K. S. Bøgh, S. Chester, and I. Assent, Skyalign : a portable, work-efficient skyline algorithm for multicore and GPU architectures, VLDB Journal, vol.25, issue.6, pp.817-841, 2016.

S. Börzsönyi, D. Kossmann, and K. Stocker, The skyline operator, Proc. of ICDE conf, pp.421-430, 2001.

N. Hanusse, P. Kamnang-wanko, and S. Maabout, Computing and summarizing the negative skycube, Proc. of CIKM Conference, pp.1733-1742, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01347960

K. Hose and A. Vlachou, A survey of skyline processing in highly distributed environments, VLDB Journal, vol.21, issue.3, pp.359-384, 2012.

J. Liu, L. Xiong, J. Pei, J. Luo, and H. Zhang, Finding pareto optimal groups : Group-based skyline, PVLDB, vol.8, issue.13, pp.2086-2097, 2015.

Y. Park, J. Min, and K. Shim, Efficient processing of skyline queries using mapreduce, IEEE Trans. Knowl. Data Eng, vol.29, issue.5, pp.1031-1044, 2017.

J. Pei, B. Jiang, X. Lin, and Y. Yuan, Probabilistic skylines on uncertain data, Proceedings of the 33rd International Conference on Very Large Data Bases, pp.15-26, 2007.

N. Bidoit, M. Herschel, and A. Tzompanaki, Efficient computation of polynomial explanations of why-not questions, Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp.713-722, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01182101

W. Fan and F. Geerts, Relative Information Completeness, ACM Trans. Database Syst, vol.35, issue.4, p.44, 2010.

F. Hannou, B. Amann, and M. Baazizi, Explaining Query Answer Completeness and Correctness with Minimal Pattern Covers, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01982575

M. Herschel and M. A. Hernández, Explaining missing answers to spjua queries, Proceedings of the VLDB Endowment, vol.3, pp.185-196, 2010.

M. Herschel, A. Mauricio, W. Hernández, and . Tan, Artemis: A system for analyzing missing answers, Proceedings of the VLDB Endowment, vol.2, pp.1550-1553, 2009.

T. Imieli?ski and W. Lipski, Incomplete information in relational databases, Readings in Artificial Intelligence and Databases, pp.342-360, 1988.

W. Lang, R. V. Nehme, E. Robinson, and J. F. Naughton, Partial results in database systems, International Conference on Management of Data, SIGMOD, pp.1275-1286, 2014.

A. Motro, Integrity = Validity + Completeness. ACM Trans. Database Syst, vol.14, issue.4, pp.480-502, 1989.

S. Razniewski, F. Korn, W. Nutt, and D. Srivastava, Identifying the extent of completeness of query answers over partially complete databases, Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, pp.561-576, 2015.

A. Shoshani, OLAP and statistical databases: Similarities and differences, Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems, pp.185-196, 1997.

. +-17]-bruhathi, P. Sundarmurthy, W. Koutris, J. F. Lang, V. Naughton et al., m-tables: Representing missing data, 20th International Conference on Database Theory, ICDT, vol.21, pp.1-21, 2017.

T. Quoc, C. Tran, and . Chan, How to conquer why-not questions, Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, pp.15-26, 2010.

A. Gandomi and M. Haider, Beyond the hype: Big data concepts, methods, and analytics, International Journal of Information Management, vol.35, pp.137-144, 2015.

W. Inoubli, S. Aridhi, H. Mezni, M. Maddouri, and E. M. Nguifo, An experimental survey on big data frameworks, Future Generation Computer Systems, vol.86, pp.546-564, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01926259

A. Oguntimilehin and . Eo-ademola, A Review of Big Data Management, Benefits and Challenges. A Review of Big Data Management, Benefits and Challenges, vol.5, pp.433-438, 2014.

L. Breiman, J. Friedman, J. Charles, R. A. Stone, and . Olshen, Classification and regression trees, 1984.

R. Fagin, A. Lotem, and M. Naor, Optimal aggregation algorithms for middleware, Journal of computer and system sciences, vol.66, pp.614-656, 2003.

J. Han, Data Mining: Concepts and Techniques, 2005.

S. Lloyd, Least Squares Quantization in PCM, IEEE Trans. Inf. Theor, vol.28, pp.129-137, 2006.

C. Mishra and N. Koudas, Interactive query refinement, Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology, pp.862-873, 2009.

L. Parida and N. Ramakrishnan, Redescription mining: Structure theory and algorithms, AAAI, vol.5, pp.837-844, 2005.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: Machine Learning in Python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

C. Quoc-trung-tran, S. Chan, and . Parthasarathy, Query Reverse Engineering, The VLDB Journal, vol.23, pp.721-746, 2014.

, Hpc geophysical simulation test suite

O. Y. Al-jarrah, P. D. Yoo, S. Muhaidat, G. K. Karagiannidis, and K. Taha, Efficient machine learning for big data: A review, Big Data Research, vol.2, issue.3, pp.87-93, 2015.

