94,670 research outputs found
Towards a Novel Cooperative Logistics Information System Framework
Supply Chains and Logistics have a growing importance in global economy.
Supply Chain Information Systems over the world are heterogeneous and each one
can both produce and receive massive amounts of structured and unstructured
data in real-time, which are usually generated by information systems,
connected objects or manually by humans. This heterogeneity is due to Logistics
Information Systems components and processes that are developed by different
modelling methods and running on many platforms; hence, decision making process
is difficult in such multi-actor environment. In this paper we identify some
current challenges and integration issues between separately designed Logistics
Information Systems (LIS), and we propose a Distributed Cooperative Logistics
Platform (DCLP) framework based on NoSQL, which facilitates real-time
cooperation between stakeholders and improves decision making process in a
multi-actor environment. We included also a case study of Hospital Supply Chain
(HSC), and a brief discussion on perspectives and future scope of work
Middleware-based Database Replication: The Gaps between Theory and Practice
The need for high availability and performance in data management systems has
been fueling a long running interest in database replication from both academia
and industry. However, academic groups often attack replication problems in
isolation, overlooking the need for completeness in their solutions, while
commercial teams take a holistic approach that often misses opportunities for
fundamental innovation. This has created over time a gap between academic
research and industrial practice.
This paper aims to characterize the gap along three axes: performance,
availability, and administration. We build on our own experience developing and
deploying replication systems in commercial and academic settings, as well as
on a large body of prior related work. We sift through representative examples
from the last decade of open-source, academic, and commercial database
replication systems and combine this material with case studies from real
systems deployed at Fortune 500 customers. We propose two agendas, one for
academic research and one for industrial R&D, which we believe can bridge the
gap within 5-10 years. This way, we hope to both motivate and help researchers
in making the theory and practice of middleware-based database replication more
relevant to each other.Comment: 14 pages. Appears in Proc. ACM SIGMOD International Conference on
Management of Data, Vancouver, Canada, June 200
Windows .NET Network Distributed Basic Local Alignment Search Toolkit (W.ND-BLAST)
BACKGROUND: BLAST is one of the most common and useful tools for Genetic Research. This paper describes a software application we have termed Windows .NET Distributed Basic Local Alignment Search Toolkit (W.ND-BLAST), which enhances the BLAST utility by improving usability, fault recovery, and scalability in a Windows desktop environment. Our goal was to develop an easy to use, fault tolerant, high-throughput BLAST solution that incorporates a comprehensive BLAST result viewer with curation and annotation functionality. RESULTS: W.ND-BLAST is a comprehensive Windows-based software toolkit that targets researchers, including those with minimal computer skills, and provides the ability increase the performance of BLAST by distributing BLAST queries to any number of Windows based machines across local area networks (LAN). W.ND-BLAST provides intuitive Graphic User Interfaces (GUI) for BLAST database creation, BLAST execution, BLAST output evaluation and BLAST result exportation. This software also provides several layers of fault tolerance and fault recovery to prevent loss of data if nodes or master machines fail. This paper lays out the functionality of W.ND-BLAST. W.ND-BLAST displays close to 100% performance efficiency when distributing tasks to 12 remote computers of the same performance class. A high throughput BLAST job which took 662.68 minutes (11 hours) on one average machine was completed in 44.97 minutes when distributed to 17 nodes, which included lower performance class machines. Finally, there is a comprehensive high-throughput BLAST Output Viewer (BOV) and Annotation Engine components, which provides comprehensive exportation of BLAST hits to text files, annotated fasta files, tables, or association files. CONCLUSION: W.ND-BLAST provides an interactive tool that allows scientists to easily utilizing their available computing resources for high throughput and comprehensive sequence analyses. The install package for W.ND-BLAST is freely downloadable from . With registration the software is free, installation, networking, and usage instructions are provided as well as a support forum
When the Hammer Meets the Nail: Multi-Server PIR for Database-Driven CRN with Location Privacy Assurance
We show that it is possible to achieve information theoretic location privacy
for secondary users (SUs) in database-driven cognitive radio networks (CRNs)
with an end-to-end delay less than a second, which is significantly better than
that of the existing alternatives offering only a computational privacy. This
is achieved based on a keen observation that, by the requirement of Federal
Communications Commission (FCC), all certified spectrum databases synchronize
their records. Hence, the same copy of spectrum database is available through
multiple (distinct) providers. We harness the synergy between multi-server
private information retrieval (PIR) and database- driven CRN architecture to
offer an optimal level of privacy with high efficiency by exploiting this
observation. We demonstrated, analytically and experimentally with deployments
on actual cloud systems that, our adaptations of multi-server PIR outperform
that of the (currently) fastest single-server PIR by a magnitude of times with
information theoretic security, collusion resiliency, and fault-tolerance
features. Our analysis indicates that multi-server PIR is an ideal
cryptographic tool to provide location privacy in database-driven CRNs, in
which the requirement of replicated databases is a natural part of the system
architecture, and therefore SUs can enjoy all advantages of multi-server PIR
without any additional architectural and deployment costs.Comment: 10 pages, double colum
Storage Solutions for Big Data Systems: A Qualitative Study and Comparison
Big data systems development is full of challenges in view of the variety of
application areas and domains that this technology promises to serve.
Typically, fundamental design decisions involved in big data systems design
include choosing appropriate storage and computing infrastructures. In this age
of heterogeneous systems that integrate different technologies for optimized
solution to a specific real world problem, big data system are not an exception
to any such rule. As far as the storage aspect of any big data system is
concerned, the primary facet in this regard is a storage infrastructure and
NoSQL seems to be the right technology that fulfills its requirements. However,
every big data application has variable data characteristics and thus, the
corresponding data fits into a different data model. This paper presents
feature and use case analysis and comparison of the four main data models
namely document oriented, key value, graph and wide column. Moreover, a feature
analysis of 80 NoSQL solutions has been provided, elaborating on the criteria
and points that a developer must consider while making a possible choice.
Typically, big data storage needs to communicate with the execution engine and
other processing and visualization technologies to create a comprehensive
solution. This brings forth second facet of big data storage, big data file
formats, into picture. The second half of the research paper compares the
advantages, shortcomings and possible use cases of available big data file
formats for Hadoop, which is the foundation for most big data computing
technologies. Decentralized storage and blockchain are seen as the next
generation of big data storage and its challenges and future prospects have
also been discussed
When Things Matter: A Data-Centric View of the Internet of Things
With the recent advances in radio-frequency identification (RFID), low-cost
wireless sensor devices, and Web technologies, the Internet of Things (IoT)
approach has gained momentum in connecting everyday objects to the Internet and
facilitating machine-to-human and machine-to-machine communication with the
physical world. While IoT offers the capability to connect and integrate both
digital and physical entities, enabling a whole new class of applications and
services, several significant challenges need to be addressed before these
applications and services can be fully realized. A fundamental challenge
centers around managing IoT data, typically produced in dynamic and volatile
environments, which is not only extremely large in scale and volume, but also
noisy, and continuous. This article surveys the main techniques and
state-of-the-art research efforts in IoT from data-centric perspectives,
including data stream processing, data storage models, complex event
processing, and searching in IoT. Open research issues for IoT data management
are also discussed
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