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Approximate Queries with Adaptive Processing

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Advanced Query Processing

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 36))

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Abstract

The traditional query processing approach, by which queries are executed exactly according to a query execution plan selected before query execution starts, breaks down in heterogeneous and dynamic processing environments that are becoming more and more common as query processing contexts. In such environments, queries are often relaxed and query processing is forced to be adaptive and approximate, either to cope with limited processing resources or with limited data knowledge and data heterogeneity.When approximation and adaptivity are applied in order to cope with limited processing resources, possibly sacrificing result quality, we refer to as Quality of Service (QoS)-oriented techniques. On the other hand, when they are a means to improve the quality of results, in presence of limited data knowledge and data heterogeneity, we refer to as Quality of Data (QoD)-oriented techniques. While both kinds of approximation techniques have been proposed, most adaptive solutions are QoS-oriented. In this chapter, we first survey both kinds of approximation and introduce adaptive query processing techniques; then, we show that techniques which apply a QoD-oriented approximation in a QoD-oriented adaptive way, though demonstrated potentially useful on some examples, are still largely neglected.

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References

  1. Abadi, D.J., Ahmad, Y., Balazinska, M., Çetintemel, U., Cherniack, M., Hwang, J.H., Lindner, W., Maskey, A., Rasin, A., Ryvkina, E., Tatbul, N., Xing, Y., Zdonik, S.B.: The Design of the Borealis Stream Processing Engine. In: CIDR, pp. 277–289 (2005)

    Google Scholar 

  2. Acharya, S., Gibbons, P.B., Poosala, V.: Congressional Samples for Approximate Answering of Group-By Queries. In: SIGMOD Conference, pp. 487–498 (2000)

    Google Scholar 

  3. Acharya, S., Gibbons, P.B., Poosala, V., Ramaswamy, S.: Join Synopses for Approximate Query Answering. In: SIGMOD Conference, pp. 275–286 (1999)

    Google Scholar 

  4. Agrawal, S., Chaudhuri, S., Das, G., Gionis, A.: Automated Ranking of Database Query Results. In: CIDR (2003)

    Google Scholar 

  5. Amato, G., Rabitti, F., Savino, P., Zezula, P.: Region Proximity in Metric Spaces and its Use for Approximate Similarity Search. ACM Trans. Inf. Syst. 21(2), 192–227 (2003)

    Article  Google Scholar 

  6. Amer-Yahia, S., Cho, S., Srivastava, D.: Tree Pattern Relaxation. In: Jensen, C.S., Jeffery, K., Pokorný, J., Šaltenis, S., Bertino, E., Böhm, K., Jarke, M. (eds.) EDBT 2002. LNCS, vol. 2287, pp. 496–513. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  7. Amer-Yahia, S., Koudas, N., Marian, A., Srivastava, D., Toman, D.: Structure and Content Scoring for XML. In: VLDB, pp. 361–372 (2005)

    Google Scholar 

  8. Arasu, A., Manku, G.S.: Approximate Counts and Quantiles over Sliding Windows. In: PODS, pp. 286–296 (2004)

    Google Scholar 

  9. Arya, S., Mount, D.M., Netanyahu, N.S., Silverman, R., Wu, A.Y.: An Optimal Algorithm for Approximate Nearest Neighbor Searching Fixed Dimensions. J. ACM 45(6), 891–923 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  10. Augsten, N., Böhlen, M.H., Dyreson, C.E., Gamper, J.: Approximate Joins for Data-Centric XML. In: ICDE, pp. 814–823 (2008)

    Google Scholar 

  11. Avnur, R., Hellerstein, J.M.: Eddies: Continuously Adaptive Query Processing. In: SIGMOD Conference, pp. 261–272 (2000)

    Google Scholar 

  12. Azevedo, L.G., Zimbrão, G., de Souza, J.M.: Approximate Query Processing in Spatial Databases Using Raster Signatures. In: Advances in Geoinformatics, pp. 53–72 (2006)

