6,555 research outputs found
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
Reasoning & Querying – State of the Art
Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF
Shingle 2.0: generalising self-consistent and automated domain discretisation for multi-scale geophysical models
The approaches taken to describe and develop spatial discretisations of the
domains required for geophysical simulation models are commonly ad hoc, model
or application specific and under-documented. This is particularly acute for
simulation models that are flexible in their use of multi-scale, anisotropic,
fully unstructured meshes where a relatively large number of heterogeneous
parameters are required to constrain their full description. As a consequence,
it can be difficult to reproduce simulations, ensure a provenance in model data
handling and initialisation, and a challenge to conduct model intercomparisons
rigorously. This paper takes a novel approach to spatial discretisation,
considering it much like a numerical simulation model problem of its own. It
introduces a generalised, extensible, self-documenting approach to carefully
describe, and necessarily fully, the constraints over the heterogeneous
parameter space that determine how a domain is spatially discretised. This
additionally provides a method to accurately record these constraints, using
high-level natural language based abstractions, that enables full accounts of
provenance, sharing and distribution. Together with this description, a
generalised consistent approach to unstructured mesh generation for geophysical
models is developed, that is automated, robust and repeatable, quick-to-draft,
rigorously verified and consistent to the source data throughout. This
interprets the description above to execute a self-consistent spatial
discretisation process, which is automatically validated to expected discrete
characteristics and metrics.Comment: 18 pages, 10 figures, 1 table. Submitted for publication and under
revie
XML Matchers: approaches and challenges
Schema Matching, i.e. the process of discovering semantic correspondences
between concepts adopted in different data source schemas, has been a key topic
in Database and Artificial Intelligence research areas for many years. In the
past, it was largely investigated especially for classical database models
(e.g., E/R schemas, relational databases, etc.). However, in the latest years,
the widespread adoption of XML in the most disparate application fields pushed
a growing number of researchers to design XML-specific Schema Matching
approaches, called XML Matchers, aiming at finding semantic matchings between
concepts defined in DTDs and XSDs. XML Matchers do not just take well-known
techniques originally designed for other data models and apply them on
DTDs/XSDs, but they exploit specific XML features (e.g., the hierarchical
structure of a DTD/XSD) to improve the performance of the Schema Matching
process. The design of XML Matchers is currently a well-established research
area. The main goal of this paper is to provide a detailed description and
classification of XML Matchers. We first describe to what extent the
specificities of DTDs/XSDs impact on the Schema Matching task. Then we
introduce a template, called XML Matcher Template, that describes the main
components of an XML Matcher, their role and behavior. We illustrate how each
of these components has been implemented in some popular XML Matchers. We
consider our XML Matcher Template as the baseline for objectively comparing
approaches that, at first glance, might appear as unrelated. The introduction
of this template can be useful in the design of future XML Matchers. Finally,
we analyze commercial tools implementing XML Matchers and introduce two
challenging issues strictly related to this topic, namely XML source clustering
and uncertainty management in XML Matchers.Comment: 34 pages, 8 tables, 7 figure
An approximate search engine for structure
As the size of structural databases grows, the need for efficiently searching these databases arises. Thanks to previous and ongoing research, searching by attribute-value and by text has become commonplace in these databases. However, searching by topological or physical structure, especially for large databases and especially for approximate matches, is still an art.
In this dissertation, efficient search techniques are presented for retrieving trees from a database that are similar to a given query tree. Rooted ordered labeled trees, rooted unordered labeled trees and free trees are considered. Ordered labeled trees are trees in which each node has a label and the left-to-right order among siblings matters. Unordered labeled trees are trees in which the parent-child relationship is significant, but the order among siblings is unimportant. Free trees (unrooted unordered trees) are acyclic graphs. These trees find many applications in bioinformatics, Web log analysis, phyloinformatics, XML processing, etc.
Two types of similarity measures are investigated: (i) counting the mismatching paths in the query tree and a data tree, and (ii) measuring the topological relationship between the trees. The proposed approaches include storing the paths of trees in a suffix array, employing hashing techniques to speed up retrieval, and counting the number of up-down operations to move a token from one node to another node in a tree. Various filters for accelerating a search, different strategies for parallelizing these search algorithms and applications of these algorithms to XML and phylogenetic data management are discussed.
The proposed techniques have been implemented into a phylogenetic search engine which is fully operational and is available on the World Wide Web. Experimental results on comparing the similarity measures with existing tree metrics and on evaluating the efficiency of the search techniques demonstrate the effectiveness of the search engine. Future work includes extending the techniques to other structural data, as well as developing new filters and algorithms for speeding up searching and mining in complex structures
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