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An effective data placement strategy for XML documents
As XML is increasingly being used in Web applications, new
technologies need to be investigated for processing XML documents with high
performance. Parallelism is a promising solution for structured document
processing and data placement is a major factor for system performance
improvement in parallel processing. This paper describes an effective XML
document data placement strategy. The new strategy is based on a multilevel
graph partitioning algorithm with the consideration of the unique features of
XML documents and query distributions. A new algorithm, which is based on
XML query schemas to derive the weighted graph from the labelled directed
graph presentation of XML documents, is also proposed. Performance analysis
on the algorithm presented in the paper shows that the new data placement
strategy exhibits low workload skew and a high degree of parallelism
Efficient data representation for XML in peer-based systems
Purpose - New directions in the provision of end-user computing experiences mean that the best way to share data between small mobile computing devices needs to be determined. Partitioning large structures so that they can be shared efficiently provides a basis for data-intensive applications on such platforms. The partitioned structure can be compressed using dictionary-based approaches and then directly queried without firstly decompressing the whole structure. Design/methodology/approach - The paper describes an architecture for partitioning XML into structural and dictionary elements and the subsequent manipulation of the dictionary elements to make the best use of available space. Findings - The results indicate that considerable savings are available by removing duplicate dictionaries. The paper also identifies the most effective strategy for defining dictionary scope. Research limitations/implications - This evaluation is based on a range of benchmark XML structures and the approach to minimising dictionary size shows benefit in the majority of these. Where structures are small and regular, the benefits of efficient dictionary representation are lost. The authors' future research now focuses on heuristics for further partitioning of structural elements. Practical implications - Mobile applications that need access to large data collections will benefit from the findings of this research. Traditional client/server architectures are not suited to dealing with high volume demands from a multitude of small mobile devices. Peer data sharing provides a more scalable solution and the experiments that the paper describes demonstrate the most effective way of sharing data in this context. Social implications - Many services are available via smartphone devices but users are wary of exploiting the full potential because of the need to conserve battery power. The approach mitigates this challenge and consequently expands the potential for users to benefit from mobile information systems. This will have impact in areas such as advertising, entertainment and education but will depend on the acceptability of file sharing being extended from the desktop to the mobile environment. Originality/value - The original work characterises the most effective way of sharing large data sets between small mobile devices. This will save battery power on devices such as smartphones, thus providing benefits to users of such devices
DALiuGE: A Graph Execution Framework for Harnessing the Astronomical Data Deluge
The Data Activated Liu Graph Engine - DALiuGE - is an execution framework for
processing large astronomical datasets at a scale required by the Square
Kilometre Array Phase 1 (SKA1). It includes an interface for expressing complex
data reduction pipelines consisting of both data sets and algorithmic
components and an implementation run-time to execute such pipelines on
distributed resources. By mapping the logical view of a pipeline to its
physical realisation, DALiuGE separates the concerns of multiple stakeholders,
allowing them to collectively optimise large-scale data processing solutions in
a coherent manner. The execution in DALiuGE is data-activated, where each
individual data item autonomously triggers the processing on itself. Such
decentralisation also makes the execution framework very scalable and flexible,
supporting pipeline sizes ranging from less than ten tasks running on a laptop
to tens of millions of concurrent tasks on the second fastest supercomputer in
the world. DALiuGE has been used in production for reducing interferometry data
sets from the Karl E. Jansky Very Large Array and the Mingantu Ultrawide
Spectral Radioheliograph; and is being developed as the execution framework
prototype for the Science Data Processor (SDP) consortium of the Square
Kilometre Array (SKA) telescope. This paper presents a technical overview of
DALiuGE and discusses case studies from the CHILES and MUSER projects that use
DALiuGE to execute production pipelines. In a companion paper, we provide
in-depth analysis of DALiuGE's scalability to very large numbers of tasks on
two supercomputing facilities.Comment: 31 pages, 12 figures, currently under review by Astronomy and
Computin
TAPER: query-aware, partition-enhancement for large, heterogenous, graphs
Graph partitioning has long been seen as a viable approach to address Graph
DBMS scalability. A partitioning, however, may introduce extra query processing
latency unless it is sensitive to a specific query workload, and optimised to
minimise inter-partition traversals for that workload. Additionally, it should
also be possible to incrementally adjust the partitioning in reaction to
changes in the graph topology, the query workload, or both. Because of their
complexity, current partitioning algorithms fall short of one or both of these
requirements, as they are designed for offline use and as one-off operations.
The TAPER system aims to address both requirements, whilst leveraging existing
partitioning algorithms. TAPER takes any given initial partitioning as a
starting point, and iteratively adjusts it by swapping chosen vertices across
partitions, heuristically reducing the probability of inter-partition
traversals for a given pattern matching queries workload. Iterations are
inexpensive thanks to time and space optimisations in the underlying support
data structures. We evaluate TAPER on two different large test graphs and over
realistic query workloads. Our results indicate that, given a hash-based
partitioning, TAPER reduces the number of inter-partition traversals by around
80%; given an unweighted METIS partitioning, by around 30%. These reductions
are achieved within 8 iterations and with the additional advantage of being
workload-aware and usable online.Comment: 12 pages, 11 figures, unpublishe
Recent Advances in Graph Partitioning
We survey recent trends in practical algorithms for balanced graph
partitioning together with applications and future research directions
Molecular Spin Qudits for Quantum Algorithms
Presently, one of the most ambitious technological goals is the development
of devices working under the laws of quantum mechanics. One prominent target is
the quantum computer, which would allow the processing of information at
quantum level for purposes not achievable with even the most powerful computer
resources. The large-scale implementation of quantum information would be a
game changer for current technology, because it would allow unprecedented
parallelised computation and secure encryption based on the principles of
quantum superposition and entanglement. Currently, there are several physical
platforms racing to achieve the level of performance required for the quantum
hardware to step into the realm of practical quantum information applications.
Several materials have been proposed to fulfil this task, ranging from quantum
dots, Bose-Einstein condensates, spin impurities, superconducting circuits,
molecules, amongst others. Magnetic molecules are among the list of promising
building blocks, due to (i) their intrinsic monodispersity, (ii) discrete
energy levels (iii) the possibility of chemical quantum state engineering, and
(iv) their multilevel characteristics, leading to the so called Qudits (d > 2),
amongst others. Herein we review how a molecular multilevel nuclear spin qubit
(or qudit, where d = 4), known as TbPc2, gathers all the necessary requirements
to perform as a molecular hardware platform with a first generation of
molecular devices enabling even quantum algorithm operations.Comment: Chem. Soc. Rev., 2017, Advance Articl
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