LibCat » Книги » Приключения » unrecognised » Seifedine Kadry - Big Data

Seifedine Kadry - Big Data

Здесь есть возможность читать онлайн «Seifedine Kadry - Big Data» — ознакомительный отрывок электронной книги совершенно бесплатно, а после прочтения отрывка купить полную версию. В некоторых случаях можно слушать аудио, скачать через торрент в формате fb2 и присутствует краткое содержание. Жанр: unrecognised, на английском языке. Описание произведения, (предисловие) а так же отзывы посетителей доступны на портале библиотеки ЛибКат.

Читать книгу

Название:
Big Data
Автор:
Seifedine Kadry
Жанр:
unrecognised / на английском языке
Год:
неизвестен
ISBN:
нет данных
Рейтинг книги:
3 / 5. Голосов: 1
Избранное:

Добавить в избранное
Отзывы:
Написать комментарий
Ваша оценка:
- 60
- 1
- 2
- 3
- 4
- 5

Big Data: краткое содержание, описание и аннотация

Предлагаем к чтению аннотацию, описание, краткое содержание или предисловие (зависит от того, что написал сам автор книги «Big Data»). Если вы не нашли необходимую информацию о книге — напишите в комментариях, мы постараемся отыскать её.

Learn Big Data from the ground up with this complete and up-to-date resource from leaders in the field Big Data: Concepts, Technology, and Architecture You’ll learn about the creation of structured, unstructured, and semi-structured data, data storage solutions, traditional database solutions like SQL, data processing, data analytics, machine learning, and data mining. You’ll also discover how specific technologies like Apache Hadoop, SQOOP, and Flume work.
Big Data Accessibly organized,
includes illuminating case studies throughout the material, showing you how the included concepts have been applied in real-world settings. Some of those concepts include:
The common challenges facing big data technology and technologists, like data heterogeneity and incompleteness, data volume and velocity, storage limitations, and privacy concerns Relational and non-relational databases, like RDBMS, NoSQL, and NewSQL databases Virtualizing Big Data through encapsulation, partitioning, and isolating, as well as big data server virtualization Apache software, including Hadoop, Cassandra, Avro, Pig, Mahout, Oozie, and Hive The Big Data analytics lifecycle, including business case evaluation, data preparation, extraction, transformation, analysis, and visualization Perfect for data scientists, data engineers, and database managers,
also belongs on the bookshelves of business intelligence analysts who are required to make decisions based on large volumes of information. Executives and managers who lead teams responsible for keeping or understanding large datasets will also benefit from this book.

Big Data — читать онлайн ознакомительный отрывок

Ниже представлен текст книги, разбитый по страницам. Система сохранения места последней прочитанной страницы, позволяет с удобством читать онлайн бесплатно книгу «Big Data», без необходимости каждый раз заново искать на чём Вы остановились. Поставьте закладку, и сможете в любой момент перейти на страницу, на которой закончили чтение.

Тёмная тема

Шрифт:

↓

↑

Сбросить

Интервал:

↓

↑

Закладка:

Сделать

6 Chapter 6Figure 6.1 Data analytics.Figure 6.2 Analyzing a customer behavior.Figure 6.3 Analytics life cycle.Figure 6.4 Data integration with EmpId field.Figure 6.5 Illustration of extraction without transformation.Figure 6.6 (a) Positive correlation. (b) negative correlation. (c) No correl...Figure 6.7 (a) Linear regression. (b) Nonlinear regression.Figure 6.8 Data analysis cycle.Figure 6.9 Big Data analytics processing.Figure 6.10 Architecture of an integrated EDW with Big Data technologies.

7 Chapter 7Figure 7.1 Types of machine learning algorithms.Figure 7.2 Supervised machine learning.Figure 7.3 Support vector machines.Figure 7.4 (a) Support vectors with small margin. (b) Support vectors with a...Figure 7.5 Non‐separable support vector machines.Figure 7.6 Unsupervised machine learning.

8 Chapter 8Fig.8.1 Frequency plot with relative valueFig.8.1 Frequency plot with absolute valueFigure 8.1 Lattice structure of data set {a,b,c,d,e}.Figure 8.2 Apriori algorithm—frequent itemsets.Figure 8.3 Apriori algorithm—Every superset of an infrequent itemset is also...Figure 8.4 Apriori algorithm–frequent itemsets.Figure 8.5 Generation of the candidate itemsets and frequent itemsets with m...Figure 8.6 Eclat algorithm illustration.Figure 8.7 Intersection of two itemsets.Figure 8.8 Eclat algorithm.Figure 8.9 (a) FP tree for transaction 1. (b) FP tree for transaction 2. (c)...Figure 8.10 Itemset and their corresponding support count.Figure 8.11 Maximal and closed frequent itemset.Figure 8.12 Maximal and closed frequent itemset – subset of frequent itemset...Figure 8.13 Maximal and closed frequent itemset – subset of frequent itemset...Figure 8.14 GenMax Algorithm implementation.Figure 8.15 CHARM algorithm implementation.Figure 8.16 Data mining methods.Figure 8.17 K‐Nearest neighbor – classification.Figure 8.18 k‐nearest neighbor – regression.Figure 8.19 Decision tree diagram.Figure 8.20 Decision tree – Weekend plan.Figure 8.21 (a) DBSCAN with ε = 1.00 and MinPts = 4. (b) DBSCAN with ε = 1.0...Figure 8.22 Biological neural network.Figure 8.23 Time series forecasting.

