[Dbworld] Last Mile || CfP: IMMM 2017 || June 25 - 29, 2017- Venice, Italy

INVITATION:

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Please consider to contribute to and/or forward to the appropriate groups the following opportunity to submit and publish original scientific results to:
IMMM 2017, The Seventh International Conference on Advances in Information Mining and Management
ISSN: 2326-9332
ISBN: 978-1-61208-566-1
The submission deadline is February 28, 2017.
Authors of selected papers will be invited to submit extended article versions to one of the IARIA Journals: http://www.iariajournals.org
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============== IMMM 2017 | Call for Papers ===============

CALL FOR PAPERS, TUTORIALS, PANELS

IMMM 2017, The Seventh International Conference on Advances in Information Mining and Management

General page: http://www.iaria.org/conferences2017/IMMM17.html
Submission page: http://www.iaria.org/conferences2017/SubmitIMMM17.html

Event schedule: June 25 - 29, 2017- Venice, Italy

Contributions:
- regular papers [in the proceedings, digital library]
- short papers (work in progress) [in the proceedings, digital library]
- ideas: two pages [in the proceedings, digital library]
- extended abstracts: two pages [in the proceedings, digital library]
- posters: two pages [in the proceedings, digital library]
- posters:  slide only [slide-deck posted at www.iaria.org]
- presentations: slide only [slide-deck posted at www.iaria.org]
- demos: two pages [posted at www.iaria.org]
- doctoral forum submissions: [in the proceedings, digital library]

Proposals for:
- mini symposia: see http://www.iaria.org/symposium.html
- workshops: see http://www.iaria.org/workshop.html
- tutorials:  [slide-deck posed on www.iaria.org]
- panels: [slide-deck posed on www.iaria.org]

Submission deadline: February 28, 2017

Sponsored by IARIA, www.iaria.org
Extended versions of selected papers will be published in IARIA Journals:  http://www.iariajournals.org
Print proceedings will be available via Curran Associates, Inc.: http://www.proceedings.com/9769.html
Articles will be archived in the free access ThinkMind Digital Library: http://www.thinkmind.org

The topics suggested by the conference can be discussed in term of concepts, state of the art, research, standards, implementations, running experiments, applications, and industrial case studies. Authors are invited to submit complete unpublished papers, which are not under review in any other conference or journal in the following, but not limited to, topic areas.

All tracks are open to both research and industry contributions, in terms of Regular papers, Posters, Work in progress, Technical/marketing/business presentations, Demos, Tutorials, and Panels.

Before submission, please check and comply with the editorial rules: http://www.iaria.org/editorialrules.html

IMMM 2017 Topics (for topics and submission details: see CfP on the site)

Call for Papers: http://www.iaria.org/conferences2017/CfPIMMM17.html

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Trends in mining/retrieving/handling data
Big data and Genome Data; Large scale data retrieval; Graph-modeled Data; Geo-tagged photos; Tractable uncertain data; Data curation workflow analysis; Online data integration services; Streaming/real-time/active; Stream processing at scale; Real-time analytics in the same database; Temporal mining; Temporal pattern discovery; Incrementally mining temporal patterns; Keyword search in large networks; Sequence mining with hierarchies; Phonetic time series; Mining communication motifs in dynamic environments; Approximate queries on string collection; Profiling entities; Data-intensive science using scalable analytics; Data-driven discovery; Composite dynamic-and-static indexing; Differential privacy on correlated data; Massive personal time-series clustering; Persistent data sketching; Stream data cleaning; Dynamic event streams; Out-of-Order data streams; Concept-drifting data streams; Indexing metrics for uncertain data; Mining and forecasting of Big time-series Data; Holisti!
 c indexing; Crowd mining; Mining subjective properties on the Web; Data semantics and information extraction; Community detection in social networks; Mining and learning activities; Confidence-based and incomplete information

Trends in methodologies/architectures/platforms
Scalable classification frameworks; MapReduce over HybriD Clouds; Parallel Database Systems; Spatio-temporal and textual processing platforms; Deep language embedding; Large-Scale machine learning; Fault-tolerance for parallel data processing; Encrypted query processing; Enforcement of data use policies; Big Data industrial systems; Condensed representation; Temporal graphs; Graph summarization; Subspace and spectral methods; Cross-modal correlations; Open annotation data models; Elastic processing of data streams; Interactive data transformation; On-line aggregation; Interactive analysis on Big Data; Crowdsourcing applications; Distributed online tracking; Social graph partitioning methods; Monitoring of co-evolving data streams; Deep packet inspection systems; Ensemble-based data mining platforms; Sensors-oriented time-series prediction systems; Social multimedia as sensors; Sentiment representation systems

