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Calculating the value of Ck ∈ {1, ∞} class of smoothness real-valued function's derivative in point of R+ in radius of convergence of its Taylor polynomial (or series), applying an analog of Newton's binomial theorem and q-difference operator. (P,q)-power difference introduced in section 5. Additionally, by means of Newton's interpolation formula, the discrete analog of Taylor series, interpolation using q-difference and p,q-power difference is shown.
In this paper, we derive and prove, by means of Binomial theorem and Faulhaber's formula, the following identity
between $m$-order polynomials in \(T\)
\(\sum_{k=1}^{\ell}\sum_{j=0}^m A_{m,j}k^j(T-k)^j=\sum_{k=0}^{m}(-1)^{m-k}U_m(\ell,k)\cdot T^k=T^{2m+1}, \ \ell=T\in\mathbb{N}.\)
The main aim of this paper to establish the relations between forward, backward and central finite (divided) differences (that is discrete analog of the derivative) and partial & ordinary high-order derivatives of the polynomials.
En los últimos años se ha visto un auge en el uso de los sistemas de bases de datos NoSQL y junto a ello se ha popularizado la idea de aplicaciones de Persistencia Políglota. Esta consiste en que gracias a la gran variedad y cantidad de datos, y los diversos servicios que pueden dar las aplicaciones hoy en día, es probable que un único tipo de sistema de almacenamiento no sea capaz de cubrir de forma eficiente todas las necesidades de la aplicación. En este articulo se dará una idea general de las Aplicaciones de Persistencia Políglota dando información acerca de su funcionamiento, arquitectura y motivación; y ademas se hablara específicamente de como aplicar la Persistencia Políglota con MongoDB y Neo4j.
Palabras Clave: NoSQL, Persistencia Políglota, MongoDB, Neo4j, Neo4j Doc Manager
In Email Analytics, our main focus on criminal and civil investigation from large email dataset. It is very difficult to deal with challenging task for investigator due to large size of email dataset. This paper offer an interactive email analytics various to current and manually intensive technique is used for search evidence from large email dataset. In investigation process, many emails are irrelevant to the investigation so it will force investigator to search carefully through email in order to find relevant emails manually. This process is very costly in terms of money and times. To help to investigation process. We combine Elasticsearch, Logstash and Kibana for data storing, data preprocessing, data visualization and data analytics and displaying results. In this process reduce the number of email which are irrelevant for investigation. It shows the relationship between them and also analyzing the email corpus based on topic relation using text mining.
Modèle de rapport ASD v1
Ludovic Journaux 2017-2018
AgroSup Dijon
Ce modèle est destiné aux étudiants d'AgroSup Dijon pour leur rapports et mémoire de 3eme année.