Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Mobile Networks and Applications
Erschienen in: Mobile Networks and Applications 2/2014

01.04.2014

Big Data: A Survey

verfasst von: Min Chen, Shiwen Mao, Yunhao Liu

Erschienen in: Mobile Networks and Applications | Ausgabe 2/2014

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

In this paper, we review the background and state-of-the-art of big data. We first introduce the general background of big data and review related technologies, such as could computing, Internet of Things, data centers, and Hadoop. We then focus on the four phases of the value chain of big data, i.e., data generation, data acquisition, data storage, and data analysis. For each phase, we introduce the general background, discuss the technical challenges, and review the latest advances. We finally examine the several representative applications of big data, including enterprise management, Internet of Things, online social networks, medial applications, collective intelligence, and smart grid. These discussions aim to provide a comprehensive overview and big-picture to readers of this exciting area. This survey is concluded with a discussion of open problems and future directions.
Vorheriger Artikel Cloud-assisted Computing for Event-driven Mobile Services
Nächster Artikel Context-Aware Systems and Applications

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Weitere Produktempfehlungen anzeigen
Literatur
1.
Gantz J, Reinsel D (2011) Extracting value from chaos. IDC iView, pp 1–12
2.
3.
Cukier K (2010) Data, data everywhere: a special report on managing information. Economist Newspaper
4.
5.
Lohr S (2012) The age of big data. New York Times, pp 11
6.
7.
8.
9.
10.
Manyika J, McKinsey Global Institute, Chui M, Brown B, Bughin J, Dobbs R, Roxburgh C, Byers AH (2011) Big data: the next frontier for innovation, competition, and productivity. McKinsey Global Institute
11.
Mayer-Schönberger V, Cukier K (2013) Big data: a revolution that will transform how we live, work, and think. Eamon Dolan/Houghton Mifflin Harcourt
12.
Laney D (2001) 3-d data management: controlling data volume, velocity and variety. META Group Research Note, 6 February
13.
Zikopoulos P, Eaton C, et al (2011) Understanding big data: analytics for enterprise class hadoop and streaming data. McGraw-Hill Osborne Media
14.
Meijer E (2011) The world according to linq. Communications of the ACM 54(10):45–51CrossRef
15.
16.
O. R. Team (2011) Big data now: current perspectives from OReilly Radar. OReilly Media
17.
18.
Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L (2008) Detecting influenza epidemics using search engine query data. Nature 457(7232):1012–1014CrossRef
19.
DeWitt D, Gray J (1992) Parallel database systems: the future of high performance database systems. Commun ACM 35(6):85–98CrossRef
20.
Walter T (2009) Teradata past, present, and future. UCI ISG lecture series on scalable data management
21.
Ghemawat S, Gobioff H, Leung S-T (2003) The google file system. In: ACM SIGOPS Operating Systems Review, vol 37. ACM, pp 29–43
22.
Dean J, Ghemawat S (2008) Mapreduce: simplified data processing on large clusters. Commun ACM 51(1):107–113CrossRef
23.
Hey AJG, Tansley S, Tolle KM, et al (2009) The fourth paradigm: data-intensive scientific discovery
24.
Howard JH, Kazar ML, Menees SG, Nichols DA, Satyanarayanan M, Sidebotham RN, West MJ (1988) Scale and performance in a distributed file system. ACM Trans Comput Syst (TOCS) 6(1):51–81CrossRef
25.
Cattell R (2011) Scalable sql and nosql data stores. ACM SIGMOD Record 39(4):12–27CrossRef
26.
Labrinidis A, Jagadish HV (2012) Challenges and opportunities with big data. Proc VLDB Endowment 5(12):2032–2033CrossRef
27.
Chaudhuri S, Dayal U, Narasayya V (2011) An overview of business intelligence technology. Commun ACM 54(8):88–98CrossRef
