Review on geoscience time series big data similarity measurement and index method

He Zhenwen; Wu Chonglong; Liu Gang; Tian Yiping; Zhang Xialin; Chen Qiyu

doi:10.19509/j.cnki.dzkq.2020.0406

Volume 39 Issue 4

Jul. 2020

Turn off MathJax

Article Contents

Article Navigation > Bulletin of Geological Science and Technology > 2020 > 39(4): 44-50

He Zhenwen, Wu Chonglong, Liu Gang, Tian Yiping, Zhang Xialin, Chen Qiyu. Review on geoscience time series big data similarity measurement and index method[J]. Bulletin of Geological Science and Technology, 2020, 39(4): 44-50. doi: 10.19509/j.cnki.dzkq.2020.0406

Citation:

He Zhenwen, Wu Chonglong, Liu Gang, Tian Yiping, Zhang Xialin, Chen Qiyu. Review on geoscience time series big data similarity measurement and index method[J]. Bulletin of Geological Science and Technology, 2020, 39(4): 44-50. doi: 10.19509/j.cnki.dzkq.2020.0406

Citation:

PDF( 1081 KB)

Review on geoscience time series big data similarity measurement and index method

doi: 10.19509/j.cnki.dzkq.2020.0406

He Zhenwen^{1a, 1b
,},
Wu Chonglong^{1a, 1b, 2},
Liu Gang^{1a, 1b, 2},
Tian Yiping^{1a, 1b, 2},
Zhang Xialin^{1a, 1b, 2},
Chen Qiyu^{1a, 1b}

Received Date: 18 Nov 2019

Abstract

Abstract

Geoscience time series big data is a kind of multi-sensor, multi-target, multi-resolution, multi-type, and multi-source heterogeneous time data, and is an important data source for machine learning and data mining in the field of geosciences. Geosciences time series data has two categories: the time point data set, and the time interval data set. The main data representation methods, similarity measurements and data indexing methods of existing time series data focus on time-points-based time series data. The core idea of the representation method for time series data is dimensionality reduction. It is the basis of similarity measurement and indexing method, including domain-transformation-based, model-based and pieces-based representation methods. The key of similarity measurement is the similarity distance including lock-step measurement and elasticity measurement. It provides a basic guideline for the aggregation and division of index items in the index of time series data. The efficient similarity measurement and distributed indexing method of multi-source heterogeneous time series big data will be an important further direction in the field of geosciences big data.
- time series data,
- big data,
- representation,
- index,
- similarity measurement

FullText(HTML)

References(64)

