701 research outputs found
MonetDB/XQuery: a fast XQuery processor powered by a relational engine
Relational XQuery systems try to re-use mature relational data management infrastructures to create fast and scalable XML database technology. This paper describes the main features, key contributions, and lessons learned while implementing such a system. Its architecture consists of (i) a range-based encoding of XML documents into relational tables, (ii) a compilation technique that translates XQuery into a basic relational algebra, (iii) a restricted (order) property-aware peephole relational query optimization strategy, and (iv) a mapping from XML update statements into relational updates. Thus, this system implements all essential XML database functionalities (rather than a single feature) such that we can learn from the full consequences of our architectural decisions. While implementing this system, we had to extend the state-of-the-art with a number of new technical contributions, such as loop-lifted staircase join and efficient relational query evaluation strategies for XQuery theta-joins with existential semantics. These contributions as well as the architectural lessons learned are also deemed valuable for other relational back-end engines. The performance and scalability of the resulting system is evaluated on the XMark benchmark up to data sizes of 11GB. The performance section also provides an extensive benchmark comparison of all major XMark results published previously, which confirm that the goal of purely relational XQuery processing, namely speed and scalability, was met
SecuCode: Intrinsic PUF Entangled Secure Wireless Code Dissemination for Computational RFID Devices
The simplicity of deployment and perpetual operation of energy harvesting
devices provides a compelling proposition for a new class of edge devices for
the Internet of Things. In particular, Computational Radio Frequency
Identification (CRFID) devices are an emerging class of battery-free,
computational, sensing enhanced devices that harvest all of their energy for
operation. Despite wireless connectivity and powering, secure wireless firmware
updates remains an open challenge for CRFID devices due to: intermittent
powering, limited computational capabilities, and the absence of a supervisory
operating system. We present, for the first time, a secure wireless code
dissemination (SecuCode) mechanism for CRFIDs by entangling a device intrinsic
hardware security primitive Static Random Access Memory Physical Unclonable
Function (SRAM PUF) to a firmware update protocol. The design of SecuCode: i)
overcomes the resource-constrained and intermittently powered nature of the
CRFID devices; ii) is fully compatible with existing communication protocols
employed by CRFID devices in particular, ISO-18000-6C protocol; and ii) is
built upon a standard and industry compliant firmware compilation and update
method realized by extending a recent framework for firmware updates provided
by Texas Instruments. We build an end-to-end SecuCode implementation and
conduct extensive experiments to demonstrate standards compliance, evaluate
performance and security.Comment: Accepted to the IEEE Transactions on Dependable and Secure Computin
Sampling-Based Query Re-Optimization
Despite of decades of work, query optimizers still make mistakes on
"difficult" queries because of bad cardinality estimates, often due to the
interaction of multiple predicates and correlations in the data. In this paper,
we propose a low-cost post-processing step that can take a plan produced by the
optimizer, detect when it is likely to have made such a mistake, and take steps
to fix it. Specifically, our solution is a sampling-based iterative procedure
that requires almost no changes to the original query optimizer or query
evaluation mechanism of the system. We show that this indeed imposes low
overhead and catches cases where three widely used optimizers (PostgreSQL and
two commercial systems) make large errors.Comment: This is the extended version of a paper with the same title and
authors that appears in the Proceedings of the ACM SIGMOD International
Conference on Management of Data (SIGMOD 2016
Strategic directions in constraint programming
An abstract is not available
Referencing Sources of Molecular Spectroscopic Data in the Era of Data Science: Application to the HITRAN and AMBDAS Databases
The application described has been designed to create bibliographic entries
in large databases with diverse sources automatically, which reduces both the
frequency of mistakes and the workload for the administrators. This new system
uniquely identifies each reference from its digital object identifier (DOI) and
retrieves the corresponding bibliographic information from any of several
online services, including the SAO/NASA Astrophysics Data Systems (ADS) and
CrossRef APIs. Once parsed into a relational database, the software is able to
produce bibliographies in any of several formats, including HTML and BibTeX,
for use on websites or printed articles. The application is provided
free-of-charge for general use by any scientific database. The power of this
application is demonstrated when used to populate reference data for the HITRAN
and AMBDAS databases as test cases. HITRAN contains data that is provided by
researchers and collaborators throughout the spectroscopic community. These
contributors are accredited for their contributions through the bibliography
produced alongside the data returned by an online search in HITRAN. Prior to
the work presented here, HITRAN and AMBDAS created these bibliographies
manually, which is a tedious, time-consuming and error-prone process. The
complete code for the new referencing system can be found at
\url{https://github.com/hitranonline/refs}.Comment: 11 pages, 5 figures, already published online at
https://doi.org/10.3390/atoms802001
Distributed Reasoning in a Peer-to-Peer Setting: Application to the Semantic Web
In a peer-to-peer inference system, each peer can reason locally but can also
solicit some of its acquaintances, which are peers sharing part of its
vocabulary. In this paper, we consider peer-to-peer inference systems in which
the local theory of each peer is a set of propositional clauses defined upon a
local vocabulary. An important characteristic of peer-to-peer inference systems
is that the global theory (the union of all peer theories) is not known (as
opposed to partition-based reasoning systems). The main contribution of this
paper is to provide the first consequence finding algorithm in a peer-to-peer
setting: DeCA. It is anytime and computes consequences gradually from the
solicited peer to peers that are more and more distant. We exhibit a sufficient
condition on the acquaintance graph of the peer-to-peer inference system for
guaranteeing the completeness of this algorithm. Another important contribution
is to apply this general distributed reasoning setting to the setting of the
Semantic Web through the Somewhere semantic peer-to-peer data management
system. The last contribution of this paper is to provide an experimental
analysis of the scalability of the peer-to-peer infrastructure that we propose,
on large networks of 1000 peers
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