Gallery — Academic Journal

This template is for the scientific journal "Trudy MAI" and Moscow Aviation Institute.

Soft Computing and Machine Intelligence Journal is an international peer-reviewed open-access journal devoted to soft computing, machine intelligence, and engineering research, published quarterly online.

Paper template for the Northern European Journal of Language Technology

Template for the Journal of Applied Instrumentation and Control (JAIC). Check for updates. Still in beta! Features should be added in the near future.

This LaTeX template is available for authors to prepare a camera-ready paper for Trans Tech Publication Journals. Inside a package you will find information about requirements and format.

The journal «Bulletin KRAESC. Physical & Mathematical Sciences» publishes the results of basic and applied research in the area of physical and mathematical sciences (mathematical modeling, mathematical physics, information and computational technologies, educational materials) including extended abstracts of doctor or candidate of science dissertations. The journal may also publish flashes, reviews, newsletters, information on scientific events, congresses, conferences, workshops, seminars and so on. The Journal was founded in August 2010, registered at the Federal Service for Compliance with the Law in Mass Communications and Protection of Cultural Heritage (Media Organization registration certificate FS 77-41501 from 04.08.2010), and re-registered (certificate FS 77-58548 from 14.07.2014). The Journal founders are the Federal State Budget Research Institution «Vitus Bering Kamchatka State University», and the Federal State Budget Research Institution «Institute of Cosmophysical Research and Radio Wave Propagation Far Eastern Branch of the Russian Academy of Sciences». The Journal is issued in print (ISSN: 2079-6641) and online (ISSN: 2079-665X) versions four times a year in Russian and it is also translated into English, beginning in 2014 (ISSN: 2313-0156). The Journal is posted on the «Math.-Net.Ru»Russian Mathematical Portal and is indexed in Google Scholar, DOAJ, Ulrich’s Periodicals Directory, OCLC WorldCat, Bielefeld Academic Search Engine (BASE), Open Access Infrastructure for Research in Europe (OpenAIRE), DataCite, Directory of Research Journals Indexing, Kyberlenika, Soсionet.

This is an unofficial general-purpose template for articles in Nature Reviews Physics based on the information about the journal and the guide to authors available at: https://www.nature.com/natrevphys/

The efficiency of a query execution plan depends on the accuracy of the selectivity estimates given to the query optimiser by the cost model. The cost model makes simplifying assumptions in order to produce said estimates in a timely manner. These assumptions lead to selectivity estimation errors that have dramatic effects on the quality of the resulting query execution plans. A convenient assumption that is ubiquitous among current cost models is to assume that attributes are independent with each other. However, it ignores potential correlations which can have a huge negative impact on the accuracy of the cost model. In this paper we attempt to relax the attribute value independence assumption without unreasonably deteriorating the accuracy of the cost model. We propose a novel approach based on a particular type of Bayesian networks called Chow-Liu trees to approximate the distribution of attribute values inside each relation of a database. Our results on the TPC-DS benchmark show that our method is an order of magnitude. more precise than other approaches whilst remaining reasonably efficient in terms of time and space.

Plantilla para los artículos que deseen enviarse en LATEX a Almanaque Económico, revista estudiantil de la Licenciatura en Economía del Colegio de México.