References

[1]	He Z, Kraak M J, Huisman O, et al.Parallel indexing technique for spatio-temporal data[J].Isprs Journal of Photogrammetry and Remote Sensing, 2013, 78:116-128. doi: 10.1016/j.isprsjprs.2013.01.014
[2]	He Z, Wu C, Liu G, et al.Decomposition tree:A spatio-temporal indexing method for movement big data[J].Cluster Computing, 2015, 18(4):1481-1492. doi: 10.1007/s10586-015-0475-3
[3]	He Z, Ma X.A distributed indexing method for timeline similarity query[J].Algorithms, 2018, 11(4):19-41. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=MDPI000000169415
[4]	吴冲龙, 刘刚, 王力哲, 等.基于大数据的城市地质环境智能监管思路与方法[J].地质科技通报, 2020, 39(1):157-163. http://dzkjqb.cug.edu.cn/CN/abstract/abstract9936.shtml
[5]	吴冲龙, 刘刚.大数据与地质学的未来发展[J].地质通报, 2019, 38(7):1081-1088. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zgqydz201907001
[6]	吴冲龙, 刘刚, 周琦, 等.地质科学大数据统合应用的基本问题[J].地质科技通报, 2020, 39(4):1-11. http://dzkjqb.cug.edu.cn/CN/abstract/abstract9993.shtml
[7]	陈国旭, 田宜平, 张夏林, 等.基于勘探剖面的三维地质模型快速构建及不确定性分析[J].地质科技情报, 2019, 38(2):275-280. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dzkjqb201902033
[8]	张文彪, 段太忠, 刘彦锋, 等.定量地质建模技术应用现状与发展趋势[J].地质科技情报, 2019, 38(3):1-9. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dzkjqb201903029
[9]	刘刚, 吴冲龙, 何珍文, 等.面向地质时空大数据表达与存储管理的数据模型研究[J].地质科技通报, 2020, 39(1):164-174. http://dzkjqb.cug.edu.cn/CN/abstract/abstract9937.shtml
[10]	Brehmer M, Lee B, Bach B, et al.Timelines revisited:A design space and considerations for expressive storytelling[J].IEEE Transactions on Visualization and Computer Graphics, 2017, 23(9):2151-2164. doi: 10.1109/TVCG.2016.2614803
[11]	Li C, Li B, Bhuiyan M Z A, et al.FluteDB:An efficient and scalable in-memory time series database for sensor-cloud[J].Journal of Parallel and Distributed Computing, 2018, 122:95-108. doi: 10.1016/j.jpdc.2018.07.021
[12]	Noury A, Amini M.An access and inference control model for time series databases[J].Future Generation Computer Systems, 2019, 92:93-108. doi: 10.1016/j.future.2018.09.057
[13]	Aghabozorgi S, Seyed Shirkhorshidi A, Teh Y W.Time-series clustering:A decade review[J].Information Systems, 2015, 53:16-38. doi: 10.1016/j.is.2015.04.007
[14]	Agrawal R, Faloutsos C, Swami A.Efficient similarity search in sequence databases[C]//Lomet D B.Foundations of data organization and algorithms.FODO 1993.Lecture Notes in Computer Science.Berlin, Heidelberg: Springer, 1993: 69-84.
[15]	Faloutsos C, Ranganathan M, Manolopoulos Y.Fast subsequence matching in time-series databases[J].Sigmod Rec., 1994, 23(2):419-429. doi: 10.1145/191843.191925
[16]	Kin-Pong C, Ada Wai-Chee F.Efficient time series matching by wavelets[C]//Masaru K, Michael P, Calton P.Proceedings of the 15th international conference on data engineering (Cat No99CB36337), F 23-26 March 1999.Washington D.C.: IEE Computer Society, 1999: 126-133.
[17]	Kawgoe K, Ueda T.A similarity search method of time series data with combination of fourier and wavelet transforms[C]//Mchael P, Alessardro A.Proceedings of the ninth international symposium on temporal representation and Reasoning (TIME'02).Washington: IEEE Computer Society, 2002: 86-98.
[18]	Korn F, Jagadish H V, Faloutsos C.Efficiently supporting ad hoc queries in large datasets of time sequences[J].Sigmod Rec., 1997, 26(2):289-300. doi: 10.1145/253262.253332
[19]	Keogh E J, Pazzani M J.An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback[C]//Rakesh A, Paul E S.The 4th international conference on knowledge discovery and data mining.New York: AAAI Press, 1998: 239-241.
[20]	Azzouzi M, Nabney I.Analysing time series structure with hidden Markov models, neural networks for signal processing Ⅷ[C]//Mahesan N, Elizabeth W, Tony C, et al.Proceedings of the 1998 IEEE Signal Processing Society Workshop.New York: IEEE Computer Society Press, 1998: 402-408.
[21]	Yi B K, Faloutsos C.Fast time sequence indexing for arbitrary Lp norms[C]//Amr E A, Michael L B, Sharma C, et al.Proceedings of the 26th international conference on very large data bases.San Francisco: Morgan Kaufmann Publishers Inc, 2000: 385-394.
[22]	Keogh E J, Pazzani M J.A simple dimensionality reduction technique for fast similarity search in large time series databases[M].Berlin, Heidelberg:Springer, 2000.
[23]	Chakrabarti K, Keogh E, Mehrotra S, et al.Locally adaptive dimensionality reduction for indexing large time series databases[J].ACM Trans Database Syst., 2002, 27(2):188-228. doi: 10.1145/568518.568520
[24]	Chung F L, Fu T C, Luk R, et al.Flexible time series pattern matching based on perceptually important points[C]//Hector L, Bernhard N, Ron B, et al.International joint conference on artificial intelligence workshop on learning from temporal and spatial data.San Francisco: Morgan Kaufmann, 2001: 1-7.
