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An Interface for User-Centred Process and Correlation Between Large Datasets

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Design, User Experience, and Usability: UX Research and Design (HCII 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12779))

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Abstract

Standard database query systems are designed to process data on a single installation only, and do not provide optimal solutions for cases that data from multiple sources need to be queried. In these cases, the sources may have different data schemata, data representations etc., necessitating extensive coding and data transformations to retrieve partial results and combine them to reach the desired outcome. Differences in schemata and representations may be subtle and remain unnoticed, leading to the production of erroneous results. The goal of this paper is to present an easy-to-use solution for the end users, enabling them to query data from a given set of databases through a single user interface. This user interface allows users to visualize database contents and query results, while facilities for uploading and validating the data are also accommodated. To demonstrate the applicability of our approach, a use case is presented where data from two different sources are uploaded into the system and thereafter the data from the two databases can be utilized in tandem. The usability evaluation involved software developers in free evaluation scenarios.

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References

  1. Jia, F., Blome, C., Sun, H., Yang, Y., Zhi, B.: Towards an integrated conceptual framework of supply chain finance: an information processing perspective. Int. J. Prod. Econ. 219, 18ā€“30 (2020). https://doi.org/10.1016/j.ijpe.2019.05.013

    ArticleĀ  Google ScholarĀ 

  2. Ortega, J.L.: Blogs and news sources coverage in altmetrics data providers: a comparative analysis by country, language, and subject. Scientometrics 122, 555ā€“572 (2020). https://doi.org/10.1007/s11192-019-03299-2

    ArticleĀ  Google ScholarĀ 

  3. Margaris, D., Vassilakis, C., Georgiadis, P.: An integrated framework for adapting WS-BPEL scenario execution using QoS and collaborative filtering techniques. Sci. Comput. Program. 98 (2015). https://doi.org/10.1016/j.scico.2014.10.007

  4. Margaris, D., Vassilakis, C., Georgiadis, P.: An integrated framework for QoS-based adaptation and exception resolution in WS-BPEL scenarios. In: Proceedings of the 28th Annual ACM Symposium on Applied Computing - SAC 2013. p. 1900. ACM Press, New York, New York, USA (2013). https://doi.org/10.1145/2480362.2480714

  5. Yang, J., Chen, B., Xia, S.-T.: Mean-removed product quantization for approximate nearest neighbor search. In: 2019 International Conference on Data Mining Workshops (ICDMW), pp. 711ā€“718. IEEE, Beijing, China (2019). https://doi.org/10.1109/ICDMW.2019.00107

  6. Asadi, S., Mansouri, H., Darvay, Z., Zangiabadi, M., Mahdavi-Amiri, N.: Large-neighborhood infeasible predictor-corrector algorithm for horizontal linear complementarity problems over cartesian product of symmetric cones. J. Optim. Theory Appl. 180, 811ā€“829 (2019). https://doi.org/10.1007/s10957-018-1402-6

    ArticleĀ  MathSciNetĀ  MATHĀ  Google ScholarĀ 

  7. Margaris, D., Spiliotopoulos, D., Vassilakis, C., Karagiorgos, G.: A user interface for personalized web service selection in business processes. In: Stephanidis, C., et al. (eds.) HCII 2020. LNCS, vol. 12427, pp. 560ā€“573. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60152-2_41

    ChapterĀ  Google ScholarĀ 

  8. Yadav, N., Rajpoot, D.S., Dhakad, S.K.: LARAVEL: a PHP framework for e-commerce website. In: 2019 Fifth International Conference on Image Information Processing (ICIIP), pp. 503ā€“508. IEEE, Shimla, India (2019). https://doi.org/10.1109/ICIIP47207.2019.8985771

  9. Mahmood, M.T., Ashour, O.I.A.: Web application based on MVC laravel architecture for online shops. In: Proceedings of the 6th International Conference on Engineering & MIS 2020, pp. 1ā€“7. ACM, Almaty Kazakhstan (2020). https://doi.org/10.1145/3410352.3410834

  10. Spiliotopoulos, D., Kotis, K., Vassilakis, C., Margaris, D.: Semantics-driven conversational interfaces for museum chatbots. In: Rauterberg, M. (ed.) Culture and Computing, pp. 255ā€“266. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-50267-6_20

  11. Varitimiadis, S., Kotis, K., Spiliotopoulos, D., Vassilakis, C., Margaris, D.: ā€œTalkingā€ triples to museum chatbots. In: Rauterberg, M. (ed.) Culture and Computing, pp. 281ā€“299. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-50267-6_22

