696 research outputs found
Forward Scan based Plane Sweep Algorithm for Parallel Interval Joins
The interval join is a basic operation that finds application in temporal, spatial, and uncertain databases. Although a number of centralized and distributed algorithms have been proposed for the efficient
evaluation of interval joins, classic plane sweep approaches have not been considered at their full potential. A recent piece of related work proposes an optimized approach based on plane sweep
(PS) for modern hardware, showing that it greatly outperforms previous work. However, this approach depends on the development of a complex data structure and its parallelization has not been adequately
studied. In this paper, we explore the applicability of a largely ignored forward scan (FS) based plane sweep algorithm, which is extremely simple to implement. We propose two optimizations of FS that greatly reduce its cost, making it competitive to the state-of-the-art single-threaded PS algorithm while achieving a lower memory footprint. In addition, we show the drawbacks of a previously proposed hash-based partitioning approach for parallel join processing and suggest a domain-based partitioning approach that does not produce duplicate results. Within our approach we propose a novel breakdown of the partition join jobs into a small number of independent mini-join jobs with varying cost and manage
to avoid redundant comparisons. Finally, we show how these mini-joins can be scheduled in multiple CPU cores and propose an adaptive domain partitioning, aiming at load balancing. We include an experimental study that demonstrates the efficiency of our optimized FS and the scalability of our parallelization framework.published_or_final_versio
Cache-efficient sweeping-based interval joins for extended Allen relation predicates
We develop a family of efficient plane-sweeping interval join algorithms for evaluating a wide range of interval predicates such as Allen’s relationships and parameterized relationships. Our technique is based on a framework, components of which can be flexibly combined in different manners to support the required interval relation. In temporal databases, our algorithms can exploit a well-known and flexible access method, the Timeline Index, thus expanding the set of operations it supports even further. Additionally, employing a compact data structure, the gapless hash map, we utilize the CPU cache efficiently. In an experimental evaluation, we show that our approach is several times faster and scales better than state-of-the-art techniques, while being much better suited for real-time event processing
A Unified Approach for Indexed and Non-Indexed Spatial Joins
The original publication is available at www.springerlink.comL. Arge, O. Procopiuc, S. Ramaswamy, T. Suel, J. Vahrenhold, and J. S. Vitter. “A Unified Approach for Indexed and Non-Indexed Spatial Joins,” Proceedings of the 7th International Conference on Extending Database Technology (EDBT ’00), Konstanz, Germany, March 2000, published in Lecture Notes in Computer Science, Springer, 1777, Berlin, Germany, 413–429
Leveraging range joins for the computation of overlap joins
Joins are essential and potentially expensive operations in database management systems. When data is associated with time periods, joins commonly include predicates that require pairs of argument tuples to overlap in order to qualify for the result. Our goal is to enable built-in systems support for such joins. In particular, we present an approach where overlap joins are formulated as unions of range joins, which are more general purpose joins compared to overlap joins, i.e., are useful in their own right, and are supported well by B+-trees. The approach is sufficiently flexible that it also supports joins with additional equality predicates, as well as open, closed, and half-open time periods over discrete and continuous domains, thus offering both generality and simplicity, which is important in a system setting. We provide both a stand-alone solution that performs on par with the state-of-the-art and a DBMS embedded solution that is able to exploit standard indexing and clearly outperforms existing DBMS solutions that depend on specialized indexing techniques. We offer both analytical and empirical evaluations of the proposals. The empirical study includes comparisons with pertinent existing proposals and offers detailed insight into the performance characteristics of the proposals
Leveraging range joins for the computation of overlap joins
Joins are essential and potentially expensive operations in database management systems. When data is associated with time periods, joins commonly include predicates that require pairs of argument tuples to overlap in order to qualify for the result. Our goal is to enable built-in systems support for such joins. In particular, we present an approach where overlap joins are formulated as unions of range joins, which are more general purpose joins compared to overlap joins, i.e., are useful in their own right, and are supported well by B+-trees. The approach is sufficiently flexible that it also supports joins with additional equality predicates, as well as open, closed, and half-open time periods over discrete and continuous domains, thus offering both generality and simplicity, which is important in a system setting. We provide both a stand-alone solution that performs on par with the state-of-the-art and a DBMS embedded solution that is able to exploit standard indexing and clearly outperforms existing DBMS solutions that depend on specialized indexing techniques. We offer both analytical and empirical evaluations of the proposals. The empirical study includes comparisons with pertinent existing proposals and offers detailed insight into the performance characteristics of the proposals
Multi-Dimensional Joins
We present three novel algorithms for performing multi-dimensional
joins and an in-depth survey and analysis of a low-dimensional
spatial join. The first algorithm, the Iterative Spatial Join,
performs a spatial join on low-dimensional data and is based
on a plane-sweep technique.
As we show analytically and experimentally,
the Iterative Spatial Join performs well when internal memory is
limited, compared to competing methods. This suggests that
the Iterative Spatial Join would be useful for very large data sets
or in situations where internal memory is a shared resource and
is therefore limited, such as with today's database engines which
share internal memory amongst several queries. Furthermore, the
performance of the Iterative Spatial Join is predictable and has
no parameters which need to be tuned, unlike other algorithms.
The second algorithm, the Quickjoin algorithm,
performs a higher-dimensional
similarity join in which pairs of objects that lie within a
certain distance epsilon of each other are reported.
The Quickjoin algorithm overcomes drawbacks of competing methods,
such as requiring embedding methods on the data first or using
multi-dimensional indices, which limit
the ability to discriminate between objects in each
dimension, thereby degrading performance.
A formal analysis is provided of the Quickjoin method, and
experiments show that the Quickjoin method significantly outperforms
competing methods.
The third algorithm adapts
incremental join techniques to improve the
speed of calculating the Hausdorff distance, which
is used in applications such as image matching, image analysis,
and surface approximations.
The nearest neighbor incremental join technique for indices that
are based on hierarchical containment use a priority queue
of index node pairs and bounds on the distance values between
pairs, both of which need to modified in order to calculate the
Hausdorff distance. Results of experiments are described that
confirm the performance improvement.
Finally, a survey is provided which
instead of just summarizing the literature and presenting each
technique in its entirety, describes distinct components of
the different techniques, and each technique is decomposed into
an overall framework for performing a spatial join
The Complexity of Boolean Conjunctive Queries with Intersection Joins
Intersection joins over interval data are relevant in spatial and temporal
data settings. A set of intervals join if their intersection is non-empty. In
case of point intervals, the intersection join becomes the standard equality
join.
We establish the complexity of Boolean conjunctive queries with intersection
joins by a many-one equivalence to disjunctions of Boolean conjunctive queries
with equality joins. The complexity of any query with intersection joins is
that of the hardest query with equality joins in the disjunction exhibited by
our equivalence. This is captured by a new width measure called the IJ-width.
We also introduce a new syntactic notion of acyclicity called iota-acyclicity
to characterise the class of Boolean queries with intersection joins that admit
linear time computation modulo a poly-logarithmic factor in the data size.
Iota-acyclicity is for intersection joins what alpha-acyclicity is for equality
joins. It strictly sits between gamma-acyclicity and Berge-acyclicity. The
intersection join queries that are not iota-acyclic are at least as hard as the
Boolean triangle query with equality joins, which is widely considered not
computable in linear time
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