28.
Agrawal D, Bernstein P, Bertino E, Davidson S, Dayal U, Franklin M, Gehrke J, Haas L, Halevy A, Han J et al (2012) Challenges and opportunities with big data. A community white paper developed by leading researches across the United States
29.
Sun Y, Chen M, Liu B, Mao S (2013) Far: a fault-avoidant routing method for data center networks with regular topology. In: Proceedings of ACM/IEEE symposium on architectures for networking and communications systems (ANCS’13). ACM
30.
31.
Bahga A, Madisetti VK (2012) Analyzing massive machine maintenance data in a computing cloud. IEEE Transac Parallel Distrib Syst 23(10):1831–1843CrossRef
32.
Gunarathne T, Wu T-L, Choi JY, Bae S-H, Qiu J (2011) Cloud computing paradigms for pleasingly parallel biomedical applications. Concurr Comput Prac Experience 23(17):2338–2354CrossRef
33.
Gantz J, Reinsel D (2010) The digital universe decade-are you ready. External publication of IDC (Analyse the Future) information and data, pp 1–16
34.
Bryant RE (2011) Data-intensive scalable computing for scientific applications. Comput Sci Eng 13(6):25–33CrossRef
35.
Wahab MHA, Mohd MNH, Hanafi HF, Mohsin MFM (2008) Data pre-processing on web server logs for generalized association rules mining algorithm. World Acad Sci Eng Technol 48:2008
36.
Nanopoulos A, Manolopoulos Y, Zakrzewicz M, Morzy T (2002) Indexing web access-logs for pattern queries. In: Proceedings of the 4th international workshop on web information and data management. ACM, pp 63–68
37.
Joshi KP, Joshi A, Yesha Y (2003) On using a warehouse to analyze web logs. Distrib Parallel Databases 13(2):161–180MATHCrossRef
38.
Chandramohan V, Christensen K (2002) A first look at wired sensor networks for video surveillance systems. In: Proceedings LCN 2002, 27th annual IEEE conference on local computer networks. IEEE, pp 728–729
39.
Selavo L, Wood A, Cao Q, Sookoor T, Liu H, Srinivasan A, Wu Y, Kang W, Stankovic J, Young D et al (2007) Luster: wireless sensor network for environmental research. In: Proceedings of the 5th international conference on Embedded networked sensor systems. ACM, pp 103–116
40.
Barrenetxea G, Ingelrest F, Schaefer G, Vetterli M, Couach O, Parlange M (2008) Sensorscope: out-of-the-box environmental monitoring. In: Information processing in sensor networks, 2008, international conference on IPSN’08. IEEE, pp 332– 343
41.
Kim Y, Schmid T, Charbiwala ZM, Friedman J, Srivastava MB (2008) Nawms: nonintrusive autonomous water monitoring system. In: Proceedings of the 6th ACM conference on Embedded network sensor systems. ACM, pp 309–322
42.
Kim S, Pakzad S, Culler D, Demmel J, Fenves G, Glaser S, Turon M (2007) Health monitoring of civil infrastructures using wireless sensor networks. In Information Processing in Sensor Networks 2007, 6th International Symposium on IPSN 2007. IEEE, pp 254–263
43.
Ceriotti M, Mottola L, Picco GP, Murphy AL, Guna S, Corra M, Pozzi M, Zonta D, Zanon P (2009) Monitoring heritage buildings with wireless sensor networks: the torre aquila deployment. In: Proceedings of the 2009 International Conference on Information Processing in Sensor Networks. IEEE Computer Society, pp 277–288
44.
Tolle G, Polastre J, Szewczyk R, Culler D, Turner N, Tu K, Burgess S, Dawson T, Buonadonna P, Gay D et al (2005) A macroscope in the redwoods. In: Proceedings of the 3rd international conference on embedded networked sensor systems. ACM, pp 51–63
45.
Wang F, Liu J (2011) Networked wireless sensor data collection: issues, challenges, and approaches. IEEE Commun Surv Tutor 13(4):673–687CrossRef
46.
Cho J, Garcia-Molina H (2002) Parallel crawlers. In: Proceedings of the 11th international conference on World Wide Web. ACM, pp 124–135
47.