  12. Koryzis, D., Fitsilis, F., Spiliotopoulos, D., Theocharopoulos, T., Margaris, D., Vassilakis, C.: Policy making analysis and practitioner user experience. In: Stephanidis, C., Marcus, A., Rosenzweig, E., Rau, P.-P.L., Moallem, A., Rauterberg, M. (eds.) HCII 2020. LNCS, vol. 12423, pp. 415ā€“431. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60114-0_29

    ChapterĀ  Google ScholarĀ 

  13. Kouroupetroglou, G., Spiliotopoulos, D.: Usability methodologies for real-life voice user interfaces. Int. J. Inf. Technol. Web Eng. 4, 78ā€“94 (2009). https://doi.org/10.4018/jitwe.2009100105

  14. Margaris, D., Vassilakis, C., Georgiadis, P.: Query personalization using social network information and collaborative filtering techniques. Futur. Gener. Comput. Syst. 78, 440ā€“450 (2018). https://doi.org/10.1016/j.future.2017.03.015

    ArticleĀ  Google ScholarĀ 

  15. Sharma, S., Rana, V.: Web search personalization using semantic similarity measure. In: Singh, P.K., Kar, A.K., Singh, Y., Kolekar, M.H., Tanwar, S. (eds.) Proceedings of ICRIC 2019. LNEE, vol. 597, pp. 273ā€“288. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-29407-6_21

    ChapterĀ  Google ScholarĀ 

  16. Azhir, E., Jafari Navimipour, N., Hosseinzadeh, M., Sharifi, A., Darwesh, A.: Deterministic and non-deterministic query optimization techniques in the cloud computing. Concurr. Comput. Pract. Exp. 31, (2019). https://doi.org/10.1002/cpe.5240

    ArticleĀ  Google ScholarĀ 

  17. Sharma, M., Singh, G., Singh, R.: A review of different cost-based distributed query optimizers. Prog. Artif. Intell. 8, 45ā€“62 (2019). https://doi.org/10.1007/s13748-018-0154-8

    ArticleĀ  Google ScholarĀ 

  18. Demidova, E., et al.: Analysing and enriching focused semantic web archives for parliament applications. Futur. Internet. 6, 433ā€“456 (2014). https://doi.org/10.3390/fi6030433

    ArticleĀ  Google ScholarĀ 

  19. Risse, T., et al.: The ARCOMEM architecture for social- and semantic-driven web archiving. Futur. Internet. 6, 688ā€“716 (2014). https://doi.org/10.3390/fi6040688

    ArticleĀ  Google ScholarĀ 

  20. Li, Y., Shen, Z., Li, J.: SimbaQL: a query language for multi-source heterogeneous data. In: Li, J., Meng, X., Zhang, Y., Cui, W., Du, Z. (eds.) Big Scientific Data Management, pp. 275ā€“284. Springer International Publishing, Cham (2019). https://doi.org/10.1007/978-3-030-28061-1_27

  21. Hu, X., Xu, H., Jia, J., Wang, X.: Research on distributed storage and query optimization of multi-source heterogeneous meteorological data. In: Proceedings of the 2018 International Conference on Cloud Computing and Internet of Things - CCIOT 2018, pp. 12ā€“18. ACM Press, Singapore, Singapore (2018). https://doi.org/10.1145/3291064.3291068

  22. Wu, Q., Chen, C., Jiang, Y.: Multi-source heterogeneous Hakka culture heritage data management based on MongoDB. In: 2016 Fifth International Conference on Agro-Geoinformatics (Agro-Geoinformatics), pp. 1ā€“6. IEEE, Tianjin, China (2016). https://doi.org/10.1109/Agro-Geoinformatics.2016.7577628

  23. Liu, B., et al.: A Versatile event-driven data model in HBase database for multi-source data of power grid. In: 2016 IEEE International Conference on Smart Cloud (SmartCloud). pp. 208ā€“213. IEEE, New York, NY, USA (2016). https://doi.org/10.1109/SmartCloud.2016.28

  24. Chen, Z., et al.: A multi-database hybrid storage method for big data of power dispatching and control. In: 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), pp. 502ā€“507. IEEE, Leicester, United Kingdom (2019). https://doi.org/10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00127

  25. Miyamoto, N., Higuchi, K., Tsuji, T.: Incremental data migration for multi-database systems based on MySQL with spider storage engine. In: 2014 IIAI 3rd International Conference on Advanced Applied Informatics. pp. 745ā€“750. IEEE, Kokura Kita-ku, Japan (2014). https://doi.org/10.1109/IIAI-AAI.2014.151

