The proliferation of heterogeneous Linked Data on the Web poses new challenges to database systems. In particular, the capacity to store, track, and query provenance data is becoming a pivotal feature of modern triplestores. We present methods extending a native RDF store to efficiently handle the storage, tracking, and querying of provenance in RDF data. We describe a reliable and understandable specification of the way results were derived from the data and how particular pieces of data were combined to answer a query. Subsequently, we present techniques to tailor queries with provenance data. We empirically evaluate the presented methods and show that the overhead of storing and tracking provenance is acceptable. Finally, we show that tailoring a query with provenance information can also significantly improve the performance of query execution.

Despite recent advances in distributed RDF data management, processing large-amounts of RDF data in the cloud is still very challenging. In spite of its seemingly simple data model, RDF actually encodes rich and complex graphs mixing both instance and schema-level data. Sharding such data using classical techniques or partitioning the graph using traditional min-cut algorithms leads to very inefficient distributed operations and to a high number of joins. In this paper, we describe DiploCloud, an efficient and scalable distributed RDF data management system for the cloud. Contrary to previous approaches, DiploCloud runs a physiological analysis of both instance and schema information prior to partitioning the data. In this paper, we describe the architecture of DiploCloud, its main data structures, as well as the new algorithms we use to partition and distribute data. We also present an extensive evaluation of DiploCloud showing that our system is often two orders of magnitude faster than state-of-the-art systems on standard workloads.

The proliferation of heterogeneous Linked Data on the Web poses new challenges to database systems. In particular, because of this heterogeneity, the capacity to store, track, and query provenance data is becoming a pivotal feature of modern triple stores. In this paper, we tackle the problem of efficiently executing provenance-enabled queries over RDF data. We propose, implement and empirically evaluate five different query execution strategies for RDF queries that incorporate knowledge of provenance. The evaluation is conducted on Web Data obtained from two different Web crawls (The Billion Triple Challenge, and the Web Data Commons). Our evaluation shows that using an adaptive query materialization execution strategy performs best in our context. Interestingly, we find that because provenance is prevalent within Web Data and is highly selective, it can be used to improve query processing performance. This is a counterintuitive result as provenance is often associated with additional overhead.

The proliferation of heterogeneous Linked Data on the Web poses new challenges to database systems. In particular, the capacity to store, track, and query provenance data is becoming a pivotal feature of modern triple stores. In this demonstration, we present TripleProv: a new system extending a native RDF store to efficiently handle the storage, tracking and querying of provenance in RDF data. In the following, we give an overview of our approach providing a reliable and understandable specification of the way results were derived from the data and how particular pieces of data were combined to answer the query. Subsequently, we present techniques enabling to tailor queries with provenance data. Finally, we describe our demonstration and how the attendees will be able to interact with our system during the conference.

Given the heterogeneity of the data one can find on the Linked Data cloud, being able to trace back the provenance of query results is rapidly becoming a must-have feature of RDF systems. While provenance models have been extensively discussed in recent years, little attention has been given to the efficient implementation of provenance-enabled queries inside data stores. This paper introduces TripleProv: a new system extending a native RDF store to efficiently handle such queries. TripleProv implements two different storage models to physically co-locate lineage and instance data, and for each of them implements algorithms for tracing provenance at two granularity levels. In the following, we present the overall architecture of our system, its different lineage storage models, and the various query execution strategies we have implemented to efficiently answer provenance-enabled queries. In addition, we present the results of a comprehensive empirical evaluation of our system over two different datasets and workloads.