3,878 research outputs found
Towards Analytics Aware Ontology Based Access to Static and Streaming Data (Extended Version)
Real-time analytics that requires integration and aggregation of
heterogeneous and distributed streaming and static data is a typical task in
many industrial scenarios such as diagnostics of turbines in Siemens. OBDA
approach has a great potential to facilitate such tasks; however, it has a
number of limitations in dealing with analytics that restrict its use in
important industrial applications. Based on our experience with Siemens, we
argue that in order to overcome those limitations OBDA should be extended and
become analytics, source, and cost aware. In this work we propose such an
extension. In particular, we propose an ontology, mapping, and query language
for OBDA, where aggregate and other analytical functions are first class
citizens. Moreover, we develop query optimisation techniques that allow to
efficiently process analytical tasks over static and streaming data. We
implement our approach in a system and evaluate our system with Siemens turbine
data
Saber: window-based hybrid stream processing for heterogeneous architectures
Modern servers have become heterogeneous, often combining multicore CPUs with many-core GPGPUs. Such heterogeneous architectures have the potential to improve the performance of data-intensive stream processing applications, but they are not supported by current relational stream processing engines. For an engine to exploit a heterogeneous architecture, it must execute streaming SQL queries with sufficient data-parallelism to fully utilise all available heterogeneous processors, and decide how to use each in the most effective way. It must do this while respecting the semantics of streaming SQL queries, in particular with regard to window handling. We describe SABER, a hybrid high-performance relational stream processing engine for CPUs and GPGPUs. SABER executes windowbased streaming SQL queries in a data-parallel fashion using all available CPU and GPGPU cores. Instead of statically assigning query operators to heterogeneous processors, SABER employs a new adaptive heterogeneous lookahead scheduling strategy, which increases the share of queries executing on the processor that yields the highest performance. To hide data movement costs, SABER pipelines the transfer of stream data between different memory types and the CPU/GPGPU. Our experimental comparison against state-ofthe-art engines shows that SABER increases processing throughput while maintaining low latency for a wide range of streaming SQL queries with small and large windows sizes
VerdictDB: Universalizing Approximate Query Processing
Despite 25 years of research in academia, approximate query processing (AQP)
has had little industrial adoption. One of the major causes of this slow
adoption is the reluctance of traditional vendors to make radical changes to
their legacy codebases, and the preoccupation of newer vendors (e.g.,
SQL-on-Hadoop products) with implementing standard features. Additionally, the
few AQP engines that are available are each tied to a specific platform and
require users to completely abandon their existing databases---an unrealistic
expectation given the infancy of the AQP technology. Therefore, we argue that a
universal solution is needed: a database-agnostic approximation engine that
will widen the reach of this emerging technology across various platforms.
Our proposal, called VerdictDB, uses a middleware architecture that requires
no changes to the backend database, and thus, can work with all off-the-shelf
engines. Operating at the driver-level, VerdictDB intercepts analytical queries
issued to the database and rewrites them into another query that, if executed
by any standard relational engine, will yield sufficient information for
computing an approximate answer. VerdictDB uses the returned result set to
compute an approximate answer and error estimates, which are then passed on to
the user or application. However, lack of access to the query execution layer
introduces significant challenges in terms of generality, correctness, and
efficiency. This paper shows how VerdictDB overcomes these challenges and
delivers up to 171 speedup (18.45 on average) for a variety of
existing engines, such as Impala, Spark SQL, and Amazon Redshift, while
incurring less than 2.6% relative error. VerdictDB is open-sourced under Apache
License.Comment: Extended technical report of the paper that appeared in Proceedings
of the 2018 International Conference on Management of Data, pp. 1461-1476.
ACM, 201
The Family of MapReduce and Large Scale Data Processing Systems
In the last two decades, the continuous increase of computational power has
produced an overwhelming flow of data which has called for a paradigm shift in
the computing architecture and large scale data processing mechanisms.
MapReduce is a simple and powerful programming model that enables easy
development of scalable parallel applications to process vast amounts of data
on large clusters of commodity machines. It isolates the application from the
details of running a distributed program such as issues on data distribution,
scheduling and fault tolerance. However, the original implementation of the
MapReduce framework had some limitations that have been tackled by many
research efforts in several followup works after its introduction. This article
provides a comprehensive survey for a family of approaches and mechanisms of
large scale data processing mechanisms that have been implemented based on the
original idea of the MapReduce framework and are currently gaining a lot of
momentum in both research and industrial communities. We also cover a set of
introduced systems that have been implemented to provide declarative
programming interfaces on top of the MapReduce framework. In addition, we
review several large scale data processing systems that resemble some of the
ideas of the MapReduce framework for different purposes and application
scenarios. Finally, we discuss some of the future research directions for
implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author
Snapshot Semantics for Temporal Multiset Relations (Extended Version)
Snapshot semantics is widely used for evaluating queries over temporal data:
temporal relations are seen as sequences of snapshot relations, and queries are
evaluated at each snapshot. In this work, we demonstrate that current
approaches for snapshot semantics over interval-timestamped multiset relations
are subject to two bugs regarding snapshot aggregation and bag difference. We
introduce a novel temporal data model based on K-relations that overcomes these
bugs and prove it to correctly encode snapshot semantics. Furthermore, we
present an efficient implementation of our model as a database middleware and
demonstrate experimentally that our approach is competitive with native
implementations and significantly outperforms such implementations on queries
that involve aggregation.Comment: extended version of PVLDB pape
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