179,961 research outputs found
Supporting Incremental Change in Large System Models
International audienceWhen reengineering large systems, software developers would like to assess and compare the impact of multiple change scenarios without actually performing these changes. A change can be ef- fected by applying a tool to the source code, or by a manual refac- toring. In addition, tools run over a model are costly to redevelop. It raises an interesting challenge for tools implementors: how to support modification of large source code models to enable com- parison of multiple versions. One naive approach is to copy the entire model after each modification. However, such an approach is too expensive in memory and execution time. In this paper we ex- plore different implementations that source code metamodels sup- port multiple versions of a system. We propose a solution based on dynamic binding of entities between multiple versions, providing good access performance while minimizing memory consumption
An ensemble-based computational approach for incremental learning in non-stationary environments related to schema- and scaffolding-based human learning
The principal dilemma in a learning process, whether human or computer, is adapting to new information, especially in cases where this new information conflicts with what was previously learned. The design of computer models for incremental learning is an emerging topic for classification and prediction of large-scale data streams undergoing change in underlying class distributions (definitions) over time; yet currently, they often ignore significant foundational learning theory that has been developed in the domain of human learning. This shortfall leads to many deficiencies in the ability to organize existing knowledge and to retain relevant knowledge for long periods of time. In this work, we introduce a unique computer-learning algorithm for incremental knowledge acquisition using an ensemble of classifiers, Learn++.NSE (Non-Stationary Environments), specifically for the case where the nature of knowledge to be learned is evolving. Learn++.NSE is a novel approach to evaluating and organizing existing knowledge (classifiers) according to the most recent data environment. Under this architecture, we address the learning problem at both the learner and supervisor end, discussing and implementing three main approaches: knowledge weighting/organization, forgetting prior knowledge, and change/drift detection. The framework is evaluated on a variety of canonical and real-world data streams (weather prediction, electricity price prediction, and spam detection). This study reveals the catastrophic effect of forgetting prior knowledge, supporting the organization technique proposed by Learn++.NSE as the most consistent performer during various drift scenarios, while also addressing the sheer difficulty in designing a system that strikes a balance between maintaining all knowledge and making decisions based only on relevant knowledge, especially in severe, unpredictable environments which are often encountered in the real-world
Incremental Consistency Checking in Delta-oriented UML-Models for Automation Systems
Automation systems exist in many variants and may evolve over time in order
to deal with different environment contexts or to fulfill changing customer
requirements. This induces an increased complexity during design-time as well
as tedious maintenance efforts. We already proposed a multi-perspective
modeling approach to improve the development of such systems. It operates on
different levels of abstraction by using well-known UML-models with activity,
composite structure and state chart models. Each perspective was enriched with
delta modeling to manage variability and evolution. As an extension, we now
focus on the development of an efficient consistency checking method at several
levels to ensure valid variants of the automation system. Consistency checking
must be provided for each perspective in isolation, in-between the perspectives
as well as after the application of a delta.Comment: In Proceedings FMSPLE 2016, arXiv:1603.0857
A requirements engineering framework for integrated systems development for the construction industry
Computer Integrated Construction (CIC) systems are computer environments through which
collaborative working can be undertaken. Although many CIC systems have been developed to demonstrate the
communication and collaboration within the construction projects, the uptake of CICs by the industry is still
inadequate. This is mainly due to the fact that research methodologies of the CIC development projects are
incomplete to bridge the technology transfer gap. Therefore, defining comprehensive methodologies for the
development of these systems and their effective implementation on real construction projects is vital.
Requirements Engineering (RE) can contribute to the effective uptake of these systems because it drives the
systems development for the targeted audience. This paper proposes a requirements engineering approach for
industry driven CIC systems development. While some CIC systems are investigated to build a broad and deep
contextual knowledge in the area, the EU funded research project, DIVERCITY (Distributed Virtual Workspace
for Enhancing Communication within the Construction Industry), is analysed as the main case study project
because its requirements engineering approach has the potential to determine a framework for the adaptation of
requirements engineering in order to contribute towards the uptake of CIC systems
The Green Investment Report: The Ways and Means to Unlock Private Finance for Green Growth
This report is a first step by the Green Growth Action Alliance to deliver on the G20 Leaders' request. It aims to provide a common point of reference to guide policy-makers, financial institutions and investors as they seek to better understand, and address, the global gap in green investment. This report documents and synthesizes the best available green investment data, research and case studies from a number of leading organizations, including Bloomberg New Energy Finance, the Climate Policy Initiative, the International Energy Agency, the Organization of Economic Cooperation and Development, the United Nations Environment Programme, the World Bank Group and the World Resources Institute, and provides important messages for different groups of stakeholders. New analysis is also presented on clean-energy asset finance flows, the findings of which can be used to guide investment decisions and priorities in other sectors
Adaptive development and maintenance of user-centric software systems
A software system cannot be developed without considering the various facets of its environment. Stakeholders â including the users that play a central role â have their needs, expectations, and perceptions of a system. Organisational and technical aspects of the environment are constantly changing. The ability to adapt a software system and its requirements to its environment throughout its
full lifecycle is of paramount importance in a constantly changing environment. The continuous involvement of users is as important as the constant evaluation of the system and the observation of evolving environments. We present a methodology for adaptive software systems development and
maintenance. We draw upon a diverse range of accepted methods including participatory design, software architecture, and evolutionary design. Our focus is on user-centred software systems
i2MapReduce: Incremental MapReduce for Mining Evolving Big Data
As new data and updates are constantly arriving, the results of data mining
applications become stale and obsolete over time. Incremental processing is a
promising approach to refreshing mining results. It utilizes previously saved
states to avoid the expense of re-computation from scratch.
In this paper, we propose i2MapReduce, a novel incremental processing
extension to MapReduce, the most widely used framework for mining big data.
Compared with the state-of-the-art work on Incoop, i2MapReduce (i) performs
key-value pair level incremental processing rather than task level
re-computation, (ii) supports not only one-step computation but also more
sophisticated iterative computation, which is widely used in data mining
applications, and (iii) incorporates a set of novel techniques to reduce I/O
overhead for accessing preserved fine-grain computation states. We evaluate
i2MapReduce using a one-step algorithm and three iterative algorithms with
diverse computation characteristics. Experimental results on Amazon EC2 show
significant performance improvements of i2MapReduce compared to both plain and
iterative MapReduce performing re-computation
Prototype system for supporting the incremental modelling of vague geometric configurations
In this paper the need for Intelligent Computer Aided Design (Int.CAD) to jointly support design and learning assistance is introduced. The paper focuses on presenting and exploring the possibility of realizing learning assistance in Int.CAD by introducing a new concept called Shared Learning. Shared Learning is proposed to empower CAD tools with more useful learning capabilities than that currently available and thereby provide a stronger interaction of learning between a designer and a computer. Controlled computational learning is proposed as a means whereby the Shared Learning concept can be realized. The viability of this new concept is explored by using a system called PERSPECT. PERSPECT is a preliminary numerical design tool aimed at supporting the effective utilization of numerical experiential knowledge in design. After a detailed discussion of PERSPECT's numerical design support, the paper presents the results of an evaluation that focuses on PERSPECT's implementation of controlled computational learning and ability to support a designer's need to learn. The paper then discusses PERSPECT's potential as a tool for supporting the Shared Learning concept by explaining how a designer and PERSPECT can jointly learn. There is still much work to be done before the full potential of Shared Learning can be realized. However, the authors do believe that the concept of Shared Learning may hold the key to truly empowering learning in Int.CAD
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