Retrieval of the most relevant facts from data streams joined with slowly evolving dataset published on the web of data

Finding the most relevant facts among dynamic and hetero- geneous data published on theWeb of Data is getting a growing attention in recent years. RDF Stream Processing (RSP) engines offer a baseline solution to integrate and process streaming data with data distributed on the Web. Unfortunately, the time to access and fetch the distributed data can be so high to put the RSP engine at risk of losing reactiveness, especially when the distributed data is slowly evolving. State of the art work addressed this problem by proposing an architectural solution that keeps a local replica of the distributed data and a baseline maintenance policy to refresh it over time. This doctoral thesis is investigating advance policies that let RSP engines continuously answer top-k queries, which require to join data streams with slowly evolving datasets published on the Web of Data, without violating the reactiveness constrains imposed by the users. In particular, it proposes policies that focus on freshing only the data in the replica that contributes to the correctness of the top-k results

Towards a Top-K SPARQL Query Benchmark Generator

The research on optimization of top-k SPARQL query would largely benefit from the establishment of a benchmark that allows comparing different approaches. For such a benchmark to be meaningful, at least two requirements should hold: 1) the benchmark should resemble reality as much as possible, and 2) it should stress the features of the topk SPARQL queries both from a syntactic and performance perspective. In this paper we propose Top-k DBPSB: an extension of the DBpedia SPARQL benchmark (DBPSB), a benchmark known to resemble reality, with the capabilities required to compare SPARQL engines on top-k queries.Web Information System

Using Rank Aggregation in Continuously Answering SPARQL Queries on Streaming and Quasi-static Linked Data

Web applications that combine dynamic data stream with distributed background data are getting a growing attention in recent years. Answering in a timely fashion, i.e., reactiveness, is one of the most important performance indicators for those applications. The Semantic Web community showed that RDF Stream Processing (RSP) is an adequate framework to develop this type of applications. However, RSP engines may lose their reactiveness due to the time necessary to access the background data when it is distributed over the Web. State-of-the-art RSP engines remain reactive using a local replica of the background data, but it progressively becomes stale if not updated to reflect the changes in the remote background data. For this reason, recently, the RSP community has investigated maintenance policies of the local replica that guarantee reactiveness while maximizing the freshness of the replica. Previous works simplified the problem with several assumptions. In this paper, we investigate how to remove some of those simplification assumptions. In particular, we target a class of queries for which multiple policies may be used simultaneously and we show that rank aggregation can be effectively used to fairly consider their alternative suggestions. We provide extensive empirical evidence that rank aggregation is key to move a step forward to the practical solution of this problem in the RSP context

Towards a top-K SPARQL query benchmark generator

The research on optimization of top-k SPARQL query would largely benefit from the establishment of a benchmark that allows comparing different approaches. For such a benchmark to be meaningful, at least two requirements should hold: 1) the benchmark should resemble reality as much as possible, and 2) it should stress the features of the topk SPARQL queries both from a syntactic and performance perspective. In this paper we propose Top-k DBPSB: an extension of the DBpedia SPARQL benchmark (DBPSB), a benchmark known to resemble reality, with the capabilities required to compare SPARQL engines on top-k queries

Towards a top-K SPARQL query benchmark generator

The research on optimization of top-k SPARQL query would largely benefit from the establishment of a benchmark that allows comparing different approaches. For such a benchmark to be meaningful, at least two requirements should hold: 1) the benchmark should resemble reality as much as possible, and 2) it should stress the features of the topk SPARQL queries both from a syntactic and performance perspective. In this paper we propose Top-k DBPSB: an extension of the DBpedia SPARQL benchmark (DBPSB), a benchmark known to resemble reality, with the capabilities required to compare SPARQL engines on top-k queries.</p

Towards a top-K SPARQL query benchmark generator

The research on optimization of top-k SPARQL query would largely benefit from the establishment of a benchmark that allows comparing different approaches. For such a benchmark to be meaningful, at least two requirements should hold: 1) the benchmark should resemble reality as much as possible, and 2) it should stress the features of the topk SPARQL queries both from a syntactic and performance perspective. In this paper we propose Top-k DBPSB: an extension of the DBpedia SPARQL benchmark (DBPSB), a benchmark known to resemble reality, with the capabilities required to compare SPARQL engines on top-k queries.Web Information System