Choudhary S, Dincturk ME, Mirtaheri SM, Moosavi A, von Bochmann G, Jourdan G-V, Onut I-V (2012) Crawling rich internet applications: the state of the art. In: CASCON. pp 146–160
48.
Ghani N, Dixit S, Wang T-S (2000) On ip-over-wdm integration. IEEE Commun Mag 38(3):72–84CrossRef
49.
Manchester J, Anderson J, Doshi B, Dravida S, Ip over sonet (1998). IEEE Commun Mag 36(5):136–142CrossRef
50.
Jinno M, Takara H, Kozicki B (2009) Dynamic optical mesh networks: drivers, challenges and solutions for the future. In: Optical communication, 2009, 35th European conference on ECOC’09. IEEE, pp 1–4
51.
Barroso LA, Hölzle U (2009) The datacenter as a computer: an introduction to the design of warehouse-scale machines. Synt Lect Comput Archit 4(1):1–108
52.
Armstrong J (2009) Ofdm for optical communications. J Light Technol 27(3):189–204CrossRef
53.
54.
Cisco data center interconnect design and deployment guide (2010)
55.
Greenberg A, Hamilton JR, Jain N, Kandula S, Kim C, Lahiri P, Maltz DA, Patel P, Sengupta S (2009) Vl2: a scalable and flexible data center network. In ACM SIGCOMM computer communication review, vol 39. ACM, pp 51–62
56.
Guo C, Lu G, Li D, Wu H, Zhang X, Shi Y, Tian C, Zhang Y, Lu S (2009) Bcube: a high performance, server-centric network architecture for modular data centers. ACM SIGCOMM Comput Commun Rev 39(4):63–74CrossRef
57.
Farrington N, Porter G, Radhakrishnan S, Bazzaz HH, Subramanya V, Fainman Y, Papen G, Vahdat A (2011) Helios: a hybrid electrical/optical switch architecture for modular data centers. ACM SIGCOMM Comput Commun Rev 41(4):339–350
58.
Abu-Libdeh H, Costa P, Rowstron A, O’Shea G, Donnelly A (2010) Symbiotic routing in future data centers. ACM SIGCOMM Comput Commun Rev 40(4):51–62CrossRef
59.
Lam C, Liu H, Koley B, Zhao X, Kamalov V, Gill V, Fiber optic communication technologies: what’s needed for datacenter network operations (2010). IEEE Commun Mag 48(7):32–39CrossRef
60.
Wang G, Andersen DG, Kaminsky M, Papagiannaki K, Ng TS, Kozuch M, Ryan M (2010) c-through: Part-time optics in data centers. In: ACM SIGCOMM Computer Communication Review, vol 40. ACM, pp 327–338
61.
Ye X, Yin Y, Yoo SJB, Mejia P, Proietti R, Akella V (2010) Dos: a scalable optical switch for datacenters. In Proceedings of the 6th ACM/IEEE symposium on architectures for networking and communications systems. ACM, p 24
62.
Singla A, Singh A, Ramachandran K, Xu L, Zhang Y (2010) Proteus: a topology malleable data center network. In Proceedings of the 9th ACM SIGCOMM workshop on hot topics in networks. ACM, p 8
63.
Liboiron-Ladouceur O, Cerutti I, Raponi PG, Andriolli N, Castoldi P (2011) Energy-efficient design of a scalable optical multiplane interconnection architecture. IEEE J Sel Top Quantum Electron 17(2):377–383CrossRef
64.
Kodi AK, Louri A (2011) Energy-efficient and bandwidth-reconfigurable photonic networks for high-performance computing (hpc) systems. IEEE J Sel Top Quantum Electron 17(2):384–395CrossRef
65.
Zhou X, Zhang Z, Zhu Y, Li Y, Kumar S, Vahdat A, Zhao BY, Zheng H (2012) Mirror mirror on the ceiling: flexible wireless links for data centers. ACM SIGCOMM Comput Commun Rev 42(4):443–454CrossRef
66.
Lenzerini M (2002) Data integration: a theoretical perspective. In: Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on principles of database systems. ACM, pp 233–246
67.
Cafarella MJ, Halevy A, Khoussainova N (2009) Data integration for the relational web. Proc VLDB Endowment 2(1):1090–1101CrossRef
68.
Maletic JI, Marcus A (2000) Data cleansing: beyond integrity analysis. In: IQ. Citeseer, pp 200–209
69.
Kohavi R, Mason L, Parekh R, Zheng Z (2004) Lessons and challenges from mining retail e-commerce data. Mach Learn 57(1-2):83–113CrossRef
70.
Chen H, Ku W-S, Wang H, Sun M-T (2010) Leveraging spatio-temporal redundancy for rfid data cleansing. In: Proceedings of the 2010 ACM SIGMOD international conference on management of data. ACM, pp 51–62
