QROWD: Because big data integration is humanly possible

We present QROWD, a project funded by the Horizon 2020 research programme, which aims at offering socio-technical solution to cross-sectorial Big Data integration in a European urban Smart Transportation context through a hybrid architecture for Big Data integration and analytics.

[1]  Yusak O. Susilo,et al.  Future directions of research for automatic travel diary collection , 2018 .

[2]  Scott Lundberg,et al.  A Unified Approach to Interpreting Model Predictions , 2017, NIPS.

[3]  Mikhail Belkin,et al.  Automatic Annotation of Daily Activity from Smartphone-Based Multisensory Streams , 2012, MobiCASE.

[4]  Markus Freudenberg,et al.  DataID: towards semantically rich metadata for complex datasets , 2014, SEM '14.

[5]  Anthony G. Cohn,et al.  Qualitative Spatial Representation and Reasoning with the Region Connection Calculus , 1997, GeoInformatica.

[6]  Luciano Bononi,et al.  Custom Dual Transportation Mode Detection By Smartphone Devices Exploiting Sensor Diversity , 2018, 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops).

[7]  Michael L. Littman,et al.  Activity Recognition from Accelerometer Data , 2005, AAAI.

[8]  Diogo R. Ferreira,et al.  Preprocessing techniques for context recognition from accelerometer data , 2010, Personal and Ubiquitous Computing.

[9]  Marcia Lei Zeng,et al.  Information and documentation - Thesauri and interoperability with other vocabularies , 2013 .

[10]  Moshe Ben-Akiva,et al.  Future Mobility Survey , 2013 .

[11]  Jens Lehmann,et al.  Class expression learning for ontology engineering , 2011, J. Web Semant..

[12]  Evgeniy Gabrilovich,et al.  A Review of Relational Machine Learning for Knowledge Graphs , 2015, Proceedings of the IEEE.

[13]  Michael J. Franklin,et al.  Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing , 2012, NSDI.

[14]  James A. Landay,et al.  MyExperience: a system for in situ tracing and capturing of user feedback on mobile phones , 2007, MobiSys '07.

[15]  Sunshin Lee Geo-Locating Tweets with Latent Location Information , 2017 .

[16]  Antoine Zimmermann,et al.  A SPARQL Extension for Generating RDF from Heterogeneous Formats , 2017, ESWC.

[17]  Sian Lun Lau,et al.  Movement recognition using the accelerometer in smartphones , 2010, 2010 Future Network & Mobile Summit.

[18]  Fausto Giunchiglia,et al.  Personal Context Recognition Via Reliable Human-Machine Collaboration , 2018, 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops).

[19]  Sean Gillies,et al.  The GeoJSON Format , 2016, RFC.

[20]  Max A. Little,et al.  Generalized methods and solvers for noise removal from piecewise constant signals. I. Background theory , 2011, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[21]  Shiyali Ramamrita Ranganathan,et al.  Prolegomena to Library Classification , 1967 .

[22]  B. West,et al.  The Quality of Paradata: A Literature Review , 2013 .

[23]  Jens Lehmann,et al.  LinkedGeoData: A core for a web of spatial open data , 2012, Semantic Web.

[24]  Ling Shao,et al.  Multimedia Interaction and Intelligent User Interfaces , 2010 .

[25]  Bashar Nuseibeh,et al.  Requirements engineering: a roadmap , 2000, ICSE '00.

[26]  Jörg Widmer,et al.  Survey on Energy Consumption Entities on the Smartphone Platform , 2011, 2011 IEEE 73rd Vehicular Technology Conference (VTC Spring).

[27]  Gernot Heiser,et al.  An Analysis of Power Consumption in a Smartphone , 2010, USENIX Annual Technical Conference.

[28]  Fausto Giunchiglia,et al.  Multi-device activity logging , 2014, UbiComp Adjunct.

[29]  Bošnjak Ivan,et al.  Different approaches to the modal split calculation in urban areas , 2009 .

[30]  Jens Lehmann,et al.  Triplify: light-weight linked data publication from relational databases , 2009, WWW '09.

