第一次 IEEE/ ACM国际分布式大数据管理研讨会 1st IEEE/ACM International Workshop on Distributed Big Data Management (DBDM 2016)

活动简介

Big Data Management is of relevant interest at now, and it can be considered as one of the most emerging research topics we deal with. In particular, the management of Big Data in distributed settings (like Clouds) is demanding for innovative models, techniques and algorithms capable of dealing with the well-known Vs of such kind of data.

Indeed, traditional approaches are not suitable to manage Big Data in distributed environments, due to the Volume, Velocity and Variety of Big Data. Starting from this evidence, recently we experienced novel proposals that are trying to creating applications and systems that, running on top of distributed settings like Clouds, effectively and efficiently manage Big Data as to support a wide range of contexts, among which analytics, knowledge discovery and cybersecurity methods are just some relevant examples.

Despite these initiatives, lot of work still needs to be done in such research area, as it encompasses a large collection of topics ranging from data management algorithms to high-performance techniques. Topics are both of theoretical nature (e.g., managing uncertain and imprecise distributed Big Data) and practical nature (e.g., Big Data dissemination in distributed environments).

The aim of the 1st International Workshop on Distributed Big Data Management (DBDM 2016) is to capture the new research trends and results in terms of models, techniques, algorithms, architecture and applications for the management of Big Data in distributed environments. This workshop will also identify potential research directions and technologies that will drive innovations within this domain. We anticipate this workshop to establish a pathway for the development of future-generation large-scale Big Data management systems.

征稿信息

征稿范围

The DBDM 2016 workshop focuses on all the research aspects of distributed management of Big Data. Among these, an unrestricted list is the following one:

Distributed Big Data: Fundamentals
Distributed Big Data: Modelling
Distributed Big Data: Statistical Approaches
Distributed Big Data: Novel Paradigms
Distributed Big Data: Innovative Protocols
Distributed Big Data: Algorithms
Distributed Big Data: Query Optimization
Distributed Big Data: Non-Conventional Environments (e.g., Spatio-Temporal Data, Streaming Data, Cloud Data, Probabilistic Data, Uncertain Data)
Distributed Big Data: Systems
Distributed Big Data: Architectures
Distributed Big Data: Advanced Topics (e.g., NoSQL Databases)
Distributed Big Data: Case Studies and Applications
Innovative Models for Big Data Management in Distributed Settings
Innovative Techniques for Big Data Management in Distributed Settings
Innovative Algorithms for Big Data Management in Distributed Settings
Innovative Architectures for Big Data Management in Distributed Settings
Query Processing Approach for Big Data in Distributed Settings
Approximate Query Processing of Big Data in Distributed Settings
Uncertain and Imprecise Big Data Management in Distributed Settings
Privacy Preserving Big Data Management in Distributed Settings
Secure Big Data Management in Distributed Settings
Scalable Big Data Analytics in Distributed Settings
Data Warehousing over Big Data in Distributed Settings
OLAP over Big Data in Distributed Settings
Big Graph Data Management in Distributed Settings
Big RDF Data Management in Distributed Settings
Streaming Big Data Management in Distributed Settings
Virtual Big Data Management in Distributed Settings
Indexing Approaches for Big Data in Distributed Settings
Theoretical Models for Big Data Representation in Distributed Settings
Big Data Exchange Models and Algorithms in Distributed Settings
Big Data Fusion Models and Algorithms in Distributed Settings
Big Data Integration Models and Algorithms in Distributed Settings
Big Data Availability Models and Algorithms in Distributed Settings
Big Data Reliability Models and Algorithms in Distributed Settings