Being able to query online public knowledge graphs such as Wikidata or DBpedia is extremely valuable. However, these queries can be interrupted due to the fair use policies enforced by SPARQL endpoint providers, leading to incomplete results. While these policies help maintain the responsiveness of public SPARQL endpoints, they compromise the completeness of query results, which limits the feasibility of various downstream tasks. Ideally, we should not have to choose between completeness and responsiveness. To address this issue, we introduce and formalize the concept of SPARQL continuation queries. When a SPARQL endpoint interrupts a query, it returns partial results along with a SPARQL continuation query to retrieve the remaining results. If the continuation query is also interrupted, the process repeats, generating further continuation queries until the complete results are obtained. In our experimentation, we show that our continuation server PASSAGE ensures completeness and responsiveness while delivering high performance.

Processing SPARQL queries over large federations of SPARQL endpoints is crucial for keeping the Semantic Web decentralized. Despite the existence of hundreds of SPARQL endpoints, current federation engines only scale to dozens. One major issue comes from the current definition of the source selection problem, i.e., finding the minimal set of SPARQL endpoints to contact per triple pattern. Even if such a source selection is minimal, only a few combinations of sources may return results. Consequently, most of the query processing time is wasted evaluating combinations that return no results. In this paper, we introduce the concept of Result-Aware query plans. This concept ensures that every subquery of the query plan effectively contributes to the result of the query. To compute a Result-Aware query plan, we propose FedUP, a new federation engine able to produce Result-Aware query plans by tracking the provenance of query results. However, getting query results requires computing source selection, and computing source selection requires query results. To break this vicious cycle, FedUP computes results and provenances on tiny quotient summaries of federations at the cost of source selection accuracy. Experimental results on federated benchmarks demonstrate that FedUP outperforms state-of-the-art federation engines by orders of magnitude in the context of large-scale federations.

To provide stable and responsive public SPARQL query services, data providers enforce quotas on server usage. Queries which exceed these quotas are interrupted and deliver partial results. Such interruption is not an issue if it is possible to resume queries execution afterward. Unfortunately, there is no preemption model for the Web that allows for suspending and resuming SPARQL queries. In this paper, we propose SaGe: a SPARQL query engine based on Web preemption. SaGe allows SPARQL queries to be suspended by the Web server after a fixed time quantum and resumed upon client request. Web preemption is tractable only if its cost in time is negligible compared to the time quantum. The challenge is to support the full SPARQL query language while keeping the cost of preemption negligible. Experimental results demonstrate that SaGe outperforms existing SPARQL query processing approaches by several orders of magnitude in term of the average total query execution time and the time for first results.

Distributed collaborative editing systems allow users to work distributed in time, space and across organizations. Trending distributed collaborative editors such as Google Docs, Etherpad or Git have grown in popularity over the years. A new kind of distributed editors based on a family of distributed data structure replicated on several sites called Conflict-free Replicated Data Type (CRDT for short) appeared recently. This paper considers a CRDT that represents a distributed sequence of basic elements that can be lines, words or characters (sequence CRDT). The possible operations on this sequence are the insertion and the deletion of elements. Compared to the state of the art, this approach is more decentralized and better scales in terms of the number of participants. However, its space complexity is linear with respect to the total number of inserts and the insertion points in the document. This makes the overall performance of such editors dependent on the editing behaviour of users. This paper proposes and models LSEQ, an adaptive allocation strategy for a sequence CRDT. LSEQ achieves in the average a sub-linear spatial-complexity whatever is the editing behaviour. A series of experiments validates LSEQ showing that it outperforms existing approaches.

Many relations existing in DBpedia are missing in Wikipedia yielding up an information gap between the semantic web and the social web. Inserting these missing relations requires to automatically discover Wikipedia conventions. From pairs linked by a property p in DBpedia, we find path queries that link the same pairs in Wikipedia. We make the hypothesis that the shortest path query with maximal containment captures the Wikipedia convention for p. We computed missing links and conventions for different DBpedia queries. Next, we inserted some missing links according to computed conventions in Wikipedia and evaluated Wikipedians feedback. Nearly all contributions has been accepted. In this paper, we detail the path indexing algorithms, the results of evaluations and give some details about social feedback.

In multi-synchronous collaboration users replicate shared data, modify it and redistribute modified versions of this data without the need of a central authority. However, in this model, no usage restriction mechanism was proposed to control what users can do with the data after it has been released to them. In this paper, we extend the multi-synchronous collaboration model with contracts that express usage restrictions and that are checked a posteriori by users when they receive the modified data. We propose a merging algorithm that deals not only with changes on data but also with contracts. A log auditing protocol is used to detect users who do not respect contracts and to adjust user trust levels. Our contract-based model was implemented and evaluated by using PeerSim simulator.

This paper proposes a collective intelligence strategy for emerging semantic data. It presents a combination of social web practices with semantic web technologies to enrich existing web resources with semantic data. The paper introduces a social semantic tagging approach called Semdrops. Semdrops defines a conceptual model which is an extension of the Gruber's tag model where the tag concept is extended to semantic tag. Semdrops is implemented as a Firefox add-on tool that turns the web browser into a collaborative semantic data editor. To validate Semdrops's approach, we conducted an evaluation and usability studies and compared the results with automatic generation methods of semantic data such as DBpedia. The studies demonstrated that Semdrops is an effective and complementary approach to produce adequate semantic data on the Web.

DSMW is a distributed semantic wiki that offers new collaboration modes to semantic wiki users and supports dataflow-oriented processes.

In this paper, we address data reconciliation in peer-to-peer (P2P) collaborative applications. We propose P2P-LTR (Logging and Timestamping for Reconciliation) which provides P2P logging and timestamping services for P2P reconciliation over a distributed hash table (DHT). While updating at collaborating peers, updates are timestamped and stored in a highly available P2P log. During reconciliation, these updates are retrieved in total order to enforce eventual consistency. In this paper, we first give an overview of P2P-LTR with its model and its main procedures. We then present our prototype used to validate P2P-LTR. To demonstrate P2P-LTR, we propose several scenarios that test our solutions and measure performance. In particular, we demonstrate how P2P-LTR handles the dynamic behavior of peers with respect to the DHT.