    Google Scholar 

  13. Babcock, B., Babu, S., Datar, M., Motwani, R., Thomas, D.: Operator Scheduling in Data Stream Systems. VLDB J. 13(4), 333–353 (2004)

    Article  Google Scholar 

  14. Babcock, B., Datar, M., Motwani, R.: Sampling From a Moving Window over Streaming Data. In: SODA, pp. 633–634 (2002)

    Google Scholar 

  15. Babcock, B., Datar, M., Motwani, R.: Load Shedding for Aggregation Queries over Data Streams. In: ICDE, pp. 350–361 (2004)

    Google Scholar 

  16. Babu, S., Bizarro, P.: Adaptive Query Processing in the Looking Glass. In: CIDR, pp. 238–249 (2005)

    Google Scholar 

  17. Babu, S., Bizarro, P., DeWitt, D.J.: Proactive Re-optimization. In: SIGMOD Conference, pp. 107–118 (2005)

    Google Scholar 

  18. Babu, S., Motwani, R., Munagala, K., Nishizawa, I., Widom, J.: Adaptive Ordering of Pipelined Stream Filters. In: SIGMOD Conference, pp. 407–418 (2004)

    Google Scholar 

  19. Babu, S., Munagala, K., Widom, J., Motwani, R.: Adaptive Caching for Continuous Queries. In: ICDE, pp. 118–129 (2005)

    Google Scholar 

  20. Babu, S., Srivastava, U., Widom, J.: Exploiting k-Constraints to Reduce Memory Overhead in Continuous Queries over Data Streams. ACM Trans. Database Syst. 29(3), 545–580 (2004)

    Article  Google Scholar 

  21. Babu, S., Widom, J.: StreaMon: An Adaptive Engine for Stream Query Processing. In: SIGMOD Conference, pp. 931–932 (2004)

    Google Scholar 

  22. Bar-Yossef, Z., Jayram, T.S., Kumar, R., Sivakumar, D., Trevisan, L.: Counting Distinct Elements in a Data Stream. In: Rolim, J.D.P., Vadhan, S.P. (eds.) RANDOM 2002. LNCS, vol. 2483, pp. 1–10. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  23. Barbará, D., DuMouchel, W., Faloutsos, C., Haas, P.J., Hellerstein, J.M., Ioannidis, Y.E., Jagadish, H.V., Johnson, T., Ng, R.T., Poosala, V., Ross, K.A., Sevcik, K.C.: The New Jersey Data Reduction Report. IEEE Data Eng. Bull. 20(4), 3–45 (1997)

    Google Scholar 

  24. Belussi, A., Boucelma, O., Catania, B., Lassoued, Y., Podestà, P.: Towards Similarity-Based Topological Query Languages. In: Grust, T., Höpfner, H., Illarramendi, A., Jablonski, S., Fischer, F., Müller, S., Patranjan, P.-L., Sattler, K.-U., Spiliopoulou, M., Wijsen, J. (eds.) EDBT 2006. LNCS, vol. 4254, pp. 675–686. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  25. Bizarro, P., Babu, S., DeWitt, D.J., Widom, J.: Content-Based Routing: Different Plans for Different Data. In: VLDB, pp. 757–768 (2005)

    Google Scholar 

  26. Börzsönyi, S., Kossmann, D., Stocker, K.: The Skyline Operator. In: ICDE, pp. 421–430 (2001)

    Google Scholar 

  27. Braumandl, R., Keidl, M., Kemper, A., Kossmann, D., Kreutz, A., Seltzsam, S., Stocker, K.: ObjectGlobe: Ubiquitous Query Processing on the Internet. VLDB J. 10(1), 48–71 (2001)

    MATH  Google Scholar 

  28. Carney, D., Çetintemel, U., Cherniack, M., Convey, C., Lee, S., Seidman, G., Stonebraker, M., Tatbul, N., Zdonik, S.B.: Monitoring Streams - A New Class of Data Management Applications. In: VLDB, pp. 215–226 (2002)

