80,635 research outputs found

    A subset of precise UML for Model-based Testing

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    This paper presents an original model-based testing approach that takes a UML behavioural view of the system under test and automatically generates test cases and executable test scripts according to model coverage criteria. This approach is embedded in the LEIRIOS Test Designer tool and is currently deployed in domains such as Enterprise IT and electronic transaction applications. This model-based testing approach makes it possible to automatically produce the traceability matrix from requirements to test cases as part of the test generation process. This paper defines the subset of UML used for model-based testing and illustrates it using a small example

    Efficient & Effective Selective Query Rewriting with Efficiency Predictions

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    To enhance effectiveness, a user's query can be rewritten internally by the search engine in many ways, for example by applying proximity, or by expanding the query with related terms. However, approaches that benefit effectiveness often have a negative impact on efficiency, which has impacts upon the user satisfaction, if the query is excessively slow. In this paper, we propose a novel framework for using the predicted execution time of various query rewritings to select between alternatives on a per-query basis, in a manner that ensures both effectiveness and efficiency. In particular, we propose the prediction of the execution time of ephemeral (e.g., proximity) posting lists generated from uni-gram inverted index posting lists, which are used in establishing the permissible query rewriting alternatives that may execute in the allowed time. Experiments examining both the effectiveness and efficiency of the proposed approach demonstrate that a 49% decrease in mean response time (and 62% decrease in 95th-percentile response time) can be attained without significantly hindering the effectiveness of the search engine

    The new import regulation; More reliability for imported organic products? in The New EU Regulation for organic food and farming: (EC) No 834/2007

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    The European market for organic products is growing at a dynamic pace. Increasingly, processing and marketing companies are entering this market, which has a very promising future. However, organic farm production at the inter-European level has not increased at the same rate as the market for organic products

    Multi-project scheduling with 2-stage decomposition

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    A non-preemptive, zero time lag multi-project scheduling problem with multiple modes and limited renewable and nonrenewable resources is considered. A 2-stage decomposition approach is adopted to formulate the problem as a hierarchy of 0-1 mathematical programming models. At stage one, each project is reduced to a macro-activity with macro-modes resulting in a single project network where the objective is the maximization of the net present value and the cash flows are positive. For setting the time horizon three different methods are developed and tested. A genetic algorithm approach is designed for this problem, which is also employed to generate a starting solution for the exact solution procedure. Using the starting times and the resource profiles obtained in stage one each project is scheduled at stage two for minimum makespan. The result of the first stage is subjected to a post-processing procedure to distribute the remaining resource capacities. Three new test problem sets are generated with 81, 84 and 27 problems each and three different configurations of solution procedures are tested

    The teaching of Relational On-Line Analytical Processing (ROLAP) in Advanced Database courses.

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    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission. © 2012 Higher Education AcademyDatabases are taught as a core subject at undergraduate and postgraduate levels in computer science degrees. The subject is often divided into conceptual database design and database technologies including physical database design. One of the problems with teaching the topic of ROLAP and Data Warehousing is the difficulty of providing practical exposure to the students. The approach taken in this work is intended to help students apply their knowledge learnt throughout the subject and gain some practical understanding leading to its application and implementation which furthermore provides the basis for analysis and evaluation at the level of critical analysis that would be expected on an advanced level course and at the highest level of learning taxonomies. In our work we have reverse engineered a large Oracle sample dataset and adapted it for use in the teaching of ROLAP. We have also produced our own smaller sample dataset for simplification and understanding of complex queries involving Slicing, Dicing , Pivoting, Rollup and Cube operations. In our examples ROLAP operations and their alternative SQL approaches are performed to provide multi-dimensional results

    Enhanced services for targeted information retrieval by event extraction and data mining

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    Where Information Retrieval (IR) and Text Categorization delivers a set of (ranked) documents according to a query, users of large document collections would rather like to receive answers. Question-answering from text has already been the goal of the Message Understanding Conferences. Since then, the task of text understanding has been reduced to several more tractable tasks, most prominently Named Entity Recognition (NER) and Relation Extraction. Now, pieces can be put together to form enhanced services added on an IR system. In this paper, we present a framework which combines standard IR with machine learning and (pre-)processing for NER in order to extract events from a large document collection. Some questions can already be answered by particular events. Other questions require an analysis of a set of events. Hence, the extracted events become input to another machine learning process which delivers the final output to the user's question. Our case study is the public collection of minutes of plenary sessions of the German parliament and of petitions to the German parliament. --
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