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A comprehensive analysis of wavelet tree based indexing schemes in GIR systems

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Abstract

Correct and accurate retrieval of geographical information is still a challenging task as the contents on Internet are growing massively every day. Indexing geographical information from Web, so that even a naive user can get the required information with lesser time, plays an important role. In this paper, a recent indexing technique, named wavelet tree is reviewed and analyzed. In fact, wavelet tree was initially designed for text compression, but further has been used for indexing and retrieval of geographical information from the Web. Among several applications of wavelet tree such as data compression, string processing, computational geometry and many more, this study emphasizes to fill the gap by discussing how wavelet tree can be used in the field of indexing of web contents.

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References

  1. Hian GC, Hongjun Lu, Chin OB, Lee TK (1996) Indexing temporal data using existing B+-trees. Data Knowl Eng 18(2):147–165

    Article  Google Scholar 

  2. Tzouramanis T, Manolopoulos Y, Lorentzos N (1999) Overlapping B+-trees: an implementation of a transaction time access method. Data Knowl Eng 29(3):381–404

    Article  Google Scholar 

  3. Bliujute R, Jensen CS, Saltenis S, Slivinskas G (1998) R-tree based indexing of nowrelative bitemporal data. In proceedings 24th conference on very large data bases (VLDB’98), 345–356.

  4. Li Z, Lee KCK, Zheng B, Lee W-C, Lee D, Wang X (2011) IR-tree: an efficient index for geographic document search. IEEE Trans Know Data Eng 23(4):9

    Article  Google Scholar 

  5. Song M, Kitagava H (2009) Managing frequent updates in R- trees for update-intensive applications IEEE Trans. Knowledge Data Eng (TKDE) 21(1):45–55

    Google Scholar 

  6. Zhou Y, Xie X, Wang C, Gong Y, Ma W-Y (2005) Hybrid index struc-tures for location-based web search, Proc 14th ACM Int’l Conf information and knowledge management (CIKM ’05), 155–162.

  7. Zhao JH, Wang XZ, Wang FY, Shen ZH, Zhou YC, Wang YL (2017) A novel approach of indexing and retrieving spatial polygons for efficient spatial region queries, ISPRS annals of the photogrammetry. Remote Sensing Spatial Inform Sci 4:131–138

    Google Scholar 

  8. Aung SN, Sein MM (2016) Index structure for nearest neighbors search with required keywords on spatial database in genetic and evolutionary computing GEC 2015. Adv Intell Syst Comput 388:457–467. https://doi.org/10.1007/978-3-319-23207-2_47

    Article  Google Scholar 

  9. Grossi R, Gupta A, Vitter JS (2003) High-order entropy-compressed text indexes, in: Proc. 14th symposium on discrete algorithms. SODA, 841–850.

  10. Brisaboa N, Cillero Y, Farina A, Ladra S, Pedreira O (2007) A new approach for document indexing using wavelet trees. DEXA Workshops 8:69–73

    Google Scholar 

  11. Ferragina P, Giancarlo R, Manzinni G (2009) The myriad virtues of wavelet trees. J Inform Comput 207:849–866. https://doi.org/10.1016/j.ic.2008.12.010

    Article  MathSciNet  MATH  Google Scholar 

  12. Gagie T, Navarro G, Puglisi SJ (2012) New algorithms on wavelet trees and applications to information retrieval. Theoretical Comput Sci Elsevier 426(427):25–41. https://doi.org/10.1016/j.tcs.2011.12.002

    Article  MathSciNet  MATH  Google Scholar 

  13. Yadav AK, Yadav D (2019) Wavelet tree based dual indexing technique for geographical search. Internat Arab J Inform Technol 16(4):624–632

    Google Scholar 

  14. Gagie T, Puglisi SJ, Turpin A (2009) Range quantile queries: another virtue of wavelet trees international symposium on string processing and information retrieval. Springer, pp 1–6

    Google Scholar 

  15. Makris C (2012) Wavelet trees: a survey. Comput Sci Inf Syst 9(2):585–625

    Article  Google Scholar 

  16. Nieves R, Brisaboa Miguel, Luaces R, Gonzalo Navarro, Diego Seco (2009) A new point access method based on wavelet trees, international conference on conceptual modelling. Springer, 297–306.

  17. Mishra SP, Prasad R, Singh G (2018) Fast pattern matching in compressed text using wavelet tree. IETE J Res 64(1):87–99

    Article  Google Scholar 

  18. Barbay J, Gagie T, Navarro G, Nekrich Y (2010) Alphabet partitioning for compressed rank/select and applications. ISAAC 2:315–326

    MathSciNet  MATH  Google Scholar 

  19. Farina A, Ladra S, Pedreira O, Places A (2009) Rank and select for succinct data structures. Electr Notes Theor Comput Sci 236:131–145

    Article  Google Scholar 

  20. Chazelle B (1998) A functional approach to data structures and its use in multidimensional searching. SIAMJ Comput 17(3):427–462

    Article  MathSciNet  Google Scholar 

  21. Jacobson G. (1989) Space-efficient static trees and graphs. Proc.30th FOCS, 549–554.

  22. Golynski A (2006) Optimal lower bounds for rank and select indexes. ICALP LNCS 4051:370–381

    MathSciNet  MATH  Google Scholar 

  23. Golynski A (2007) Optimal lower bounds for rank and select indexes. Theor Comput Sci 387(3):348–359

    Article  MathSciNet  Google Scholar 

  24. Foschini L, Grossi R, Gupta A, Vitter J (2006) When indexing equals compression: experiments with compressing suffix arrays and applications. ACM Trans Algorith 2(4):611–639