    Google Scholar 

  29. Catania, B., Guerrini, G.: Towards Adaptively Approximated Search in Distributed Architectures. In: Vakali, A., Jain, L.C. (eds.) New Directions in Web Data Management 1. Studies in Computational Intelligence, vol. 331, pp. 171–212. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  30. Chakrabarti, K., Garofalakis, M.N., Rastogi, R., Shim, K.: Approximate Query Processing using Wavelets. VLDB J. 10(2-3), 199–223 (2001)

    MATH  Google Scholar 

  31. Chandrasekaran, S., Cooper, O., Deshpande, A., Franklin, M.J., Hellerstein, J.M., Hong, W., Krishnamurthy, S., Madden, S., Raman, V., Reiss, F., Shah, M.A.: TelegraphCQ: Continuous Dataflow Processing for an Uncertain World. In: CIDR (2003)

    Google Scholar 

  32. Chaudhuri, S., Das, G., Narasayya, V.R.: Optimized Stratified Sampling for Approximate Query Processing. ACM Trans. Database Syst. 32(2), 9 (2007)

    Article  Google Scholar 

  33. Chaudhuri, S., Ganti, V., Kaushik, R.: A Primitive Operator for Similarity Joins in Data Cleaning. In: ICDE, p. 5 (2006)

    Google Scholar 

  34. Chen, J., DeWitt, D.J., Tian, F., Wang, Y.: NiagaraCQ: A Scalable Continuous Query System for Internet Databases. In: SIGMOD Conference, pp. 379–390 (2000)

    Google Scholar 

  35. Ciaccia, P., Patella, M.: PAC Nearest Neighbor Queries: Approximate and Controlled Search in High-Dimensional and Metric Spaces. In: ICDE, p. 244 (2000)

    Google Scholar 

  36. Considine, J., Hadjieleftheriou, M., Li, F., Byers, J.W., Kollios, G.: Robust Approximate Aggregation in Sensor Data Management Systems. ACM Trans. Database Syst. 34(1) (2009)

    Google Scholar 

  37. Considine, J., Li, F., Kollios, G., Byers, J.W.: Approximate Aggregation Techniques for Sensor Databases. In: ICDE, pp. 449–460 (2004)

    Google Scholar 

  38. Cormode, G., Garofalakis, M.N.: Sketching Streams Through the Net: Distributed Approximate Query Tracking. In: VLDB, pp. 13–24 (2005)

    Google Scholar 

  39. Corral, A., Cañadas, J., Vassilakopoulos, M.: Approximate Algorithms for Distance-Based Queries in High-Dimensional Data Spaces Using R-Trees. In: Manolopoulos, Y., Návrat, P. (eds.) ADBIS 2002. LNCS, vol. 2435, pp. 163–176. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  40. Das, A., Gehrke, J., Riedewald, M.: Approximate Join Processing Over Data Streams. In: SIGMOD Conference, pp. 40–51 (2003)

    Google Scholar 

  41. Das, G., Gunopulos, D., Koudas, N., Sarkas, N.: Ad-hoc Top-k Query Answering for Data Streams. In: VLDB, pp. 183–194 (2007)

    Google Scholar 

  42. Datar, M., Gionis, A., Indyk, P., Motwani, R.: Maintaining Stream Statistics over Sliding Windows. SIAM J. Comput. 31(6), 1794–1813 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  43. Deshpande, A., Guestrin, C., Madden, S., Hellerstein, J.M., Hong, W.: Model-Driven Data Acquisition in Sensor Networks. In: VLDB, pp. 588–599 (2004)

    Google Scholar 

  44. Deshpande, A., Ives, Z.G., Raman, V.: Adaptive Query Processing. Foundations and Trends in Databases 1(1), 1–140 (2007)

    Article  MATH  Google Scholar 

  45. Elmagarmid, A.K., Ipeirotis, P.G., Verykios, V.S.: Duplicate Record Detection: A Survey. IEEE Trans. Knowl. Data Eng. 19(1), 1–16 (2007)

