2,220 research outputs found
NORA: Scalable OWL reasoner based on NoSQL databasesand Apache Spark
Reasoning is the process of inferring new knowledge and identifying inconsis-tencies within ontologies. Traditional techniques often prove inadequate whenreasoning over large Knowledge Bases containing millions or billions of facts.This article introduces NORA, a persistent and scalable OWL reasoner built ontop of Apache Spark, designed to address the challenges of reasoning over exten-sive and complex ontologies. NORA exploits the scalability of NoSQL databasesto effectively apply inference rules to Big Data ontologies with large ABoxes. Tofacilitatescalablereasoning,OWLdata,includingclassandpropertyhierarchiesand instances, are materialized in the Apache Cassandra database. Spark pro-grams are then evaluated iteratively, uncovering new implicit knowledge fromthe dataset and leading to enhanced performance and more efficient reasoningover large-scale ontologies. NORA has undergone a thorough evaluation withdifferent benchmarking ontologies of varying sizes to assess the scalability of thedeveloped solution.Funding for open access charge: Universidad de Málaga / CBUA
This work has been partially funded by grant (funded by MCIN/AEI/10.13039/501100011033/) PID2020-112540RB-C41,AETHER-UMA (A smart data holistic approach for context-aware data analytics: semantics and context exploita-tion). Antonio Benítez-Hidalgo is supported by Grant PRE2018-084280 (Spanish Ministry of Science, Innovation andUniversities)
Efficient classification using parallel and scalable compressed model and Its application on intrusion detection
In order to achieve high efficiency of classification in intrusion detection,
a compressed model is proposed in this paper which combines horizontal
compression with vertical compression. OneR is utilized as horizontal
com-pression for attribute reduction, and affinity propagation is employed as
vertical compression to select small representative exemplars from large
training data. As to be able to computationally compress the larger volume of
training data with scalability, MapReduce based parallelization approach is
then implemented and evaluated for each step of the model compression process
abovementioned, on which common but efficient classification methods can be
directly used. Experimental application study on two publicly available
datasets of intrusion detection, KDD99 and CMDC2012, demonstrates that the
classification using the compressed model proposed can effectively speed up the
detection procedure at up to 184 times, most importantly at the cost of a
minimal accuracy difference with less than 1% on average
GraphX: Unifying Data-Parallel and Graph-Parallel Analytics
From social networks to language modeling, the growing scale and importance
of graph data has driven the development of numerous new graph-parallel systems
(e.g., Pregel, GraphLab). By restricting the computation that can be expressed
and introducing new techniques to partition and distribute the graph, these
systems can efficiently execute iterative graph algorithms orders of magnitude
faster than more general data-parallel systems. However, the same restrictions
that enable the performance gains also make it difficult to express many of the
important stages in a typical graph-analytics pipeline: constructing the graph,
modifying its structure, or expressing computation that spans multiple graphs.
As a consequence, existing graph analytics pipelines compose graph-parallel and
data-parallel systems using external storage systems, leading to extensive data
movement and complicated programming model.
To address these challenges we introduce GraphX, a distributed graph
computation framework that unifies graph-parallel and data-parallel
computation. GraphX provides a small, core set of graph-parallel operators
expressive enough to implement the Pregel and PowerGraph abstractions, yet
simple enough to be cast in relational algebra. GraphX uses a collection of
query optimization techniques such as automatic join rewrites to efficiently
implement these graph-parallel operators. We evaluate GraphX on real-world
graphs and workloads and demonstrate that GraphX achieves comparable
performance as specialized graph computation systems, while outperforming them
in end-to-end graph pipelines. Moreover, GraphX achieves a balance between
expressiveness, performance, and ease of use
LINVIEW: Incremental View Maintenance for Complex Analytical Queries
Many analytics tasks and machine learning problems can be naturally expressed
by iterative linear algebra programs. In this paper, we study the incremental
view maintenance problem for such complex analytical queries. We develop a
framework, called LINVIEW, for capturing deltas of linear algebra programs and
understanding their computational cost. Linear algebra operations tend to cause
an avalanche effect where even very local changes to the input matrices spread
out and infect all of the intermediate results and the final view, causing
incremental view maintenance to lose its performance benefit over
