Scalable management of storage for massive quality-adjustable sensor data

Computing

Volumn 97, Issue 8, 2015, Pages 769-793

  Lee, Dongeun ^a   Ryu, Junhee ^a   Shin, Heonshik ^a  

^a Seoul National University   (South Korea)

Author keywords

Big data; Data aging; Optimal rate allocation; Quality adjustable sensor data; Scalable quality management

Indexed keywords

BUDGET CONTROL; DIGITAL STORAGE; INFORMATION MANAGEMENT; QUALITY MANAGEMENT; STORAGE MANAGEMENT;

ACCESS PATTERNS; DATA ARCHIVING; DATA GENERATION; OPTIMAL RATE ALLOCATION; SENSOR DATA; SPATIOTEMPORAL CORRELATION; STORAGE SPACES; TOTAL DISTORTION;

BIG DATA;

EID: 84937972932     PISSN: 0010485X     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00607-015-0465-6     Document Type: Article

References (40)

1. Satyanarayanan M (2010) Mobile computing: the next decade. Paper presented at the 1st ACM workshop on mobile cloud computing and services: social networks and beyond, San Francisco
2. Campbell AT, Eisenman SB, Lane ND, Miluzzo E, Peterson RA (2006) People-centric urban sensing. Paper presented at the 2nd annual international workshop on wireless internet, Boston
3. Roddick JF, Hoel E, Egenhofer MJ, Papadias D, Salzberg B (2004) Spatial, temporal and spatio-temporal databases—hot issues and directions for PhD research. SIGMOD Rec 33(2):126–131. doi:10.1145/1024694.1024724
4. Kitsuregawa M, Nishida T (2010) Special issue on information explosion. New Gener Comput 28(3):207–215. doi:10.1007/s00354-010-0086-8
5. Sweeney L (2001) Information explosion. In: Zayatz L, Doyle P, Theeuwes J, Lane J (eds) Confidentiality, disclosure, and data access: theory and practical applications for statistical agencies. Urban Institute, Washington
6. Gantz JF, Chute C (2008) The diverse and exploding digital universe: an updated forecast of worldwide information growth through 2011. 2008 IDC
7. Hilbert M, López P (2011) The world’s technological capacity to store, communicate, and compute information. Science 332(6025):60–65. doi:10.1126/science.1200970
8. Sampson A, Nelson J, Strauss K, Ceze L (2013) Approximate storage in solid-state memories. Paper presented at the 46th IEEE/ACM international symposium on microarchitecture (MICRO), Davis
9. Esmaeilzadeh H, Sampson A, Ceze L, Burger D (2012) Architecture support for disciplined approximate programming. Paper presented at the 17th international conference on architectural support for programming languages and operating systems (ASPLOS), London
10. Ganesan D, Estrin D, Heidemann J (2003) Dimensions: why do we need a new data handling architecture for sensor networks? SIGCOMM Comput Commun Rev 33(1):143–148. doi:10.1145/774763.774786
11. Ganesan D, Greenstein B, Estrin D, Heidemann J, Govindan R (2005) Multiresolution storage and search in sensor networks. ACM Trans Storage 1(3):277–315. doi:10.1145/1084779.1084780
12. Srisooksai T, Keamarungsi K, Lamsrichan P, Araki K (2012) Practical data compression in wireless sensor networks: a survey. J Netw Comput Appl 35(1):37–59. doi:10.1016/j.jnca.2011.03.001
13. Vuran MC, Akan ÖB, Akyildiz IF (2004) Spatio-temporal correlation: theory and applications for wireless sensor networks. Comput Netw 45(3):245–259. doi:10.1016/j.comnet.2004.03.007
14. Smith H (2011) Data center storage: cost-effective strategies, implementation, and management. CRC Press, Boca Raton
15. Sensirion Home Page. http://www.sensirion.com/en/home/. Accessed 12 Jan 2015
16. Cao H, Wolfson O, Trajcevski G (2006) Spatio-temporal data reduction with deterministic error bounds. VLDB J 15(3):211–228
17. Palmer M (2005) Seven principles of effective RFID data management. Progress Software