    Article  Google Scholar 

  46. Eurviriyanukul, K., Paton, N.W., Fernandes, A.A.A., Lynden, S.J.: Adaptive Join Processing in Pipelined Plans. In: EDBT, pp. 183–194 (2010)

    Google Scholar 

  47. Gedik, B., Wu, K.L., Yu, P.S., Liu, L.: Adaptive Load Shedding for Windowed Stream Joins. In: CIKM, pp. 171–178 (2005)

    Google Scholar 

  48. Gedik, B., Wu, K.L., Yu, P.S., Liu, L.: CPU Load Shedding for Binary Stream Joins. Knowl. Inf. Syst. 13(3), 271–303 (2007)

    Article  Google Scholar 

  49. Gedik, B., Wu, K.L., Yu, P.S., Liu, L.: GrubJoin: An Adaptive, Multi-Way, Windowed Stream Join with Time Correlation-Aware CPU Load Shedding. IEEE Trans. Knowl. Data Eng. 19(10), 1363–1380 (2007)

    Article  Google Scholar 

  50. Gibbons, P.B., Matias, Y.: Synopsis Data Structures for Massive Data Sets. In: Abello, J.M., Vitter, J.S. (eds.) External Memory Algorithms, pp. 39–70. American Mathematical Society (1999)

    Google Scholar 

  51. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning, 1st edn. Addison-Wesley Longman Publishing Co., Inc., Boston (1989)

    MATH  Google Scholar 

  52. Gounaris, A., Smith, J., Paton, N.W., Sakellariou, R., Fernandes, A.A.A., Watson, P.: Adaptive Workload Allocation in Query Processing in Autonomous Heterogeneous Environments. Distributed and Parallel Databases 25(3), 125–164 (2009)

    Article  Google Scholar 

  53. Gravano, L., Ipeirotis, P.G., Jagadish, H.V., Koudas, N., Muthukrishnan, S., Srivastava, D.: Approximate String Joins in a Database (Almost) for Free. In: VLDB, pp. 491–500 (2001)

    Google Scholar 

  54. Guha, S., Jagadish, H.V., Koudas, N., Srivastava, D., Yu, T.: Approximate XML Joins. In: SIGMOD Conference, pp. 287–298 (2002)

    Google Scholar 

  55. Guo, L., Shao, F., Botev, C., Shanmugasundaram, J.: XRANK: Ranked Keyword Search over XML Documents. In: SIGMOD Conference, pp. 16–27 (2003)

    Google Scholar 

  56. Guttman, A.: R-Trees: A Dynamic Index Structure for Spatial Searching. In: SIGMOD Conference, pp. 47–57 (1984)

    Google Scholar 

  57. Han, D., Wang, G., Xiao, C., Zhou, R.: Load Shedding for Window Joins over Streams. J. Comput. Sci. Technol. 22(2), 182–189 (2007)

    Article  Google Scholar 

  58. Ilyas, I.F., Aref, W.G., Elmagarmid, A.K., Elmongui, H.G., Shah, R., Vitter, J.S.: Adaptive Rank-aware Query Optimization in Relational Databases. ACM Trans. Database Syst. 31(4), 1257–1304 (2006)

    Article  Google Scholar 

  59. Ilyas, I.F., Beskales, G., Soliman, M.A.: A Survey of Top-k Query Processing Techniques in Relational Database Systems. ACM Comput. Surv. 40(4) (2008)

    Google Scholar 

  60. Ioannidis, Y.E., Kang, Y.: Randomized Algorithms for Optimizing Large Join Queries. SIGMOD Rec. 19, 312–321 (1990)

    Article  Google Scholar 

  61. Ioannidis, Y.E., Ng, R.T., Shim, K., Sellis, T.K.: Parametric Query Optimization. In: VLDB, pp. 103–114 (1992)

    Google Scholar 

  62. Ioannidis, Y.E., Poosala, V.: Histogram-Based Approximation of Set-Valued Query-Answers. In: VLDB, pp. 174–185 (1999)