B. Bohn, J. Garcke, R. Iza-teran, A. Paprotny, B. Peherstorfer et al., Analysis of car crash simulation data with nonlinear machine learning methods, Int. Conf. on Computational Science ICCS, pp.621-630, 2013.

R. Campisano, F. Porto, E. Pacitti, F. Masseglia, and E. S. Ogasawara, Spatial sequential pattern mining for seismic data, Simpósio Brasileiro de Banco de Dados (SBBD), pp.241-246, 2016.

T. Condie, P. Mineiro, N. Polyzotis, and M. Weimer, Machine learning on big data, 29th IEEE Int. Conf. on Data Engineering, ICDE, pp.1242-1244, 2013.

S. Fotheringham, C. Brunsdon, and M. Charlton, Quantitative Geography: Perspectives on Spatial Data Analysis, 2000.

M. A. Friedl and C. E. Brodley, Decision tree classification of land cover from remotely sensed data, Remote Sensing of Environment, vol.61, issue.3, pp.399-409, 1997.

L. O. Hall, N. V. Chawla, and K. W. Bowyer, Decision tree learning on very large data sets, IEEE Int. Conf. on Systems, Man and Cybernetics, pp.2579-2584, 1998.

R. G-e-hinton and . Salakhutdinov, Reducing the dimensionality of data with neural networks, Science, vol.313, issue.5786, pp.504-507, 2006.

F. Kathryn, J. T. Oden, and D. Faghihi, A bayesian framework for adaptive selection, calibration, and validation of coarse-grained models of atomistic systems, Journal of Computational Physics, vol.295, pp.189-208, 2015.

J. Liu, E. Pacitti, and P. Valduriez, A survey of scheduling frameworks in big data systems, International Journal of Cloud Computing, p.27, 2018.
URL : https://hal.archives-ouvertes.fr/lirmm-01692229

S. Marelli and B. Sudret, UQLab: A Framework for Uncertainty Quantification in MATLAB, 2014.

C. Michele, T. Stefano, and S. Andrea, Sensitivity and uncertainty analysis in spatial modelling based on gis, Ecosystems & Environment, vol.81, issue.1, pp.71-79, 2000.

E. E. Prudencio and K. W. Schulz, The parallel C++ statistical library 'queso': Quantification of uncertainty for estimation, simulation and optimization, Euro-Par: Parallel Processing Workshops, pp.398-407, 2011.

S. Suthaharan, Big data classification: Problems and challenges in network intrusion prediction with machine learning, ACM SIGMETRICS Performance Evaluation Review, vol.41, issue.4, pp.70-73, 2014.

G. Trajcevski, Uncertainty in spatial trajectories, Computing with Spatial Trajectories, pp.63-107, 2011.

M. Zaharia, M. Chowdhury, M. J. Franklin, S. Shenker, and I. Stoica, Spark: Cluster computing with working sets, USENIX Workshop on Hot Topics in Cloud Computing (HotCloud), 2010.

R. Fagin, A. Lotem, and M. Naor, Optimal aggregation algorithms for middleware, J. Comput. Syst. Sci, vol.66, pp.614-656, 2003.

B. Hore, S. Mehrotra, M. Canim, and M. Kantarcioglu, Secure multidimensional range queries over outsourced data, J. VLDB, vol.21, pp.333-358, 2012.

R. Li, A. X. Liu, A. L. Wang, and B. Bruhadeshwar, Fast Range Query Processing with Strong Privacy Protection for Cloud Computing, PVLDB, vol.7, pp.1953-1964, 2014.

S. Mahboubi, R. Akbarinia, and P. Valduriez, Privacy-Preserving Top-k Query Processing in Distributed Systems, 24th Europ. Conf. on Parallel and Distributed Computing, 2018.
URL : https://hal.archives-ouvertes.fr/lirmm-02265730

W. Javier-d-fernández, M. A. Beek, M. Martínez-prieto, and . Arias, LOD-a-lot, International Semantic Web Conference, pp.75-83, 2017.

M. A. Javier-d-fernández, C. Martínez-prieto, A. Gutiérrez, M. Polleres, and . Arias, Binary RDF representation for publication and exchange (HDT), Web Semantics: Science, Services and Agents on the World Wide Web, vol.19, pp.22-41, 2013.

