91,520 research outputs found
Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure
Big data research has attracted great attention in science, technology,
industry and society. It is developing with the evolving scientific paradigm,
the fourth industrial revolution, and the transformational innovation of
technologies. However, its nature and fundamental challenge have not been
recognized, and its own methodology has not been formed. This paper explores
and answers the following questions: What is big data? What are the basic
methods for representing, managing and analyzing big data? What is the
relationship between big data and knowledge? Can we find a mapping from big
data into knowledge space? What kind of infrastructure is required to support
not only big data management and analysis but also knowledge discovery, sharing
and management? What is the relationship between big data and science paradigm?
What is the nature and fundamental challenge of big data computing? A
multi-dimensional perspective is presented toward a methodology of big data
computing.Comment: 59 page
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
A characteristics framework for Semantic Information Systems Standards
Semantic Information Systems (IS) Standards play a critical role in the development of the networked economy. While their importance is undoubted by all stakeholders—such as businesses, policy makers, researchers, developers—the current state of research leaves a number of questions unaddressed. Terminological confusion exists around the notions of “business semantics”, “business-to-business interoperability”, and “interoperability standards” amongst others. And, moreover, a comprehensive understanding about the characteristics of Semantic IS Standards is missing. The paper addresses this gap in literature by developing a characteristics framework for Semantic IS Standards. Two case studies are used to check the applicability of the framework in a “real-life” context. The framework lays the foundation for future research in an important field of the IS discipline and supports practitioners in their efforts to analyze, compare, and evaluate Semantic IS Standard
Business integration models in the context of web services.
E-commerce development and applications have
been bringing the Internet to business and marketing
and reforming our current business styles and
processes. The rapid development of the Web, in
particular, the introduction of the semantic web and
web service technologies, enables business
processes, modeling and management to enter an
entirely new stage. Traditional web based business
data and transactions can now be analyzed,
extracted and modeled to discover new business
rules and to form new business strategies, let alone
mining the business data in order to classify
customers or products. In this paper, we investigate
and analyze the business integration models in the
context of web services using a micro-payment
system because a micro-payment system is
considered to be a service intensive activity, where
many payment tasks involve different forms of
services, such as payment method selection for
buyers, security support software, product price
comparison, etc. We will use the micro-payment case
to discuss and illustrate how the web services
approaches support and transform the business
process and integration model.
Managing contextual information in semantically-driven temporal information systems
Context-aware (CA) systems have demonstrated the provision of a robust solution for personalized information delivery in the current content-rich and dynamic information age we live in. They allow software agents to autonomously interact with users by modeling the user’s environment (e.g. profile, location, relevant public information etc.) as dynamically-evolving and interoperable contexts. There is a flurry of research activities in a wide spectrum at context-aware research areas such as managing the user’s profile, context acquisition from external environments, context storage, context representation and interpretation, context service delivery and matching of context attributes to users‘ queries etc. We propose SDCAS, a Semantic-Driven Context Aware System that facilitates public services recommendation to users at temporal location. This paper focuses on information management and service recommendation using semantic technologies, taking into account the challenges of relationship complexity in temporal and contextual information
Improving lifecycle query in integrated toolchains using linked data and MQTT-based data warehousing
The development of increasingly complex IoT systems requires large
engineering environments. These environments generally consist of tools from
different vendors and are not necessarily integrated well with each other. In
order to automate various analyses, queries across resources from multiple
tools have to be executed in parallel to the engineering activities. In this
paper, we identify the necessary requirements on such a query capability and
evaluate different architectures according to these requirements. We propose an
improved lifecycle query architecture, which builds upon the existing Tracked
Resource Set (TRS) protocol, and complements it with the MQTT messaging
protocol in order to allow the data in the warehouse to be kept updated in
real-time. As part of the case study focusing on the development of an IoT
automated warehouse, this architecture was implemented for a toolchain
integrated using RESTful microservices and linked data.Comment: 12 pages, worksho
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