71.
Zhao Z, Ng W (2012) A model-based approach for rfid data stream cleansing. In Proceedings of the 21st ACM international conference on information and knowledge management. ACM, pp 862–871
72.
Khoussainova N, Balazinska M, Suciu D (2008) Probabilistic event extraction from rfid data. In: Data Engineering, 2008. IEEE 24th international conference on ICDE 2008. IEEE, pp 1480–1482
73.
Herbert KG, Wang JTL (2007) Biological data cleaning: a case study. Int J Inf Qual 1(1):60–82CrossRef
74.
Tsai T-H, Lin C-Y (2012) Exploring contextual redundancy in improving object-based video coding for video sensor networks surveillance. IEEE Transac Multmed 14(3):669–682CrossRef
75.
Sarawagi S, Bhamidipaty A (2002) Interactive deduplication using active learning. In Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 269–278
76.
Kamath U, Compton J, Dogan RI, Jong KD, Shehu A (2012) An evolutionary algorithm approach for feature generation from sequence data and its application to dna splice site prediction. IEEE/ACM Transac Comput Biol Bioinforma (TCBB) 9(5):1387–1398CrossRef
77.
Leung K-S, Lee KH, Wang J-F, Ng EYT, Chan HLY, Tsui SKW, Mok TSK, Tse PC-H, Sung JJ-Y (2011) Data mining on dna sequences of hepatitis b virus. IEEE/ACM Transac Comput Biol Bioinforma 8(2):428–440CrossRef
78.
Huang Z, Shen H, Liu J, Zhou X (2011) Effective data co-reduction for multimedia similarity search. In Proceedings of the 2011 ACM SIGMOD International Conference on Management of data. ACM, pp 1021–1032
79.
Bleiholder J, Naumann F (2008) Data fusion. ACM Comput Surv (CSUR) 41(1):1CrossRef
80.
Brewer EA (2000) Towards robust distributed systems. In: PODC. p 7
81.
Gilbert S, Lynch N (2002) Brewer’s conjecture and the feasibility of consistent, available, partition-tolerant web services. ACM SIGACT News 33(2):51–59CrossRef
82.
McKusick MK, Quinlan S (2009) Gfs: eqvolution on fast-forward. ACM Queue 7(7):10CrossRef
83.
Chaiken R, Jenkins B, Larson P-Å, Ramsey B, Shakib D, Weaver S, Zhou J (2008) Scope: easy and efficient parallel processing of massive data sets. Proc VLDB Endowment 1(2):1265–1276CrossRef
84.
Beaver D, Kumar S, Li HC, Sobel J, Vajgel P et al (2010) Finding a needle in haystack: facebook’s photo storage. In OSDI, vol 10. pp 1–8
85.
DeCandia G, Hastorun D, Jampani M, Kakulapati G, Lakshman A, Pilchin A, Sivasubramanian S, Vosshall P, Vogels W (2007) Dynamo: amazon’s highly available key-value store. In: SOSP, vol 7. pp 205–220
86.
Karger D, Lehman E, Leighton T, Panigrahy R, Levine M, Lewin D (1997) Consistent hashing and random trees: distributed caching protocols for relieving hot spots on the world wide web. In: Proceedings of the twenty-ninth annual ACM symposium on theory of computing. ACM, pp 654–663
87.
Chang F, Dean J, Ghemawat S, Hsieh WC, Wallach DA, Burrows M, Chandra T, Fikes A, Gruber RE (2008) Bigtable: a distributed storage system for structured data. ACM Trans Comput Syst (TOCS) 26(2):4CrossRef
88.
Burrows M (2006) The chubby lock service for loosely-coupled distributed systems. In: Proceedings of the 7th symposium on Operating systems design and implementation. USENIX Association, pp 335–350
89.
Lakshman A, Malik P (2009) Cassandra: structured storage system on a p2p network. In: Proceedings of the 28th ACM symposium on principles of distributed computing. ACM, pp 5–5
90.
George L (2011) HBase: the definitive guide. O’Reilly Media Inc
91.
Judd D (2008) hypertable-0.9. 0.4-alpha
92.
Chodorow K (2013) MongoDB: the definitive guide. O’Reilly Media Inc
93.
Crockford D (2006) The application/json media type for javascript object notation (json)
94.
Murty J (2009) Programming amazon web services: S3, EC2, SQS, FPS, and SimpleDB. O’Reilly Media Inc
95.
Anderson JC, Lehnardt J, Slater N (2010) CouchDB: the definitive guide. O’Reilly Media Inc
