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RETRACTED ARTICLE: Research outlook and state-of-the-art methods in context awareness data modeling and retrieval

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Abstract

As the data or information gets increased in various applications, it is very much essential to make the retrieval and modeling easier and simple. Number of modeling aspects already exists for this crisis. Yet, context awareness modeling plays a significant role in this. However, there requires some advancement in modeling system with the incorporation of advanced technologies. Hence, this survey intends to formulate a review on the context-aware modeling in two aspects: context data retrieval and context data modeling. Here, the literature analyses on diverse techniques associated with context awareness modeling. It reviews 60 research papers and states the significant analysis. Initially, the analysis depicts various applications that are contributed in different papers. Subsequently, the analysis also focuses on various features such as web application, time series model, intelligence models and performance measure. Moreover, this survey gives the detailed study regarding the chronological review and performance achievements in each contribution. Finally, it extends the various research issues, mainly the adoption of Evolutionary algorithms, which can be useful for the researchers to accomplish further research on context-aware system.

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References

  1. Foschini L, Montanari R, Boukerche A, Corradi A (2016) Scalable and mobile context data retrieval and distribution for community response heterogeneous wireless networks. IEEE Commun Mag 54(4):101–107

    Article  Google Scholar 

  2. Brown PJ, Jones GJF (2001) Context-aware retrieval: exploring a new environment for information retrieval and information filtering. Pers Ubiquit Comput 5(4):253–263

    Article  Google Scholar 

  3. Roussaki I, Strimpakou M, Pils C (2007) Distributed context retrieval and consistency control in pervasive computing. J Netw Syst Manag 15(1):57–74

    Article  Google Scholar 

  4. Mizzaro S, Vassena L (2011) A social approach to context-aware retrieval. World Wide Web 14(4):377–405

    Article  Google Scholar 

  5. Singh S, Kumar P (2018) User specific context construction for personalized multimedia retrieval. Multimed Tools Appl 77(11):13459–13486

    Article  Google Scholar 

  6. Eito-Brun R (2016) Remote access to EAC-CPF context and authority records for metadata indexing: a solution based on open information retrieval standards. Arch Sci 16(2):149–165

    Article  Google Scholar 

  7. Zakos J, Verma B (2006) A novel context-based technique for web information retrieval. World Wide Web 9(4):485–503

    Article  MATH  Google Scholar 

  8. Golitsyna OL, Maksimov NV (2011) Information retrieval models in the context of retrieval tasks. Autom Doc Math Linguist 45(1):20–32

    Article  Google Scholar 

  9. Hyman Sincich T, Will R, Agrawal M, Padmanabhan B, FridyIII W (2015) A process model for information retrieval context learning and knowledge discovery. Artif Intell Law 23(2):103–132

    Article  Google Scholar 

  10. Iqbal R (2014) Information retrieval, decision making process and user needs in the context of ubiquitous and collaborative computing. J Ambient Intell Humaniz Comput 5(1):91–92

    Article  Google Scholar 

  11. Yu Y, Li J, Yu J, Guan H, Wang C (2014) Pairwise three-dimensional shape context for partial object matching and retrieval on mobile laser scanning data. IEEE Geosci Remote Sens Lett 11(5):1019–1023

    Article  Google Scholar 

  12. Li S, Purushotham S, Chen C, Ren Y, Kuo CCJ (2017) Measuring and predicting tag importance for image retrieval. IEEE Trans Pattern Anal Mach Intell 39(12):2423–2436

    Article  Google Scholar 

  13. Tariq A, Foroosh H (2017) a context-driven extractive framework for generating realistic image descriptions. IEEE Trans Image Process 26(2):619–632

    Article  MathSciNet  MATH  Google Scholar 

  14. Sheikh I, Fohr D, Illina I, Linarès G (2017) Modelling semantic context of OOV words in large vocabulary continuous speech recognition. IEEE/ACM Trans Audio Speech Lang Process 25(3):598–610