18. Cohen E, Kaplan H (2001) Aging through cascaded caches: performance issues in the distribution of web content. SIGCOMM Comput Commun Rev 31(4):41–53
19. Rimoldi B (1994) Successive refinement of information: characterization of the achievable rates. IEEE Trans Inf Theory 40(1):253–259. doi:10.1109/18.272493
20. Equitz WHR, Cover TM (1991) Successive refinement of information. IEEE Trans Inf Theory 37(2):269–275. doi:10.1109/18.75242
21. Schwarz H, Marpe D, Wiegand T (2007) Overview of the scalable video coding extension of the H.264/AVC standard. IEEE Trans Circuits Syst Video Technol 17(9):1103–1120. doi:10.1109/TCSVT.2007.905532
22. Vidyo Home Page. http://www.vidyo.com. Accessed 12 Jan 2015
23. Sayood K (2005) Introduction to data compression, 3rd edn. Morgan Kaufmann, San Francisco
24. Xiong Z, Ramchandran K, Orchard MT, Zhang Y-Q (1999) A comparative study of DCT- and wavelet-based image coding. IEEE Trans Circuits Syst Video Technol 9(5):692–695. doi:10.1109/76.780358
25. Iverson V, McVeigh J, Reese B (2004) Real-time H.264/AVC codec on Intel architectures. Paper presented at the 11th IEEE international conference on image processing (ICIP), Singapore
26. Lappalainen V, Hallapuro A, Hämäläinen TD (2003) Performance of H. 26L video encoder on general-purpose processor. J VLSI Signal Process Syst Signal Image Video Technol 34(3):239–249
27. Diao Y, Ganesan D, Mathur G, Shenoy PJ (2007) Rethinking data management for storage-centric sensor networks. Paper presented at the 3rd biennial conference on innovative data systems research (CIDR), Pacific Grove
28. SensorScope: sensor networks for environmental monitoring. http://lcav.epfl.ch/op/edit/sensorscope-en. Accessed 12 Jan 2015
29. RD models derivation Home Page. http://pail.unist.ac.kr/papers/DELEE_COMPUTING_RD.pdf. Accessed 12 Jan 2015
30. Li D, Sun X (2006) Nonlinear integer programming. Springer, New York
31. Shoham Y, Gersho A (1988) Efficient bit allocation for an arbitrary set of quantizers. IEEE Trans Acoust Speech Signal Process 36(9):1445–1453. doi:10.1109/29.90373
32. Everett H (1963) Generalized Lagrange multiplier method for solving problems of optimum allocation of resources. Oper Res 11(3):399–417
33. Forney GD Jr (1973) The viterbi algorithm. Proc IEEE 61(3):268–278. doi:10.1109/PROC.1973.9030
34. Wang Y-C, Hsieh Y-Y, Tseng Y-C (2009) Multiresolution spatial and temporal coding in a wireless sensor network for long-term monitoring applications. IEEE Trans Comput 58(6):827–838
35. Palpanas T, Vlachos M, Keogh E, Gunopulos D, Truppel W (2004) Online amnesic approximation of streaming time series. Paper presented at the 20th international conference on data engineering (ICDE), Boston
36. Schneidewind NF (1993) Software reliability model with optimal selection of failure data. IEEE Trans Softw Eng 19(11):1095–1104
37. Chakrabarti K, Garofalakis M, Rastogi R, Shim K (2001) Approximate query processing using wavelets. VLDB J 10(2–3):199–223. doi:10.1007/s007780100049
38. Cesari M, Favalli L, Folli M (2009) Quality modeling for the medium grain scalability option of H.264/SVC. Paper presented at the 5th international ICST mobile multimedia communications conference (MOBIMEDIA), London
39. Mansour H, Krishnamurthy V, Nasiopoulos P (2008) Rate and distortion modeling of medium grain scalable video coding. Paper presented at the 15th IEEE international conference on image processing (ICIP), San Diego
40. Dai M, Loguinov D, Radha HM (2006) Rate-distortion analysis and quality control in scalable internet streaming. IEEE Trans Multimed 8(6):1135–1146. doi:10.1109/TMM.2006.884626