[25]	Cai Y, Ng R.Indexing spatio-temporal trajectories with Chebyshev polynomials[C]//Arnd C K, Stefan D.Proceedings of the 2004 ACM SIGMOD international conference on management of data.Paris: ACM, 2004: 599-610.
[26]	Lin J, Keogh E, Lonardi S, et al.A symbolic representation of time series, with implications for streaming algorithms[C]//Mohamned J Z, Charu C A.Proceedings of the 8th ACM SIGMOD workshop on research issues in data mining and knowledge discovery.San Diego, California: ACM, 2003: 2-11.
[27]	Lin J, Keogh E, Wei L, et al.Experiencing SAX:A novel symbolic representation of time series[J].Data Mining Knowledge Discovery, 2007, 15(2):107-144. http://nar.oxfordjournals.org/external-ref?access_num=10.1007/s10618-007-0064-z&link_type=DOI
[28]	Ratamahatana C J, Keogh E, Bagnall A, et al.A novel bit level time series representation with implication of similarity search and clustering[C]//Tu B H, David C, Huan L.Advances in knowledge discovery and data mining PAKDD 2005.Berlin, Heidelberg: Springer, 2005: 771-777.
[29]	Chen Q, Chen L, Lian X, et al.Indexable PLA for efficient similarity search[C]//Wolfgang K, Erich J N.Proceedings of the 33rd international conference on very large data bases.Vienna, Austria: VLDB Endowment.2007: 435-446.
[30]	Serra J, Kantz H, Serra X, et al.Predictability of music descriptor time series and its application to cover song detection[J].Audio, Speech, and Language Processing, IEEE Transactions on, 2012, 20:514-525. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=b267ec14db1c90d4e0b6cd5a9ca85568
[31]	Stefan A, Athitsos V, Das G.The move-split-merge metric for time series[J].IEEE Trans Knowl Data Eng., 2013, 25(6):1425-1438. doi: 10.1109/TKDE.2012.88
[32]	Baydogan M G, Runger G.Learning a symbolic representation for multivariate time series classification[J].Data Mining Knowledge Discovery, 2015, 29(2):400-422. doi: 10.1007/s10618-014-0349-y
[33]	Zhang Z, Tang P, Duan R.Dynamic time warping under pointwise shape context[J].Information Sciences, 2015, 315:88-101. doi: 10.1016/j.ins.2015.04.007
[34]	Baydogan M G, Runger G.Time series representation and similarity based on local autopatterns[J].Data Mining Knowledge Discovery, 2016, 30(2):476-509. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=3239893eea2eb9924ed78f89e25691e2
[35]	Ye Y Q, Jiang J, Ge B F, et al.Similarity measures for time series data classification using grid representation and matrix distance[J].Knowledge Information Systems, 2019, 60(2):1105-1134. doi: 10.1007/s10115-018-1264-0
[36]	Har-Peled S, Raichel B.Net and prune:A linear time algorithm for euclidean distance problems[J].J.ACM, 2015, 62(6):1-35. http://dl.acm.org/citation.cfm?id=2831230
[37]	Oregi I, Pérez A, Del Ser J, et al.On-line elastic similarity measures for time series[J].Pattern Recognition, 2019, 88:506-517. doi: 10.1016/j.patcog.2018.12.007
[38]	陈海燕, 刘晨晖, 孙博.时间序列数据挖掘的相似性度量综述[J].控制与决策, 2017, 32(1):1-11. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=kzyjc201701001
[39]	王一舒, 袁野, 刘萌, 等.大规模时序图数据的查询处理与挖掘技术综述[J].计算机研究与发展, 2018, 55(9):1889-902. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jsjyjyfz201809005
[40]	Bai S, Qi H D, Xiu N.Constrained best euclidean distance embedding on a sphere:A matrix optimization approach[J].SIAM Journal on Optimization, 2015, 25(1):439-467. doi: 10.1137/13094918X
[41]	Junejo I N, Junejo K N, Aghbari Z A.Silhouette-based human action recognition using SAX-Shapes[J].The Visual Computer, 2014, 30(3):259-269. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=c9eeb52a7e591c430b542d28131dd133
[42]	Hsu C J, Huang K S, Yang C B, et al.Flexible dynamic time warping for time series classification[J].Procedia Computer Science, 2015, 51(28):38-42. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=2771883c82adae9bff875df64ae86ef6
[43]	Roggen D, Cuspinera L P, Pombo G, et al.Limited-memory warping LCSS for real-time low-power pattern recognition in wireless nodes[M].Cham:Springer International Publishing, 2015.
[44]	Chairunnanda P, Gopalkrishnan V, Lei C.Enhancing edit distance on real sequences filters using histogram distance on fixed reference ordering[C]//Tang Y Y, Wang S P, Lorette G, et al.Proceedings of the 18th international conference on pattern recognition (ICPR'06), F 20-24 Aug.Los Alamtes: IEEE, 2006: 582-585.
[45]	Chen L, Ng R.On the marriage of Lp-norms and edit distance[C]//Mario A N, Özsu M T, Donald K, et al.Proceedings of the thirtieth international conference on very large data bases-Volume 30.Toronto, Canada: VLDB Endowment, 2004: 792-803.