re-evaluation. We develop techniques based on matrix factorizations to contain
such epidemics of change. As a consequence, our techniques make incremental
view maintenance of linear algebra practical and usually substantially cheaper
than re-evaluation. We show, both analytically and experimentally, the
usefulness of these techniques when applied to standard analytics tasks. Our
evaluation demonstrates the efficiency of LINVIEW in generating parallel
incremental programs that outperform re-evaluation techniques by more than an
order of magnitude.Comment: 14 pages, SIGMO
Bench-Ranking: ettekirjutav analüüsimeetod suurte teadmiste graafide päringutele
Relatsiooniliste suurandmete (BD) töötlemisraamistike kasutamine suurte teadmiste graafide töötlemiseks kätkeb endas võimalust päringu jõudlust optimeerimida. Kaasaegsed BD-süsteemid on samas keerulised andmesüsteemid, mille konfiguratsioonid omavad olulist mõju jõudlusele. Erinevate raamistike ja konfiguratsioonide võrdlusuuringud pakuvad kogukonnale parimaid tavasid parema jõudluse saavutamiseks. Enamik neist võrdlusuuringutest saab liigitada siiski vaid kirjeldavaks ja diagnostiliseks analüütikaks. Lisaks puudub ühtne standard nende uuringute võrdlemiseks kvantitatiivselt järjestatud kujul. Veelgi enam, suurte graafide töötlemiseks vajalike konveierite kavandamine eeldab täiendavaid disainiotsuseid mis tulenevad mitteloomulikust (relatsioonilisest) graafi töötlemise paradigmast. Taolisi disainiotsuseid ei saa automaatselt langetada, nt relatsiooniskeemi, partitsioonitehnika ja salvestusvormingute valikut. Käesolevas töös käsitleme kuidas me antud uurimuslünga täidame. Esmalt näitame disainiotsuste kompromisside mõju BD-süsteemide jõudluse korratavusele suurte teadmiste graafide päringute tegemisel. Lisaks näitame BD-raamistike jõudluse kirjeldavate ja diagnostiliste analüüside piiranguid suurte graafide päringute tegemisel. Seejärel uurime, kuidas lubada ettekirjutavat analüütikat järjestamisfunktsioonide ja mitmemõõtmeliste optimeerimistehnikate (nn "Bench-Ranking") kaudu. See lähenemine peidab kirjeldava tulemusanalüüsi keerukuse, suunates praktiku otse teostatavate teadlike otsusteni.Leveraging relational Big Data (BD) processing frameworks to process large knowledge graphs yields a great interest in optimizing query performance. Modern BD systems are yet complicated data systems, where the configurations notably affect the performance. Benchmarking different frameworks and configurations provides the community with best practices for better performance. However, most of these benchmarking efforts are classified as descriptive and diagnostic analytics. Moreover, there is no standard for comparing these benchmarks based on quantitative ranking techniques. Moreover, designing mature pipelines for processing big graphs entails considering additional design decisions that emerge with the non-native (relational) graph processing paradigm. Those design decisions cannot be decided automatically, e.g., the choice of the relational schema, partitioning technique, and storage formats. Thus, in this thesis, we discuss how our work fills this timely research gap. Particularly, we first show the impact of those design decisions’ trade-offs on the BD systems’ performance replicability when querying large knowledge graphs. Moreover, we showed the limitations of the descriptive and diagnostic analyses of BD frameworks’ performance for querying large graphs. Thus, we investigate how to enable prescriptive analytics via ranking functions and Multi-Dimensional optimization techniques (called ”Bench-Ranking”). This approach abstracts out from the complexity of descriptive performance analysis, guiding the practitioner directly to actionable informed decisions.https://www.ester.ee/record=b553332
Alexandria: Extensible Framework for Rapid Exploration of Social Media
The Alexandria system under development at IBM Research provides an
extensible framework and platform for supporting a variety of big-data
analytics and visualizations. The system is currently focused on enabling rapid
exploration of text-based social media data. The system provides tools to help
with constructing "domain models" (i.e., families of keywords and extractors to
enable focus on tweets and other social media documents relevant to a project),
to rapidly extract and segment the relevant social media and its authors, to
apply further analytics (such as finding trends and anomalous terms), and
visualizing the results. The system architecture is centered around a variety
of REST-based service APIs to enable flexible orchestration of the system
capabilities; these are especially useful to support knowledge-worker driven
iterative exploration of social phenomena. The architecture also enables rapid
integration of Alexandria capabilities with other social media analytics
system, as has been demonstrated through an integration with IBM Research's
SystemG. This paper describes a prototypical usage scenario for Alexandria,
along with the architecture and key underlying analytics.Comment: 8 page
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