T. Minier, G. Montoya, H. Skaf-molli, and P. Molli, Parallelizing Federated SPARQL Queries in Presence of Replicated Data, The Semantic Web: ESWC 2017 Satellite Events, Revised Selected Papers, pp.181-196, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01591791

G. Montoya, H. Skaf-molli, P. Molli, and M. Vidal, Federated SPARQL queries processing with replicated fragments, International Semantic Web Conference, pp.36-51, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01169601

G. Montoya, H. Skaf-molli, P. Molli, and M. Vidal, Decomposing federated queries in presence of replicated fragments, Web Semantics: Science, Services and Agents on the World Wide Web, vol.42, pp.1-18, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01496241

M. Saleem, A. Ngomo, J. Xavier-parreira, H. F. Deus, and M. Hauswirth, DAW: Duplicate-AWare Federated Query Processing over the Web of Data, The Semantic Web -ISWC 2013, pp.574-590, 2013.

R. Verborgh, M. Vander-sande, O. Hartig, J. Van-herwegen, L. D. Vocht et al., Triple Pattern Fragments: A low-cost knowledge graph interface for the Web, Web Semantics: Science, Services and Agents on the World Wide Web, vol.37, pp.184-206, 2016.

E. Daga, E. Mathieu-d'aquin, A. Motta, and . Gangemi, A Bottom-up Approach for Licences Classification and Selection, International Semantic Web Conference (ISWC), 2015.

E. Dorothy and . Denning, A Lattice Model of Secure Information Flow, Commun. ACM, vol.19, pp.236-243, 1976.

M. Gr-gangadharan, . Weiss, D. Vincenzo, R. Andrea, and . Iannella, Service License Composition and Compatibility Analysis, International Conference on Service-Oriented Computing (ICSOC), pp.257-269, 2007.

G. Governatori, A. Rotolo, S. Villata, and F. Gandon, One License to Compose Them All. A Deontic Logic Approach to Data Licensing on the Web of Data, International Semantic Web Conference (ISWC), 2013.
URL : https://hal.archives-ouvertes.fr/hal-00907883

M. Georgia, F. Kapitsaki, N. Kramer, and . Tselikas, Automating the License Compatibility Process in Open Source Software With SPDX, Journal of Systems and Software, vol.131, pp.386-401, 2017.

M. Mesiti, P. Perlasca, and S. Valtolina, On the Composition of Digital Licenses in Collaborative Environments, Conference on Database and Expert Systems Applications (DEXA), 2013.

R. S. Sandhu, Lattice-Based Access Control Models, Computer, vol.26, pp.9-19, 1993.

V. Soto-mendoza, P. Serrano-alvarado, E. Desmontils, and J. A. Garcia-macias, Policies Composition Based on Data Usage Context, Consuming Linked Data (COLD) in International Semantic Web Conference (ISWC), 2015.
URL : https://hal.archives-ouvertes.fr/hal-01184660

S. Villata and F. Gandon, Licenses Compatibility and Composition in the Web of Data, Consuming Linked Data (COLD) in International Semantic Web Conference (ISWC), vol.905, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01171124

. Références,

C. Charu, C. Aggarwal, and . Zhai, A Survey of Text Clustering Algorithms, Mining Text Data, pp.77-128, 2012.

R. Alghamdi and K. Alfalqi, A Survey of Topic Modeling in Text Mining, International Journal of Advanced Computer Science and Applications, vol.6, pp.147-153, 2015.

P. Bellot, A. Doucet, S. Geva, S. Gurajada, J. Kamps et al., SIGIR Forum, vol.47, pp.21-32, 2013.

A. Bifet and E. Frank, Sentiment Knowledge Discovery in Twitter Streaming Data, Discovery Science, pp.1-15, 2010.

S. Bringay, N. Béchet, F. Bouillot, P. Poncelet, M. Roche et al., Towards an On-Line Analysis of Tweets Processing, International Conference on Database and Expert Systems Applications (DEXA), pp.154-161, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00636285

M. Crane, J. S. Culpepper, and J. Lin, A Comparison of Document-at-a-Time and Score-at-a-Time Query Evaluation, 10th ACM International Conference on Web Search and Data Mining (WSDM), pp.201-210, 2017.

J. Ferrarons, M. Adhana, C. Colmenares, S. Pietrowska, F. Bentayeb et al., PRIMEBALL : a Parallel Processing Framework Benchmark for Big Data Applications in the Cloud, 5th TPC Technology Conference on Performance Evaluation and Benchmarking (TPC-TC), vol.839, pp.109-124, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00921822

A. E. Gattiker, H. Fadi, H. Gebara, J. D. Peter-hofstee, A. Hayes et al., Big Data text-oriented benchmark creation for Hadoop, IBM Journal of Research and Development, vol.57, issue.4, pp.1-10, 2013.

J. Gray, The Benchmark Handbook for Database and Transaction Systems, 1993.

A. Guille and C. Favre, Event detection, tracking, and visualization in Twitter : a mention-anomaly-based approach, Social Network Analysis and Mining, vol.5, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01154825

S. Huang, J. Huang, J. Dai, T. Xie, and B. Huang, The Hi-Bench benchmark suite : Characterization of the MapReduce-based data analysis, Workshops Proceedings of the 26th International Conference on Data Engineering (ICDE), pp.41-51, 2010.

