The document discusses the evolution of the meaning and technology of computers over time. It describes how in the 1930s and 1940s, a "computer" referred to a person who performed calculations. It then explains how the modern digital computer emerged in the 1960s to automatically perform calculations, distinguishing digital from analog computers. The document also provides some historical context on early computers like ENIAC and mentions pioneers in the field like Turing and Von Neumann.
August 2016 HUG: Open Source Big Data Ingest with StreamSets Data Collector Yahoo Developer Network
Big data tools such as Hadoop and Spark allow you to process data at unprecedented scale, but keeping your processing engine fed can be a challenge. Upstream data sources can 'drift' due to infrastructure, OS and application changes, causing ETL tools and hand-coded solutions to fail. StreamSets Data Collector (SDC) is an open source platform for building big data ingest pipelines that allows you to design, execute and monitor robust data flows. In this session we'll look at how SDC's "intent-driven" approach keeps the data flowing, whether you're processing data 'off-cluster', in Spark, or in MapReduce.
StreamSets software delivers performance management for data flows that feed the next generation of big data applications. Its mission is to bring operational excellence to the management of data in motion, so that data arrives on time and with quality, accelerating analysis and decision making. StreamSets Data Collector is in use at hundreds of companies where it brings unprecedented visibility into and control over data as it moves between an expanding variety of sources and destinations.
Speakers:
Pat Patterson has been working with Internet technologies since 1997, building software and working with communities at Sun Microsystems, Huawei, Salesforce and StreamSets. At Sun, Pat was the community lead for the OpenSSO open source project, while at Huawei he developed cloud storage infrastructure software. Part of the developer evangelism team at Salesforce, Pat focused on identity, integration and the Internet of Things. Now community champion at StreamSets, Pat is responsible for the care and feeding of the StreamSets open source community.
August 2016 HUG: Better together: Fast Data with Apache Spark™ and Apache Ign...Yahoo Developer Network
Spark and Ignite are two of the most popular open source projects in the area of high-performance Big Data and Fast Data. But did you know that one of the best ways to boost performance for your next generation real-time applications is to use them together? In this session, Dmitriy Setrakyan, Apache Ignite Project Management Committee Chairman and co-founder and CPO at GridGain will explain in detail how IgniteRDD — an implementation of native Spark RDD and DataFrame APIs — shares the state of the RDD across other Spark jobs, applications and workers. Dmitriy will also demonstrate how IgniteRDD, with its advanced in-memory indexing capabilities, allows execution of SQL queries many times faster than native Spark RDDs or Data Frames. Don't miss this opportunity to learn from one of the experts how to use Spark and Ignite better together in your projects.
Speakers:
Dmitriy Setrakyan, is a founder and CPO at GridGain Systems. Dmitriy has been working with distributed architectures for over 15 years and has expertise in the development of various middleware platforms, financial trading systems, CRM applications and similar systems. Prior to GridGain, Dmitriy worked at eBay where he was responsible for the architecture of an add-serving system processing several billion hits a day. Currently Dmitriy also acts as PMC chair of Apache Ignite project.
First part of the talk will describe the anatomy of a typical data pipeline and how Apache Oozie meets the demands of large-scale data pipelines. In particular, we will focus on recent advancements in Oozie for dependency management among pipeline stages, incremental and partial processing, combinatorial, conditional and optional processing, priority processing, late processing and BCP management. Second part of the talk will focus on out of box support for spark jobs.
Speakers:
Purshotam Shah is a senior software engineer with the Hadoop team at Yahoo, and an Apache Oozie PMC member and committer.
Satish Saley is a software engineer at Yahoo!. He contributes to Apache Oozie.
Slide-uri folosite la cursul 6. Din păcate exemplele in care am folosit animaţia nu se văd. Acolo unde sunt imagini am pus la titlu tipul de animaţie folosit. Pentru mai multe detalii despre curs vedeti pagina http://statagro.wordpress.com/
Prezentarea Facultății de Îmbunătățiri Funciare și Ingineria MediuluiCristian-Mihai Pomohaci
Prezentarea Facultății de Îmbunătățiri Funciare și Ingineria Mediului din cadrul Universității de Științe Agronomice și Medicină Veterinară București. Pentru mai multe detalii puteți vizita pagina de web a facultății: http://www.fifim.ro/
PARTENERIAT TRANSFRONTALIER REPUBLICA MOLDOVA-ROMÂNIAFlorinaTrofin
olaborarea la nivel transfrontalier prin împărtășirea opiniilor, practicilor, metodelor și strategiilor de lucru cu cadrele didactice din Republica Moldova și România pentru îmbunătățirea procesului educațional cu finalități comune.
PROIECT DE PARTENERIAT TRANSFRONTALIER „Educație online fără hotare”DusikaLevinta1
Colaborarea la nivel transfrontalier prin împărtășirea opiniilor, practicilor, metodelor și strategiilor de lucru cu cadrele didactice Republica Moldova și România pentru îmbunătățirea procesului educațional cu finalități comune.
OBIECTIVE Contribuirea la dezvoltarea unei educații de calitate;
Încurajarea formării continue a cadrelor didactice și manageriale;
Facilitarea accesului transfrontalier la resurse educative;
Promovarea dimensiunii interculturale a educației;
Încurajarea inovărilor în elaborarea materialelor didactice;
Utilizarea noilor tehnologii în educație.
Poveștile pentru copii au un rol complex și benefic în dezvoltarea lor, le vor oferi nu doar divertisment, ci și oportunități de învățare și creștere personală.