  26. Daniel, G., et al.: NeoEMF: a multi-database model persistence framework for very large models. Sci. Comput. Program. 149, 9ā€“14 (2017). https://doi.org/10.1016/j.scico.2017.08.002

    ArticleĀ  Google ScholarĀ 

  27. Rachman, M.A.F., Saptawati, G.A.P.: Database integration based on combination schema matching approach (case study: Multi-database of district health information system). In: 2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE), pp. 430ā€“435. IEEE, Yogyakarta (2017). https://doi.org/10.1109/ICITISEE.2017.8285544

  28. Phungtua-Eng, T., Chittayasothorn, S.: A multi-database access system with instance matching. In: Nguyen, N.T., Tojo, S., Nguyen, L.M., Trawiński, B. (eds.) Intelligent Information and Database Systems, pp. 312ā€“321. Springer International Publishing, Cham (2017). https://doi.org/10.1007/978-3-319-54472-4_30

  29. Xydas, G., Spiliotopoulos, D., Kouroupetroglou, G.: Modeling prosodic structures inĀ linguisticallyĀ enrichedĀ environments. In: Sojka, P., Kopeček, I., Pala, K. (eds.) TSD 2004. LNCS (LNAI), vol. 3206, pp. 521ā€“528. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30120-2_66

    ChapterĀ  Google ScholarĀ 

  30. Spiliotopoulos, D., Xydas, G., Kouroupetroglou, G., Argyropoulos, V., Ikospentaki, K.: Auditory universal accessibility of data tables using naturally derived prosody specification. Univers. Access Inf. Soc. 9(2), 169ā€“183 (2010). https://doi.org/10.1007/s10209-009-0165-0

  31. Xydas, G., Spiliotopoulos, D., Kouroupetroglou, G.: Modeling improved prosody generation from high-level linguistically annotated corpora. IEICE Trans. Inf. Syst. E88-D, 510ā€“518 (2005). https://doi.org/10.1093/ietisy/e88-d.3.510

  32. Naik, S.T.: Accessing data from multiple heterogeneous distributed database systems. In: Applying Integration Techniques and Methods in Distributed Systems and Technologies: IGI Global (2019). https://doi.org/10.4018/978-1-5225-8295-3.ch008

  33. Chen, C.: Information visualization. Wiley Interdiscip. Rev. Comput. Stat. 2, 387ā€“403 (2010). https://doi.org/10.1002/wics.89

    ArticleĀ  Google ScholarĀ 

  34. Dasari, V., Allen, S., Brown, S.E.: Dynamic visualization of large scale tactical network simulations. In: 2019 IEEE International Conference on Big Data (Big Data), pp. 3951ā€“3954. IEEE, Los Angeles, CA, USA (2019). https://doi.org/10.1109/BigData47090.2019.9005641

  35. Sun, Y.: Third-party library integration. In: Practical Application Development with AppRun, pp. 163ā€“190. Apress, Berkeley, CA (2019)

    Google ScholarĀ 

  36. Lu, T., Zhang, P., Li, H.: Practice teaching reform of discrete mathematics model based on D3.js. In: 2019 14th International Conference on Computer Science & Education (ICCSE), pp. 379ā€“384. IEEE, Toronto, ON, Canada (2019). https://doi.org/10.1109/ICCSE.2019.8845409

  37. Urmela, S., Nandhini, M.: Collective dendrogram clustering with collaborative filtering for distributed data mining on electronic health records. In: 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT), pp. 1ā€“5. IEEE. Coimbatore (2017). https://doi.org/10.1109/ICECCT.2017.8117876

  38. Arief, V.N., DeLacy, I.H., Basford, K.E., Dieters, M.J.: Application of a dendrogram seriation algorithm to extract pattern from plant breeding data. Euphytica 213, 85 (2017). https://doi.org/10.1007/s10681-017-1870-z

    ArticleĀ  Google ScholarĀ 

  39. Darmawan, I., Rahmatulloh, A., Nuralam, I.M.F., Gunawan, R.: Optimizing data storage in handling dynamic input fields with JSON string compression. In: 2020 8th International Conference on Information and Communication Technology (ICoICT), pp. 1ā€“5. IEEE, Yogyakarta, Indonesia (2020). https://doi.org/10.1109/ICoICT49345.2020.9166458

  40. Pezoa, F., Reutter, J.L., Suarez, F., Ugarte, M., Vrgoč, D.: Foundations of JSON schema. In: Proceedings of the 25th International Conference on World Wide Web, pp. 263ā€“273. International World Wide Web Conferences Steering Committee, MontrĆ©al QuĆ©bec Canada (2016). https://doi.org/10.1145/2872427.2883029

