2,048 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
A Call to Arms: Revisiting Database Design
Good database design is crucial to obtain a sound, consistent database, and -
in turn - good database design methodologies are the best way to achieve the
right design. These methodologies are taught to most Computer Science
undergraduates, as part of any Introduction to Database class. They can be
considered part of the "canon", and indeed, the overall approach to database
design has been unchanged for years. Moreover, none of the major database
research assessments identify database design as a strategic research
direction.
Should we conclude that database design is a solved problem?
Our thesis is that database design remains a critical unsolved problem.
Hence, it should be the subject of more research. Our starting point is the
observation that traditional database design is not used in practice - and if
it were used it would result in designs that are not well adapted to current
environments. In short, database design has failed to keep up with the times.
In this paper, we put forth arguments to support our viewpoint, analyze the
root causes of this situation and suggest some avenues of research.Comment: Removed spurious column break. Nothing else was change
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
Control Infrastructure for a Pulsed Ion Accelerator
We report on updates to the accelerator controls for the Neutralized Drift
Compression Experiment II, a pulsed induction-type accelerator for heavy ions.
The control infrastructure is built around a LabVIEW interface combined with an
Apache Cassandra backend for data archiving. Recent upgrades added the storing
and retrieving of device settings into the database, as well as ZeroMQ as a
message broker that replaces LabVIEW's shared variables. Converting to ZeroMQ
also allows easy access via other programming languages, such as Python
The potential of semantic paradigm in warehousing of big data
Big data have analytical potential that was hard to realize with available technologies. After new storage paradigms intended for big data such as NoSQL databases emerged, traditional systems got pushed out of the focus. The current research is focused on their reconciliation on different levels or paradigm replacement. Similarly, the emergence of NoSQL databases has started to push traditional (relational) data warehouses out of the research and even practical focus. Data warehousing is known for the strict modelling process, capturing the essence of the business processes. For that reason, a mere integration to bridge the NoSQL gap is not enough. It is necessary to deal with this issue on a higher abstraction level during the modelling phase. NoSQL databases generally lack clear, unambiguous schema, making the comprehension of their contents difficult and their integration and analysis harder. This motivated involving semantic web technologies to enrich NoSQL database contents by additional meaning and context. This paper reviews the application of semantics in data integration and data warehousing and analyses its potential in integrating NoSQL data and traditional data warehouses with some focus on document stores. Also, it gives a proposal of the future pursuit directions for the big data warehouse modelling phases
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