Um Experimento em um Ambiente de Business Intelligence Industrial para melhoria da manutenção de cargas de dados
Resumo
Aplicações Business Intelligence (BI) e Data Analytics efetivas dependem de um Data Warehouse (DW), um repositório histórico de dados projetado para dar suporte a processos de tomada de decisão. Sem um DW eficiente, as organizações não podem extrair, em um tempo aceitável, os dados que viabilizam ações estratégicas, táticas e operacionais mais eficazes. Este artigo apresenta uma abordagem e uma ferramenta de desenvolvimento rápido de aplicações (RAD) para aumentar a eficiência e a eficácia do desenvolvimento e manutenção de programas ETL (Extract, Transform and Load). Além disto, também é descrito um experimento controlado feito na indústria para analisar a eficiência e eficácia da ferramenta na manutenção de procedimentos ETL. Os resultados do experimento específico aqui apresentado indicam que a nossa abordagem pode de fato ser usada como método para acelerar e melhorar a manutenção do processo de ETL.
Palavras-chave:
Engenharia de Software Experimental, RAD, Data Warehouse, ETL, Data Analytics, Manutenção
Referências
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H. Liang, J. Yuan, and D. Thalmann. Parsing the hand in depth images. IEEE Trans. on Multimedia, 16(5):1241–1253, aug. 2014.
J. Liu, B. Liu, S. Zhang, F. Yang, P. Yang, D. N. Metaxas, and C. Neidle. Non-manual grammatical marker recognition based on multi-scale, spatio-temporal analysis of head pose and facial expressions. Image Vision Comput., 32(10):671–681, 2014.
J. Liu, X. Wang, T. Li, and J. Yang. Spatio-temporal semantic features for human action recognition. KSII Trans. on Internet and Inf. Syst., 6(10):2632–2649, 2012.
S. Liu, J. Feng, C. Domokos, H. Xu, J. Huang, Z. Hu, and S. Yan. Fashion parsing with weak color-category labels. IEEE Trans. on Multimedia, 16(1):253–265, jan. 2014.
S. Liu, X. Liang, L. Liu, K. Lu, L. Lin, X. Cao, and S. Yan. Fashion parsing with video context. IEEE Trans. on Multimedia, 17(8):1347–1358, aug. 2015.
R. Lun and W. Zhao. A survey of applications and human motion recognition with microsof kinect. Int. J. of Pattern Recogn. and Art. Intel., 29(5):1–48, 2015.
A. L¨ucking, K. Bergmann, F. Hahn, S. Kopp, and H. Rieser. Data-based analysis of speech and gesture: the Bielefeld Speech and Gesture Alignment corpus (SaGA) and its applications. Journal on Multimodal User Interfaces, 7(1-2), 2013.
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S. Mitra and T. Acharya. Gesture recognition: A survey. IEEE Trans. on Syst., Man, and Cyber., Part C: App. and Rev., 37(3):311–324, May 2007.
T. D. Nguyen and S. Ranganath. Facial expressions in american sign language: Tracking and recognition. Pattern Recogn., 45(5):1877–1891, 2012.
S. C. W. Ong and S. Ranganath. Automatic sign language analysis: A survey and the future beyond lecical meaning. IEEE Trans. on Pattern Analysis and Machine Intel., 27(6):873–891, jun. 2005.
P. K. Pisharady and M. Saerbeck. Recent methods and databases in vision-based hand gesture recognition: A review. Computer Vision and Image Underst., 141:152–165, 2015.
A. Rosani, N. Conci, and F. G. B. D. Natale. Human behavior recognition using a context-free grammar. J. Electronic Imaging, 23(3):033016, 2014.
N. A. Smith. Linguistic Structure Prediction. Morgan & Claypool, 2011.
W. Xu and E.-J. Lee. Hand gesture recognition using improved hidden markov models. J. of Korea Multimedia Soc., 14(7):866–871, 2011.
X. Zhang, C. Li, W. Hu, X. Tong, S. J. Maybank, and Y. Zhang. Human pose estimation and tracking via parsing a tree structure based human model. IEEE Trans. Syst., Man, and Cyber.: Systems, 44(5):580–592, 2014.
L. Zhao, X. Gao, D. Tao, and X. Li. Tracking human pose using max-margin markov models. IEEE Trans. on Image Proces., 24(12):5274–5287, 2015.
L. Zhu, Y. Chen, C. Lin, and A. L. Yuille. Max margin learning of hierarchical configural deformable templates (hcdts) for efficient object parsing and pose estimation. Int. J. of Comp. Vision, 93(1):1–21, 2011.
J. Biolchini, P. G. Mian, A. C. C. Natali, and G. H. Travassos. Systematic rev. in soft. eng. Technical report, System Engineering and Computer Science Department COPPE/UFRJ, Technical Report ES–679–05, 2005.
B. Caramiaux, M. M. Wanderley, and F. Bevilacqua. Segmenting and parsing instrumentalists’ gestures. J. of New Music Research, 41(1):13–29, 2012.
G. Caridakis, S. Asteriadis, and K. Karpouzis. Non-manual cues in automatic sign language recognition. Personal and Ubiquitous Comput., 18(1):37–46, 2014.
C. Chen, R. Jafari, and N. Kehtarbavaz. A survey of depth and inertial sensor fusion for human action recognition. Multimedia Tools App., 2015. to appear.
N. H. Dardas and N. D. Georganas. Real-time hand gesture detection and recognition using bag-of-features and support vector machine techniques. IEEE Trans. Inst. and Measurement, 60(11):3592–3607, 2011.
N. H. Dardas, J. M. Silva, and A. El-Saddik. Target-shooting exergame with a hand gesture control. Multimedia Tools Appl., 70(3):2211–2233, 2014.