  41. Vyas, S., Vaishnav, P.: A comparative study of various ETL process and their testing techniques in data warehouse. J. Stat. Manag. Syst. 20, 753ā€“763 (2017). https://doi.org/10.1080/09720510.2017.1395194

    ArticleĀ  Google ScholarĀ 

  42. Biswas, N., Chattopadhyay, S., Mahapatra, G., Chatterjee, S., Mondal, K.C.: SysML based conceptual ETL process modeling. In: Mandal, J.K., Dutta, P., Mukhopadhyay, S. (eds.) Computational Intelligence, Communications, and Business Analytics, pp. 242ā€“255. Springer Singapore, Singapore (2017). https://doi.org/10.1007/978-981-10-6430-2_19

  43. Pereira, A.P., Cardoso, B.P., Laureano, R.M.S.: Business intelligence: performance and sustainability measures in an ETL process. In: 2018 13th Iberian Conference on Information Systems and Technologies (CISTI), pp. 1ā€“7. IEEE, Caceres (2018). https://doi.org/10.23919/CISTI.2018.8399473

  44. Georgiou, M.A., Paphitis, A., Sirivianos, M., Herodotou, H.: Hihooi: A database replication middleware for scaling transactional databases consistently. IEEE Trans. Knowl. Data Eng. 1 (2020). https://doi.org/10.1109/TKDE.2020.2987560

  45. Dong, L., Liu, W., Li, R., Zhang, T., Zhao, W.: Replica-aware partitioning design in parallel database systems. In: Rivera, F.F., Pena, T.F., Cabaleiro, J.C. (eds.) Euro-Par 2017: Parallel Processing, pp. 303ā€“316. Springer International Publishing, Cham (2017). https://doi.org/10.1007/978-3-319-64203-1_22

  46. Spiliotopoulos, D., Margaris, D., Vassilakis, C.: Data-assisted persona construction using social media data. Big Data Cogn. Comput. 4, 21ā€“21 (2020). https://doi.org/10.3390/bdcc4030021

    ArticleĀ  Google ScholarĀ 

  47. Margaris, D., Vassilakis, C., Spiliotopoulos, D.: Handling uncertainty in social media textual information for improving venue recommendation formulation quality in social networks. Soc. Netw. Anal. Min. 9, 64 (2019). https://doi.org/10.1007/s13278-019-0610-x

    ArticleĀ  Google ScholarĀ 

  48. Preece, A., et al.: https://doi.org/10.1109/access.2020.2981567. IEEE Trans. Comput. Soc. Syst. 5, 118ā€“131 (2018). https://doi.org/10.1109/TCSS.2017.2763684

  49. Aivazoglou, M., et al.: A fine-grained social network recommender system. Soc. Netw. Anal. Min. 10, 8 (2020). https://doi.org/10.1007/s13278-019-0621-7

    ArticleĀ  Google ScholarĀ 

  50. Margaris, D., Kobusinska, A., Spiliotopoulos, D., Vassilakis, C.: An adaptive social network-aware collaborative filtering algorithm for improved rating prediction accuracy. IEEE Access. 8, 68301ā€“68310 (2020). https://doi.org/10.1109/ACCESS.2020.2981567

    ArticleĀ  Google ScholarĀ 

  51. Winter, S., Maslowska, E., Vos, A.L.: The effects of trait-based personalization in social media advertising. Comput. Hum. Behav. 114, (2021). https://doi.org/10.1016/j.chb.2020.106525

    ArticleĀ  Google ScholarĀ 

  52. Margaris, D., Vassilakis, C., Spiliotopoulos, D.: What makes a review a reliable rating in recommender systems? Inf. Process. Manag. 57, (2020). https://doi.org/10.1016/j.ipm.2020.102304

    ArticleĀ  Google ScholarĀ 

  53. Margaris, D., Spiliotopoulos, D., Vassilakis, C.: Social relations versus near neighbours: reliable recommenders in limited information social network collaborative filtering for online advertising. In: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2019), pp. 1160ā€“1167. ACM, Vancouver, B.C., Canada (2019). https://doi.org/10.1145/3341161.3345620

  54. Metz, M., Kruikemeier, S., Lecheler, S.: Personalization of politics on facebook: examining the content and effects of professional, emotional and private self-personalization. Inf. Commun. Soc. 23, 1481ā€“1498 (2020). https://doi.org/10.1080/1369118X.2019.1581244