J. Lin, M. Crane, A. Trotman, J. Callan, I. Chattopadhyaya et al., Toward Reproducible Baselines : The Open-Source IR Reproducibility Challenge, Advances in Information Retrieval, pp.408-420, 2016.

F. Raiber and O. Kurland, Kullback-Leibler Divergence Revisited, Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval, pp.117-124, 2017.

F. Ravat, O. Teste, R. Tournier, and G. Zurfluh, Top_Keyword : an Aggregation Function for Textual Document OLAP, 10th International Conference on Data Warehousing and Knowledge Discovery (DaWaK), pp.55-64, 2008.

, TPC Express Benchmark HS Standard Specification Version 1, 2016.

J. Ciprian-octavian-truic? and . Darmont, T 2 K 2 : The Twitter Top-K Keywords Benchmark, 21st European Conference on Advances in Databases and Information Systems (ADBIS). CCIS, pp.21-28, 2017.

J. Ciprian-octavian-truic?, A. Darmont, F. Boicea, and . R?dulescu, Benchmarking Top-K Keyword and Top-K Document Processing with T 2 K 2 and T 2 K 2 D 2, Future Generation Computer Systems, vol.85, pp.60-75, 2018.

J. Ciprian-octavian-truic?, J. Darmont, and . Velcin, A Scalable Document-based Architecture for Text Analysis, International Conference on Advanced Data Mining and Applications (ADMA). LNAI 10086, pp.481-494, 2016.

L. Wang, J. Zhan, C. Luo, Y. Zhu, Q. Yang et al., BigDataBench : A big data benchmark suite from internet services, 20th IEEE International Symposium on High Performance Computer Architecture (HPCA), pp.488-499, 2014.

T. Abdessalem, Approche des versions de bases de données : représentation et interrogation des versions, Ph.D. Dissertation. Univ. Paris, 1997.

T. Abdessalem and G. Jomier, VQL: A Query Language for Multiversion Databases, DBPL, pp.160-179, 1997.

T. Abdessalem, C. Medeiros, W. Cellary, M. Manouvrier, M. Rukoz et al., Les Versions de Bases de Données, Livre Recherche des 50 ans de, 2019.

M. L. Ba, T. Abdessalem, and P. Senellart, Merging Uncertain Multi-Version XML Documents, Int. Workshop on Doc. Changes: Modeling, Detection, Storage and Visualization, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01113246

W. Cellary and G. Jomier, Consistency of Versions in Object-Oriented Databases, VLDB, pp.432-441, 1990.

L. Cura, Processing versions in geographic databases, 1997.

, Master's thesis

S. Gançarski and G. Jomier, A framework for programming multiversion databases, Data Knowl. Eng, vol.36, pp.29-53, 2001.

S. Gançarski, Versions et bases de données; modèle formel, supports de langage et d'interface utilisateur, Ph.D. Dissertation. Univ. Paris XI, 1994.

K. Jouini, Optimisation de la localité spatiale des données temporelles et multiversions, Ph.D. Dissertation. Univ. Paris, 2008.

K. Jouini and G. Jomier, Indexing multiversion databases, ACM CIKM, pp.915-918, 2007.

J. S. Longo, Management of Integrity Constraints for Multi-scale geospatial Data, 2013.

J. Savio, C. Longo, L. Camargo, C. Bauzer, A. Medeiros et al., Using the DBV model to maintain versions of multi-scale geospatial data, SeCoGIS, vol.7518, 2012.

J. Savio, C. Longo, and C. Bauzer-medeiros, Providing multi-scale consistency for multi-scale geospatial data, SSDBM, 2013.

M. Manouvrier, Objets Similaires de Grande Taille dans les Bases de Données, Ph.D. Dissertation. Univ. Paris, 2000.

M. Manouvrier, M. Rukoz, and G. Jomier, Quadtree representations for storage and manipulation of clusters of images, Image Vision Comput, vol.20, pp.513-527, 2002.
URL : https://hal.archives-ouvertes.fr/hal-00004817

C. Bauzer-medeiros, M. Bellosta, and G. Jomier, Managing Multiple Representations of Georeferenced Elements, DEXA, pp.364-371, 1996.

C. Bauzer-medeiros, M. Joliveau, G. Jomier, and F. De-vuyst, Managing sensor traffic data and forecasting unusual behaviour propagation, Geoinformatica, vol.14, pp.279-305, 2010.

C. Bauzer-medeiros and G. Jomier, Managing Alternatives and Data Evolution in GIS, ACM Workshop on Advances in Geographic Info. Sys, pp.36-39, 1993.

M. Peerbocus, Gestion de l'évolution spatiotemporelle dans une base de données géographiques, Ph.D. Dissertation. Univ. Paris, 2001.

