Big Data Analytics

Recommendations

Info

160 D. Sitaram and K.V. Subramaniam 16. Eyers D, Freudenreich T, Margara A, Frischbier S, Pietzuch P, Eugster P (2012) Living in the present: on-the-fly information processing in scalable web architectures. In: Proceedings of the 2nd international workshop on cloud computing platforms, p 6. ACM 17. Gangemi A, Guarino N, Masolo C, Oltramari A, Schneider L (2012) Sweetening ontologies with DOLCE. In: Knowledge engineering and knowledge management: Ontologies and the semantic Web. Springer, Berlin, pp 166–181 18. Gedik B, Andrade H, Wu KL, Yu PS, Doo M (2008) SPADE: the system S declarative stream processing engine. In: Proceedings of the 2008 ACM SIGMOD international conference on Management of data. ACM, pp 1123–1134 19. Gregersen H (2006) The formal semantics of the timeER model. In: Proceedings of the 3rd Asia-Pacific conference on Conceptual modelling, vol 53. Australian Computer Society, Inc., pp 35–44 20. Gupta A, Jain R (2011) Managing event information: Modeling, retrieval, and applications. Synth Lect Data Manag 3(4):1–141 21. Gutierrez C, Hurtado C, Vaisman A (2005) Temporal rdf. In: The semantic web: research and applications. Springer, Berlin, pp 93–107 22. Johnson T, Shkapenyuk V, Hadjieleftheriou M (2015) Data stream warehousing in Tidalrace. CIDR 23. Lam W, Liu L, Prasad STS, Rajaraman A, Vacheri Z, Doan AH (2012) Muppet: MapReduce-style processing of fast data. Proc VLDB Endowment 5(12):1814–1825 24. Leibiusky J, Eisbruch G, Simonassi D (2012) Getting started with storm. O’Reilly Media, Inc. 25. Gianmarco De Francisci M, Bifet A (2015) SAMOA: scalable advanced massive online analysis. J Mach Learn Res 16:149–153 26. Motwani R, Widom J, Arasu A, Babcock B, Babu S, Datar M, Manku G, Olston C, Rosenstein J, Varma R Query processing, resource management, and approximation in a data stream management system. CIDR 27. Neumeyer L, Robbins B, Nair A, Kesari A (2010) S4: distributed stream computing platform. In: 2010 IEEE International conference on data mining workshops (ICDMW), pp 170–177. IEEE 28. Papadimitriou S, Sun J, Faloutsos C (2005) Streaming pattern discovery in multiple time-series. In: Proceedings of the 31st international conference on Very large data bases, pp 697–708. VLDB Endowment 29. Perry M, Hakimpour F, Sheth A (2006) Analyzing theme, space, and time: an ontology-based approach. In: Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems, pp 147–154. ACM 30. Scherp A, Franz T, Saathoff C, Staab S (2015) http://ontologydesignpatterns.org/wiki/ Ontology:Event_Model_F. Accessed 4 Sept 2015 31. Scherp A, Franz T, Saathoff C, Staab S (2009) F–a model of events based on the foundational ontology dolce + DnS ultralight. In: Proceedings of the fifth international conference on Knowledge capture, pp 137–144. ACM 32. Sheth A, Perry M (2008) Traveling the semantic web through space, time, and theme. IEEE Internet Comput 12(2):81–86 33. Sony Corporation, Leading semiconductor wafer surface cleaning technologies that support the next generation of semiconductor devices. http://www.sony.net/Products/SC-HP/cx_ news_archives/img/pdf/vol_36/featuring36.pdf 34. Stonebraker M, Çetintemel U, Zdonik S (2005) The 8 requirements of real-time stream processing. ACM SIGMOD Rec 34(4):42–47 35. Van Hage WR, Malaisé V, Segers R, Hollink L, Schreiber G (2011) Design and use of the Simple Event Model (SEM). Web Semant: Sci, Serv Agents World Wide Web 9(2):128–136 36. Westermann U, Jain R (2006) E—A generic event model for event-centric multimedia data management in eChronicle applications. In: Proceedings 22nd International conference on data engineering workshops, 2006, pp x106–x106. IEEE 37. Westermann U, Jain R (2007) Toward a common event model for multimedia applications. IEEE Multim 1:19–29
