9,331 research outputs found
Query Workload-based RDF Graph Fragmentation and Allocation
As the volume of the RDF data becomes increasingly large, it is essential for
us to design a distributed database system to manage it. For distributed RDF
data design, it is quite common to partition the RDF data into some parts,
called fragments, which are then distributed. Thus, the distribution design
consists of two steps: fragmentation and allocation. In this paper, we propose
a method to explore the intrinsic similarities among the structures of queries
in a workload for fragmentation and allocation, which aims to reduce the number
of crossing matches and the communication cost during SPARQL query processing.
Specifically, we mine and select some frequent access patterns to reflect the
characteristics of the workload. Here, although we prove that selecting the
optimal set of frequent access patterns is NP-hard, we propose a heuristic
algorithm which guarantees both the data integrity and the approximation ratio.
Based on the selected frequent access patterns, we propose two fragmentation
strategies, vertical and horizontal fragmentation strategies, to divide RDF
graphs while meeting different kinds of query processing objectives. Vertical
fragmentation is for better throughput and horizontal fragmentation is for
better performance. After fragmentation, we discuss how to allocate these
fragments to various sites. Finally, we discuss how to process a query based on
the results of fragmentation and allocation. Extensive experiments confirm the
superior performance of our proposed solutions.Comment: 13 page
Accelerating Partial Evaluation in Distributed SPARQL Query Evaluation
Partial evaluation has recently been used for processing SPARQL queries over
a large resource description framework (RDF) graph in a distributed
environment. However, the previous approach is inefficient when dealing with
complex queries. In this study, we further improve the "partial evaluation and
assembly" framework for answering SPARQL queries over a distributed RDF graph,
while providing performance guarantees. Our key idea is to explore the
intrinsic structural characteristics of partial matches to filter out
irrelevant partial results, while providing performance guarantees on a network
trace (data shipment) or the computational cost (response time). We also
propose an efficient assembly algorithm to utilize the characteristics of
partial matches to merge them and form final results. To improve the efficiency
of finding partial matches further, we propose an optimization that
communicates variables' candidates among sites to avoid redundant computations.
In addition, although our approach is partitioning-tolerant, different
partitioning strategies result in different performances, and we evaluate
different partitioning strategies for our approach. Experiments over both real
and synthetic RDF datasets confirm the superiority of our approach.Comment: 15 page
On The Marriage of SPARQL and Keywords
Although SPARQL has been the predominant query language over RDF graphs, some
query intentions cannot be well captured by only using SPARQL syntax. On the
other hand, the keyword search enjoys widespread usage because of its intuitive
way of specifying information needs but suffers from the problem of low
precision. To maximize the advantages of both SPARQL and keyword search, we
introduce a novel paradigm that combines both of them and propose a hybrid
query (called an SK query) that integrates SPARQL and keyword search. In order
to answer SK queries efficiently, a structural index is devised, based on a
novel integrated query algorithm is proposed. We evaluate our method in large
real RDF graphs and experiments demonstrate both effectiveness and efficiency
of our method.Comment: 14 page
3D Dense Separated Convolution Module for Volumetric Image Analysis
With the thriving of deep learning, 3D Convolutional Neural Networks have
become a popular choice in volumetric image analysis due to their impressive 3D
contexts mining ability. However, the 3D convolutional kernels will introduce a
significant increase in the amount of trainable parameters. Considering the
training data is often limited in biomedical tasks, a tradeoff has to be made
between model size and its representational power. To address this concern, in
this paper, we propose a novel 3D Dense Separated Convolution (3D-DSC) module
to replace the original 3D convolutional kernels. The 3D-DSC module is
constructed by a series of densely connected 1D filters. The decomposition of
3D kernel into 1D filters reduces the risk of over-fitting by removing the
redundancy of 3D kernels in a topologically constrained manner, while providing
the infrastructure for deepening the network. By further introducing nonlinear
layers and dense connections between 1D filters, the network's representational
power can be significantly improved while maintaining a compact architecture.