M. A. Peerbocus, C. Medeiros, G. Jomier, and A. Voisard, A System for Change Documentation Based on a Spatiotemporal Database, GeoInformatica, vol.8, pp.173-204, 2004.

M. S. Pierre, Access Control in Multiversion Databases (in Portuguese, 2007.

A. Santanchè, J. Sávio, C. Longo, G. Jomier, M. Zam et al., Multi-focus Research and Geospatial Data -anthropocentric concerns, JJIDM, vol.5, pp.146-160, 2014.

A. Voisard, C. Bauzer-medeiros, and G. Jomier, Database Support for Cooperative Work Documentation, COOP, pp.275-290, 2000.

M. Zam, Contribution à la traçabilité du processus de conception en génie logiciel, Ph.D. Dissertation. Univ. Paris IX, 1998.

M. Zam, G. Dodinet, and G. Jomier, Software objects fairy tales: merging design and runtime objects into the cloud with mydraft, ACM SIGPLAN OOPSLA, 2011.

F. Muhammad-intizar-ali, A. Gao, and . Mileo, CityBench: A Configurable Benchmark to Evaluate RSP Engines Using Smart City Datasets, proceedings of ISWC 2015 -14th International Semantic Web Conference. W3C, pp.374-389, 2015.

W. Huiwen, W. Yuan, and H. Lele, Incremental algorithm of multiple linear regression model, Journal of Beijing University of Aeronautics and Astronsutics, vol.40, pp.1487-1491, 2014.

. Pan-liqiang, . Li-jianzhong, and . Luo-jizhou, A Multiple-Regression-Model-Based Missing Values Imputation Algorithm in Wireless Sensor Network, Journal of Computer Research and Development, vol.33, pp.1-11, 2010.

S. Rs and R. Nedunchezhian, Evaluation of three simple imputation methods for enhancing preprocessing of data with missing values, International Journal of Computer Applications, vol.21, p.10, 2011.

M. Schleich, D. Olteanu, and R. Ciucanu, Learning linear regression models over factorized joins, pp.3-18, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01330113

. Références,

I. Assent, R. Krieger, F. Afschari, and T. Seidl, The TS-tree : Efficient Time Series Search and Retrieval, EDBT, 2008.

A. Camerra, J. Shieh, T. Palpanas, T. Rakthanmanon, and E. J. Keogh, Beyond one billion time series : indexing and mining very large time series collections with i SAX2+, Knowl. Inf. Syst, 2014.

P. Esling and C. Agon, Time-series Data Mining, ACM Comput. Surv. 45, 1, Article, vol.12, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01577883

P. Huijse, P. A. Estévez, P. Protopapas, J. C. Principe, and P. Zegers, Computational Intelligence Challenges and Applications on Large-Scale Astronomical Time Series Databases, IEEE Comp. Int. Mag, vol.9, pp.27-39, 2014.

H. Kondylakis and N. Dayan, Coconut : A Scalable Bottom-Up Approach for Building Data Series Indexes, PVLDB, vol.11, p.6, 2018.

M. Linardi and T. Palpanas, ULISSE : ULtra Compact Index for Variable-Length Similarity Search in Data Series, ICDE, 2018.

M. Linardi and T. Palpanas, Scalable, Variable-Length Similarity Search in Data Series : The ULISSE Approach. PVLDB, 2019.

M. Linardi, Y. Zhu, T. Palpanas, and E. J. Keogh, Matrix Profile X : VALMOD -Scalable Discovery of Variable-Length Motifs in Data Series, SIGMOD, 2018.

M. Linardi, Y. Zhu, T. Palpanas, and E. J. Keogh, VAL-MOD : A Suite for Easy and Exact Detection of Variable Length Motifs in Data Series, SIGMOD, 2018.

T. Palpanas, Data Series Management : The Road to Big Sequence Analytics, SIGMOD Record, vol.44, pp.47-52, 2015.

T. Palpanas, Big Sequence Management : A glimpse of the Past, the Present, and the Future, SOFSEM, 2016.

T. Palpanas, The Parallel and Distributed Future of Data Series Mining, International Conference on High Performance Computing & Simulation, HPCS, 2017.

T. Rakthanmanon, B. Campana, A. Mueen, G. Batista, B. Westover et al., Searching and Mining Trillions of Time Series Subsequences Under Dynamic Time Warping, KDD, 2012.

U. Raza, A. Camerra, A. L. Murphy, T. Palpanas, and G. P. Picco, Practical Data Prediction for Real-World Wireless Sensor Networks, TKDE, 2015.

W. H. Baumgartner, Long-term variability of AGN at hard X-rays, Astronomy & Astrophysics, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01171251

J. Shieh and E. Keogh, iSAX : Disk-aware mining and indexing of massive time series datasets, DMKD, vol.19, pp.24-57, 2009.