96.
Blanas S, Patel JM, Ercegovac V, Rao J, Shekita EJ, Tian Y (2010) A comparison of join algorithms for log processing in mapreduce. In: Proceedings of the 2010 ACM SIGMOD international conference on management of data. ACM, pp 975–986
97.
Yang H-C, Parker DS (2009) Traverse: simplified indexing on large map-reduce-merge clusters. In: Database systems for advanced applications. Springer, pp 308–322
98.
Pike R, Dorward S, Griesemer R, Quinlan S (2005) Interpreting the data: parallel analysis with sawzall. Sci Program 13(4):277–298
99.
Gates AF, Natkovich O, Chopra S, Kamath P, Narayanamurthy SM, Olston C, Reed B, Srinivasan S, Srivastava U (2009) Building a high-level dataflow system on top of map-reduce: the pig experience. Proceedings VLDB Endowment 2(2):1414–1425CrossRef
100.
Thusoo A, Sarma JS, Jain N, Shao Z, Chakka P, Anthony S, Liu H, Wyckoff P, Murthy R (2009) Hive: a warehousing solution over a map-reduce framework. Proc VLDB Endowment 2(2):1626–1629CrossRef
101.
Isard M, Budiu M, Yu Y, Birrell A, Fetterly D (2007) Dryad: distributed data-parallel programs from sequential building blocks. ACM SIGOPS Oper Syst Rev 41(3):59–72CrossRef
102.
Yu Y, Isard M, Fetterly D, Budiu M, Erlingsson Ú, Gunda PK, Currey J (2008) Dryadlinq: a system for general-purpose distributed data-parallel computing using a high-level language. In: OSDI, vol 8. pp 1–14
103.
Moretti C, Bulosan J, Thain D, Flynn PJ (2008) All-pairs: an abstraction for data-intensive cloud computing. In: Parallel and distributed processing, 2008. IEEE international symposium on IPDPS 2008. IEEE, pp 1–11
104.
Malewicz G, Austern MH, Bik AJC, Dehnert JC, Horn I, Leiser N, Czajkowski G (2010) Pregel: a system for large-scale graph processing. In: Proceedings of the 2010 ACM SIGMOD international conference on management of data. ACM, pp 135–146
105.
Bu Y, Bill H, Balazinska M, Ernst MD (2010) Haloop: efficient iterative data processing on large clusters. Proc VLDB Endowment 3(1-2):285–296CrossRef
106.
Ekanayake J, Li H, Zhang B, Gunarathne T, Bae S-H, Qiu J, Fox G (2010) Twister: a runtime for iterative mapreduce. In Proceedings of the 19th ACM international symposium on high performance distributed computing. ACM, pp 810–818
107.
Zaharia M, Chowdhury M, Das T, Dave A, Ma J, McCauley M, Franklin M, 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
108.
Bhatotia P, Wieder A, Rodrigues R, Acar UA, Pasquin R (2011) Incoop: mapreduce for incremental computations. In: Proceedings of the 2nd ACM symposium on cloud computing. ACM, p 7
109.
Murray DG, Schwarzkopf M, Smowton C, Smith S, Madhavapeddy A, Hand S (2011) Ciel: a universal execution engine for distributed data-flow computing. In: Proceedings of the 8th USENIX conference on Networked systems design and implementation. p 9
110.
Anderson TW (1958) An introduction to multivariate statistical analysis, vol 2. Wiley, New York
111.
Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, Motoda H, McLachlan GJ, Ng A, Liu B, Philip SY et al (2008) Top 10 algorithms in data mining. Knowl Inf Syst 14(1):1–37CrossRef
112.
113.
Berthold MR, Cebron N, Dill F, Gabriel TR, Kötter T, Meinl T, Ohl P, Sieb C, Thiel K, Wiswedel B (2008) KNIME: the Konstanz information miner. Springer
114.
Sallam RL, Richardson J, Hagerty J, Hostmann B (2011) Magic quadrant for business intelligence platforms. CT, Gartner Group, Stamford
115.
Beyond the PC. Special Report on Personal Technology (2011)
116.
Goff SA, Vaughn M, McKay S, Lyons E, Stapleton AE, Gessler D, Matasci N, Wang L, Hanlon M, Lenards A et al (2011) The iplant collaborative: cyberinfrastructure for plant biology. Front Plant Sci 34(2):1–16. doi:10.​3389/​fpls.​2011.​00034
117.
Baah GK, Gray A, Harrold MJ (2006) On-line anomaly detection of deployed software: a statistical machine learning approach. In: Proceedings of the 3rd international workshop on Software quality assurance. ACM, pp 70–77