    Article  Google Scholar 

  15. Wang L, Zhao X, Si Y, Cao L, Liu Y (2017) Context-associative hierarchical memory model for human activity recognition and prediction. IEEE Trans Multimed 19(3):646–659

    Article  Google Scholar 

  16. Erickson Tim (2006) Stealing from physics: modeling with mathematical functions in data-rich contexts. Teach Math Appl Int J IMA 25(1):23–32

    Google Scholar 

  17. Zhou N, Zhao WX, Zhang X, Wen JR, Wang S (2016) A general multi-context embedding model for mining human trajectory data. IEEE Trans Knowl Data Eng 28(8):1945–1958

    Article  Google Scholar 

  18. Huynh T, Gao Y, Kang J, Wang L, Zhang P, Lian J, Shen D (2015) Estimating CT Image from MRI data using structured random forest and auto-context model. IEEE Trans Med Imaging 35(1):174–183

    Article  Google Scholar 

  19. Dai W, Xiong H, Wang J, Cheng S, Zheng YF (2015) Generalized context modeling with multi-directional structuring and mdl-based model selection for heterogeneous data compression. IEEE Trans Signal Process 63(21):5650–5664

    Article  MathSciNet  MATH  Google Scholar 

  20. Nedic A, Tomlin D, Holmes P, Prentice DA, Cohen JD (2012) A decision task in a social context: human experiments, models, and analyses of behavioral data. Proc IEEE 100(3):713–733

    Article  Google Scholar 

  21. Yang E-H, He D-K (2003) Efficient universal lossless data compression algorithms based on a greedy sequential grammar transform. 2. With context models. IEEE Trans Inf Theory 49(11):2874–2894

    Article  MathSciNet  MATH  Google Scholar 

  22. Yang E-H, Kieffer JC (2000) Efficient universal lossless data compression algorithms based on a greedy sequential grammar transform. I. Without context models. IEEE Trans Inf Theory 46(3):755–777

    Article  MathSciNet  MATH  Google Scholar 

  23. Prabha R, Ramesh MV, Rangan VP, Ushakumari PV, Hemalatha T (2017) Energy efficient data acquisition techniques using context aware sensing for landslide monitoring systems. IEEE Sens J 17(18):6006–6018

    Article  Google Scholar 

  24. Milne D, Watling D (2018) Big data and understanding change in the context of planning transport systems. Elsevier, Amsterdam

    Google Scholar 

  25. Vasilecas O, Kalibatiene D, Lavbic D (2016) Rule- and context-based dynamic business process modelling and simulation. J Syst Softw 22:1–15

    Article  Google Scholar 

  26. Gasparic M, Murphy C, Ricci F (2017) A context model for IDE-based recommendation systems. J Syst Softw 128:200–219

    Article  Google Scholar 

  27. Chen S, Wang X (2018) Semiparametric estimation of panel data models without monotonicity or separability. J Econom 206:515–530

    Article  MathSciNet  MATH  Google Scholar 

  28. Hoyos JR, García-Molina J, Botía JA (2013) A domain-specific language for context modeling in context-aware systems. J Syst Softw 86(11):2890–2905

    Article  Google Scholar 

  29. Erfani M, Zandi M, Rilling J, Keivanloo I (2016) Context-awareness in the software domain—a semantic web enabled modeling approach. J Syst Softw 121:345–357

    Article  Google Scholar 

  30. ElSayed NA, Smith RT, Marriott K, Thomas BH (2018) Context-aware design pattern for situated analytics: blended model view controller. J Vis Lang Comput 44:1–12

    Article  Google Scholar 

  31. Coakes JM, Coakes EW (2000) Specifications in context: stakeholders, systems and modelling of conflict. Requir Eng 5(2):103–113

    Article  Google Scholar 

  32. Papaioannou A, Dovriki E, Rigas N, Plageras P, Rigas I, Kokkora M, Papastergiou P (2010) Assessment and modelling of groundwater quality data by environmetric methods in the context of public health. Water Resour Manag 24(12):3257–3278

