2,375 research outputs found
A Selectivity based approach to Continuous Pattern Detection in Streaming Graphs
Cyber security is one of the most significant technical challenges in current
times. Detecting adversarial activities, prevention of theft of intellectual
properties and customer data is a high priority for corporations and government
agencies around the world. Cyber defenders need to analyze massive-scale,
high-resolution network flows to identify, categorize, and mitigate attacks
involving networks spanning institutional and national boundaries. Many of the
cyber attacks can be described as subgraph patterns, with prominent examples
being insider infiltrations (path queries), denial of service (parallel paths)
and malicious spreads (tree queries). This motivates us to explore subgraph
matching on streaming graphs in a continuous setting. The novelty of our work
lies in using the subgraph distributional statistics collected from the
streaming graph to determine the query processing strategy. We introduce a
"Lazy Search" algorithm where the search strategy is decided on a
vertex-to-vertex basis depending on the likelihood of a match in the vertex
neighborhood. We also propose a metric named "Relative Selectivity" that is
used to select between different query processing strategies. Our experiments
performed on real online news, network traffic stream and a synthetic social
network benchmark demonstrate 10-100x speedups over selectivity agnostic
approaches.Comment: in 18th International Conference on Extending Database Technology
(EDBT) (2015
Graphulo Implementation of Server-Side Sparse Matrix Multiply in the Accumulo Database
The Apache Accumulo database excels at distributed storage and indexing and
is ideally suited for storing graph data. Many big data analytics compute on
graph data and persist their results back to the database. These graph
calculations are often best performed inside the database server. The GraphBLAS
standard provides a compact and efficient basis for a wide range of graph
applications through a small number of sparse matrix operations. In this
article, we implement GraphBLAS sparse matrix multiplication server-side by
leveraging Accumulo's native, high-performance iterators. We compare the
mathematics and performance of inner and outer product implementations, and
show how an outer product implementation achieves optimal performance near
Accumulo's peak write rate. We offer our work as a core component to the
Graphulo library that will deliver matrix math primitives for graph analytics
within Accumulo.Comment: To be presented at IEEE HPEC 2015: http://www.ieee-hpec.org
Proceedings of the 3rd Workshop on Domain-Specific Language Design and Implementation (DSLDI 2015)
The goal of the DSLDI workshop is to bring together researchers and
practitioners interested in sharing ideas on how DSLs should be designed,
implemented, supported by tools, and applied in realistic application contexts.
We are both interested in discovering how already known domains such as graph
processing or machine learning can be best supported by DSLs, but also in
exploring new domains that could be targeted by DSLs. More generally, we are
interested in building a community that can drive forward the development of
modern DSLs. These informal post-proceedings contain the submitted talk
abstracts to the 3rd DSLDI workshop (DSLDI'15), and a summary of the panel
discussion on Language Composition
UpStream: storage-centric load management for streaming applications with update semantics
This paper addresses the problem of minimizing the staleness of query results for streaming applications with update semantics under overload conditions. Staleness is a measure of how out-of-date the results are compared with the latest data arriving on the input. Real-time streaming applications are subject to overload due to unpredictably increasing data rates, while in many of them, we observe that data streams and queries in fact exhibit "update semantics” (i.e., the latest input data are all that really matters when producing a query result). Under such semantics, overload will cause staleness to build up. The key to avoid this is to exploit the update semantics of applications as early as possible in the processing pipeline. In this paper, we propose UpStream, a storage-centric framework for load management over streaming applications with update semantics. We first describe how we model streams and queries that possess the update semantics, providing definitions for correctness and staleness for the query results. Then, we show how staleness can be minimized based on intelligent update key scheduling techniques applied at the queue level, while preserving the correctness of the results, even for complex queries that involve sliding windows. UpStream is based on the simple idea of applying the updates in place, yet with great returns in terms of lowering staleness and memory consumption, as we also experimentally verify on the Borealis syste
Querying XML data streams from wireless sensor networks: an evaluation of query engines
As the deployment of wireless sensor networks increase and their application domain widens, the opportunity for effective use of XML filtering and streaming query engines is ever more present. XML filtering engines aim to provide efficient real-time querying of streaming XML encoded data. This paper provides a detailed analysis of several such engines, focusing on the technology involved, their capabilities, their support for XPath and their performance. Our experimental evaluation identifies which filtering engine is best suited to process a given query based on its properties. Such metrics are important in establishing the best approach to filtering XML streams on-the-fly
Asymptotically near-optimal RRT for fast, high-quality, motion planning
We present Lower Bound Tree-RRT (LBT-RRT), a single-query sampling-based
algorithm that is asymptotically near-optimal. Namely, the solution extracted
from LBT-RRT converges to a solution that is within an approximation factor of
1+epsilon of the optimal solution. Our algorithm allows for a continuous
interpolation between the fast RRT algorithm and the asymptotically optimal
RRT* and RRG algorithms. When the approximation factor is 1 (i.e., no
approximation is allowed), LBT-RRT behaves like RRG. When the approximation
factor is unbounded, LBT-RRT behaves like RRT. In between, LBT-RRT is shown to
produce paths that have higher quality than RRT would produce and run faster
than RRT* would run. This is done by maintaining a tree which is a sub-graph of
the RRG roadmap and a second, auxiliary graph, which we call the lower-bound
graph. The combination of the two roadmaps, which is faster to maintain than
the roadmap maintained by RRT*, efficiently guarantees asymptotic
near-optimality. We suggest to use LBT-RRT for high-quality, anytime motion
planning. We demonstrate the performance of the algorithm for scenarios ranging
from 3 to 12 degrees of freedom and show that even for small approximation
factors, the algorithm produces high-quality solutions (comparable to RRG and
RRT*) with little running-time overhead when compared to RRT
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
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