[46]	Sun Y, Li J, Liu J, et al.An improvement of symbolic aggregate approximation distance measure for time series[J].Neurocomputing, 2014, 138:189-198. doi: 10.1016/j.neucom.2014.01.045
[47]	Abughali I K A, Minz S.Binarizing change for fast trend similarity based clustering of time series data[M].Cham:Springer International Publishing, 2015.
[48]	Nakamura T, Taki K, Nomiya H, et al.A shape-based similarity measure for time series data with ensemble learning[J].Pattern Analysis and Applications, 2013, 16(4):535-548. doi: 10.1007/s10044-011-0262-6
[49]	He X, Shao C, Xiong Y.A new similarity measure based on shape information for invariant with multiple distortions[J].Neurocomputing, 2014, 129:556-569. doi: 10.1016/j.neucom.2013.09.003
[50]	Ares J, Lara J A, Lizcano D, et al.A soft computing framework for classifying time series based on fuzzy sets of events[J].Information Sciences, 2016, 330:125-144. doi: 10.1016/j.ins.2015.10.014
[51]	Kondylakis H, Dayan N, Zoumpatianos K, et al.Coconut:A scalable bottom-up approach for building data series indexes[J].Proceedings of the VLDB Endowment, 2018, 11(6):1-14. http://www.researchgate.net/publication/323869978_Coconut_A_Scalable_Bottom-Up_Approach_for_Building_Data_Series_Indexes
[52]	Shieh J, Keogh E.iSAX:Disk-aware mining and indexing of massive time series datasets[J].Data Mining Knowledge Discovery, 2009, 19(1):24-57. http://www.springerlink.com/content/7357q35x63121n56/
[53]	Camerra A, Palpanas T, Shieh J, et al.iSAX 2.0: Indexing and mining one billion time series[C]//Geoffrey I W, Liu B, Zhang C, et al.Proceedings of the 10th IEEE international conference on data mining.ICDM 2010, December 14, 2010- December 17, 2010, Sydney, NSW, Australia.Sydney: Institute of Electrical and Electronics Engineers Inc, 2010: 58-67.
[54]	Zoumpatianos K, Idreos S, Palpanas T.ADS:The adaptive data series index[J].The VLDB Journal, 2016, 25(6):843-66. doi: 10.1007/s00778-016-0442-5
[55]	Yagoubi D E, Akbarinia R, Masseglia F, et al.DPiSAX: Massively distributed partitioned iSAX[C]//Raju G, Ning X, Dong G Z, et al.Proceedings of the 17th IEEE international conference on data mining, ICDM 2017, November 18- November 21, 2017, New Orleans USA.New Orleans: IEEE Computer Society Press, 2017: 1135-1140.
[56]	曾碧贵, 叶少珍.时间序列数据摘要与索引机制[J].工业控制计算机, 2017, 30(1):59-60. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gykzjsj201701026
[57]	Camerra A, Shieh J, Palpanas T, et al.Beyond one billion time series:Indexing and mining very large time series collections with iSAX2+[J].Knowledge Information Systems, 2014, 39(1):123-151. doi: 10.1007/s10115-012-0606-6
[58]	Keogh E, Chakrabarti K, Pazzani M, et al.Dimensionality reduction for fast similarity search in large time series databases[J].Knowledge Information Systems, 2001, 3(3):263-286. doi: 10.1007/PL00011669
[59]	Yagoubi D E, Akbarinia R, Masseglia F, et al.Massively distributed time series indexing and querying[J].IEEE Transactions on Knowledge and Data Engineering, 2018, 11:1-14 http://ieeexplore.ieee.org/document/8528881/
[60]	Kaufmann M, Manjili A A, Vagenas P, et al.Timeline index: A unified data structure for processing queries on temporal data in SAP HANA[C]//Kenneth R, Divesh S.Proceedings of the 2013 ACM SIGMOD international conference on management of data.New York: ACM, 2013: 1173-1184.
[61]	何锦源.TQIndex: 面向超大规模数据进行时序检索的高效数据索引结构[D].广州: 中山大学, 2015.
[62]	Leon-Alcaide P, Rodriguez-Benitez L, Castillo-Herrear E, et al.An evolutionary approach for efficient prototyping of large time series datasets[J].Information Sciences, 2020, 511:74-93. doi: 10.1016/j.ins.2019.09.044
[63]	Palpanas T.Evolution of a Data Series Index[C]//Flouris G, Laurent D, Plexousakis D, et al.International workshop on information search, integration, and personalization.Cham: Springer International Publishing, 2020: 68-83.
[64]	Ye Y Q, Jiang J, Ge B F, et al.Similarity measures for time series data classification using grid representation and matrix distance[J].Knowledge and Information Systems, 2019, 60(2):1105-1134. doi: 10.1007/s10115-018-1264-0

Relative Articles

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Get Citation

PDF

XML

Article Metrics

Article Views(393) PDF Downloads(1669)

Review on geoscience time series big data similarity measurement and index method

doi: 10.19509/j.cnki.dzkq.2020.0406

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Proportional views

Related

Review on geoscience time series big data similarity measurement and index method

doi: 10.19509/j.cnki.dzkq.2020.0406

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Proportional views

Related

Export File

Citation

Format

Content