57,354 research outputs found
University of Twente @ TREC 2009: Indexing half a billion web pages
This report presents results for the TREC 2009 adhoc task, the diversity task, and the relevance feedback task. We present ideas for unsupervised tuning of search system, an approach for spam removal, and the use of categories and query log information for diversifying search results
Neo: A Learned Query Optimizer
Query optimization is one of the most challenging problems in database
systems. Despite the progress made over the past decades, query optimizers
remain extremely complex components that require a great deal of hand-tuning
for specific workloads and datasets. Motivated by this shortcoming and inspired
by recent advances in applying machine learning to data management challenges,
we introduce Neo (Neural Optimizer), a novel learning-based query optimizer
that relies on deep neural networks to generate query executions plans. Neo
bootstraps its query optimization model from existing optimizers and continues
to learn from incoming queries, building upon its successes and learning from
its failures. Furthermore, Neo naturally adapts to underlying data patterns and
is robust to estimation errors. Experimental results demonstrate that Neo, even
when bootstrapped from a simple optimizer like PostgreSQL, can learn a model
that offers similar performance to state-of-the-art commercial optimizers, and
in some cases even surpass them
An Analytical Study of Large SPARQL Query Logs
With the adoption of RDF as the data model for Linked Data and the Semantic
Web, query specification from end- users has become more and more common in
SPARQL end- points. In this paper, we conduct an in-depth analytical study of
the queries formulated by end-users and harvested from large and up-to-date
query logs from a wide variety of RDF data sources. As opposed to previous
studies, ours is the first assessment on a voluminous query corpus, span- ning
over several years and covering many representative SPARQL endpoints. Apart
from the syntactical structure of the queries, that exhibits already
interesting results on this generalized corpus, we drill deeper in the
structural char- acteristics related to the graph- and hypergraph represen-
tation of queries. We outline the most common shapes of queries when visually
displayed as pseudographs, and char- acterize their (hyper-)tree width.
Moreover, we analyze the evolution of queries over time, by introducing the
novel con- cept of a streak, i.e., a sequence of queries that appear as
subsequent modifications of a seed query. Our study offers several fresh
insights on the already rich query features of real SPARQL queries formulated
by real users, and brings us to draw a number of conclusions and pinpoint
future di- rections for SPARQL query evaluation, query optimization, tuning,
and benchmarking
Forecasting the cost of processing multi-join queries via hashing for main-memory databases (Extended version)
Database management systems (DBMSs) carefully optimize complex multi-join
queries to avoid expensive disk I/O. As servers today feature tens or hundreds
of gigabytes of RAM, a significant fraction of many analytic databases becomes
memory-resident. Even after careful tuning for an in-memory environment, a
linear disk I/O model such as the one implemented in PostgreSQL may make query
response time predictions that are up to 2X slower than the optimal multi-join
query plan over memory-resident data. This paper introduces a memory I/O cost
model to identify good evaluation strategies for complex query plans with
multiple hash-based equi-joins over memory-resident data. The proposed cost
model is carefully validated for accuracy using three different systems,
including an Amazon EC2 instance, to control for hardware-specific differences.
Prior work in parallel query evaluation has advocated right-deep and bushy
trees for multi-join queries due to their greater parallelization and
pipelining potential. A surprising finding is that the conventional wisdom from
shared-nothing disk-based systems does not directly apply to the modern
shared-everything memory hierarchy. As corroborated by our model, the
performance gap between the optimal left-deep and right-deep query plan can
grow to about 10X as the number of joins in the query increases.Comment: 15 pages, 8 figures, extended version of the paper to appear in
SoCC'1
Predicting Destinations by Nearest Neighbor Search on Training Vessel Routes
The DEBS Grand Challenge 2018 is set in the context of maritime route
prediction. Vessel routes are modeled as streams of Automatic Identification
System (AIS) data points selected from real-world tracking data. The challenge
requires to correctly estimate the destination ports and arrival times of
vessel trips, as early as possible. Our proposed solution partitions the
training vessel routes by reported destination port and uses a nearest neighbor
search to find the training routes that are closer to the query AIS point.
Particular improvements have been included as well, such as a way to avoid
changing the predicted ports frequently within one query route and automating
the parameters tuning by the use of a genetic algorithm. This leads to
significant improvements on the final score
JetWeb: A WWW Interface and Database for Monte Carlo Tuning and Validation
A World Wide Web interface to a Monte Carlo validation and tuning facility is
described. The aim of the package is to allow rapid and reproducible
comparisons to be made between detailed measurements at high-energy physics
colliders and general physics simulation packages. The package includes a
relational database, a Java servlet query and display facility, and clean
interfaces to simulation packages and their parameters.Comment: See http://jetweb.hep.ucl.ac.uk for further informatio
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