7,809 research outputs found
Collaborative Reuse of Streaming Dataflows in IoT Applications
Distributed Stream Processing Systems (DSPS) like Apache Storm and Spark
Streaming enable composition of continuous dataflows that execute persistently
over data streams. They are used by Internet of Things (IoT) applications to
analyze sensor data from Smart City cyber-infrastructure, and make active
utility management decisions. As the ecosystem of such IoT applications that
leverage shared urban sensor streams continue to grow, applications will
perform duplicate pre-processing and analytics tasks. This offers the
opportunity to collaboratively reuse the outputs of overlapping dataflows,
thereby improving the resource efficiency. In this paper, we propose
\emph{dataflow reuse algorithms} that given a submitted dataflow, identifies
the intersection of reusable tasks and streams from a collection of running
dataflows to form a \emph{merged dataflow}. Similar algorithms to unmerge
dataflows when they are removed are also proposed. We implement these
algorithms for the popular Apache Storm DSPS, and validate their performance
and resource savings for 35 synthetic dataflows based on public OPMW workflows
with diverse arrival and departure distributions, and on 21 real IoT dataflows
from RIoTBench.Comment: To appear in IEEE eScience Conference 201
Graph Summarization
The continuous and rapid growth of highly interconnected datasets, which are
both voluminous and complex, calls for the development of adequate processing
and analytical techniques. One method for condensing and simplifying such
datasets is graph summarization. It denotes a series of application-specific
algorithms designed to transform graphs into more compact representations while
preserving structural patterns, query answers, or specific property
distributions. As this problem is common to several areas studying graph
topologies, different approaches, such as clustering, compression, sampling, or
influence detection, have been proposed, primarily based on statistical and
optimization methods. The focus of our chapter is to pinpoint the main graph
summarization methods, but especially to focus on the most recent approaches
and novel research trends on this topic, not yet covered by previous surveys.Comment: To appear in the Encyclopedia of Big Data Technologie
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
A High-Throughput Solver for Marginalized Graph Kernels on GPU
We present the design and optimization of a linear solver on General Purpose GPUs for the efficient and high-throughput evaluation of the marginalized graph kernel between pairs of labeled graphs. The solver implements a preconditioned conjugate gradient (PCG) method to compute the solution to a generalized Laplacian equation associated with the tensor product of two graphs. To cope with the gap between the instruction throughput and the memory bandwidth of current generation GPUs, our solver forms the tensor product linear system on-the-fly without storing it in memory when performing matrix-vector dot product operations in PCG. Such on-the-fly computation is accomplished by using threads in a warp to cooperatively stream the adjacency and edge label matrices of individual graphs by small square matrix blocks called tiles, which are then staged in registers and the shared memory for later reuse. Warps across a thread block can further share tiles via the shared memory to increase data reuse. We exploit the sparsity of the graphs hierarchically by storing only non-empty tiles using a coordinate format and nonzero elements within each tile using bitmaps. Besides, we propose a new partition-based reordering algorithm for aggregating nonzero elements of the graphs into fewer but denser tiles to improve the efficiency of the sparse format.We carry out extensive theoretical analyses on the graph tensor product primitives for tiles of various density and evaluate their performance on synthetic and real-world datasets. Our solver delivers three to four orders of magnitude speedup over existing CPU-based solvers such as GraKeL and GraphKernels. The capability of the solver enables kernel-based learning tasks at unprecedented scales
Streaming Similarity Self-Join
We introduce and study the problem of computing the similarity self-join in a
streaming context (SSSJ), where the input is an unbounded stream of items
arriving continuously. The goal is to find all pairs of items in the stream
whose similarity is greater than a given threshold. The simplest formulation of
the problem requires unbounded memory, and thus, it is intractable. To make the
problem feasible, we introduce the notion of time-dependent similarity: the
similarity of two items decreases with the difference in their arrival time. By
leveraging the properties of this time-dependent similarity function, we design
two algorithmic frameworks to solve the sssj problem. The first one, MiniBatch
(MB), uses existing index-based filtering techniques for the static version of
the problem, and combines them in a pipeline. The second framework, Streaming
(STR), adds time filtering to the existing indexes, and integrates new
time-based bounds deeply in the working of the algorithms. We also introduce a
new indexing technique (L2), which is based on an existing state-of-the-art
indexing technique (L2AP), but is optimized for the streaming case. Extensive
experiments show that the STR algorithm, when instantiated with the L2 index,
is the most scalable option across a wide array of datasets and parameters
Recent Advances in Graph Partitioning
We survey recent trends in practical algorithms for balanced graph
partitioning together with applications and future research directions
Network Sampling: From Static to Streaming Graphs
Network sampling is integral to the analysis of social, information, and
biological networks. Since many real-world networks are massive in size,
continuously evolving, and/or distributed in nature, the network structure is
often sampled in order to facilitate study. For these reasons, a more thorough
and complete understanding of network sampling is critical to support the field
of network science. In this paper, we outline a framework for the general
problem of network sampling, by highlighting the different objectives,
population and units of interest, and classes of network sampling methods. In
addition, we propose a spectrum of computational models for network sampling
methods, ranging from the traditionally studied model based on the assumption
of a static domain to a more challenging model that is appropriate for
streaming domains. We design a family of sampling methods based on the concept
of graph induction that generalize across the full spectrum of computational
models (from static to streaming) while efficiently preserving many of the
topological properties of the input graphs. Furthermore, we demonstrate how
traditional static sampling algorithms can be modified for graph streams for
each of the three main classes of sampling methods: node, edge, and
topology-based sampling. Our experimental results indicate that our proposed
family of sampling methods more accurately preserves the underlying properties
of the graph for both static and streaming graphs. Finally, we study the impact
of network sampling algorithms on the parameter estimation and performance
evaluation of relational classification algorithms
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