[31]  Vicki A Freedman,et al.  Interviewer and Respondent Interactions and Quality Assessments in a Time Diary Study. , 2013, Electronic international journal of time use research.

[32]  Sören Auer,et al.  LIMES - A Time-Efficient Approach for Large-Scale Link Discovery on the Web of Data , 2011, IJCAI.

[33]  Benjamin B. Bederson,et al.  Human computation: a survey and taxonomy of a growing field , 2011, CHI.

[34]  Guan Le,et al.  Survey on NoSQL database , 2011, 2011 6th International Conference on Pervasive Computing and Applications.

[35]  Patrick Seeling,et al.  Towards the run and walk activity classification through step detection - An android application , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[36]  Marc A. Viredaz,et al.  Energy Management on Handheld Devices , 2003, ACM Queue.

[37]  Robert J. Meijer,et al.  Sensor Data Storage Performance: SQL or NoSQL, Physical or Virtual , 2012, 2012 IEEE Fifth International Conference on Cloud Computing.

[38]  Jens Lehmann,et al.  DBpedia - A large-scale, multilingual knowledge base extracted from Wikipedia , 2015, Semantic Web.

[39]  Jens Lehmann,et al.  Simplified RDB2RDF Mapping , 2015, LDOW@WWW.

[40]  Wei Zhang,et al.  Knowledge vault: a web-scale approach to probabilistic knowledge fusion , 2014, KDD.

[41]  Yusak O. Susilo,et al.  Lessons from a trial of MEILI, a smartphone based semi-automatic activity-travel diary collector, in Stockholm city, Sweden , 2016 .

[42]  Jens Lehmann,et al.  DL-Learner - A framework for inductive learning on the Semantic Web , 2016, J. Web Semant..

[43]  Johann Schrammel,et al.  Comparison of Travel Diaries Generated from Smartphone Data and Dedicated GPS Devices , 2015 .

[44]  Nicola Guarino,et al.  Evaluating ontological decisions with OntoClean , 2002, CACM.

[45]  Thomas R. Gruber,et al.  A translation approach to portable ontology specifications , 1993, Knowl. Acquis..

[46]  Gary M. Weiss,et al.  Activity recognition using cell phone accelerometers , 2011, SKDD.

[47]  Mario Platzer,et al.  Field Evaluation of the Smartphone-based Travel Behaviour Data Collection App “SmartMo”☆ , 2015 .

[48]  Derya Birant,et al.  ST-DBSCAN: An algorithm for clustering spatial-temporal data , 2007, Data Knowl. Eng..

[49]  Anupam Basu,et al.  An Agreement Measure for Determining Inter-Annotator Reliability of Human Judgements on Affective Text , 2008, Proceedings of the Workshop on Human Judgements in Computational Linguistics - HumanJudge '08.

[50]  Norbert Brändle,et al.  Supporting large-scale travel surveys with smartphones – A practical approach , 2014 .

[51]  Bin Guo,et al.  From participatory sensing to Mobile Crowd Sensing , 2014, 2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS).

[52]  Jun Yang,et al.  Physical Activity Recognition with Mobile Phones: Challenges, Methods, and Applications , 2010 .

[53]  Daniel Gatica-Perez,et al.  By their apps you shall understand them: mining large-scale patterns of mobile phone usage , 2010, MUM.

[54]  Fausto Giunchiglia,et al.  From Knowledge Organization to Knowledge Representation , 2014 .

[55]  Elsevier Sdol,et al.  Transportation Research Part C: Emerging Technologies , 2009 .

[56]  M. Ben-Akiva,et al.  The Future Mobility Survey: Experiences in developing a smartphone-based travel survey in Singapore , 2013 .

[57]  Go Hirakawa,et al.  A Large Scale Gathering System for Activity Data with Mobile Sensors , 2011, 2011 15th Annual International Symposium on Wearable Computers.

[58]  Dirk Merkel,et al.  Docker: lightweight Linux containers for consistent development and deployment , 2014 .

[59]  Frauke Kreuter,et al.  Improving Surveys with Paradata: Analytic Uses of Process Information , 2013 .