    Google Scholar 

  63. Ives, Z.G., Deshpande, A., Raman, V.: Adaptive Query Processing: Why, How, When, and What Next? In: VLDB, pp. 1426–1427 (2007)

    Google Scholar 

  64. Ives, Z.G., Florescu, D., Friedman, M., Levy, A.Y., Weld, D.S.: An Adaptive Query Execution System for Data Integration. In: SIGMOD Conference, pp. 299–310 (1999)

    Google Scholar 

  65. Ives, Z.G., Halevy, A.Y., Weld, D.S.: Adapting to Source Properties in Processing Data Integration Queries. In: SIGMOD Conference, pp. 395–406 (2004)

    Google Scholar 

  66. Jiao, Y.: Maintaining Stream Statistics over Multiscale Sliding Windows. ACM Trans. Database Syst. 31, 1305–1334 (2006)

    Article  Google Scholar 

  67. Kabra, N., DeWitt, D.J.: Efficient Mid-Query Re-Optimization of Sub-Optimal Query Execution Plans. In: SIGMOD Conference, pp. 106–117 (1998)

    Google Scholar 

  68. Kadlag, A., Wanjari, A.V., Freire, J.-L., Haritsa, J.R.: Supporting Exploratory Queries in Databases. In: Lee, Y., Li, J., Whang, K.-Y., Lee, D. (eds.) DASFAA 2004. LNCS, vol. 2973, pp. 594–605. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  69. Kang, J., Naughton, J.F., Viglas, S.: Evaluating Window Joins over Unbounded Streams. In: ICDE, pp. 341–352 (2003)

    Google Scholar 

  70. Kirkpatrick, S., Gelatt Jr., C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220, 671–680 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  71. Kossmann, D., Ramsak, F., Rost, S.: Shooting Stars in the Sky: An Online Algorithm for Skyline Queries. In: VLDB, pp. 275–286 (2002)

    Google Scholar 

  72. Koudas, N., Li, C., Tung, A.K.H., Vernica, R.: Relaxing Join and Selection Queries. In: VLDB, pp. 199–210 (2006)

    Google Scholar 

  73. Koudas, N., Sarawagi, S., Srivastava, D.: Record Linkage: Similarity Measures and Algorithms. In: SIGMOD Conference, pp. 802–803 (2006)

    Google Scholar 

  74. Koudas, N., Srivastava, D.: Approximate Joins: Concepts and Techniques. In: VLDB, p. 1363 (2005)

    Google Scholar 

  75. Kulkarni, D., Ravishankar, C.V.: iJoin: Importance-Aware Join Approximation Over Data Streams. In: Ludäscher, B., Mamoulis, N. (eds.) SSDBM 2008. LNCS, vol. 5069, pp. 541–548. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  76. Lee, D.: Query Relaxation for XML Model. Ph.D. thesis. University of California (2002)

    Google Scholar 

  77. Lengu, R., Missier, P., Fernandes, A.A.A., Guerrini, G., Mesiti, M.: Time-completeness Trade-offs in Record Linkage using Adaptive Query Processing. In: EDBT, pp. 851–861 (2009)

    Google Scholar 

  78. Li, Q., Shao, M., Markl, V., Beyer, K.S., Colby, L.S., Lohman, G.M.: Adaptively Reordering Joins during Query Execution. In: ICDE, pp. 26–35 (2007)

    Google Scholar 

  79. Liu, B., Zhu, Y., Jbantova, M., Momberger, B., Rundensteiner, E.A.: A Dynamically Adaptive Distributed System for Processing Complex Continuous Queries. In: VLDB, pp. 1338–1341 (2005)

    Google Scholar 

  80. Liu, H., Wang, X., Yang, Y.: Comments on ”An Integrated Efficient Solution for Computing Frequent and Top-k Elements in Data Streams”. ACM Trans. Database Syst. 35(2) (2010)

    Google Scholar 

  81. Liu, X., Dong, X.L., Ooi, B.C., Srivastava, D.: Online Data Fusion. In: VLDB (2011)

    Google Scholar 

  82. Liu, Y., Li, J., Gao, H., Fang, X.: Enabling epsilon-Approximate Querying in Sensor Networks. PVLDB 2(1), 169–180 (2009)