We demonstrate the superiority of 3D-DSC on volumetric image classification and
segmentation, which are two challenging tasks often encountered in biomedical
image computing.Comment: 7 pages,5 figure
Analog-to-digital conversion revolutionized by deep learning
As the bridge between the analog world and digital computers,
analog-to-digital converters are generally used in modern information systems
such as radar, surveillance, and communications. For the configuration of
analog-to-digital converters in future high-frequency broadband systems, we
introduce a revolutionary architecture that adopts deep learning technology to
overcome tradeoffs between bandwidth, sampling rate, and accuracy. A photonic
front-end provides broadband capability for direct sampling and speed
multiplication. Trained deep neural networks learn the patterns of system
defects, maintaining high accuracy of quantized data in a succinct and adaptive
manner. Based on numerical and experimental demonstrations, we show that the
proposed architecture outperforms state-of-the-art analog-to-digital
converters, confirming the potential of our approach in future
analog-to-digital converter design and performance enhancement of future
information systems
Feature Selection via Sparse Approximation for Face Recognition
Inspired by biological vision systems, the over-complete local features with
huge cardinality are increasingly used for face recognition during the last
decades. Accordingly, feature selection has become more and more important and
plays a critical role for face data description and recognition. In this paper,
we propose a trainable feature selection algorithm based on the regularized
frame for face recognition. By enforcing a sparsity penalty term on the minimum
squared error (MSE) criterion, we cast the feature selection problem into a
combinatorial sparse approximation problem, which can be solved by greedy
methods or convex relaxation methods. Moreover, based on the same frame, we
propose a sparse Ho-Kashyap (HK) procedure to obtain simultaneously the optimal
sparse solution and the corresponding margin vector of the MSE criterion. The
proposed methods are used for selecting the most informative Gabor features of
face images for recognition and the experimental results on benchmark face
databases demonstrate the effectiveness of the proposed methods
Fast and Accurate Graph Stream Summarization
A graph stream is a continuous sequence of data items, in which each item
indicates an edge, including its two endpoints and edge weight. It forms a
dynamic graph that changes with every item in the stream. Graph streams play
important roles in cyber security, social networks, cloud troubleshooting
systems and other fields. Due to the vast volume and high update speed of graph
streams, traditional data structures for graph storage such as the adjacency
matrix and the adjacency list are no longer sufficient. However, prior art of
graph stream summarization, like CM sketches, gSketches, TCM and gMatrix,
either supports limited kinds of queries or suffers from poor accuracy of query
results. In this paper, we propose a novel Graph Stream Sketch (GSS for short)
to summarize the graph streams, which has the linear space cost (O(|E|), E is
the edge set of the graph) and the constant update time complexity (O(1)) and
supports all kinds of queries over graph streams with the controllable errors.
Both theoretical analysis and experiment results confirm the superiority of our
solution with regard to the time/space complexity and query results' precision
compared with the state-of-the-art
Computing Longest Increasing Subsequence Over Sequential Data Streams
In this paper, we propose a data structure, a quadruple neighbor list
(QN-list, for short), to support real time queries of all longest increasing
subsequence (LIS) and LIS with constraints over sequential data streams. The
QN-List built by our algorithm requires space, where is the time
window size. The running time for building the initial QN-List takes time. Applying the QN-List, insertion of the new item takes
time and deletion of the first item takes time. To the best of our
knowledge, this is the first work to support both LIS enumeration and LIS with
constraints computation by using a single uniform data structure for real time
sequential data streams. Our method outperforms the state-of-the-art methods in
both time and space cost, not only theoretically, but also empirically.Comment: 20 pages (12+8
Phonon induced spin squeezing based on geometric phase
A scheme to achieve spin squeezing using a geometric phase induced by a
single mechanical mode is proposed. The analytical and numerical results show
that the ultimate degree of spin squeezing depends on the parameter
, which is the ratio between the thermal
excitation, the quality factor and square root of ensemble size. The undesired
coupling between the spin ensemble and the bath can be efficiently suppressed
by Bang-Bang control pulses. With high quality factor, the ultimate limit of
the ideal one-axis twisting spin squeezing can be obtained for an NV ensemble
in diamond
Detuning Enhanced Cavity Spin Squeezing
The unconditionally squeezing of the collective spin of an atomic ensemble in
a laser driven optical cavity (I. D. Leroux, M. H. Schleier-Smith, and V.
Vuletic, Phys. Rev. Lett 104, 073602 (2010)) is studied and analyzed
theoretically. Surprisingly, we find that the largely detuned driving laser can
improve the scaling of cavity squeezing from to , where S
is the total atomic spin. Moreover, we also demonstrate that the experimental
imperfection of photon scattering into free space can be efficiently suppressed
by detuning.Comment: 5 pages, 3 figure
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