W. Yang, W. Peng, P. Jian, W. Wei, and H. Sheng, A Data-adaptive and Dynamic Segmentation Index for Whole Matching on, Time Series. PVLDB, 2013.

L. Ye and E. J. Keogh, Time series shapelets : a new primitive for data mining, KDD, 2009.

C. C. Yeh, Y. Zhu, L. Ulanova, N. Begum, Y. Ding et al., Matrix Profile I : All Pairs Similarity Joins for Time Series : A Unifying View That Includes Motifs, Discords and Shapelets, 2016.

K. Zoumpatianos, T. Idreos, and . Palpanas, Indexing for Interactive Exploration of Big Data Series, SIGMOD Conf, 2014.

K. Zoumpatianos, ADS : the adaptive data series index, Stratos Idreos, and Themis Palpanas, vol.25, pp.843-866, 2016.

K. Zoumpatianos and T. Palpanas, Data Series Management : Fulfilling the Need for Big Sequence Analytics, ICDE, 2018.

K. Alami, R. Ciucanu, and M. N. Engelbert, Egg: A framework for generating evolving rdf graphs, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01591713

G. Bagan, gMark: Schema-driven generation of graphs and queries, IEEE TKDE, vol.29, issue.4, pp.856-869, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01402575

M. Bentounsi and C. S. Deme, Procédé sécurisé d'analyse externe de données d'exploitation d'une infrastructure de traitement de données, vol.3043809, 2017.

K. Chodorow and M. Dirolf, MongoDB -The Definitive Guide: Powerful and Scalable Data Storage, 2010.

M. Fazel-zarandi, S. Mark, and . Fox, An ontology for skill and competency management, FOIS, pp.89-102, 2012.

P. Föll and . Frédéric-thiesse, Aligning is curriculum with industry skill expectations: a text mining approach, 25th European Conference on Information Systems, 2017.

V. Ha-thuc, Search by ideal candidates: next generation of talent search at linkedin, Proceedings of the 25th International Conference on World Wide Web, pp.195-198, 2016.

M. Vinaya-r-kudatarkar, . Ramannavar, S. Nandini, and . Sidnal, An unstructured text analytics approach for qualitative evaluation of resumes, International Journal of Innovative Research in Advanced Engineering, pp.2349-2163, 2014.

C. Tankard, What the GDPR means for businesses, Network Security, pp.5-8, 2016.

T. Minier, H. Skaf-molli, P. Molli, and M. Vidal, Intelligent clients for replicated Triple Pattern Fragments, Proceedings of the 15th Extended Semantic Web Conference, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01789409

G. Montoya, H. Skaf-molli, P. Molli, and M. Vidal, Federated SPARQL queries processing with replicated fragments, International Semantic Web Conference, pp.36-51, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01169601

G. Montoya, H. Skaf-molli, P. Molli, and M. Vidal, Decomposing federated queries in presence of replicated fragments, Web Semantics: Science, Services and Agents on the World Wide Web, vol.42, pp.1-18, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01496241

C. Yoshikawa, B. Chun, P. Eastham, A. Vahdat, T. Anderson et al., Using smart clients to build scalable services, Proceedings of the 1997 USENIX Technical Conference. CA, p.105, 1997.

C. Bizer, T. Heath, and T. Berners-lee, Linked data-the story so far, International journal on semantic web and information systems, vol.5, pp.1-22, 2009.

F. Goasdoué, I. Manolescu, and A. Roati?, Efficient query answering against dynamic RDF databases, Proceedings of the 16th International Conference on Extending Database Technology, pp.299-310, 2013.

R. Gu, S. Wang, F. Wang, C. Yuan, and Y. Huang, Cichlid: efficient large scale RDFS/OWL reasoning with spark, Parallel and Distributed Processing Symposium (IPDPS), pp.700-709, 2015.

J. Leskovec, A. Rajaraman, and J. D. Ullman, Mining of massive datasets, 2014.

M. Stocker and E. Sirin, PelletSpatial: A Hybrid RCC-8 and RDF/OWL Reasoning and Query Engine, OWLED, vol.529, 2009.

J. Urbani, S. Kotoulas, J. Maassen, F. Van-harmelen, and H. Bal, WebPIE: A web-scale parallel inference engine using MapReduce, Web Semantics: Science, Services and Agents on the World Wide Web, vol.10, pp.59-75, 2012.

X. H. Wang, Q. Zhang, T. Gu, and H. K. Pung, Ontology based context modeling and reasoning using OWL, Proceedings of the Second IEEE Annual Conference on. Ieee, pp.18-22, 2004.

T. Alves and D. Felton, TrustZone: Integrated Hardware and Software Security-Enabling Trusted Computing in Embedded Systems, 2004.