    Google Scholar 

  83. Lu, H., Zhou, Y., Haustad, J.: Continuous Skyline Monitoring Over Distributed Data Streams. In: Gertz, M., Ludäscher, B. (eds.) SSDBM 2010. LNCS, vol. 6187, pp. 565–583. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  84. Madden, S., Shah, M.A., Hellerstein, J.M., Raman, V.: Continuously Adaptive Continuous Queries over Streams. In: SIGMOD Conference, pp. 49–60 (2002)

    Google Scholar 

  85. Manku, G.S., Motwani, R.: Approximate Frequency Counts over Data Streams. In: VLDB, pp. 346–357 (2002)

    Google Scholar 

  86. Marian, A., Amer-Yahia, S., Koudas, N., Srivastava, D.: Adaptive Processing of Top-k Queries in XML. In: ICDE, pp. 162–173 (2005)

    Google Scholar 

  87. Markl, V., Raman, V., Simmen, D.E., Lohman, G.M., Pirahesh, H.: Robust Query Processing through Progressive Optimization. In: SIGMOD Conference, pp. 659–670 (2004)

    Google Scholar 

  88. Metwally, A., Agrawal, D., Abbadi, A.E.: An Integrated Efficient Solution for Computing Frequent and Top-k Elements in Data Streams. ACM Trans. Database Syst. 31(3), 1095–1133 (2006)

    Article  Google Scholar 

  89. Mishra, C., Koudas, N.: Interactive Query Refinement. In: EDBT, pp. 862–873 (2009)

    Google Scholar 

  90. Motwani, R., Widom, J., Arasu, A., Babcock, B., Babu, S., Datar, M., Manku, G.S., Olston, C., Rosenstein, J., Varma, R.: Query Processing, Approximation, and Resource Management in a Data Stream Management System. In: CIDR (2003)

    Google Scholar 

  91. Mouratidis, K., Bakiras, S., Papadias, D.: Continuous Monitoring of Top-k Queries over Sliding Windows. In: SIGMOD Conference, pp. 635–646 (2006)

    Google Scholar 

  92. Mozafari, B., Zaniolo, C.: Optimal Load Shedding with Aggregates and Mining Queries. In: ICDE, pp. 76–88 (2010)

    Google Scholar 

  93. Munagala, K., Srivastava, U., Widom, J.: Optimization of Continuous Queries with Shared Expensive Filters. In: PODS, pp. 215–224 (2007)

    Google Scholar 

  94. Olston, C., Jiang, J., Widom, J.: Adaptive Filters for Continuous Queries over Distributed Data Streams. In: SIGMOD Conference, pp. 563–574 (2003)

    Google Scholar 

  95. Pan, L., Luo, J., Li, J.: Probing Queries in Wireless Sensor Networks. In: ICDCS, pp. 546–553 (2008)

    Google Scholar 

  96. Papadias, D., Arkoumanis, D.: Approximate Processing of Multiway Spatial Joins in Very Large Databases. In: Jensen, C.S., Jeffery, K., Pokorný, J., Šaltenis, S., Bertino, E., Böhm, K., Jarke, M. (eds.) EDBT 2002. LNCS, vol. 2287, pp. 179–196. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  97. Papadias, D., Mantzourogiannis, M., Kalnis, P., Mamoulis, N., Ahmad, I.: Content-based Retrieval using Heuristic Search. In: SIGIR, pp. 168–175. ACM, New York (1999)

    Google Scholar 

  98. Papadias, D., Tao, Y., Fu, G., Seeger, B.: Progressive Skyline Computation in Database Systems. ACM Trans. Database Syst. 30(1), 41–82 (2005)

    Article  Google Scholar 

  99. Poosala, V., Ganti, V., Ioannidis, Y.E.: Approximate Query Answering using Histograms. IEEE Data Eng. Bull. 22(4), 5–14 (1999)

