328 research outputs found
Geographica: A Benchmark for Geospatial RDF Stores
Geospatial extensions of SPARQL like GeoSPARQL and stSPARQL have recently
been defined and corresponding geospatial RDF stores have been implemented.
However, there is no widely used benchmark for evaluating geospatial RDF stores
which takes into account recent advances to the state of the art in this area.
In this paper, we develop a benchmark, called Geographica, which uses both
real-world and synthetic data to test the offered functionality and the
performance of some prominent geospatial RDF stores
Weiterentwicklung analytischer Datenbanksysteme
This thesis contributes to the state of the art in analytical database systems. First, we identify and explore extensions to better support analytics on event streams. Second, we propose a novel polygon index to enable efficient geospatial data processing in main memory. Third, we contribute a new deep learning approach to cardinality estimation, which is the core problem in cost-based query optimization.Diese Arbeit trĂ€gt zum aktuellen Forschungsstand von analytischen Datenbanksystemen bei. Wir identifizieren und explorieren Erweiterungen um Analysen auf Eventströmen besser zu unterstĂŒtzen. Wir stellen eine neue Indexstruktur fĂŒr Polygone vor, die eine effiziente Verarbeitung von Geodaten im Hauptspeicher ermöglicht. Zudem prĂ€sentieren wir einen neuen Ansatz fĂŒr KardinalitĂ€tsschĂ€tzungen mittels maschinellen Lernens
The {RDF}-3X Engine for Scalable Management of {RDF} Data
RDF is a data model for schema-free structured information that is gaining momentum in the context of Semantic-Web data, life sciences, and also Web 2.0 platforms. The ``pay-as-you-go'' nature of RDF and the flexible pattern-matching capabilities of its query language SPARQL entail efficiency and scalability challenges for complex queries including long join paths. This paper presents the RDF-3X engine, an implementation of SPARQL that achieves excellent performance by pursuing a RISC-style architecture with streamlined indexing and query processing. The physical design is identical for all RDF-3X databases regardless of their workloads, and completely eliminates the need for index tuning by exhaustive indexes for all permutations of subject-property-object triples and their binary and unary projections. These indexes are highly compressed, and the query processor can aggressively leverage fast merge joins with excellent performance of processor caches. The query optimizer is able to choose optimal join orders even for complex queries, with a cost model that includes statistical synopses for entire join paths. Although RDF-3X is optimized for queries, it also provides good support for efficient online updates by means of a staging architecture: direct updates to the main database indexes are deferred, and instead applied to compact differential indexes which are later merged into the main indexes in a batched manner. Experimental studies with several large-scale datasets with more than 50 million RDF triples and benchmark queries that include pattern matching, manyway star-joins, and long path-joins demonstrate that RDF-3X can outperform the previously best alternatives by one or two orders of magnitude
Adaptive Geospatial Joins for Modern Hardware
Geospatial joins are a core building block of connected mobility
applications. An especially challenging problem are joins between streaming
points and static polygons. Since points are not known beforehand, they cannot
be indexed. Nevertheless, points need to be mapped to polygons with low
latencies to enable real-time feedback.
We present an adaptive geospatial join that uses true hit filtering to avoid
expensive geometric computations in most cases. Our technique uses a
quadtree-based hierarchical grid to approximate polygons and stores these
approximations in a specialized radix tree. We emphasize on an approximate
version of our algorithm that guarantees a user-defined precision. The exact
version of our algorithm can adapt to the expected point distribution by
refining the index. We optimized our implementation for modern hardware
architectures with wide SIMD vector processing units, including Intel's brand
new Knights Landing. Overall, our approach can perform up to two orders of
magnitude faster than existing techniques
Adaptive geospatial joins for modern hardware
Geospatial joins are a core building block of connected
mobility applications. An especially challenging problem
are joins between streaming points and static polygons. Since
points are not known beforehand, they cannot be indexed.
Nevertheless, points need to be mapped to polygons with low
latencies to enable real-time feedback.
We present an adaptive geospatial join that uses true hit
filtering to avoid expensive geometric computations in most
cases. Our technique uses a quadtree-based hierarchical grid
to approximate polygons and stores these approximations in a
specialized radix tree. We emphasize on an approximate version
of our algorithm that guarantees a user-defined precision. The
exact version of our algorithm can adapt to the expected point
distribution by refining the index. We optimized our implementation
for modern hardware architectures with wide SIMD vector
processing units, including Intelâs brand new Knights Landing.
Overall, our approach can perform up to two orders of magnitude
faster than existing techniques
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Supporting Scientific Analytics under Data Uncertainty and Query Uncertainty
Data management is becoming increasingly important in many applications, in particular, in large scientific databases where (1) data can be naturally modeled by continuous random variables, and (2) queries can involve complex predicates and/or be difficult for users to express explicitly. My thesis work aims to provide efficient support to both the data uncertainty and the query uncertainty .
When data is uncertain, an important class of queries requires query answers to be returned if their existence probabilities pass a threshold. I start with optimizing such threshold query processing for continuous uncertain data in the relational model by (i) expediting selections by reducing dimensionality of integration and using faster filters, (ii) expediting joins using new indexes on uncertain data, and (iii) optimizing a query plan using a dynamic, per-tuple based approach. Evaluation results using real-world data and benchmark queries show the accuracy and efficiency of my techniques and the dynamic query planning has over 50% performance gains in most cases over a state-of-the-art threshold query optimizer and is very close to the optimal planning in all cases.
Next I address uncertain data management in the array model, which has gained popu- larity for scientific data processing recently due to performance benefits. I define the formal semantics of array operations on uncertain data involving both value uncertainty within individual tuples and position uncertainty regarding where a tuple should belong in an array given uncertain dimension attributes, and propose a suite of storage and evaluation strategies for array operators, with a focus on a novel scheme that bounds the overhead of querying by strategically placing a few replicas of the tuples with large variances. Evaluation results show that for common workloads, my best-performing techniques outperform baselines up to 1 to 2 orders of magnitude while incurring only small storage overhead.
Finally, to bridge the increasing gap between the fast growth of data and the limited human ability to comprehend data and help the user retrieve high-value content from data more effectively, I propose to build interactive data exploration as a new database service, using an approach called âexplore-by-exampleâ. To build an effective system, my work is grounded in a rigorous SVM-based active learning framework and focuses on the following three problems: (i) accuracy-based and convergence-based stopping criteria, (ii) expediting example acquisition in each iteration, and (iii) expediting the final result retrieval. Evaluation results using real-world data and query patterns show that my system significantly outperforms state-of-the-art systems in accuracy (18x accuracy improvement for 4-dimensional workloads) while achieving desired efficiency for interactive exploration (2 to 5 seconds per iteration)
Efficient similarity-based operations for data integration
Similarity-based operations, similarity join, similarity grouping, data integrationMagdeburg, Univ., Fak. fĂŒr Informatik, Diss., 2004von Eike Schalleh
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