Real-time processing of data coming from multiple heterogeneous data streams and static databases is a typical task in many industrial scenarios such as diagnostics of large machines. A complex diagnostic task may require a collection of up to hundreds of queries over such data. Although many of these queries retrieve data of the same kind, such as temperature measurements, they access structurally different data sources. In this work we show how Semantic Technologies implemented in our system optique can simplify such complex diagnostics by providing an abstraction layer---ontology---that integrates heterogeneous data. In a nutshell, optique allows complex diagnostic tasks to be expressed with just a few high-level semantic queries. The system can then automatically enrich these queries, translate them into a collection with a large number of low-level data queries, and finally optimise and efficiently execute the collection in a heavily distributed environment. We will demo the benefits of optique on a real world scenario from Siemens.

MapReduce is a computing paradigm that has gained a lot of popularity as it allows non-expert users to easily run complex analytical tasks at very large-scale. At such scale, task and node failures are no longer an exception but rather a characteristic of large-scale systems. This makes fault-tolerance a critical issue for the efficient operation of any application. MapReduce automatically reschedules failed tasks to available nodes, which in turn recompute such tasks from scratch. However, this policy can significantly decrease performance of applications. In this paper, we propose a family of Recovery Algorithms for Fast-Tracking (RAFT) MapReduce. As ease-of-use is a major feature of MapReduce, RAFT focuses on simplicity and also non-intrusiveness, in order to be implementation-independent. To efficiently recover from task failures, RAFT exploits the fact that MapReduce produces and persists intermediate results at several points in time. RAFT piggy-backs checkpoints on the task progress computation. To deal with multiple node failures, we propose query metadata checkpointing. We keep track of the mapping between input key-value pairs and intermediate data for all reduce tasks. Thereby, RAFT does not need to re-execute completed map tasks entirely. Instead RAFT only recomputes intermediate data that were processed for local reduce tasks and hence not shipped to another node for processing. We also introduce a scheduling strategy taking full advantage of these recovery algorithms. We implemented RAFT on top of Hadoop and evaluated it on a 45-node cluster using three common analytical tasks. Overall, our experimental results demonstrate that RAFT outperforms Hadoop runtimes by 23% on average under task and node failures. The results also show that RAFT has negligible runtime overhead.

The MapReduce framework is typically deployed on very large computing clusters where task and node failures are no longer an exception but the rule. Thus, fault-tolerance is an important aspect for the efficient operation of MapReduce jobs. However, currently MapReduce implementations fully recompute failed tasks (subparts of a job) from the beginning. This can significantly decrease the runtime performance of MapReduce applications. We present an alternative system that implements RAFT ideas. RAFT is a family of powerful and inexpensive Recovery Algorithms for Fast-Tracking MapReduce jobs under task and node failures. To recover from task failures, RAFT exploits the intermediate results persisted by MapReduce at several points in time. RAFT piggybacks checkpoints on the task progress computation. To recover from node failures, RAFT maintains a per-map task list of all input key-value pairs producing intermediate results and pushes intermediate results to reducers. In this demo, we demonstrate that RAFT recovers efficiently from both task and node failures. Further, the audience can compare RAFT with Hadoop via an easy-to-use web interface.

Recently, the SPARQL query language for RDF has reached the W3C recommendation status. In response to this emerging standard, the database community is currently exploring efficient storage techniques for RDF data and evaluation strategies for SPARQL queries. A meaningful analysis and comparison of these approaches necessitates a comprehensive and universal benchmark platform. To this end, we have developed SP2Bench, a publicly available, language-specific SPARQL performance benchmark. SP2Bench is settled in the DBLP scenario and comprises both a data generator for creating arbitrarily large DBLP-like documents and a set of carefully designed benchmark queries. The generated documents mirror key characteristics and social-world distributions encountered in the original DBLP data set, while the queries implement meaningful requests on top of this data, covering a variety of SPARQL operator constellationsand RDF access patterns. As a proof of concept, we apply SP2Bench to existing engines and discuss their strengths and weaknesses that follow immediately from the benchmark results.