Communications of the ACM
A database perspective on knowledge discovery
Communications of the ACM
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Dynamic hierarchical mega models: comprehensive traceability and its efficient maintenance
Software and Systems Modeling (SoSyM)
Deductive and Inductive Stream Reasoning for Semantic Social Media Analytics
IEEE Intelligent Systems
Large knowledge collider: a service-oriented platform for large-scale semantic reasoning
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
CrowdDB: answering queries with crowdsourcing
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Mining mobility user profiles for car pooling
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Unveiling the complexity of human mobility by querying and mining massive trajectory data
The VLDB Journal — The International Journal on Very Large Data Bases
A classification for community discovery methods in complex networks
Statistical Analysis and Data Mining
An inductive database system based on virtual mining views
Data Mining and Knowledge Discovery
Answering search queries with CrowdSearcher
Proceedings of the 21st international conference on World Wide Web
Web Semantics: Science, Services and Agents on the World Wide Web
Towards mega-modeling: a walk through data analysis experiences
ACM SIGMOD Record
Extending ER models to capture database transformations to build data sets for data mining
Data & Knowledge Engineering
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The availability of huge amounts of data ("big data") is changing our attitude towards science, which is moving from specialized to massive experiments and from very focused to very broad research questions. Models of all kinds, from analytic to numeric, from exact to stochastic, from simulative to predictive, from behavioral to ontological, from patterns to laws, enable massive data analysis and mining, often in real time. Scientific discovery in most cases stems from complex pipelines of data analysis and data mining methods on top of "big" experimental data, confronted and contrasted with state-of-art knowledge. In this setting, we propose mega-modelling as a new holistic data and model management system for the acquisition, composition, integration, management, querying and mining of data and models, capable of mastering the co-evolution of data and models and of supporting the creation of what-if analyses, predictive analytics and scenario explorations.