An Exploratory Study of Data Quality Management Practices in the ERP Software Systems Context

Quality data are not only relevant for successful Data Warehousing or Business Intelligence applications; they are also a precondition for efficient and effective use of Enterprise Resource Planning (ERP) systems. ERP professionals in all kinds of businesses are concerned with data quality issues, as a survey, conducted by the Institute of Information Systems at the University of Bern, has shown. This paper demonstrates, by using results of this survey, why data quality problems in modern ERP systems can occur and suggests how ERP researchers and practitioners can handle issues around the quality of data in an ERP software Environment.

Comparative Study of Digital Rights Management Systems for Music and Text Files

Digital Rights Management Systems (DRMS) are seen by content providers as the appropriate tool to, on the one hand, fight piracy and, on the other hand, monetize their assets. Although these systems claim to be very powerful and include multiple protection technologies, there is a lack of understanding about how such systems are currently being implemented and used by content providers. The aim of this paper is twofold. First, it provides a theoretical basis through which we present shortly the seven core protection technologies of a DRMS. Second, this paper provides empirical evidence that the seven protection technologies outlined in the first section of this paper are the most commonly used technologies. It further evaluates to what extent these technologies are being used within the music and print industry. It concludes that the three main Technologies are encryption, password, and payment systems. However, there are some industry differences: the number of protection technologies used, the requirements for a DRMS, the required investment, or the perceived success of DRMS in fighting piracy.

The Application of Digital Rights Management Systems in the Music Industry - An Empirical Investigation

Technology advances in hardware, software and IP-networks such as the Internet or peer-to-peer file sharing systems are threatening the music business. The result has been an increasing amount of illegal copies available on-line as well as off-line. With the emergence of digital rights management systems (DRMS), the music industry seems to have found the appropriate tool to simultaneously fight piracy and to monetize their assets. Although these systems are very powerful and include multiple technologies to prevent piracy, it is as of yet unknown to what extent such systems are currently being used by content providers. We provide empirical analyses, results, and conclusions related to digital rights management systems and the protection of digital content in the music industry. It shows that most content providers are protecting their digital content through a variety of technologies such as passwords or encryption. However, each protection technology has its own specific goal, and not all prevent piracy. The majority of the respondents are satisfied with their current protection but want to reinforce it for the future, due to fear of increasing piracy. Surprisingly, although encryption is seen as the core DRM technology, only few companies are currently using it. Finally, half of the respondents do not believe in the success of DRMS and their ability to reduce piracy.

Die Bedeutung von Business Rules im Customer Relationship Management

Der Beitrag diskutiert Möglichkeiten zur Automatisierung von Kundenbeziehungsprozessen im Customer Relationship Management mit Hilfe von Business Rules. Anhand einer CRM-Architektur werden Anwendungsmöglichkeiten erörtert und am Beispiel einer Cross-Selling-Kampagne vertieft. Technische Aspekte werden dabei nicht im Detail betrachtet. Der Schwerpunkt liegt vielmehr in der Diskussion von Automatisierungs- und Integrationspotenzialen durch den Einsatz von Business Rules, wie sie in zunehmend individualisierten Kundenbeziehungen in Massenmärkten gegeben sind.

Enhanced Knowledge Management by Synchronizing Mind Maps and Fuzzy Cognitive Maps

This paper presents a conceptual approach to enhance knowledge management by synchronizing mind maps and fuzzy cognitive maps. The use of mind maps allows taking advantage of human creativity, while the application of fuzzy cognitive maps enables to store information expressed in natural language. By applying cognitive computing, it makes possible to gather and extract relevant information out of a data pool. Therefore, this approach is supposed to give a framework that enhances knowledge management. To demonstrate the potential of this framework, a use case concerning the development of a smart city app is presented.

The use of computers for graduate education in Project Management. Improving the integration to the industry.

This paper presents an initiative for monitoring the competence acquisition by a team of students with different backgrounds facing the experience of being working by projects and in a project. These students are graduated bachelor engineering are inexperienced in the project management field and they play this course on a time-shared manner along with other activities. The goal of this experience is to increase the competence levels acquired by using an structured web based portfolio tool helping to reinforce how relevant different project management approaches can result for final products and how important it becomes to maintain the integration along the project. Monitoring is carried out by means of have a look on how the work is being done and measuring different technical parameters per participant. The use of this information could make possible to bring additional information to the students involved in terms of their individual competencies and the identification of new opportunities of personal improvement. These capabilities are strongly requested by companies in their daily work as well as they can be very convenient too for students when they try to organize their PhD work.

An Intelligent Assistant for Public Transport Management

This paper describes the architecture of a computer system conceived as an intelligent assistant for public transport management. The goal of the system is to help operators of a control center in making strategic decisions about how to solve problems of a fleet of buses in an urban network. The system uses artificial intelligence techniques to simulate the decision processes. In particular, a complex knowledge model has been designed by using advanced knowledge engineering methods that integrates three main tasks: diagnosis, prediction and planning. Finally, the paper describes two particular applications developed following this architecture for the cities of Torino (Italy) and Vitoria (Spain).

An abstract machine based execution model for computer architecture design and efficient implementation of logic programs in parallel.