    Article  MathSciNet  Google Scholar 

  25. Raman R, Raman V, Rao S (2002) Succinct indexable dictionaries with applications to encoding k-ary trees and multisets. SODA 4:233–242

    MATH  Google Scholar 

  26. Ferragina P, Manzini G, Makinen V, Navarro G (2007) Compressed representations of sequences and full-text indexes. ACM Trans Algorithms 3(2):9. https://doi.org/10.1145/1240233.1240243

    Article  MathSciNet  MATH  Google Scholar 

  27. Lee S, Park K (2007) Dynamic rank-select structures with applications to run-length encoded texts. In Proc. 18th CPM. LNCS 4580:95–106

    MATH  Google Scholar 

  28. Makinen V, Navarro G (2006) Dynamic entropy-compressed sequences and full-text indexes. CPM LNCS 4009:307–318

    MathSciNet  MATH  Google Scholar 

  29. Burrows M, Wheeler DJ (1994) A block-sorting lossless data compression algorithm. Technical report.

  30. Claude F, Navarro G (2008) Practical rank/select queries over arbitrary sequences. SPIRE

    Book  Google Scholar 

  31. Gautam A (1984) R-trees: a dynamic index structure for spatial searching. ACM SIGMOD

    Google Scholar 

  32. Comer D (1979) The ubiquitous B-Tree. ACM Comput Surv 11(2):121–137

    Article  MathSciNet  Google Scholar 

  33. Markl V (1999) MISTRAL: processing relational queries using a multidimensional access technique. Infix Verlag

    MATH  Google Scholar 

  34. Chen S, Ooi BC, Tan KL, Nascimento MA (2008) ST2B-tree: a self-tunable spatio-temporal b+-tree index for moving objects, Proceedings of the 2008 ACM SIGMOD international conference on Management of data, 29–42, https://doi.org/10.1145/1376616.1376622

  35. Lin HY (2008) A compact index structure with high data retrieval efficiency, 2008 international conference on service systems and service management. 2008, pp. 1–5, doi: https://doi.org/10.1109/ICSSSM.2008.4598528.

  36. Sellis TK, Roussopoulos N, Faloutsos C (1987) The R+-Tree: a dynamic index for multi-dimensional objects, Proc. VLDB 13th international conference on very large data Bases, 507–518.

  37. Beckmann N, Kriegel HP, Schneider R, Seeger B (1990) The R*-tree: an efficient and robust access method for points and rectangles, Proc. ACM SIGMOD international conference on Management of data, 322–331.

  38. Berchtold S, Keim DA, Kriegel HP (1996) The X-tree: an index structure for high-dimensional data, proc. 22th international conference on very large data bases, 28–39.

  39. Paolo C, Patella M, Pavel Z (1997) M-tree an efficient access method for similarity search in metric spaces, vldb '97: proceedings of the 23rd international conference on very large data bases, 426–435.

  40. Kamel I, Faloutsos C (1994) Hilbert R-tree: an improved r-tree using fractals, proc. 20th international conference on very large data bases, 500–509.

  41. Hua Y, Xiao B, Wang J (2009) BR-tree: a scalable prototype for supporting multiple queries of multidimensional data. IEEE Trans Comput 58(12):1585–1598. https://doi.org/10.1109/TC.2009.97

    Article  MathSciNet  MATH  Google Scholar 

  42. Huaqing M, Fuling B (2007) Design and implementation of QR+Tree index algorithms, international conference on digital object identifier, 5987-5990.

  43. Patel P, Garg D (2012) Comparison of advance tree data structures. Internat J Comput Appl 41(2):11–20

    Google Scholar 

  44. Yadav AK, Yadav D, Prasad R (2016) Efficient textual web retrieval using wavelet tree. Int J Inf Retr Res 6(4):16–29

    Google Scholar 

  45. Shun J (2020) Improved parallel construction of wavelet trees and rank/select structures. Inform Comput 5:273

    MathSciNet  MATH  Google Scholar 

  46. Yadav AK, Yada D (2015) Wavelet tree based hybrid geo-textual indexing technique for geographical search. Indian J Sci Technol 8(33):1–7

    Article  Google Scholar 

  47. Yadav AK, Rai D (2016) Efficient methods to generate inverted indexes for ir information systems design and intelligent applications. Springer, pp 431–440

    Google Scholar 

  48. Gupta A, Yadav D (2021) A novel approach to perform context-based automatic spoken document retrieval of political speeches based on wavelet tree indexing. Multimed Tools Appl. https://doi.org/10.1007/s11042-021-10800-8

    Article  Google Scholar 

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Authors and Affiliations

  1. AKTU, Lucknow (UP), India

    Dharmendra Kumar

  2. IET, Lucknow (UP), India

    D. S. Yadav

  3. NIT, Hamirpur (HP), India

    Divakar Yadav

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  1. Dharmendra Kumar
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  2. D. S. Yadav
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  3. Divakar Yadav
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Correspondence to Divakar Yadav.

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Kumar, D., Yadav, D.S. & Yadav, D. A comprehensive analysis of wavelet tree based indexing schemes in GIR systems. Int. j. inf. tecnol. 13, 2227–2236 (2021). https://doi.org/10.1007/s41870-021-00683-1

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  • DOI: https://doi.org/10.1007/s41870-021-00683-1

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