    Article  Google Scholar 

  33. Degrandsart S, Demeyer S, Van den Bergh J, Mens T (2014) A transformation-based approach to context-aware modelling. Softw Syst Model 13(1):191–208

    Article  Google Scholar 

  34. Cabrera O, Franch X, Marco J (2017) 3LConOnt: a three-level ontology for context modelling in context-aware computing. Softw Syst Model 18:1–34

    Google Scholar 

  35. Baele G, Van de Peer Y, Vansteelandt S (2010) Modelling the ancestral sequence distribution and model frequencies in context-dependent models for primate non-coding sequences. BMC Evol Biol 10:244

    Article  Google Scholar 

  36. Petrelli D, Not E, Zancanaro M, Strapparava C, Stock O (2001) Modelling and adapting to context. Pers Ubiquit Comput 5(1):20–24

    Article  Google Scholar 

  37. Guotao H, Sivakumar A, Polak JW (2012) Modelling travellers’ risky choice in a revealed preference context: a comparison of EUT and non-EUT approaches. Transportation 39(4):825–841

    Article  Google Scholar 

  38. Shanahan M, Shakeshaft A, Mattick RP (2006) Modelling the costs and outcomes of changing rates of screening for alcohol misuse by gps in the australian context. Appl Health Econ Health Policy 5(3):155–166

    Article  Google Scholar 

  39. Ayotte Keith W, Davy Robert J, Coppin Peter A (2001) A simple temporal and spatial analysis of flow in complex terrain in the context of wind energy modelling. Bound-Layer Meteorol 98(2):275–295

    Article  Google Scholar 

  40. Lasagna M, Caviglia C, De Luca DA (2014) Simulation modelling for groundwater safety in an overexploitation situation: the Maggiore Valley context (Piedmont, Italy). Bull Eng Geol Environ 73(2):341–355

    Google Scholar 

  41. Zorom M, Barbier B, Gouba E, Somé B (2018) Mathematical modelling of the dynamics of the socio-economic vulnerability of rural Sahelian households in a context of climatic variability. Model Earth Syst Environ 4:1–11

    Article  Google Scholar 

  42. Martins J, Richardson DM, Henriques R, Marchante E, Marchante H, Alves P, Gaertner M, Honrado JP, Vicente JR (2016) A multi-scale modelling framework to guide management of plant invasions in a transboundary context. For Ecosyst 3:17

    Article  Google Scholar 

  43. Gross T, Prinz W (2004) Modelling shared contexts in cooperative environments: concept, implementation, and evaluation. Comput Support Coop Work (CSCW) 13(3–4):283–303

    Article  Google Scholar 

  44. Webb B, Gallagher S (2009) Action in context and context in action: modelling complexity in multimedia systems development. J Inf Technol 24(1):126–138

    Article  Google Scholar 

  45. Heesch D, Petrou M (2010) Markov random fields with asymmetric interactions for modelling spatial context in structured scene labelling. J Signal Process Syst 61(1):95–103

    Article  Google Scholar 

  46. Moroni D, Salvetti M, Salvetti O (2010) Shape analysis, semantic annotation and context modelling for the retrieval of 3D anatomical structures. Pattern Recognit Image Anal 20(1):86–93

    Article  Google Scholar 

  47. El Morjani ZEA, Ebener S, Boos J, Abdel Ghaffar EA, Musani A (2007) Modelling the spatial distribution of five natural hazards in the context of the WHO/EMRO atlas of disaster risk as a step towards the reduction of the health impact related to disasters. Int J Health Geogr 6:8

    Article  Google Scholar 

  48. Yu Z, Wong RK, Chi CH (2017) Efficient role mining for context-aware service recommendation using a high-performance cluster. IEEE Trans Serv Comput 10(6):914–926

    Article  Google Scholar 

  49. Maran V, Machado A, Machadoa GM, Augustin I, de Oliveira JP (2018) Domain content querying using ontology-based context-awareness in information systems. Data Knowl Eng 115:152–173

