8,228 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
Temporal Stream Algebra
Data stream management systems (DSMS) so far focus on
event queries and hardly consider combined queries to both
data from event streams and from a database. However,
applications like emergency management require combined
data stream and database queries. Further requirements are
the simultaneous use of multiple timestamps after different
time lines and semantics, expressive temporal relations between multiple time-stamps and
exible negation, grouping
and aggregation which can be controlled, i. e. started and
stopped, by events and are not limited to fixed-size time
windows. Current DSMS hardly address these requirements.
This article proposes Temporal Stream Algebra (TSA) so
as to meet the afore mentioned requirements. Temporal
streams are a common abstraction of data streams and data-
base relations; the operators of TSA are generalizations of
the usual operators of Relational Algebra. A in-depth 'analysis of temporal relations guarantees that valid TSA expressions are non-blocking, i. e. can be evaluated incrementally.
In this respect TSA differs significantly from previous algebraic approaches which use specialized operators to prevent
blocking expressions on a "syntactical" level
S-Store: Streaming Meets Transaction Processing
Stream processing addresses the needs of real-time applications. Transaction
processing addresses the coordination and safety of short atomic computations.
Heretofore, these two modes of operation existed in separate, stove-piped
systems. In this work, we attempt to fuse the two computational paradigms in a
single system called S-Store. In this way, S-Store can simultaneously
accommodate OLTP and streaming applications. We present a simple transaction
model for streams that integrates seamlessly with a traditional OLTP system. We
chose to build S-Store as an extension of H-Store, an open-source, in-memory,
distributed OLTP database system. By implementing S-Store in this way, we can
make use of the transaction processing facilities that H-Store already
supports, and we can concentrate on the additional implementation features that
are needed to support streaming. Similar implementations could be done using
other main-memory OLTP platforms. We show that we can actually achieve higher
throughput for streaming workloads in S-Store than an equivalent deployment in
H-Store alone. We also show how this can be achieved within H-Store with the
addition of a modest amount of new functionality. Furthermore, we compare
S-Store to two state-of-the-art streaming systems, Spark Streaming and Storm,
and show how S-Store matches and sometimes exceeds their performance while
providing stronger transactional guarantees
Blazes: Coordination Analysis for Distributed Programs
Distributed consistency is perhaps the most discussed topic in distributed
systems today. Coordination protocols can ensure consistency, but in practice
they cause undesirable performance unless used judiciously. Scalable
distributed architectures avoid coordination whenever possible, but
under-coordinated systems can exhibit behavioral anomalies under fault, which
are often extremely difficult to debug. This raises significant challenges for
distributed system architects and developers. In this paper we present Blazes,
a cross-platform program analysis framework that (a) identifies program
locations that require coordination to ensure consistent executions, and (b)
automatically synthesizes application-specific coordination code that can
significantly outperform general-purpose techniques. We present two case
studies, one using annotated programs in the Twitter Storm system, and another
using the Bloom declarative language.Comment: Updated to include additional materials from the original technical
report: derivation rules, output stream label
Analysing Temporal Relations – Beyond Windows, Frames and Predicates
This article proposes an approach to rely on the standard
operators of relational algebra (including grouping and ag-
gregation) for processing complex event without requiring
window specifications. In this way the approach can pro-
cess complex event queries of the kind encountered in appli-
cations such as emergency management in metro networks.
This article presents Temporal Stream Algebra (TSA) which
combines the operators of relational algebra with an analy-
sis of temporal relations at compile time. This analysis de-
termines which relational algebra queries can be evaluated
against data streams, i. e. the analysis is able to distinguish
valid from invalid stream queries. Furthermore the analysis
derives functions similar to the pass, propagation and keep
invariants in Tucker's et al. \Exploiting Punctuation Seman-
tics in Continuous Data Streams". These functions enable
the incremental evaluation of TSA queries, the propagation
of punctuations, and garbage collection. The evaluation of
TSA queries combines bulk-wise and out-of-order processing
which makes it tolerant to workload bursts as they typically
occur in emergency management. The approach has been
conceived for efficiently processing complex event queries on
top of a relational database system. It has been deployed
and tested on MonetDB
Knowledge-infused and Consistent Complex Event Processing over Real-time and Persistent Streams
Emerging applications in Internet of Things (IoT) and Cyber-Physical Systems
(CPS) present novel challenges to Big Data platforms for performing online
analytics. Ubiquitous sensors from IoT deployments are able to generate data
streams at high velocity, that include information from a variety of domains,
and accumulate to large volumes on disk. Complex Event Processing (CEP) is
recognized as an important real-time computing paradigm for analyzing
continuous data streams. However, existing work on CEP is largely limited to
relational query processing, exposing two distinctive gaps for query
specification and execution: (1) infusing the relational query model with
higher level knowledge semantics, and (2) seamless query evaluation across
temporal spaces that span past, present and future events. These allow
accessible analytics over data streams having properties from different
disciplines, and help span the velocity (real-time) and volume (persistent)
dimensions. In this article, we introduce a Knowledge-infused CEP (X-CEP)
framework that provides domain-aware knowledge query constructs along with
temporal operators that allow end-to-end queries to span across real-time and
persistent streams. We translate this query model to efficient query execution
over online and offline data streams, proposing several optimizations to
mitigate the overheads introduced by evaluating semantic predicates and in
accessing high-volume historic data streams. The proposed X-CEP query model and
execution approaches are implemented in our prototype semantic CEP engine,
SCEPter. We validate our query model using domain-aware CEP queries from a
real-world Smart Power Grid application, and experimentally analyze the
benefits of our optimizations for executing these queries, using event streams
from a campus-microgrid IoT deployment.Comment: 34 pages, 16 figures, accepted in Future Generation Computer Systems,
October 27, 201
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
Towards Ideal Semantics for Analyzing Stream Reasoning
The rise of smart applications has drawn interest to logical reasoning over
data streams. Recently, different query languages and stream
processing/reasoning engines were proposed in different communities. However,
due to a lack of theoretical foundations, the expressivity and semantics of
these diverse approaches are given only informally. Towards clear
specifications and means for analytic study, a formal framework is needed to
define their semantics in precise terms. To this end, we present a first step
towards an ideal semantics that allows for exact descriptions and comparisons
of stream reasoning systems.Comment: International Workshop on Reactive Concepts in Knowledge
Representation (ReactKnow 2014), co-located with the 21st European Conference
on Artificial Intelligence (ECAI 2014). Proceedings of the International
Workshop on Reactive Concepts in Knowledge Representation (ReactKnow 2014),
pages 17-22, technical report, ISSN 1430-3701, Leipzig University, 2014.
http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-150562 2014,
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