The term "Logic Programming" refers to a variety of computer languages and execution models which are based on the traditional concept of Symbolic Logic. The expressive power of these languages offers promise to be of great assistance in facing the programming challenges of present and future symbolic processing applications in Artificial Intelligence, Knowledge-based systems, and many other areas of computing. The sequential execution speed of logic programs has been greatly improved since the advent of the first interpreters. However, higher inference speeds are still required in order to meet the demands of applications such as those contemplated for next generation computer systems. The execution of logic programs in parallel is currently considered a promising strategy for attaining such inference speeds. Logic Programming in turn appears as a suitable programming paradigm for parallel architectures because of the many opportunities for parallel execution present in the implementation of logic programs. This dissertation presents an efficient parallel execution model for logic programs. The model is described from the source language level down to an "Abstract Machine" level suitable for direct implementation on existing parallel systems or for the design of special purpose parallel architectures. Few assumptions are made at the source language level and therefore the techniques developed and the general Abstract Machine design are applicable to a variety of logic (and also functional) languages. These techniques offer efficient solutions to several areas of parallel Logic Programming implementation previously considered problematic or a source of considerable overhead, such as the detection and handling of variable binding conflicts in AND-Parallelism, the specification of control and management of the execution tree, the treatment of distributed backtracking, and goal scheduling and memory management issues, etc. A parallel Abstract Machine design is offered, specifying data areas, operation, and a suitable instruction set. This design is based on extending to a parallel environment the techniques introduced by the Warren Abstract Machine, which have already made very fast and space efficient sequential systems a reality. Therefore, the model herein presented is capable of retaining sequential execution speed similar to that of high performance sequential systems, while extracting additional gains in speed by efficiently implementing parallel execution. These claims are supported by simulations of the Abstract Machine on sample programs.

GreenDisc: a HW/SW energy optimization framework in globally distributed computation

In recent future, wireless sensor networks (WSNs) will experience a broad high-scale deployment (millions of nodes in the national area) with multiple information sources per node, and with very specific requirements for signal processing. In parallel, the broad range deployment of WSNs facilitates the definition and execution of ambitious studies, with a large input data set and high computational complexity. These computation resources, very often heterogeneous and driven on-demand, can only be satisfied by high-performance Data Centers (DCs). The high economical and environmental impact of the energy consumption in DCs requires aggressive energy optimization policies. These policies have been already detected but not successfully proposed. In this context, this paper shows the following on-going research lines and obtained results. In the field of WSNs: energy optimization in the processing nodes from different abstraction levels, including reconfigurable application specific architectures, efficient customization of the memory hierarchy, energy-aware management of the wireless interface, and design automation for signal processing applications. In the field of DCs: energy-optimal workload assignment policies in heterogeneous DCs, resource management policies with energy consciousness, and efficient cooling mechanisms that will cooperate in the minimization of the electricity bill of the DCs that process the data provided by the WSNs.

MIA - a free and open source software for gray scale medical image analysis

Background Gray scale images make the bulk of data in bio-medical image analysis, and hence, the main focus of many image processing tasks lies in the processing of these monochrome images. With ever improving acquisition devices, spatial and temporal image resolution increases, and data sets become very large. Various image processing frameworks exists that make the development of new algorithms easy by using high level programming languages or visual programming. These frameworks are also accessable to researchers that have no background or little in software development because they take care of otherwise complex tasks. Specifically, the management of working memory is taken care of automatically, usually at the price of requiring more it. As a result, processing large data sets with these tools becomes increasingly difficult on work station class computers. One alternative to using these high level processing tools is the development of new algorithms in a languages like C++, that gives the developer full control over how memory is handled, but the resulting workflow for the prototyping of new algorithms is rather time intensive, and also not appropriate for a researcher with little or no knowledge in software development. Another alternative is in using command line tools that run image processing tasks, use the hard disk to store intermediate results, and provide automation by using shell scripts. Although not as convenient as, e.g. visual programming, this approach is still accessable to researchers without a background in computer science. However, only few tools exist that provide this kind of processing interface, they are usually quite task specific, and don’t provide an clear approach when one wants to shape a new command line tool from a prototype shell script. Results The proposed framework, MIA, provides a combination of command line tools, plug-ins, and libraries that make it possible to run image processing tasks interactively in a command shell and to prototype by using the according shell scripting language. Since the hard disk becomes the temporal storage memory management is usually a non-issue in the prototyping phase. By using string-based descriptions for filters, optimizers, and the likes, the transition from shell scripts to full fledged programs implemented in C++ is also made easy. In addition, its design based on atomic plug-ins and single tasks command line tools makes it easy to extend MIA, usually without the requirement to touch or recompile existing code. Conclusion In this article, we describe the general design of MIA, a general purpouse framework for gray scale image processing. We demonstrated the applicability of the software with example applications from three different research scenarios, namely motion compensation in myocardial perfusion imaging, the processing of high resolution image data that arises in virtual anthropology, and retrospective analysis of treatment outcome in orthognathic surgery. With MIA prototyping algorithms by using shell scripts that combine small, single-task command line tools is a viable alternative to the use of high level languages, an approach that is especially useful when large data sets need to be processed.

Experiments in the use of the electrochemical cell as a binary memory element /

Includes bibliographical references.

SAGA, a system to automate the management of software production /

"UILU-ENG 81 1702"--Cover.

Coaxial cable memory /

"January 10, 1957"

Medical data management /

Thesis (M.S.)--University of Illinois at Urbana-Champaign.