As the development of World Wide Web, social networks , wikis and social tagging communities
are becoming more and more popular. Along with the interaction between users and computers,
more and more personalized information is potentially mined from the Web by using semantic
computing technology, such as ontology engineering for social network and personalization,
mining user reviews, learners and courses modeling, user profiling in social network, sentiment
analysis for user opinion mining and so on. Connecting semantic computing and personalized can
enhance classic information management and retrieval approaches. It combines data mining with
semantic computing as a promising direction and offers opportunities for developing novel
algorithms and tools ranging from text and multimedia.
Topics of interest include, but are not limited to the exploitation of the Web of Data,
the identification of semantics underlying social anno tations of multimedia contents, and
the application of semantic based techniques and technologies in research fields (but not limited)
The 4th International Workshop on Graph Data Management and Analysis (GDMA 2020)
Recently, there has been a lot of interest in the application of graphs in different domains.
They have been widely used for data modeling of different application domains such as multimedia
databases, protein networks, social networks and semantic web. With the continued emergence and
increase of massive and complex structural graph data, a graph database that efficiently supports
elementary data management mechanisms is crucially required to effectively understand and utilize any collection of graphs.
The overall goal of the workshop is to bring people from different fields together, exchange research
ideas and results, and encourage discussion about how to provide efficient graph data management techniques
in different application domains and to understand the research challenges of such area.
The proliferation of new technologies such as Internet of things,
social networks and cloud computing produces datasets whose volume, velocity, and variability
is beyond the ability of commonly-used software tools. Efficient processing of big data creates
notable research challenges and opportunities for traditional databases and infrastructure.
On the other hand, computing and software are turned into commodity services by cloud computing,
so it is necessary to examine Big Data as a Service (BDaaS) from both technology and business aspects.
This BDMS 2020 workshop intends to bring together researchers, practitioners,
and developers from academia and industry to discuss cutting edge research on big data management
and service, such that the interactions among the experts of these areas can be promoted and new
interdisciplinary technologies can be developed. The scope of the workshop includes big data management
techniques such as big data modeling, analytics, and toolkits, as well as techniques and case studies
for providing big data as a service.
Big data quality management is in demand to decrease the harm of data quality problems and computes
high quality problem from big data. Big data quality management has become one of the hottest
issues not only in database community but also in artificial intelligence, data mining and other
related area. The goal of this workshop is to raise the awareness of quality issues in big data
and promote approaches to evalua te and improve big data quality.
The workshop topics include, but are not limited to: Data Quality Models and Theory,
Data Cleaning Algorithms, Record Linkage and Entity Resolution, Privacy Preservation,
Error Tolerate Computation and so on.
Artificial intelligence(AI) recently represented by the deep learning has become very important techniques to various
applications, and, especially, big data is definitely linked to the AI. The big data can be the essential training input
of the AI and the AI can be the useful tool to prepare, manage and analysis the big data. The aim of this workshop is to
present and discuss how to improve both of AI and big data technologies by interacting with researcher from various areas.
The scope of this workshop includes all technologies and applications related to AI, machine learning, knowledge
discovery, data mining and database management but not limited.