281 research outputs found
Implementing Performance Competitive Logical Recovery
New hardware platforms, e.g. cloud, multi-core, etc., have led to a
reconsideration of database system architecture. Our Deuteronomy project
separates transactional functionality from data management functionality,
enabling a flexible response to exploiting new platforms. This separation
requires, however, that recovery is described logically. In this paper, we
extend current recovery methods to work in this logical setting. While this is
straightforward in principle, performance is an issue. We show how ARIES style
recovery optimizations can work for logical recovery where page information is
not captured on the log. In side-by-side performance experiments using a common
log, we compare logical recovery with a state-of-the art ARIES style recovery
implementation and show that logical redo performance can be competitive.Comment: VLDB201
Enabling Operator Reordering in Data Flow Programs Through Static Code Analysis
In many massively parallel data management platforms, programs are
represented as small imperative pieces of code connected in a data flow. This
popular abstraction makes it hard to apply algebraic reordering techniques
employed by relational DBMSs and other systems that use an algebraic
programming abstraction. We present a code analysis technique based on reverse
data and control flow analysis that discovers a set of properties from user
code, which can be used to emulate algebraic optimizations in this setting.Comment: 4 pages, accepted and presented at the First International Workshop
on Cross-model Language Design and Implementation (XLDI), affiliated with
ICFP 2012, Copenhage
Near-Optimal Sensor Scheduling for Batch State Estimation: Complexity, Algorithms, and Limits
In this paper, we focus on batch state estimation for linear systems. This
problem is important in applications such as environmental field estimation,
robotic navigation, and target tracking. Its difficulty lies on that limited
operational resources among the sensors, e.g., shared communication bandwidth
or battery power, constrain the number of sensors that can be active at each
measurement step. As a result, sensor scheduling algorithms must be employed.
Notwithstanding, current sensor scheduling algorithms for batch state
estimation scale poorly with the system size and the time horizon. In addition,
current sensor scheduling algorithms for Kalman filtering, although they scale
better, provide no performance guarantees or approximation bounds for the
minimization of the batch state estimation error. In this paper, one of our
main contributions is to provide an algorithm that enjoys both the estimation
accuracy of the batch state scheduling algorithms and the low time complexity
of the Kalman filtering scheduling algorithms. In particular: 1) our algorithm
is near-optimal: it achieves a solution up to a multiplicative factor 1/2 from
the optimal solution, and this factor is close to the best approximation factor
1/e one can achieve in polynomial time for this problem; 2) our algorithm has
(polynomial) time complexity that is not only lower than that of the current
algorithms for batch state estimation; it is also lower than, or similar to,
that of the current algorithms for Kalman filtering. We achieve these results
by proving two properties for our batch state estimation error metric, which
quantifies the square error of the minimum variance linear estimator of the
batch state vector: a) it is supermodular in the choice of the sensors; b) it
has a sparsity pattern (it involves matrices that are block tri-diagonal) that
facilitates its evaluation at each sensor set.Comment: Correction of typos in proof
Performance guarantees for greedy maximization of non-submodular controllability metrics
A key problem in emerging complex cyber-physical networks is the design of
information and control topologies, including sensor and actuator selection and
communication network design. These problems can be posed as combinatorial set
function optimization problems to maximize a dynamic performance metric for the
network. Some systems and control metrics feature a property called
submodularity, which allows simple greedy algorithms to obtain provably
near-optimal topology designs. However, many important metrics lack
submodularity and therefore lack provable guarantees for using a greedy
optimization approach. Here we show that performance guarantees can be obtained
for greedy maximization of certain non-submodular functions of the
controllability and observability Gramians. Our results are based on two key
quantities: the submodularity ratio, which quantifies how far a set function is
from being submodular, and the curvature, which quantifies how far a set
function is from being supermodular
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