This paper contains a modern vision of the parallelization techniques used for evolutionary algorithms (EAs). The work is motivated by two fundamental facts: 1) the different families of EAs have naturally converged in the last decade while parallel EAs (PEAs) are still lack of unified studies; and 2) there is a large number of improvements in these algorithms and in their parallelization that raise the need for a comprehensive survey. We stress the differences between the EA model and its parallel implementation throughout the paper. We discuss the advantages and drawbacks of PEAs. Also, successful applications are mentioned and open problems are identified. We propose potential solutions to these problems and classify the different ways in which recent results in theory and practice are helping to solve them. Finally, we provide a highly structured background relating to PEAs in order to make researchers aware of the benefits of decentralizing and parallelizing an EA.

/pdf/parallelism-and-evolutionary-algorithms-hitdkwys5k.pdf

Parallelism and evolutionary algorithms

One certain way to improve the quality of data : improve its use ! In the movie War Games, set in the U.S. Strategic Air Command Center under a mountain near Colorado Springs, there is a critical scene : on a huge map of the globe are arrows indicating large numbers of nuclear missiles approaching the U.S. from the Soviet Union. The military official in charge is trying to decide if he should ask the President for permission to launch a retaliatory attack because the technicians are telling him the threat is real. At this point, the scientist who originally designed the system (and who knows that something is wrong with the system itself) says, General, what you see on that board is not reality, it is a computer-generated hallucination !

Data quality and systems theory

Many companies see Data Governance as a promising approach to ensuring data quality and maintaining its value as a company asset. While the practitioners' community has been vigorously discussing the topic for quite some time, Data Governance as a field of scientific study is still in its infancy. This article reports on the findings of a case study on the organization of Data Governance in two large telecommunications companies, namely BT and Deutsche Telekom. The article proposes that large, service-providing companies in general have a number of options when designing Data Governance and that the individual organizational design is context-contingent.Despite their many similarities, BT and Deutsche Telekom differ with regard to their Data Governance organization. BT has followed a more project-driven, bottom-up philosophy; Deutsche Telekom, on the other hand, favors a rather constitutive, top-down approach. The article also proposes a research agenda for further studies in the field of Data Governance organization.

/pdf/organizing-data-governance-findings-from-the-3sdezfeg1e.pdf

Organizing Data Governance: Findings from the Telecommunications Industry and Consequences for Large Service Providers

Unlike previous decades where enterprises prized independence, the next decade will be one of business alliances and competing, end-to-end value chains. Enterprise value chains comprised of powerful business alliance partners will exceedingly compete as single entities for customers. Such extended corporations reach out not only with business relationships; they must integrate their business processes and information systems. In this paper, we review the business and technological requirements of modern extended organizations and explain how adaptive business objects and controlled interoperability are the key enabling technologies to the challenge of integrated value chains.

Integrated value chains and their implications from a business and technology standpoint

A component- and push-based architectural style for ajax applications

Application (re)engineering : building Web-based applications and dealing with legacies

1. Introduction and Overview of Distributed Computing 2. Communications Network Technologies 3. Network Architectures and Network Interconnectivity 4. Network Management 5. Client-Server Systems and Application Interconnectivity 6. Distributed Data and Transaction Management 7. Distributed Operating Systems and Distributed Computing Platforms 8. Distributed Applications and Application Downsizing/Rightsizing 9. Open Systems: Interoperability, Portability, and Integration Standards 10. Management and Support Considerations Appendices

Distributed computing and client-server systems

Enterprise data—the data that is created, used and shared by a corporation in conducting business—is a critical business resource that must be analyzed, architected and managed with data quality as a guiding principle. This paper presents results, practical insights, and lessons learned from a large scale study conducted in the telecommunications industry that synthesizes data quality issues into an architectural and management approach. We describe the real life case study and show how requirements for data quality were collected, how the data quality metrics were defined, what guidelines were established for intersystem data flows, what COTS (commercial off-the-shelf) technologies were used, and what results were obtained through a prototype effort. As a result of experience gained and lessons learned, we propose a comprehensive data quality approach that combines data quality and data architectures into a single framework with a series of steps, procedures, checklists, and tools. Our approach takes into account the technology, process, and people issues and extends the extant literature on data quality.

Enterprise Data Quality: A Pragmatic Approach

Distributed computing: a practical synthesis of networks, client-server systems, distributed applications, and open systems

Object-oriented client/server Internet environments : the modern it infrastructure

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Amjad Umar

Author Tools

Chat about Author