118.
Moeng M, Melhem R (2010) Applying statistical machine learning to multicore voltage & frequency scaling. In: Proceedings of the 7th ACM international conference on computing frontiers. ACM, pp 277–286
119.
Gaber MM, Zaslavsky A, Krishnaswamy S (2005) Mining data streams: a review. ACM Sigmod Record 34(2):18–26CrossRef
120.
Verykios VS, Bertino E, Fovino IN, Provenza LP, Saygin Y, Theodoridis Y (2004) State-of-the-art in privacy preserving data mining. ACM Sigmod Record 33(1):50–57CrossRef
121.
van der Aalst W (2012) Process mining: overview and opportunities. ACM Transac Manag Inform Syst (TMIS) 3(2):7
122.
Manning CD, Schütze H (1999) Foundations of statistical natural language processing, vol 999. MIT Press
123.
Pal SK, Talwar V, Mitra P (2002) Web mining in soft computing framework, relevance, state of the art and future directions. IEEE Transac Neural Netw 13(5):1163–1177CrossRef
124.
Chakrabarti S (2000) Data mining for hypertext: a tutorial survey. ACM SIGKDD Explor Newsl 1(2):1–11CrossRef
125.
Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. Comput Netw ISDN Syst 30(1):107–117CrossRef
126.
Konopnicki D, Shmueli O (1995) W3qs: a query system for the world-wide web. In: VLDB, vol 95. pp 54–65
127.
Chakrabarti S, Van den Berg M, Dom B (1999) Focused crawling: a new approach to topic-specific web resource discovery. Comput Netw 31(11):1623–1640CrossRef
128.
Ding D, Metze F, Rawat S, Schulam PF, Burger S, Younessian E, Bao L, Christel MG, Hauptmann A (2012) Beyond audio and video retrieval: towards multimedia summarization. In: Proceedings of the 2nd ACM international conference on multimedia retrieval. ACM, pp 2
129.
Wang M, Ni B, Hua X-S, Chua T-S (2012) Assistive tagging: a survey of multimedia tagging with human-computer joint exploration. ACM Comput Surv (CSUR) 44(4):25CrossRef
130.
Lew MS, Sebe N, Djeraba C, Jain R (2006) Content-based multimedia information retrieval: state of the art and challenges. ACM Trans Multimed Comput Commun Appl (TOMCCAP) 2(1):1–19CrossRef
131.
Hu W, Xie N, Li L, Zeng X, Maybank S (2011) A survey on visual content-based video indexing and retrieval. IEEE Trans Syst Man Cybern Part C Appl Rev 41(6):797–819CrossRef
132.
Park Y-J, Chang K-N (2009) Individual and group behavior-based customer profile model for personalized product recommendation. Expert Syst Appl 36(2):1932–1939MathSciNetCrossRef
133.
Barragáns-Martínez AB, Costa-Montenegro E, Burguillo JC, Rey-López M, Mikic-Fonte FA, Peleteiro A (2010) A hybrid content-based and item-based collaborative filtering approach to recommend tv programs enhanced with singular value decomposition. Inf Sci 180(22):4290–4311CrossRef
134.
Naphade M, Smith JR, Tesic J, Chang S-F, Hsu W, Kennedy L, Hauptmann A, Curtis J (2006) Large-scale concept ontology for multimedia. IEEE Multimedia 13(3):86–91CrossRef
135.
Ma Z, Yang Y, Cai Y, Sebe N, Hauptmann AG (2012) Knowledge adaptation for ad hoc multimedia event detection with few exemplars. In: Proceedings of the 20th ACM international conference on multimedia. ACM, pp 469–478
136.
Hirsch JE (2005) An index to quantify an individual’s scientific research output. Proc Natl Acad Sci USA 102(46):16569CrossRef
137.
Watts DJ (2004) Six degrees: the science of a connected age. WW Norton & Company
138.
Aggarwal CC (2011) An introduction to social network data analytics. Springer
139.
Scellato S, Noulas A, Mascolo C (2011) Exploiting place features in link prediction on location-based social networks. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 1046–1054
140.
Ninagawa A, Eguchi K (2010) Link prediction using probabilistic group models of network structure. In: Proceedings of the 2010 ACM symposium on applied Computing. ACM, pp 1115–1116