S. Brenner, Confidential ZooKeeper using Intel SGX. Middleware, 2016.

V. Costan and S. Devadas, Intel SGX Explained. IACR Cryptology ePrint Archive, vol.086, pp.1-118, 2016.

B. Fuhry, Practical and secure index with SGX. IFIP Annual Conference on Data and Applications Security and Privacy, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01684366

P. Kocher, Spectre attacks: Exploiting speculative execution, S&P, 2019.

. Lipp and . Moritz, Reading Kernel Memory from User Space. USENIX Security Symposium, 2018.

F. Mckeen, Innovative instructions and software model for isolated execution, HASP@ ISCA, vol.10, 2013.

C. Priebe, K. Vaswani, and M. Costa, EnclaveDB: A Secure Database using SGX. EnclaveDB: A Secure Database using SGX, 2018.

J. Abawajy, Comprehensive Analysis of Big Data Variety Landscape, International Journal of Parallel, Emergent and Distributed Systems, vol.30, pp.5-14, 2015.

A. Botta, V. Walter-de-donato, A. Persico, and . Pescapé, Integration of Cloud computing and Internet of Things: A survey, Future Generation Computer Systems, vol.56, pp.684-700, 2016.

C. Delimitrou and C. Kozyrakis, Paragon: QoS-aware Scheduling for Heterogeneous Datacenters, SIGARCH Comput. Archit. News, vol.41, pp.77-88, 2013.

S. García, S. Ramírez-gallego, J. Luengo, J. M. Benítez, and F. Herrera, Big data preprocessing: methods and prospects, Big Data Analytics, vol.1, issue.1, 2016.

J. Mars, L. Tang, and R. Hundt, Heterogeneity in "Homogeneous" Warehouse-Scale Computers: A Performance Opportunity, IEEE Computer Architecture Letters, vol.10, issue.2, pp.29-32, 2011.

A. Oussous and F. Benjelloun, Big Data technologies: A survey, Ayoub Ait Lahcen, and Samir Belfkih, 2017.

U. Sivarajah, M. Kamal, Z. Irani, and V. Weerakkody, Critical analysis of Big Data challenges and analytical methods, vol.70, 2016.

L. Wang, Heterogeneous Data and Big Data Analytics, Automatic Control and Information Sciences, vol.3, pp.8-15, 2017.

S. Cohen-boulakia, K. Belhajjame, O. Collin, J. Chopard, C. Froidevaux et al., Scientific workflows for computational reproducibility in the life sciences: Status, challenges and opportunities, Future Generation Computer Systems, vol.75, pp.284-298, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01516082

K. A. Daniel-de-oliveira, F. Ocaña, M. Baião, and . Mattoso, A provenance-based adaptive scheduling heuristic for parallel scientific workflows in clouds, Journal of Grid Computing, vol.10, pp.521-552, 2012.

J. Liu, E. Pacitti, P. Valduriez, D. D. Oliveira, and M. Mattoso, Multi-objective scheduling of scientific workflows in multisite clouds, Future Generation Computer Systems, vol.63, pp.76-95, 2016.
URL : https://hal.archives-ouvertes.fr/lirmm-01342203

J. Liu, E. Pacitti, P. Valduriez, and M. Mattoso, Scientific workflow scheduling with provenance support in multisite cloud, International Conference on Vector and Parallel Processing, pp.206-219, 2016.
URL : https://hal.archives-ouvertes.fr/lirmm-01342190

C. Pradal, S. Artzet, J. Chopard, D. Dupuis, C. Fournier et al., InfraPhenoGrid: a scientific workflow infrastructure for plant phenomics on the grid, Future Generation Computer Systems, vol.67, pp.341-353, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01336655

C. Pradal, S. Dufour-kowalski, F. Boudon, C. Fournier, and C. Godin, OpenAlea: a visual programming and componentbased software platform for plant modelling, Functional plant biology, vol.35, pp.751-760, 2008.

F. Tardieu, L. Cabrera-bosquet, T. Pridmore, and M. Bennett, Plant phenomics, from sensors to knowledge, Current Biology, vol.27, pp.770-783, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01608414

, Riad Ladjel Inria & U. Versailles Versailles, France riad.ladjel@inria.fr Nicolas Anciaux Inria & U. Versailles Versailles, France nicolas.anciaux@inria.fr

G. Scerri and U. ,

I. Anati, Innovative technology for CPU based attestation and sealing, HASP, 2013.

J. Götzfried, Cache attacks on Intel SGX, Proceedings of the 10th European Workshop on Systems Security, 2017.

A. Molina-markham, Private memoirs of a smart meter, Embedded sensing systems for energy-efficiency in building, 2010.

C. Gentry, A fully homomorphic encryption scheme, 2009.

A. Joseph and . Cruz, Applications of machine learning in cancer prediction and prognosis, Cancer informatics, 2006.

R. Cramer, Secure multiparty computation, 2015.

T. Allard, Lightweight privacy-preserving averaging for the internet of things, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01421986

Y. Lv, Traffic flow prediction with big data: a deep learning approach, IEEE Transactions on Intelligent Transportation Systems, 2015.