    Google Scholar 

  100. Raman, V., Deshpande, A., Hellerstein, J.M.: Using State Modules for Adaptive Query Processing. In: ICDE, p. 353 (2003)

    Google Scholar 

  101. Reiss, F., Hellerstein, J.M.: Data Triage: An Adaptive Architecture for Load Shedding in TelegraphCQ. In: VLDB (2004)

    Google Scholar 

  102. Rundensteiner, E.A., Ding, L., Sutherland, T.M., Zhu, Y., Pielech, B., Mehta, N.: CAPE: Continuous Query Engine with Heterogeneous-Grained Adaptivity. In: VLDB, pp. 1353–1356 (2004)

    Google Scholar 

  103. Rusu, F., Dobra, A.: Sketching Sampled Data Streams. In: ICDE, pp. 381–392 (2009)

    Google Scholar 

  104. Sanz, I., Mesiti, M., Guerrini, G., Llavori, R.B.: Fragment-based Approximate Retrieval in Highly Heterogeneous XML Collections. Data Knowl. Eng. 64(1), 266–293 (2008)

    Article  Google Scholar 

  105. Sarawagi, S., Kirpal, A.: Efficient Set Joins on Similarity Predicates. In: SIGMOD Conference, pp. 743–754 (2004)

    Google Scholar 

  106. Sarkas, N., Das, G., Koudas, N., Tung, A.K.H.: Categorical Skylines for Streaming Data. In: SIGMOD Conference, pp. 239–250 (2008)

    Google Scholar 

  107. Shah, M.A., Hellerstein, J.M., Chandrasekaran, S., Franklin, M.J.: Flux: An Adaptive Partitioning Operator for Continuous Query Systems. In: ICDE, pp. 25–36 (2003)

    Google Scholar 

  108. Silberstein, A., Braynard, R., Ellis, C.S., Munagala, K., Yang, J.: A Sampling-Based Approach to Optimizing Top-k Queries in Sensor Networks. In: ICDE, p. 68 (2006)

    Google Scholar 

  109. Silva, Y.N., Aref, W.G., Ali, M.H.: The similarity join database operator. In: ICDE, pp. 892–903 (2010)

    Google Scholar 

  110. Skordylis, A., Trigoni, N., Guitton, A.: A Study of Approximate Data Management Techniques for Sensor Networks. In: Intelligent Solutions in Embedded Systems, pp. 1–12 (2006)

    Google Scholar 

  111. Spiegel, J., Polyzotis, N.: TuG Synopses for Approximate Query Answering. ACM Trans. Database Syst. 34(1) (2009)

    Google Scholar 

  112. Srivastava, U., Widom, J.: Memory-Limited Execution of Windowed Stream Joins. In: VLDB, pp. 324–335 (2004)

    Google Scholar 

  113. Sun, S., Huang, Z., Zhong, H., Dai, D., Liu, H., Li, J.: Efficient Monitoring of Skyline Queries over Distributed Data Streams. Knowl. Inf. Syst. 25(3), 575–606 (2010)

    Article  Google Scholar 

  114. Tan, K.L., Eng, P.K., Ooi, B.C.: Efficient Progressive Skyline Computation. In: VLDB, pp. 301–310 (2001)

    Google Scholar 

  115. Tao, Y., Papadias, D.: Maintaining Sliding Window Skylines on Data Streams. IEEE Trans. Knowl. Data Eng. 18(2), 377–391 (2006)

    Google Scholar 

  116. Tatbul, N., Çetintemel, U., Zdonik, S.B., Cherniack, M., Stonebraker, M.: Load Shedding in a Data Stream Manager. In: VLDB, pp. 309–320 (2003)

    Google Scholar 

  117. Tatbul, N., Zdonik, S.B.: Window-Aware Load Shedding for Aggregation Queries over Data Streams. In: VLDB, pp. 799–810 (2006)

    Google Scholar 

  118. Theobald, M., Bast, H., Majumdar, D., Schenkel, R., Weikum, G.: TopX: Efficient and Versatile Top-k Query Processing for Semistructured Data. VLDB J. 17(1), 81–115 (2008)