T. G. Dietterich. Machine learning for sequential data: A review”. In Proc. of Joint Structural, Syntactic, and Statistical Pattern Recogn. International Workshops, pages 15–30. Springer Berlin Heidelberg, 2002.
T. Hachaj and M. R. Ogiela. Rule-based approach to recognizing human body poses and gestures in real time. Multimedia Syst., 20(1):81–99, 2014.
T. Hachaj and M. R. Ogiela. Full body movements recognition – unsupervised learning approach with heuristic r-gdl method. Digital Signal Proces., 46:239–252, 2015.
J. Han, L. Shao, D. Xu, and J. Shotton. Enhanced computer vision with microsoft kinect sensor: A review. IEEE Trans. on Cyber., 43(5):1318–1334, 2013.
B. A. Kitchenham. Systematic review in software engineering: Where we are and where we should be going. In Proc. of the 2Nd Int. Workshop on Evidential Assessment of Soft. Tech., EAST ’12, pages 1–2, New York, NY, USA, 2012. ACM.
M. Kyan, G. Sun, H. Li, L. Zhong, P. Muneesawang, N. Dong, B. Elder, and L. Guan. An approach to ballet dance training through ms kinect and visualization in a cave virtual reality environment. ACM Trans. Intell. Syst. Technol., 6(2):23:1–23:37, mar. 2015.
S. H. Ladewig and J. Bressem. New insights into the medium hand: Discovering recurrent structures in gestures. Semiotica, 2013(197):203–231, Oct. 2013.
H. Liang, J. Yuan, and D. Thalmann. Parsing the hand in depth images. IEEE Trans. on Multimedia, 16(5):1241–1253, aug. 2014.
J. Liu, B. Liu, S. Zhang, F. Yang, P. Yang, D. N. Metaxas, and C. Neidle. Non-manual grammatical marker recognition based on multi-scale, spatio-temporal analysis of head pose and facial expressions. Image Vision Comput., 32(10):671–681, 2014.
J. Liu, X. Wang, T. Li, and J. Yang. Spatio-temporal semantic features for human action recognition. KSII Trans. on Internet and Inf. Syst., 6(10):2632–2649, 2012.
S. Liu, J. Feng, C. Domokos, H. Xu, J. Huang, Z. Hu, and S. Yan. Fashion parsing with weak color-category labels. IEEE Trans. on Multimedia, 16(1):253–265, jan. 2014.
S. Liu, X. Liang, L. Liu, K. Lu, L. Lin, X. Cao, and S. Yan. Fashion parsing with video context. IEEE Trans. on Multimedia, 17(8):1347–1358, aug. 2015.
R. Lun and W. Zhao. A survey of applications and human motion recognition with microsof kinect. Int. J. of Pattern Recogn. and Art. Intel., 29(5):1–48, 2015.
A. L¨ucking, K. Bergmann, F. Hahn, S. Kopp, and H. Rieser. Data-based analysis of speech and gesture: the Bielefeld Speech and Gesture Alignment corpus (SaGA) and its applications. Journal on Multimodal User Interfaces, 7(1-2), 2013.
R. C. B. Madeo, P. K. Wagner, and S. M. Peres. A review of temporal aspects of hand gesture analysis applied to discourse analysis and natural conversation. Int. J. of C. Sci. & Inf. Tech., 5(4), dec. 2013.
S. Mitra and T. Acharya. Gesture recognition: A survey. IEEE Trans. on Syst., Man, and Cyber., Part C: App. and Rev., 37(3):311–324, May 2007.
T. D. Nguyen and S. Ranganath. Facial expressions in american sign language: Tracking and recognition. Pattern Recogn., 45(5):1877–1891, 2012.
S. C. W. Ong and S. Ranganath. Automatic sign language analysis: A survey and the future beyond lecical meaning. IEEE Trans. on Pattern Analysis and Machine Intel., 27(6):873–891, jun. 2005.
P. K. Pisharady and M. Saerbeck. Recent methods and databases in vision-based hand gesture recognition: A review. Computer Vision and Image Underst., 141:152–165, 2015.
A. Rosani, N. Conci, and F. G. B. D. Natale. Human behavior recognition using a context-free grammar. J. Electronic Imaging, 23(3):033016, 2014.
N. A. Smith. Linguistic Structure Prediction. Morgan & Claypool, 2011.
W. Xu and E.-J. Lee. Hand gesture recognition using improved hidden markov models. J. of Korea Multimedia Soc., 14(7):866–871, 2011.
X. Zhang, C. Li, W. Hu, X. Tong, S. J. Maybank, and Y. Zhang. Human pose estimation and tracking via parsing a tree structure based human model. IEEE Trans. Syst., Man, and Cyber.: Systems, 44(5):580–592, 2014.
L. Zhao, X. Gao, D. Tao, and X. Li. Tracking human pose using max-margin markov models. IEEE Trans. on Image Proces., 24(12):5274–5287, 2015.
L. Zhu, Y. Chen, C. Lin, and A. L. Yuille. Max margin learning of hierarchical configural deformable templates (hcdts) for efficient object parsing and pose estimation. Int. J. of Comp. Vision, 93(1):1–21, 2011.
Publicado
17/05/2016
Como Citar
COSTA, Juli Kelle Góis; SANTOS, Igor Peterson Oliveira; COLAÇO JÚNIOR, Methanias; NASCIMENTO, André Vinícius R. P..
Um Experimento em um Ambiente de Business Intelligence Industrial para melhoria da manutenção de cargas de dados. In: SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 12. , 2016, Florianópolis.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2016
.
p. 534-541.
DOI: https://doi.org/10.5753/sbsi.2016.6004.