Proiect transfrontalier ”Povestea are fir bogat”.pptx
Analiza matem pentru economisti notițe de curs
1. Elemente de analiza matematica
pentru economisti
Educatia nu este raspunsul la o intrebare, educatia
este calea spre raspunsul la toate intrebarile
“Nimic nu costa mai mult decat nestiinta”
Grigore Moisil (1906-1973)
2. Functii
• Euler (in anul 1794): functia este o marime variabila care depinde de alta
marime variabila.
• Definitie: Functia este relatia prin care
asociem oricarui element din domeniul de
definitie un unic element in codomeniu.
• Notatie: f: A → B, unde A=domeniul de
definitie, B=codomeniul, A si B sunt multimi
nevide.
3. • x f(x) y
• x = argument, variabila independenta, punct;
• y=f(x) = imaginea lui x prin functia f, variabila dependenta de
x, valoarea lui f in punctul x;
• Definitie: Graficul functiei f :A → B este multimea
( , ) / , , ( ) f G x y xA yB y f x
• Observatie:
f G A B
5. Functii reale
Fie functia f: A→ B
Daca A si B sunt multimi de numere reale,
atunci f se numeste functie reala de o variabila
reala.
Daca A RnsiB R
, atunci f se numeste
functie reala de mai multe variabile reale (sau
de variabila vectoriala).
12. Functii reale de mai multe variabile
reale
z
x2 + y2 = 16
y
x
f(x, y) = x2 + y2
x2 + y2 = 4
x2 + y2 = 1
x2 + y2 = 0
x2 + y2 = 9
13. Functii reale de doua variabile reale
Moduri de a descrie o functie reala de doua
variabile reale:
Grafic sau diagrama
Tabel de valori
Exprimare prin text
Analitic (printr-o formula sau ecuatie
algebrica)
14. O functie reala de doua variabile reale este o
functie definita pe o submultime D RR
cu
valori in R prin care asociem oricarei perechi
ordonate (x,y) din D un unic numar real notat
z= f(x,y)
15. Exemple
1. Fie functia f definita prin
f (x, y) x xy y2 2
• Calculati f(0, 0), f(1, 2), si f(2, 1).
Solutie:
2 f (0,0) 0 (0)(0) 0 2 2
2 f (1,2) 1 (1)(2) 2 2 9
2 f (2,1) 2 (2)(1) 1 2 7
• Domeniul de definitie al unei functii reale de doua
variabile reale este o submultime a planului xOy, iar
reprezentarea grafica a functiei este o multime de
puncte in spatiul fizic .
Example 1, page 536
17. 17
3.
Se da functia f sub forma
tabelara .Calculati:
f(20, 10) = ?
f(40, 20) = ?
f(10, 20) f(20, 10) = ?
Solutie:
x
y
10 20 30 40
10 1 107 162 3
20 6 194 294 14
30 11 281 426 25
40 16 368 558 36
f 20,10107
f 40,20 14
f 10,20 f 20,10 6 107 113
18. 4. In regions with severe winter weather, the wind-chill
index is often used to describe the apparent
severity of the cold.
• This index W is a subjective temperature that
depends on the actual temperature T and the
wind speed v.
• So W is a function of T and v, and we can write W
= f (T, v).
19. • Table 1 records values of W compiled by the
National Weather Service of the US and the
Meteorological Service of Canada.
Wind-chill index as a function of air temperature and wind speed
Table 1
20. Domeniul de definitie al functiilor de
doua variabile
1. Aflati domeniul de definitie al functiei
f (x, y) 2x xy 0,3y3 7y
Solutie: Domeniul este intregul plan xOy.
24. Exemplul 3
h(x, y) 1 x2 y2
Solutie:
• Observam ca 1 – x2 – y2 0 este echivalent cu
x2 + y2 1 care este multimea punctelor (x, y)
ce se afla in interiorul cercului de raza 1 cu
centrul in origine:
x
y
x2 + y2 = 1
1
–1 1
–1
Example 2, page 536
25. Reprezentarea grafica a functiilor reale
de doua variabile reale
• Reprezentarea grafica a functiei z=f(x,y) este o
suprafata in spatiu.
26. Pentru fiecare(x, y) din domeniul lui f, exista o valoare z pe
(x, y, z)
(x, y)
suprafata.
z
y
x
z = f(x, y)
27. Derivate partiale
f
x
y
f
x
f
y
x
y
f f
2 f f
x y x y
2
f f
2
y y y
x
y
2
2
x x x
2 f f
y x y x
2 2 f f
y x x y
When both are
continuous
28. Derivate partiale de ordinul intai
• Presupunem ca f(x, y) este o functie de doua
variabile x si y.
• Atunci, derivata partiala a lui f in raport cu x in
punctul (x, y) este
f f x h y f x y
x h
0
presupunand ca limita exista.
• Derivata partiala a lui f in raport cu y in
punctul (x, y) este
f f x y k f x y
y k
daca limita exista.
( , ) ( , )
lim
h
0
( , ) ( , )
lim
k
29. Interpretarea geometrica aderivatelor
partiale
z
f(x, y)
y = b plan
a
x
b y
(a, b)
( , )
f
panta f x b
x
dreptei
f(x, b)
f
x
Ce inseamna ?
30. x
f(c, y) ( , )
y
Interpretarea geometrica aderivatelor
partiale
f(x, y)
c
(c, d)
x = c plan
f
panta dreptei f c y
y
f
y
Ce inseamna ?
z
d