437 research outputs found
Work-Efficient Query Evaluation with PRAMs
The paper studies query evaluation in parallel constant time in the PRAM model. While it is well-known that all relational algebra queries can be evaluated in constant time on an appropriate CRCW-PRAM, this paper is interested in the efficiency of evaluation algorithms, that is, in the number of processors or, asymptotically equivalent, in the work. Naive evaluation in the parallel setting results in huge (polynomial) bounds on the work of such algorithms and in presentations of the result sets that can be extremely scattered in memory. The paper first discusses some obstacles for constant time PRAM query evaluation. It presents algorithms for relational operators that are considerably more efficient than the naive approaches. Further it explores three settings, in which efficient sequential query evaluation algorithms exist: acyclic queries, semi-join algebra queries, and join queries - the latter in the worst-case optimal framework. Under natural assumptions on the representation of the database, the work of the given algorithms matches the best sequential algorithms in the case of semi-join queries, and it comes close in the other two settings. An important tool is the compaction technique from Hagerup (1992)
A Parallel semantics for normal logic programs plus time
It is proposed that Normal Logic Programs with an explicit time ordering are a suitable basis for a general purpose parallel programming language. Examples show that such a language can accept real-time external inputs and outputs, and mimic assignment, all without departing from its pure logical semantics. This paper describes a fully incremental bottom-up interpreter that supports a wide range of parallel execution strategies and can extract significant potential parallelism from programs with complex dependencies
Classified Stable Matching
We introduce the {\sc classified stable matching} problem, a problem
motivated by academic hiring. Suppose that a number of institutes are hiring
faculty members from a pool of applicants. Both institutes and applicants have
preferences over the other side. An institute classifies the applicants based
on their research areas (or any other criterion), and, for each class, it sets
a lower bound and an upper bound on the number of applicants it would hire in
that class. The objective is to find a stable matching from which no group of
participants has reason to deviate. Moreover, the matching should respect the
upper/lower bounds of the classes.
In the first part of the paper, we study classified stable matching problems
whose classifications belong to a fixed set of ``order types.'' We show that if
the set consists entirely of downward forests, there is a polynomial-time
algorithm; otherwise, it is NP-complete to decide the existence of a stable
matching.
In the second part, we investigate the problem using a polyhedral approach.
Suppose that all classifications are laminar families and there is no lower
bound. We propose a set of linear inequalities to describe stable matching
polytope and prove that it is integral. This integrality allows us to find
various optimal stable matchings using Ellipsoid algorithm. A further
ramification of our result is the description of the stable matching polytope
for the many-to-many (unclassified) stable matching problem. This answers an
open question posed by Sethuraman, Teo and Qian
Scalable String and Suffix Sorting: Algorithms, Techniques, and Tools
This dissertation focuses on two fundamental sorting problems: string sorting
and suffix sorting. The first part considers parallel string sorting on
shared-memory multi-core machines, the second part external memory suffix
sorting using the induced sorting principle, and the third part distributed
external memory suffix sorting with a new distributed algorithmic big data
framework named Thrill.Comment: 396 pages, dissertation, Karlsruher Instituts f\"ur Technologie
(2018). arXiv admin note: text overlap with arXiv:1101.3448 by other author
Specific-to-General Learning for Temporal Events with Application to Learning Event Definitions from Video
We develop, analyze, and evaluate a novel, supervised, specific-to-general
learner for a simple temporal logic and use the resulting algorithm to learn
visual event definitions from video sequences. First, we introduce a simple,
propositional, temporal, event-description language called AMA that is
sufficiently expressive to represent many events yet sufficiently restrictive
to support learning. We then give algorithms, along with lower and upper
complexity bounds, for the subsumption and generalization problems for AMA
formulas. We present a positive-examples--only specific-to-general learning
method based on these algorithms. We also present a polynomial-time--computable
``syntactic'' subsumption test that implies semantic subsumption without being
equivalent to it. A generalization algorithm based on syntactic subsumption can
be used in place of semantic generalization to improve the asymptotic
complexity of the resulting learning algorithm. Finally, we apply this
algorithm to the task of learning relational event definitions from video and
show that it yields definitions that are competitive with hand-coded ones
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