1. Department of Information and Electrical Engineering, Ludong University, Shandong 264025, China 2. Department of Software, East China Normal University, Shanghai 200062, China
The time management model for event processing in internet of things has a special and important requirement. Many events in real world applications are long-lasting events which have different time granularity with order or out-of-order. The temporal relationships among those events are often complex. An important issue of complex event processing is to extract patterns from event streams to support decision making in real-time. However, current time management model does not consider the unified solution about time granularity, time interval, time disorder, and the difference between workday calendar systems in different organizations. In this work, we analyze the preliminaries of temporal semantics of events. A tree-plan model of out-of-order durable events is proposed. A hybrid solution is correspondingly introduced. A case study is illustrated to explain the time constraints and the time optimization. Extensive experimental studies demonstrate the efficiency of our approach.
. [J]. Frontiers of Computer Science, 2019, 13(3): 471-488.
Chunjie ZHOU, Xiaoling WANG, Zhiwang ZHANG, Zhenxing ZHANG, Haiping QU. The time model for event processing in internet of things. Front. Comput. Sci., 2019, 13(3): 471-488.
P M MRodrigues, NSalish. Modeling and forecasting interval time series with threshold models. Advances in Data Analysis and Classification, 2015, 9(1): 1–17 https://doi.org/10.1007/s11634-014-0170-x
2
DZurita, M Delgado, J ACarino, J AOrtega, GClerc. Industrial time series modelling by means of the neo-fuzzy neuron. IEEE Access, 2017, 4: 6151–6160 https://doi.org/10.1109/ACCESS.2016.2611649
3
THu, XLin, BNan. Cross-ratio estimation for bivariate failure times with left truncation. Lifetime Data Analysis, 2014, 20(1): 23–37 https://doi.org/10.1007/s10985-013-9263-7
4
R LPrentice. Nonparametric inference on bivariate survival data with interval sampling: association estimation and testing. Biometrika, 2014, 101(3): 519–533 https://doi.org/10.1093/biomet/asu005
5
BDrinkwater, M A Charleston. A time and space complexity reduction for coevolutionary analysis of trees generated under both a yule and uniform model. Computational Biology and Chemistry, 2015, 57(C): 61–71 https://doi.org/10.1016/j.compbiolchem.2015.02.003
6
I BFidaner, A Cankorur-Cetinkaya, DDikicioglu, BKirdar. CLUSTERnGO: a user-defined modelling platform for two-stage clustering of time-series data. Bioinformatics, 2016, 32(3): 388–397 https://doi.org/10.1093/bioinformatics/btv532
7
XChen, D Worthington. Staffing of time-varying queues using a geometric discrete time modelling approach. Annals of Operations Research, 2017, 252(1): 63–64 https://doi.org/10.1007/s10479-015-2058-3
8
EBen Abdallah, T Ribeiro, MMagnin, ORoux, KInoue. Modeling delayed dynamics in biological regulatory networks from time series data. Algorithms, 2017, 10(1): 8 https://doi.org/10.3390/a10010008
9
IAl-Darabsah, YYuan. A time-delayed epidemic model for ebola disease transmission. Applied Mathematics and Computation, 2016, 290: 307–325 https://doi.org/10.1016/j.amc.2016.05.043
10
SBabu, U Srivastava, JWidom. Exploiting K-constraints to reduce memory overhead in continuous queries over data streams. ACM Transaction on Database Systems, 2004, 29(3): 545–580 https://doi.org/10.1145/1016028.1016032
11
M AHammad, M J Franklin, W GAref, A KElmagarmid. Scheduling for shared window joins over data streams. In: Proceedings of the 29th International Conference on Very Large Data Bases. 2003, 297–308 https://doi.org/10.1016/B978-012722442-8/50034-3
12
CLiu, NLu, QZhang, J Li, PLiu. Modeling and analysis in a preypredator system with commercial harvesting and double time delays. Applied Mathematics and Computation, 2016, 281: 77–101 https://doi.org/10.1016/j.amc.2016.01.039
13
E B MBashier, K C Patidar. Optimal control of an epidemiological model with multiple time delays. Applied Mathematics and Computation, 2017, 292: 47–56 https://doi.org/10.1016/j.amc.2016.07.009
14
YMei, SMadden. ZStream: a cost-based query processor for adaptively detecting composite events. In: Proceedings of the 35th SIGMOD International Conference on Management of Data (SIGMOD). 2009, 193–206 https://doi.org/10.1145/1559845.1559867
15
JEder, E Panagos, HPozewaunig, MRabinovich. Time management in workflow systems. In: Proceedings of the 3rd International Conference on Business Information Systems. 1999, 265–280 https://doi.org/10.1007/978-1-4471-0875-7_22
16
JChen, YYang. Multiple states based temporal consistency for dynamic verification of fixed time constraints in grid workflow systems. Concurrency and Computation Practice and Experience, 2010, 19(7): 965–982 https://doi.org/10.1002/cpe.1088
17
CFan, S WMyint, S JRey, W Li. Time series evaluation of landscape dynamics using annual landsat imagery and spatial statistical modeling: evidence from the phoenix metropolitan region. International Journal of Applied Earth Observation and Geoinformation, 2017, 58: 12–25 https://doi.org/10.1016/j.jag.2017.01.009
18
HWang, HDai, BFu. Accelerated failure time models for censored survival data under referral bias. Biostatistics, 2013, 14(2): 313–326 https://doi.org/10.1093/biostatistics/kxs041
CBettini, X S Bettini, SJajodia. Temporal reasoning in workflow systems. Distributed and Parallel Databases, 2002, 11(3): 269–306 https://doi.org/10.1023/A:1014048800604
21
SDu, JTan, GLu. The description and analysis of multi-granularity time restriction in the workflow model. Chinese Journal of Software, 2003, 14(11): 1834–1840
22
MLiu, MLi, DGolovnya, E A Rundensteiner, KClaypool. Sequence pattern query processing over out-of-order event streams. In: Proceedings of the 25th International Conference on Data Engineering (ICDE). 2009, 274–295 https://doi.org/10.1109/ICDE.2009.95
23
L PSong, R PZhang, L PFeng, Q Shi. Pattern dynamics of a spatial epidemic model with time delay. Applied Mathematics and Computation. 2017, 292: 390–399 https://doi.org/10.1016/j.amc.2016.07.013
24
R E DGrande, A Boukerche, RAlkharboush. Time series-oriented load prediction model and migration policies for distributed simulation systems. IEEE Transactions on Parallel Distribution System, 2017, 28(1): 215–229 https://doi.org/10.1109/TPDS.2016.2552174
25
P SKam, A WFu. Discovering temporal patterns for interval-based events. In: Proceedings of the 2nd International Conference on Data Warehousing and Knowledge Discovery (DaWak). 2000, 317–326 https://doi.org/10.1007/3-540-44466-1_32
26
PPapapetrou, G Kollios, SSclaroff, DGunopulos. Discovering frequent arrangements of temporal intervals. In: Proceedings of the IEEE International Conference on Data Mining. 2005, 354–361 https://doi.org/10.1109/ICDM.2005.50
27
S YWu, Y LChen. Mining nonambiguous temporal patterns for interval-based events. IEEE Transactions on Knowledge and Data Engineering, 2007, 19(6): 742–758 https://doi.org/10.1109/TKDE.2007.190613
28
DPatel, WHsu, M LLee. Mining relationships among interval-based events for classification. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data. 2008, 393–404 https://doi.org/10.1145/1376616.1376658
