co-evolution and devise an approach for conflict resolution during co-evolution of RDF datasets. Our proposed approach is property-oriented and allows for exploiting semantics about RDF properties during co-evolution management. The quality of our approach is empirically evaluated in different scenarios on the DBpedia-live dataset. Experimental results suggest that proposed proposed techniques have a positive impact on the quality of data in source datasets and replicas.

show abstract

“…A variety of existing applications is related to change detection of query results on dynamic RDF datasets, such as (external) query caching [13], partial dataset update [3], as well as notification services [14]. However, even though Williams and Weaver show how the Last-Modified date can be computed with reasonable modifications to a state-of-the-art SPARQL processor [18], working implementations are rare.…”

Section: Related Workmentioning

confidence: 99%

“…Further hints for possible changes may be obtained from metadata about datasets; for instance, the DCAT recommendation suggests to use dcterms:modified or dcterms:accrualPeriodicity to describe update frequencies of a dataset. 3 Since the aforementioned cache indicators and hints for change detection are missing almost entirely in practice, we rely on re-execution of queries. Apparently, such an approach causes overhead in terms of additional network traffic and server load.…”

Section: Related Workmentioning

confidence: 99%

Scheduling Refresh Queries for Keeping Results from a SPARQL Endpoint Up-to-Date (Short Paper)

Knuth

Hartig

Sack

2016

On the Move to Meaningful Internet Systems: OTM 2016 Conferences

View full text Add to dashboard Cite

Abstract. Many datasets change over time. As a consequence, long-running applications that cache and repeatedly use query results obtained from a SPARQL endpoint may resubmit the queries regularly to ensure up-to-dateness of the results. While this approach may be feasible if the number of such regular refresh queries is manageable, with an increasing number of applications adopting this approach, the SPARQL endpoint may become overloaded with such refresh queries. A more scalable approach would be to use a middle-ware component at which the applications register their queries and get notified with updated query results once the results have changed. Then, this middle-ware can schedule the repeated execution of the refresh queries without overloading the endpoint. In this paper, we study the problem of scheduling refresh queries for a large number of registered queries by assuming an overload-avoiding upper bound on the length of a regular time slot available for testing refresh queries. We investigate a variety of scheduling strategies and compare them experimentally in terms of time slots needed before they recognize changes and number of changes that they miss.

show abstract

Interest-Based RDF Update Propagation

Cited by 10 publications

References 12 publications

Decomposing federated queries in presence of replicated fragments

Decomposing federated queries in presence of replicated fragments

Co-evolution of RDF Datasets

Scheduling Refresh Queries for Keeping Results from a SPARQL Endpoint Up-to-Date (Short Paper)

Contact Info

Product

Resources

About