26,220 research outputs found
Financing local government in Hungary
Hungary has undertaken a bold and far-ranging reform of its system of subnational finances. This paper outlines the changes introduced in the system of local finance as a result of the 1990 Local Self-Government Act, and the 1990 Act on Local Taxes and provides a preliminary assessment of their implications as well as the need for further reform. These Acts, together with the annual Act on the Budget, define the overall scope and authorities of Hungary's approximately 3100 new local self-governments. These Acts: (i) define the new assignment of expenditures between central and local government; (ii) define the new local revenue sources; and (iii) establish the economic foundation, property rights and entrepreneurial functions of the localities. The paper outlines the historical evolution of the system, provides international comparisons, and describes its present-day form. Drawing on this background, it suggests some revised policies that should not only both help avert the potentially undesirable outcomes of the current system but, more positively, help Hungary to achieve its goal of a smaller, more efficient government sector without unduly exacerbating social inequalities. In turn, issues and recommendations are discussed in the following areas: local finance; assignment of expenditures; assignment of taxes; design of the transfer system; role of the localities in property management; capital investment; and other requisites for sound local finance.Banks&Banking Reform,Municipal Financial Management,National Governance,Urban Economics,Public Sector Economics&Finance
Real-time detection of grid bulk transfer traffic
The current practice of physical science research has yielded a continuously growing demand for interconnection network bandwidth to support the sharing of large datasets. Academic research networks and internet service providers have provisioned their networks to handle this type of load, which generates prolonged, high-volume traffic between nodes on the network. Maintenance of QoS for all network users demands that the onset of these (Grid bulk) transfers be detected to enable them to be reengineered through resources specifically provisioned to handle this type of traffic. This paper describes a real-time detector that operates at full-line-rate on Gb/s links, operates at high connection rates, and can track the use of ephemeral or non-standard ports
Dynamic integration of context model constraints in web service processes
Autonomic Web service composition has been a challenging topic for some years. The context in which composition takes places determines essential aspects. A context model can provide meaningful composition information for services process composition. An ontology-based approach for context information integration is the basis of a constraint approach to dynamically integrate context validation into service processes. The dynamic integration of context constraints into an orchestrated service process is a necessary direction to achieve autonomic service composition
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A monitoring approach for runtime service discovery
Effective runtime service discovery requires identification of services based on different service characteristics such as structural, behavioural, quality, and contextual characteristics. However, current service registries guarantee services described in terms of structural and sometimes quality characteristics and, therefore, it is not always possible to assume that services in them will have all the characteristics required for effective service discovery. In this paper, we describe a monitor-based runtime service discovery framework called MoRSeD. The framework supports service discovery in both push and pull modes of query execution. The push mode of query execution is performed in parallel to the execution of a service-based system, in a proactive way. Both types of queries are specified in a query language called SerDiQueL that allows the representation of structural, behavioral, quality, and contextual conditions of services to be identified. The framework uses a monitor component to verify if behavioral and contextual conditions in the queries can be satisfied by services, based on translations of these conditions into properties represented in event calculus, and verification of the satisfiability of these properties against services. The monitor is also used to support identification that services participating in a service-based system are unavailable, and identification of changes in the behavioral and contextual characteristics of the services. A prototype implementation of the framework has been developed. The framework has been evaluated in terms of comparison of its performance when using and when not using the monitor component
Astronomy in the Cloud: Using MapReduce for Image Coaddition
In the coming decade, astronomical surveys of the sky will generate tens of
terabytes of images and detect hundreds of millions of sources every night. The
study of these sources will involve computation challenges such as anomaly
detection and classification, and moving object tracking. Since such studies
benefit from the highest quality data, methods such as image coaddition
(stacking) will be a critical preprocessing step prior to scientific
investigation. With a requirement that these images be analyzed on a nightly
basis to identify moving sources or transient objects, these data streams
present many computational challenges. Given the quantity of data involved, the
computational load of these problems can only be addressed by distributing the
workload over a large number of nodes. However, the high data throughput
demanded by these applications may present scalability challenges for certain
storage architectures. One scalable data-processing method that has emerged in
recent years is MapReduce, and in this paper we focus on its popular
open-source implementation called Hadoop. In the Hadoop framework, the data is
partitioned among storage attached directly to worker nodes, and the processing
workload is scheduled in parallel on the nodes that contain the required input
data. A further motivation for using Hadoop is that it allows us to exploit
cloud computing resources, e.g., Amazon's EC2. We report on our experience
implementing a scalable image-processing pipeline for the SDSS imaging database
using Hadoop. This multi-terabyte imaging dataset provides a good testbed for
algorithm development since its scope and structure approximate future surveys.
First, we describe MapReduce and how we adapted image coaddition to the
MapReduce framework. Then we describe a number of optimizations to our basic
approach and report experimental results comparing their performance.Comment: 31 pages, 11 figures, 2 table
Use-cases on evolution
This report presents a set of use cases for evolution and reactivity for data in the Web and
Semantic Web. This set is organized around three different case study scenarios, each of them
is related to one of the three different areas of application within Rewerse. Namely, the scenarios
are: “The Rewerse Information System and Portal”, closely related to the work of A3
– Personalised Information Systems; “Organizing Travels”, that may be related to the work
of A1 – Events, Time, and Locations; “Updates and evolution in bioinformatics data sources”
related to the work of A2 – Towards a Bioinformatics Web
The Role of the Mangement Sciences in Research on Personalization
We present a review of research studies that deal with personalization. We synthesize current knowledge about these areas, and identify issues that we envision will be of interest to researchers working in the management sciences. We take an interdisciplinary approach that spans the areas of economics, marketing, information technology, and operations. We present an overarching framework for personalization that allows us to identify key players in the personalization process, as well as, the key stages of personalization. The framework enables us to examine the strategic role of personalization in the interactions between a firm and other key players in the firm's value system. We review extant literature in the strategic behavior of firms, and discuss opportunities for analytical and empirical research in this regard. Next, we examine how a firm can learn a customer's preferences, which is one of the key components of the personalization process. We use a utility-based approach to formalize such preference functions, and to understand how these preference functions could be learnt based on a customer's interactions with a firm. We identify well-established techniques in management sciences that can be gainfully employed in future research on personalization.CRM, Persoanlization, Marketing, e-commerce,
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