    Article  Google Scholar 

  50. Terama E, Clarke E, Rounsevell MDA, Fronzek S, Carter TR (2017) Modelling population structure in the context of urban land use change in Europe. Reg Environ Change 19:1–11

    Google Scholar 

  51. Ziaimatin H, Groza T, Tudorache T, Hunter J (2016) Modelling expertise at different levels of granularity using semantic similarity measures in the context of collaborative knowledge-curation platforms. J Intell Inf Syst 47(3):469–490

    Article  Google Scholar 

  52. Hudson John A, BäckströmJ A, Rutqvist L, Jing T BackersM, Chijimatsu R, Christiansson X-T, Feng A, Kobayashi T, Koyama H-S, Lee I, Neretnieks P-Z, Pan M Rinne, Shen B-T (2009) Characterising and modelling the excavation damaged zone in crystalline rock in the context of radioactive waste disposal. Environ Geol 57(6):1275–1297

    Article  Google Scholar 

  53. Sunoj SM, Vipin N (2017) Some properties of conditional partial moments in the context of stochastic modelling. Stat Pap. https://doi.org/10.1007/s00362-017-0904-x

    Article  MATH  Google Scholar 

  54. Tiwari V, Thakur RS (2015) Contextual snowflake modelling for pattern warehouse logical design. Sadhana 40(1):15–33

    Article  Google Scholar 

  55. Wong RK, Lam F, Orgun MA (2001) Modelling and manipulating multidimensional data in semistructured databases. World Wide Web 4(1–2):79–99

    Article  MATH  Google Scholar 

  56. Thu HN, Wehn U (2016) Data sharing in international transboundary contexts: the Vietnamese perspective on data sharing in the Lower Mekong Basin. J Hydrol 536:351–364

    Article  Google Scholar 

  57. Hu W, Wu B, Wang P, Yuan C, Li Y, Maybank S (2018) Context-dependent random walk graph kernels and tree pattern graph matching kernels with applications to action recognition. IEEE Trans Image Process 27(10):5060–5075

    Article  MathSciNet  MATH  Google Scholar 

  58. Gronchi G, Provenzi E (2017) A variational model for context-driven effects in perception and cognition. J Math Psychol 77:124–141

    Article  MathSciNet  MATH  Google Scholar 

  59. Pokojski J, Oleksinski K, Pruszynski J (2018) Knowledge based processes in the context of conceptual design. J Ind Inf Integr. https://doi.org/10.1016/j.jii.2018.07.002

    Article  Google Scholar 

  60. Schoenfisch J, Stuckenschmidt H (2017) Analyzing real-world SPARQL queries and ontology-based data access in the context of probabilistic data. Int J Approximate Reasoning 90:374–388

    Article  MathSciNet  MATH  Google Scholar 

  61. Choi S, Seo J, Kim M, Kang S, Han S (2017) Chrological big data curation: a study on the enhanced information retrieval system. IEEE Access 5:11269–11277

    Article  Google Scholar 

  62. Chen Z, Zhong F, Min G, Leng Y, Ying Y (2018) Supervised intra- and inter-modality similarity preserving hashing for cross-modal retrieval. IEEE Access 6:27796–27808

    Article  Google Scholar 

  63. Yang J, Jiang B, Li B, Tian K, Lv Z (2017) A fast image retrieval method designed for network big data. IEEE Trans Ind Inf 13(5):2350–2359

    Article  Google Scholar 

  64. Chi M, Plaza A, Benediktsson JA, Sun Z, Shen J, Zhu Y (2016) Big data for remote sensing: challenges and opportunities. Proc IEEE 104(11):2207–2219

    Article  Google Scholar 

  65. Tang J, Wang K, Shao L (2016) Supervised matrix factorization hashing for cross-modal retrieval. IEEE Trans Image Process 25(7):3157–3166

    Article  MathSciNet  MATH  Google Scholar 

  66. Chen Z et al (2015) A survey of bitmap index compression algorithms for Big Data. Tsinghua Sci Technol 20(1):100–115

    Article  MathSciNet  Google Scholar 

  67. Ayala-Romero JA, Alcaraz JJ, Vales-Alonso J (2018) Data-driven configuration of interference coordination parameters in HetNets. IEEE Trans Veh Technol 67(6):5174–5187