141.
Dunlavy DM, Kolda TG, Acar E (2011) Temporal link prediction using matrix and tensor factorizations. ACM Transac Knowl Discov Data (TKDD) 5(2):10
142.
Leskovec J, Lang KJ, Mahoney M (2010) Empirical comparison of algorithms for network community detection. In: Proceedings of the 19th international conference on World wide web. ACM, pp 631–640
143.
Du N, Wu B, Pei X, Wang B, Xu L (2007) Community detection in large-scale social networks. In: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis. ACM, pp 16–25
144.
Garg S, Gupta T, Carlsson N, Mahanti A (2009) Evolution of an online social aggregation network: an empirical study. In: Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference. ACM, pp 315–321
145.
Allamanis M, Scellato S, Mascolo C (2012) Evolution of a location-based online social network: analysis and models. In: Proceedings of the 2012 ACM conference on Internet measurement conference. ACM, pp 145–158
146.
Gong NZ, Xu W, Huang L, Mittal P, Stefanov E, Sekar V, Song D (2012) Evolution of social-attribute networks: measurements, modeling, and implications using google+. In: Proceedings of the 2012 ACM conference on Internet measurement conference. ACM, pp 131–144
147.
Zheleva E, Sharara H, Getoor L (2009) Co-evolution of social and affiliation networks. In: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 1007–1016
148.
Tang J, Sun J, Wang C, Yang Z (2009) Social influence analysis in large-scale networks. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 807–816
149.
Li Y, Chen W, Wang Y, Zhang Z-L (2013) Influence diffusion dynamics and influence maximization in social networks with friend and foe relationships. In Proceedings of the sixth ACM international conference on Web search and data mining. ACM, pp 657–666
150.
Dai W, Chen Y, Xue G-R, Yang Q, Yu Y (2008) Translated learning: transfer learning across different feature spaces: In: Advances in neural information processing systems. pp 353–360
151.
152.
Rhee Y, Lee J (2009) On modeling a model of mobile community: designing user interfaces to support group interaction. Interactions 16(6):46–51CrossRef
153.
Han J, Lee J-G, Gonzalez H, Li X (2008) Mining massive rfid, trajectory, and traffic data sets. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, p 2
154.
Garg MK, Kim D-J, Turaga DS, Prabhakaran B (2010) Multimodal analysis of body sensor network data streams for real-time healthcare. In: Proceedings of the international conference on multimedia information retrieval. ACM, pp 469–478
155.
Park Y, Ghosh J (2012) A probabilistic imputation framework for predictive analysis using variably aggregated, multi-source healthcare data. In: Proceedings of the 2nd ACM SIGHIT international health informatics symposium. ACM, pp 445–454
156.
Tasevski P (2011) Password attacks and generation strategies. Tartu University: Faculty of Mathematics and Computer Sciences
Metadaten
Titel
Big Data: A Survey
verfasst von
Min Chen
Shiwen Mao
Yunhao Liu
Publikationsdatum
01.04.2014
Verlag
Springer US
Erschienen in
Mobile Networks and Applications / Ausgabe 2/2014
Print ISSN: 1383-469X
Elektronische ISSN: 1572-8153
DOI
https://doi.org/10.1007/s11036-013-0489-0

Weitere Artikel der Ausgabe 2/2014

Mobile Networks and Applications 2/2014 Zur Ausgabe

OriginalPaper

VCMIA: A Novel Architecture for Integrating Vehicular Cyber-Physical Systems and Mobile Cloud Computing

OriginalPaper

Mobile Cloud Computing: A Survey, State of Art and Future Directions

OriginalPaper

Cloud-assisted Computing for Event-driven Mobile Services

OriginalPaper

A Cloudlet-Assisted Multiplayer Cloud Gaming System

OriginalPaper

Information and Service Integration Based on Agent-Oriented Ubiquitous Computing

OriginalPaper

Context-aware Composition of Semantic Web Services

Neuer Inhalt

    Bildnachweise