Mining mechanisms and methods
Contextual aggregated information retrieval; Aggregated query for workflows; Data mining algorithms; Media adaptive mining; Agent-based mining; Content-based mining; Context-aware mining; Automation of data extraction; Data mining at a large; Domain-driven data mining; Graph-based data mining; Multilabel information; Multimodal mining; Cloud-based mining; Mining using neurocomputing techniques

Mining support
Querying for mining; Questions for digital investigation; Similarity search; User-generated content; Visualizing data mining; Internationalization and localization techniques for profile/context-based visualization

Type of information mining
Concept mining; Process mining; Concept mining; Knowledge mining; Knowledge discovery; Mining image and video; Mining patterns; Opinion mining; Graph mining; Ontology mining; Semantic annotations and mining; Document mining; Spatial mining; Speech mining; Text mining; Web mining; XML data mining

Pervasive information retrieval
Context and location information retrieval; Mobile information retrieval; Geo-information retrieval; Context-aware information retrieval; Access-driven information retrieval;  Location-specific information retrieval; Spacial information retrieval;  Semantic-driven retrieval

Automated retrieval and mining
Automated information extraction; Agent-based data mining and information discovery; Agent-based knowledge; Datamining-based agents and multi-agent systems; Agent-mining intelligent applications and systems; Automated retrieval of multimedia streams; Automated retrieval from multimedia archives; Automated copyright infringement detection and watermarking; Automated content summarization; Automatic concept detection, categorization, and genre detection; Automatic speech recognition; Automated cross-media linking

Mining features
Dimensionality reduction; Feature weighting; Subset selection; Feature extraction; Feature construction; Data streams and time series; Selection in high-dimensional domains; Multilingual data mining; Multimedia mining; String processing and data mining; Mining association rules; Mining social relationships; Mining linked data; Mining sequential episodes from time series; Mining time-dependent data; Un-supervised data mining; Semi-structured data; Mining location-sensitive data; Concept-drift in data mining

Information mining and management
Data cleaning; Data updating; Segmentation and clustering; Mining transient information; Warehousing; Web syndication; Data filtering and aggregation; Optimal pruning; Data summarization; Knowledge injection, discovery and classification; Uncertainty removal; Managing incompleteness

Mining from specific sources
Bio data mining; Climate data mining; Data mining in medicine and pharmacology; Data mining in special networks (grids, sensors, etc.); Data management for mobile systems; Data management for sensors; Data mining and management for wireless systems; Dynamic network discovery; Mining from multiple sources; Mining personal semantic data; Mining from social networks; Mining from deep web; Mining from Wikipedia

Data management in special environments
Data management in sensor and mobile ad hoc networks; Data management in mobile peer-to-peer networks;  Data management for mobile applications; Data management in mobile/temporal social networks; Management of community sensing/participatory sensing data; Managing pervasive data, sensor data streams and user devices; Managing mobile semantic data; Manging data-intensive mobile computing; Management of real-time data; Managing security data streams; Managing Mobile Web 2.0 data; Managing data in mobile clouds; Data replication, migration and dissemination in mobile environments; Web data processing and security on mobile devices; Resource advertising and discovery techniques

Mining evaluation
Statistics on mining; Ranking of mining results; Provenance; Privacy issues; Patterns for mining; Credibility on data mining; Performance of mining information; Data mining and computational intelligence; Intelligent data understanding; Intelligent data analysis

Mining tools and applications
Data mining applications; Data mining tools and enabling software; Interoperability of information mining tools; Applications for large-scale mining; Content segmentation tools (e.g., shot and semantic scene segmentation);  Evaluation methods for TV and radio content analysis tools; Tools for data sets and standard resources

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IMMM 2017 Committee: http://www.iaria.org/conferences2017/ComIMMM17.html

IMMM Steering Committee
Nitin Agarwal, University of Arkansas at Little Rock, USA
Andreas Schmidt, Karlsruhe University of Applied Sciences, Germany
Michele Melchiori, Università degli Studi di Brescia, Italy
Bernhard Bauer, University of Augsburg, Germany
Mehmed Kantardzic, University of Louisville, USA
Daniel Thalmann, Institute for Media Innovation (IMI) | Nanyang Technological University, Singapore
Verena Kantere, University of Geneva, Switzerland
Duarte Trigueiros, ISCTE - University Institute of Lisbon, Portugal, Portugal

IMMM Industry/Research Advisory Committee
Dirk Labudde, Hochschule Mittweida, Germany
Adrienn Skrop, University of Pannonia, Hungary
Qing Liu, Data61 | CSIRO, Australia
Stefan Brüggemann, Airbus Defence and Space, Germany
Xuanwen Luo, Sandvik Mining, USA
Maria Luisa Villani, Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), Italy
Emir Muñoz, Fujitsu Ireland Ltd / INSIGHT Centre at NUI Galway, Ireland
Xiang Ji, Bloomberg LP, USA
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