Complex Event Processing in Big Data Systems 161 38. Wu E, Diao Y, Rizvi S (2006) High-performance complex event processing over streams. In: Proceedings of the 2006 ACM SIGMOD international conference on Management of data, pp 407–418. ACM 39. Zaharia M, Das T, Li H, Hunter T, Shenker S, Stoica I (2013) Discretized streams: Fault-tolerant streaming computation at scale. In: Proceedings of the Twenty-Fourth ACM symposium on operating systems principles, pp 423–438. ACM 40. Zaharia M, Chowdhury M, Das T, Dave A, Ma J, McCauley M, Franklin MJ, Shenker S, Stoica I (2012) Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing. In: Proceedings of the 9th USENIX conference on networked systems design and implementation. USENIX Association, pp 2–2
Page 1 and 2:
Saumyadipta Pyne · B.L.S. Prakasa
Page 3 and 4:
Saumyadipta Pyne ⋅ B.L.S. Prakasa
Page 5 and 6:
Foreword Big data is transforming t
Page 7 and 8:
viii Preface We thank all the autho
Page 9 and 10:
x Contents Managing Large-Scale Sta
Page 11 and 12:
xii About the Editors biological, n
Page 13 and 14:
2 S. Pyne et al. ethics. If data po
Page 15 and 16:
4 S. Pyne et al. Therefore, as data
Page 17 and 18:
6 S. Pyne et al. characteristics. I
Page 19 and 20:
8 S. Pyne et al. stays of high-dime
Page 21 and 22:
10 S. Pyne et al. References 1. Ken
Page 23 and 24:
12 M.K. Pusala et al. 1 Introductio
Page 25 and 26:
14 M.K. Pusala et al. to obtain hid
Page 27 and 28:
16 M.K. Pusala et al. to identify t
Page 29 and 30:
18 M.K. Pusala et al. duce works cl
Page 31 and 32:
20 M.K. Pusala et al. (ETL), as wel
Page 33 and 34:
22 M.K. Pusala et al. According to
Page 35 and 36:
24 M.K. Pusala et al. 4.2.3 Documen
Page 37 and 38:
26 M.K. Pusala et al. also offers s
Page 39 and 40:
28 M.K. Pusala et al. The Real-time
Page 41 and 42:
30 M.K. Pusala et al. Fig. 4 Implem
Page 43 and 44:
32 M.K. Pusala et al. However, thes
Page 45 and 46:
34 M.K. Pusala et al. Rack-level da
Page 47 and 48:
36 M.K. Pusala et al. 7 Summary and
Page 49 and 50:
38 M.K. Pusala et al. 15. Enhancing
Page 51 and 52:
40 M.K. Pusala et al. 64. Yuan D, C
Page 53 and 54:
42 A. Laha ability to query the dat
Page 55 and 56:
44 A. Laha as positive, negative or
Page 57 and 58:
46 A. Laha 3.2 Velocity Some of the
Page 59 and 60:
48 A. Laha angular data), variation
Page 61 and 62:
50 A. Laha that it creates a histog
Page 63 and 64:
52 A. Laha It is seen that the ASR
Page 65 and 66:
54 A. Laha However, better methods
Page 67 and 68:
Application of Mixture Models to La
Page 69 and 70:
Page 71 and 72:
Page 73 and 74:
Page 75 and 76:
Page 77 and 78:
Page 79 and 80:
Page 81 and 82:
Page 83 and 84:
Page 85 and 86:
An Efficient Partition-Repetition A
Page 87 and 88:
Page 89 and 90:
Page 91 and 92:
Page 93 and 94:
Page 95 and 96:
Page 97 and 98:
Page 99 and 100:
Page 101 and 102:
Page 103 and 104:
Page 105 and 106:
96 X. Chen and J. Huan 1 Introducti
Page 107 and 108:
98 X. Chen and J. Huan Hash or rand
Page 109 and 110:
100 X. Chen and J. Huan Fig. 1 Vert
Page 111 and 112:
102 X. Chen and J. Huan According t
Page 113 and 114:
104 X. Chen and J. Huan Fig. 3 Assi
Page 115 and 116:
106 X. Chen and J. Huan The ADG alg
Page 117 and 118: 108 X. Chen and J. Huan partition o
Page 119 and 120: 110 X. Chen and J. Huan Table 5 Ave
Page 121 and 122: 112 X. Chen and J. Huan orders. At
Page 123 and 124: 114 X. Chen and J. Huan 10. Ng AY (
Page 125 and 126: 116 Y. Simmhan and S. Perera will b
Page 127 and 128: 118 Y. Simmhan and S. Perera human-
Page 129 and 130: 120 Y. Simmhan and S. Perera IoT sy
Page 131 and 132: 122 Y. Simmhan and S. Perera Fig. 1
Page 133 and 134: 124 Y. Simmhan and S. Perera Finall
Page 135 and 136: 126 Y. Simmhan and S. Perera detect
Page 137 and 138: 128 Y. Simmhan and S. Perera Fig. 3
Page 139 and 140: 130 Y. Simmhan and S. Perera perfor
Page 141 and 142: 132 Y. Simmhan and S. Perera get fe
Page 143 and 144: 134 Y. Simmhan and S. Perera Refere
Page 145 and 146: Complex Event Processing in Big Dat
Page 167: Complex Event Processing in Big Dat
Page 171 and 172: 164 C. Hota et al. security and pri
Page 173 and 174: 166 C. Hota et al. The network is p
Page 175 and 176: 168 C. Hota et al. Fig. 5 Web usage
Page 177 and 178: 170 C. Hota et al. the P2P network
Page 179 and 180: 172 C. Hota et al. authors in [45]
Page 181 and 182: 174 C. Hota et al. Table 1 Applicat
Page 183 and 184: 176 C. Hota et al. Fig. 7 Flow-base
Page 185 and 186: 178 C. Hota et al. applications lik
Page 187 and 188: 180 C. Hota et al. Fig. 10 Botnet T
Page 189 and 190: 182 C. Hota et al. The build time o
Page 191 and 192: 184 C. Hota et al. With the edge-pa
Page 193 and 194: 186 C. Hota et al. 24. Liang J, Kum
Page 195 and 196: Application-Level Benchmarking of B
Page 207 and 208: 202 S. Batra and S. Sachdeva 1 Intr
Page 209 and 210: 204 S. Batra and S. Sachdeva In 201
Page 211 and 212: 206 S. Batra and S. Sachdeva 2.2 Du
Page 213 and 214: 208 S. Batra and S. Sachdeva One mo
Page 215 and 216: 210 S. Batra and S. Sachdeva Retrie
Page 217 and 218: 212 S. Batra and S. Sachdeva 3.4 Op
Page 219 and 220:
214 S. Batra and S. Sachdeva inform
Page 221 and 222:
216 S. Batra and S. Sachdeva Fig. 4
Page 223 and 224:
218 S. Batra and S. Sachdeva 7. IHT
Page 225 and 226:
Microbiome Data Mining for Microbia
Page 227 and 228:
Page 229 and 230:
Page 231 and 232:
Page 233 and 234:
Page 235 and 236:
Page 237 and 238:
Page 239 and 240:
Page 241 and 242:
238 K. Das and Z. Nenadic a viable
Page 243 and 244:
240 K. Das and Z. Nenadic band [47,
Page 245 and 246:
242 K. Das and Z. Nenadic (a) (b) C
Page 247 and 248:
244 K. Das and Z. Nenadic Fig. 3 Tw
Page 249 and 250:
246 K. Das and Z. Nenadic membershi
Page 251 and 252:
248 K. Das and Z. Nenadic and are e
Page 253 and 254:
250 K. Das and Z. Nenadic Fig. 6 Ex
Page 255 and 256:
252 K. Das and Z. Nenadic 3.2.3 Dis
Page 257 and 258:
254 K. Das and Z. Nenadic 3. Belhum
Page 259 and 260:
256 K. Das and Z. Nenadic 50. Ramos
Page 261 and 262:
Big Data and Cancer Research Binay
Page 263 and 264:
Big Data and Cancer Research 261 sy
Page 265 and 266:
Big Data and Cancer Research 263 Ta
Page 267 and 268:
Big Data and Cancer Research 265 in
Page 269 and 270:
Big Data and Cancer Research 267 Id
Page 271 and 272:
Big Data and Cancer Research 269 us
Page 273 and 274:
Big Data and Cancer Research 271 Ac
Page 275 and 276:
Big Data and Cancer Research 273 39
Page 277 and 278:
Big Data and Cancer Research 275 75
show all

Big Data Analytics

Create successful ePaper yourself

Delete template?

Save as template?