9 Chapter 9Figure 9.1 Clustering algorithm.Figure 9.2 Clustering based on distance.Figure 9.3 A vector in space.Figure 9.4 Manhattan distance.Figure 9.5 Hierarchical clustering.Figure 9.6 Dendrogram graph.Figure 9.7 Agglomerative and divisive clustering.Figure 9.8 K‐means clustering flowchart.Figure 9.9 (a) Initial clustered points with random centroids (b) Iteration ...Figure 9.10 (a) Initial clustered points with random centroids (b) Final Ite...Figure 9.11 Linearly separable clusters.Figure 9.12 Arbitrarily shaped clusters.Figure 9.13 (a) Original data set (b) K means (c) KK means.Figure 9.14 Univariate Gaussian distribution.Figure 9.15 Data points from two different models.Figure 9.16 Gaussian distribution.Figure 9.17 Gaussians placed in random positions.Figure 9.18 Probability estimation for the randomly placed Gaussians.Figure 9.19 Outliers.Figure 9.20 Point outlier.Figure 9.21 Contextual outlier.Figure 9.22 Collective outlier.Figure 9.23 Optimization algorithm.Figure 9.24 Particle swarm algorithm.Figure 9.25 Individual particle.Figure 9.26 Particle swarm optimization algorithm flowchart.Figure 9.27 Generating random numbers of clusters.Figure 9.28 K‐means clustering.Figure 9.29 Implementation of elbow method.Figure 9.30 Types of fuzzy clustering.

10 Chapter 10Figure 10.1 Increase in gold rate from 2004 to 2011.Figure 10.2 Bar chart—monthly sales of a company.Figure 10.3 Pie chart—favorite activities of teenagers.Figure 10.4 Scatter plot—height vs. weight.Figure 10.5 Bubble plot—Industry market share study.Figure 10.6 Visualizing and sharing with Tableau.Figure 10.7 Tableau workbook.Figure 10.8 Tableau start page.Figure 10.9 CSV file connected to Tableau.Figure 10.10 Tableau worksheet.Figure 10.11 Modifying the data type in tableau.Figure 10.12 Box plot.Figure 10.13 R Studio interface on windows.Figure 10.14Figure 10.15 Correlation between age and height.Figure 10.16 Importing data from csv file and displaying the data.

Guide

1 Cover

2 Table of Contents

3 Begin Reading

Pages

1 iii

2 iv

3 v

4 xi

5 xii

6 1

7 2

8 3

9 4

10 5

11 6

12 7

13 8

14 9

15 10

16 11

17 12

18 13

19 14

20 15

21 16

22 17

23 18

24 19

25 20

26 21

27 22

28 23

29 24

30 25

31 26

32 27

33 28

34 29

35 31

36 32

37 33

38 34

39 35

40 36

41 37

42 38

43 39

44 40

45 41

46 42

47 43

48 44

49 45

50 46

51 47

52 48

53 49

54 50

55 51

56 52

57 53

58 54

59 55

60 56

61 57

62 58

63 59

64 60

65 61

66 62

67 63

68 64

69 65

70 66

71 67

72 68

73 69

74 70

75 71

76 72

77 73

78 74

79 75

80 76

81 77

82 78

83 79

84 80

85 81

86 83

87 84

88 85

89 86

90 87

91 88

92 89

93 90

94 91

95 92

96 93

97 94

98 95

99 96

100 97

101 98

102 99

103 100

104 101

105 102

106 103

107 104

108 105

109 106

110 107

111 108

112 109

113 110

114 111

115 112

116 113

117 114

118 115

119 116

120 117

121 118

122 119

123 120

124 121

125 122

126 123

127 124

128 125

129 126

130 127

131 128

132 129

133 130

134 131

135 132

136 133

137 134

138 135

139 136

140 137

141 138

142 139

143 140

144 141

145 142

146 143

147 144

148 145

149 146

150 147

151 148

152 149

153 150

154 151

155 152

156 153

157 154

158 155

159 156

160 157

161 158

162 159

163 160

164 161

165 162

166 163

167 164

168 165

169 166

170 167

171 168

172 169

173 170

174 171

175 172

176 173

177 174

178 175

179 176

180 177

181 178

182 179

183 180

184 181

185 182

186 183

187 184

188 185

189 186

190 187

191 188

192 189

193 190

194 191

195 192

196 193

197 194

198 195

199 196

200 197

201 198

202 199

203 201

204 202

205 203

206 204

207 205

208 206

209 207

210 208

211 209

212 210

213 211

214 212

215 213

216 214

217 215

218 216

219 217

220 218

221 219

222 220

223 221

224 222

225 223

226 224

227 225

228 226

229 227

230 228

231 229

232 230

233 231

234 232

235 233

236 234

237 235

238 236

239 237

240 238

241 239

242 240

243 241

244 242

245 243

246 244

247 245

248 246

249 247

250 248

251 249

252 250

253 251

254 252

255 253

256 254

257 255

258 256

259 257

260 259

261 260

262 261

263 262

264 263

265 264

266 265

267 266

268 267

269 268

270 269

271 270

272 271

273 272

274 273

275 274

276 275

277 276

278 277

279 278

280 279

281 280

282 281

283 282