The Linked Data paradigm has become the prominent enabler for sharing huge volumes of data using Semantic Web technologies, and has created novel challenges for non-relational data management systems, such as RDF and graph engines. Efficient data access through queries is perhaps the most important data management task, and is enabled through query optimization techniques, which amount to the discovery of optimal or close to optimal execution plans for a given query.
At SIGMOD 2015, the third international workshop on Graph Datamagement Experience and Systems (GRADES2015) will be organized.
GRADES since its inception has been one of the best attended workshops at SIGMOD, and papers published in it are getting noted.
LDBC is a co-sponsor of the GRADES workshops and is being led this year by Tedd Willke (Intel Corp.) and Josep Lluis Larriba Pey (LDBC coordinator).
The workshop will be held on Sunday May 31, in Melbourne, Australia.
When talking about DATAGEN and other graph generators with social network characteristics, our attention is typically borrowed by the friendship subgraph and/or its structure. However, a social graph is more than a bunch of people being connected by friendship relations, but has a lot more of other things is worth to look at. With a quick view to commercial social networks like Facebook, Twitter or Google+, one can easily identify a lot of other elements such as text images or even video assets. More importantly, all these elements form other subgraphs within the social network!
The LDBC consortium are pleased to announce its Sixth Technical User Community (TUC) meeting.
This will be a two-day event at Universitat Politècnica de Catalunya, Barcelona on Thursday and Friday March 19/20, 2015.
The 5th LDBC Technical User Community (TUC) meeting took place in Athens on 14.11.2014 being well attended by both Graph and RDF databases industry and academia. In the morning session, members of the LDBC project gave an update on the status of the project and its benchmarks:
The SNB Driver part 1 post introduced, broadly, the challenges faced when developing a workload driver for the LDBC SNB benchmark. In this blog we'll drill down deeper into the details of what it means to execute "dependent queries" during benchmark execution, and how this is handled in the driver. First of all, as many driver-specific terms will be used, below is a listing of their definitions. There is no need to read them in detail, it is just there to serve as a point of reference.
The Semantic Publishing Instance Matching Benchmark (SPIMBench) is a novel benchmark for the assessment of instance matching techniques for RDF data with an associated schema. SPIMBench extends the state-of-the art instance matching benchmarks for RDF data in three main aspects: it allows for systematic scalability testing, supports a wider range of test cases including semantics-aware ones, and provides an enriched gold standard.
We are presently working on the SNB BI workload. Andrey Gubichev of TU Munchen and myself are going through the queries and are playing with two SQL based implementations, one on Virtuoso and the other on Hyper.
As discussed before, the BI workload has the same choke points as TPC-H as a base but pushes further in terms of graphiness and query complexity.