    Article  Google Scholar 

  119. Tian, F., DeWitt, D.J.: Tuple Routing Strategies for Distributed Eddies. In: VLDB, pp. 333–344 (2003)

    Google Scholar 

  120. Tirthapura, S., Xu, B., Busch, C.: Sketching Asynchronous Data streams over Sliding Windows. Distributed Computing 20(5), 359–374 (2008)

    Article  Google Scholar 

  121. Urhan, T., Franklin, M.J., Amsaleg, L.: Cost Based Query Scrambling for Initial Delays. In: SIGMOD Conference, pp. 130–141 (1998)

    Google Scholar 

  122. Vitter, J.S., Wang, M.: Approximate Computation of Multidimensional Aggregates of Sparse Data Using Wavelets. In: SIGMOD Conference, pp. 193–204 (1999)

    Google Scholar 

  123. Wang, J., Li, G., Feng, J.: Trie-Join: Efficient Trie-based String Similarity Joins with Edit-Distance Constraints. PVLDB 3(1), 1219–1230 (2010)

    Google Scholar 

  124. Weis, M., Naumann, F.: DogmatiX Tracks down Duplicates in XML. In: SIGMOD Conference, pp. 431–442 (2005)

    Google Scholar 

  125. Wilschut, A.N., Apers, P.M.G.: Dataflow Query Execution in a Parallel Main-Memory Environment. In: PDIS, pp. 68–77 (1991)

    Google Scholar 

  126. Wu, J., Tan, K.-L., Zhou, Y.: QoS-Oriented Multi-Query Scheduling Over Data Streams. In: Zhou, X., Yokota, H., Deng, K., Liu, Q. (eds.) DASFAA 2009. LNCS, vol. 5463, pp. 215–229. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  127. Yang, Y., Krämer, J., Papadias, D., Seeger, B.: HybMig: A Hybrid Approach to Dynamic Plan Migration for Continuous Queries. IEEE Trans. Knowl. Data Eng. 19(3), 398–411 (2007)

    Article  Google Scholar 

  128. Yi, K., Li, F., Cormode, G., Hadjieleftheriou, M., Kollios, G., Srivastava, D.: Small Synopses for Group-by Query Verification on Outsourced Data Streams. ACM Trans. Database Syst. 34(3) (2009)

    Google Scholar 

  129. Yu, H., Hwang, S.-w., Chang, K.C.C.: Enabling Ad-hoc Ranking for Data Retrieval. In: ICDE, pp. 514–515 (2005)

    Google Scholar 

  130. Zhang, Z., Hwang, S.-w., Chang, K.C.C., Wang, M., Lang, C.A.3., Chang, Y.C.: Boolean Ranking: Querying a Database by k-constrained Optimization. In: SIGMOD Conference, pp. 359–370 (2006)

    Google Scholar 

  131. Zhou, X., Gaugaz, J., Balke, W.T., Nejdl, W.: Query Relaxation using Malleable Schemas. In: SIGMOD Conference, pp. 545–556 (2007)

    Google Scholar 

  132. Zimbrao, G., de Souza, J.M.: A Raster Approximation For Processing of Spatial Joins. In: VLDB, pp. 558–569 (1998)

    Google Scholar 

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  1. University of Genoa, Genoa, Italy

    Barbara Catania & Giovanna Guerrini

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  1. Barbara Catania
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  2. Giovanna Guerrini
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  1. Dipto. Informatica e Science, dell'Informazione, Università Genova, Via Dodecaneso 35, Genova, 16146, Italy

    Barbara Catania

  2. , School of Electrical and Information, University of South Australia, Mawson Lakes Campus, Adelaide, SA 5095, South Australia, Australia

    Lakhmi C. Jain

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Catania, B., Guerrini, G. (2013). Approximate Queries with Adaptive Processing. In: Catania, B., Jain, L. (eds) Advanced Query Processing. Intelligent Systems Reference Library, vol 36. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28323-9_10

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