  55. Margaris, D., Vassilakis, C.: Improving collaborative filteringā€™s rating prediction quality in dense datasets, by pruning old ratings. In: 2017 IEEE Symposium Computer Communication, pp. 1168ā€“1174 (2017). https://doi.org/10.1109/ISCC.2017.8024683

  56. Margaris, D., Spiliotopoulos, D., Vassilakis, C., Vasilopoulos, D.: Improving collaborative filteringā€™s rating prediction accuracy by introducing the experiencing period criterion. Neural Comput. Appl. (2020). https://doi.org/10.1007/s00521-020-05460-y

    ArticleĀ  Google ScholarĀ 

  57. Wang, L., Zhang, X., Wang, R., Yan, C., Kou, H., Qi, L.: Diversified service recommendation with high accuracy and efficiency. Knowl.-Based Syst. 204, (2020). https://doi.org/10.1016/j.knosys.2020.106196

    ArticleĀ  Google ScholarĀ 

  58. Margaris, D., Vasilopoulos, D., Vassilakis, C., Spiliotopoulos, D.: Improving collaborative filteringā€™s rating prediction accuracy by introducing the common item rating past criterion. In: 2019 10th International Conference on Information, Intelligence, Systems and Applications, pp. 1ā€“8 (2019). https://doi.org/10.1109/IISA.2019.8900758

  59. Singh, P.K., Sinha, M., Das, S., Choudhury, P.: Enhancing recommendation accuracy of item-based collaborative filtering using Bhattacharyya coefficient and most similar item. Appl. Intell. 50, 4708ā€“4731 (2020). https://doi.org/10.1007/s10489-020-01775-4

    ArticleĀ  Google ScholarĀ 

  60. Margaris, D., Spiliotopoulos, D., Vassilakis, C.: Improving collaborative filteringā€™s rating prediction quality by exploiting the item adoption eagerness information. In: 2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI) 2019, pp. 342ā€“347 (2019). https://doi.org/10.1145/3350546.3352544

  61. Lian, D., Liu, Q., Chen, E.: Personalized ranking with importance sampling. In: Proceedings of The Web Conference 2020, pp. 1093ā€“1103. ACM, Taipei Taiwan (2020). https://doi.org/10.1145/3366423.3380187

  62. Hu, Z., Wang, J., Yan, Y., Zhao, P., Chen, J., Huang, J.: Neural graph personalized ranking for Top-N recommendation. Knowl.-Based Syst. 213, (2021). https://doi.org/10.1016/j.knosys.2020.106426

    ArticleĀ  Google ScholarĀ 

  63. Wu, B., Ye, Y.: BSPR: basket-sensitive personalized ranking for product recommendation. Inf. Sci. (Ny) 541, 185ā€“206 (2020). https://doi.org/10.1016/j.ins.2020.06.046

    ArticleĀ  MathSciNetĀ  Google ScholarĀ 

  64. Liu, B., Chen, T., Jia, P., Wang, L.: Effective public service delivery supported by time-decayed Bayesian personalized ranking. Knowl.-Based Syst. 206, (2020). https://doi.org/10.1016/j.knosys.2020.106376

    ArticleĀ  Google ScholarĀ 

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Authors and Affiliations

  1. Department of Management Science and Technology, University of the Peloponnese, Tripoli, Greece

    Dimitris Spiliotopoulos

  2. Department of Informatics and Telecommunications, University of the Peloponnese, Tripoli, Greece

    Ī¤heodoros Giannakopoulos,Ā Costas VassilakisĀ &Ā Manolis Wallace

  3. Department of Digital Systems, University of the Peloponnese, Sparta, Greece

    Marina Lantzouni,Ā Vassilis PoulopoulosĀ &Ā Dionisis Margaris

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  1. Dimitris Spiliotopoulos
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  3. Costas Vassilakis
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Editors and Affiliations

  1. Hunan University, School of Design, Changsha, China

    Marcelo M. Soares

  2. World Usability Day and Rabb School of Continuing Studies, Division of Graduate Professional Studies, Brandeis University, Newton Center, MA, USA

    Elizabeth Rosenzweig

  3. Aaron Marcus and Associates, Berkeley, CA, USA

    Aaron Marcus

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Spiliotopoulos, D. et al. (2021). An Interface for User-Centred Process and Correlation Between Large Datasets. In: Soares, M.M., Rosenzweig, E., Marcus, A. (eds) Design, User Experience, and Usability: UX Research and Design. HCII 2021. Lecture Notes in Computer Science(), vol 12779. Springer, Cham. https://doi.org/10.1007/978-3-030-78221-4_32

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