13,177 research outputs found
Different Perspectives on IT Business Value: An Integrative Approach
Despite growing evidence of a positive impact of Information Technology (IT) investments on firm performance, the variations in the results across organisations are still significant. This research takes a fresh approach by addressing complementarity impacts of organisational practices on three different dimensions of IT business value (ITBV). The goal is to identify important organisational practices and empirically test the synergistic relationships among them and their impacts on different dimensions of IT business value. We implemented an integrative approach to analyse the complex interactions among multiple organisational practices. First, we categorised ITBV into four dimensions based on different management objectives: strategic, informational, transactional and organisational transformation. Second, organisational configuration for each ITBV dimension is identified using regression trees. Third, a formal complementarity test was performed on each configuration pattern. Our findings indicate that the set of organisational practices affecting each dimension of ITBV is different. Hence, IT complementary factors that affect particular dimensions of ITBV do not necessarily have the same effect on other dimensions
Smart Grid Technologies in Europe: An Overview
The old electricity network infrastructure has proven to be inadequate, with respect to modern challenges such as alternative energy sources, electricity demand and energy saving policies. Moreover, Information and Communication Technologies (ICT) seem to have reached an adequate level of reliability and flexibility in order to support a new concept of electricity network—the smart grid. In this work, we will analyse the state-of-the-art of smart grids, in their technical, management, security, and optimization aspects. We will also provide a brief overview of the regulatory aspects involved in the development of a smart grid, mainly from the viewpoint of the European Unio
Context Aware Computing for The Internet of Things: A Survey
As we are moving towards the Internet of Things (IoT), the number of sensors
deployed around the world is growing at a rapid pace. Market research has shown
a significant growth of sensor deployments over the past decade and has
predicted a significant increment of the growth rate in the future. These
sensors continuously generate enormous amounts of data. However, in order to
add value to raw sensor data we need to understand it. Collection, modelling,
reasoning, and distribution of context in relation to sensor data plays
critical role in this challenge. Context-aware computing has proven to be
successful in understanding sensor data. In this paper, we survey context
awareness from an IoT perspective. We present the necessary background by
introducing the IoT paradigm and context-aware fundamentals at the beginning.
Then we provide an in-depth analysis of context life cycle. We evaluate a
subset of projects (50) which represent the majority of research and commercial
solutions proposed in the field of context-aware computing conducted over the
last decade (2001-2011) based on our own taxonomy. Finally, based on our
evaluation, we highlight the lessons to be learnt from the past and some
possible directions for future research. The survey addresses a broad range of
techniques, methods, models, functionalities, systems, applications, and
middleware solutions related to context awareness and IoT. Our goal is not only
to analyse, compare and consolidate past research work but also to appreciate
their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201
Handling Concept Drift for Predictions in Business Process Mining
Predictive services nowadays play an important role across all business
sectors. However, deployed machine learning models are challenged by changing
data streams over time which is described as concept drift. Prediction quality
of models can be largely influenced by this phenomenon. Therefore, concept
drift is usually handled by retraining of the model. However, current research
lacks a recommendation which data should be selected for the retraining of the
machine learning model. Therefore, we systematically analyze different data
selection strategies in this work. Subsequently, we instantiate our findings on
a use case in process mining which is strongly affected by concept drift. We
can show that we can improve accuracy from 0.5400 to 0.7010 with concept drift
handling. Furthermore, we depict the effects of the different data selection
strategies
An Iterative and Toolchain-Based Approach to Automate Scanning and Mapping Computer Networks
As today's organizational computer networks are ever evolving and becoming
more and more complex, finding potential vulnerabilities and conducting
security audits has become a crucial element in securing these networks. The
first step in auditing a network is reconnaissance by mapping it to get a
comprehensive overview over its structure. The growing complexity, however,
makes this task increasingly effortful, even more as mapping (instead of plain
scanning), presently, still involves a lot of manual work. Therefore, the
concept proposed in this paper automates the scanning and mapping of unknown
and non-cooperative computer networks in order to find security weaknesses or
verify access controls. It further helps to conduct audits by allowing
comparing documented with actual networks and finding unauthorized network
devices, as well as evaluating access control methods by conducting delta
scans. It uses a novel approach of augmenting data from iteratively chained
existing scanning tools with context, using genuine analytics modules to allow
assessing a network's topology instead of just generating a list of scanned
devices. It further contains a visualization model that provides a clear, lucid
topology map and a special graph for comparative analysis. The goal is to
provide maximum insight with a minimum of a priori knowledge.Comment: 7 pages, 6 figure
Development of Grid e-Infrastructure in South-Eastern Europe
Over the period of 6 years and three phases, the SEE-GRID programme has
established a strong regional human network in the area of distributed
scientific computing and has set up a powerful regional Grid infrastructure. It
attracted a number of user communities and applications from diverse fields
from countries throughout the South-Eastern Europe. From the infrastructure
point view, the first project phase has established a pilot Grid infrastructure
with more than 20 resource centers in 11 countries. During the subsequent two
phases of the project, the infrastructure has grown to currently 55 resource
centers with more than 6600 CPUs and 750 TBs of disk storage, distributed in 16
participating countries. Inclusion of new resource centers to the existing
infrastructure, as well as a support to new user communities, has demanded
setup of regionally distributed core services, development of new monitoring
and operational tools, and close collaboration of all partner institution in
managing such a complex infrastructure. In this paper we give an overview of
the development and current status of SEE-GRID regional infrastructure and
describe its transition to the NGI-based Grid model in EGI, with the strong SEE
regional collaboration.Comment: 22 pages, 12 figures, 4 table
- …