S. Abiteboul, B. André, and D. Kaplan, Managing your digital life, Commun. ACM, vol.58, pp.32-35, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01068006

M. Backes, P. Druschel, A. Haeberlen, and D. Unruh, CSAR: A Practical and Provable Technique to Make Randomized Systems Accountable, In NDSS, vol.9, pp.341-353, 2009.

C. Cloud, Cozy allow you to control your personal data (pictures, bank statements, bills, health reinbursements) in a secure and private space, 2013.

. Fing, The mesinfos project explores and implements the self data concept in france, vol.3, 2013.

T. Hunt, Have I been pwned? Check if you have an account that has been compromised in a data breach, 2013.

S. Lallali and N. Anciaux, Supporting secure keyword search in the personal cloud, Information Systems, vol.72, pp.1-26, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01660599

S. Ratnasamy, P. Francis, M. Handley, R. Karp, and S. Shenker, A scalable content-addressable network, vol.31, 2001.

A. Shamir, How to share a secret, Commun. ACM, vol.22, pp.612-613, 1979.

I. Stoica, R. Morris, D. Karger, H. Frans-kaashoek, and . Balakrishnan, Chord: A scalable peer-to-peer lookup service for internet applications, ACM SIGCOMM Computer Communication Review, vol.31, pp.149-160, 2001.

D. C. , Privacy in mobile participatory sensing: current trends and future challenges, Journal of Systems and Software, vol.116, pp.57-68, 2016.

C. Cornelius, Anonysense: privacy-aware people-centric sensing, 6th international conference on Mobile systems, applications, and services, 2008.

D. Christin, Privacy-preserving collaborative path hiding for participatory sensing applications, IEEE 8th International Conference on. IEEE, pp.341-350, 2011.

. Dai-hai-ton and . That, PAMPAS: Privacy-Aware Mobile Participatory Sensing Using Secure Probes, ACM SSDBM, 2016.

D. Kempe, Gossip-based computation of aggregate information, IEEE Symposium on Foundations of Computer Science, 2003.

H. To, A framework for protecting worker location privacy in spatial crowdsourcing, VLDB Endowment, vol.7, p.10, 2014.

I. Anati, Innovative technology for CPU based attestation and sealing, 2nd international workshop on hardware and architectural support for security and privacy, vol.13, 2013.

K. Vu, Efficient algorithms for k-anonymous location privacy in participatory sensing, IEEE INFOCOM. IEEE, pp.2399-2407, 2012.

T. Alves, TrustZone: Integrated Hardware and Software Security-Enabling Trusted Computing in Embedded Systems, 2004.

V. Raphaël, Estimation of urban noise with the assimilation of observations crowdsensed by the mobile application Ambiciti, 2017.

O. Goldreich, Secure multi-party computation. Manuscript, 1998.

J. Krumm, Inference attacks on location tracks, International Conference on Pervasive Computing, pp.127-143, 2007.

N. Pham and . Hoai, Detection of recurring software vulnerabilities, 2010.

, Graduate Theses and Dissertations, vol.11590

Z. Li, D. Zou, S. Xu, X. Ou, H. Jin et al., VulDeePecker: A Deep Learning-Based System for Vulnerability Detection, vol.5, 2018.

. Chucky, Exposing Missing Checks in Source Code for Vulnerability Discovery, 2013.

, Understanding Bag-of-Words Model: A Statistical Framework, Yin Zhang, Rong Jin

, Generating robust parsers using island grammars

J. Harer, L. Y. Kim, R. L. Russell, O. Ozdemir, L. R. Kosta et al., Automated software vulnerability detection with machine learning, 2018.

F. Yamaguchi, A. Maier, H. Gascon, K. Rieck, and U. G¨ottingen, Automatic Inference of Search Patterns for Taint-Style Vulnerabilities

J. Jang, A. Agrawal, and D. Brumley, ReDeBug: Finding unpatched code clones in entire OS distributions, Proceedings of the 33th IEEE Symposium on Security and Privacy, pp.48-62, 2012.

H. Sajnani, V. J. Saini, C. K. Svajlenkoy, C. V. Royy, and . Lopes, SourcererCC: Scaling Code Clone Detection to Big Code, 2015.

, The use of machine learning with signal-and NLP processing of source code to fingerprint, detect, and classify vulnerabilities and weaknesses with MARFCAT, Serguei A. Mokhov, 2011.

R. L. Russell, L. Kim, L. H. Hamilton, T. Lazovich, J. A. Harer et al., Automated Vulnerability Detection in Source Code Using Deep Representation Learning