    Article  Google Scholar 

  68. Shi L, Wu Y, Liu L, Sun X, Jiang L (2018) Event detection and identification of influential spreaders in social media data streams. Big Data Min Anal 1(1):34–46

    Article  Google Scholar 

  69. Li Y, Zhang Y, Huang X, Zhu H, Ma J (2018) Large-scale remote sensing image retrieval by deep hashing neural networks. IEEE Trans Geosci Remote Sens 56(2):950–965

    Article  Google Scholar 

  70. Müller H, Unay D (2017) Retrieval from and understanding of large-scale multi-modal medical datasets: a review. IEEE Trans Multimed 19(9):2093–2104

    Article  Google Scholar 

  71. Shao Z, Cai J, Wang Z (2018) Smart monitoring cameras driven intelligent processing to big surveillance video data. IEEE Trans Big Data 4(1):105–116

    Article  Google Scholar 

  72. Zhuo G, Jia Q, Guo L, Li M, Li P (2017) Privacy-preserving verifiable set operation in big data for cloud-assisted mobile crowdsourcing. IEEE Internet Things J 4(2):572–582

    Article  Google Scholar 

  73. Wu Y et al (2016) CAMP: a new bitmap index for data retrieval in traffic archival. IEEE Commun Lett 20(6):1128–1131

    Article  Google Scholar 

  74. Jiang S, Qian X, Mei T, Fu Y (2016) Personalized travel sequence recommendation on multi-source big social media. IEEE Trans Big Data 2(1):43–56

    Article  Google Scholar 

  75. Zhu Q, Shyu ML (2015) Sparse linear integration of content and context modalities for semantic concept retrieval. IEEE Trans Emerg Topics Comput 3(2):152–160

    Article  Google Scholar 

  76. Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge

    MATH  Google Scholar 

  77. Storn Rainer, Price Kenneth (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359

    Article  MathSciNet  MATH  Google Scholar 

  78. Yao X, Liu Y, Lin Guangming (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102

    Article  Google Scholar 

  79. Rechenberg I (1973) Evolution strategy. Springer, Berlin, pp 83–114

    Google Scholar 

  80. Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713

    Article  Google Scholar 

  81. Müller H, Guadagni S (2008) Regional chemotherapy for carcinoma of the lung. Surg Oncol Clin N Am 17(4):895–917. https://doi.org/10.1016/j.soc.2008.04.012

    Article  Google Scholar 

  82. Fiorentini G, Rossi S, Bernardeschi P, Cantore M, Guadagni S (2015) Is there a new drug beyond floxuridine for intra-arterial hepatic chemotherapy in liver metastases from colorectal cancer. J Clin Oncol 23(9):2105

    Article  Google Scholar 

  83. Guadagni S, Fiorentini G, Clementi M, Palumbo G, Masedu F, Deraco M, De Manzoni G, Chiominto A, Valenti M, Pellegrini C (2017) MGMT methylation correlates with melphalan pelvic perfusion survival in stage III melanoma patients. A pilot study. Melanoma Res 27:439–447

    Article  Google Scholar 

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Authors and Affiliations

  1. Hirasugar Institute of Technology, Nidasoshi, Belagavi, Karnataka, India

    S. G. Gollagi

  2. Gogte Institute of Technology, Belagavi, Karnataka, India

    M. M. Math

  3. SDM College of Engineering and Technology, Dharawad, Karnataka, India

    U. P. Kulkarni

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  1. S. G. Gollagi
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  2. M. M. Math
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Gollagi, S.G., Math, M.M. & Kulkarni, U.P. RETRACTED ARTICLE: Research outlook and state-of-the-art methods in context awareness data modeling and retrieval. Evol. Intel. 15, 1025–1036 (2022). https://doi.org/10.1007/s12065-019-00274-x

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