You are invited to participate in The First International Workshop on Knowledge Management and Big Data Analytics (KMBA), to be held as part of the IEEE International Conference on Big Data, Oct 6 – 9, 2013 at Santa Clara, CA, USA.

Data driven scientific discovery approach has already been agreed to be an important emerging paradigm for computing in areas including social, service, Internet of Things (or sensor networks), and cloud. Under this paradigm, Big Data is the core that drives new researches in many areas, from environmental to social. There are many new scientific challenges when facing this big data phenomenon, ranging from capture, curation, storage, search, sharing, analysis, and visualization. The complication here is not just the storage, I/O, query, and performance, but also the integration across heterogeneous, interdependent complex data resources for real-time decision making, collaboration, and ultimately value co-creation.

Due to the consideration of real-time performance, return on investment (ROI), complexity /practicability, and data-human interface, one important approach for predictive big data analytics is to focus on the creation and maintenance of domain specific knowledge that serves as the bridge between the high speed incoming raw data and the “last mile” self service analytics. This will significantly reduce the amount of data to be managed and processed. Its presentation view will likely be more user-centric, easily to be understood by non-IT experts for their final decision making; time taken for decision making can also be fast enough to meet most of the real-time analytics requirements.

Due to the four main properties (i.e. volume, velocity, variety, and veracity) of the big data, storing temporal knowledge from big data for real-time analytics poses many new challenges to its life cycle maintenance of knowledge in Big Data analytics. KMBA aims to foster a dialogue among researchers, industry practitioners, as well as potential users of Big Data, discuss new opportunities and investigations to promote the best actionable analytics framework for wide range of applications.

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Topics of interest include, but are not limited to:

  • Data and Knowledge Modeling
  • Knowledge Mapping from Big Data Sources
  • Knowledge Creation through Crowdsourcing
  • Knowledge-inspired Big Data Indexing and Query Processing
  • Dynamic Knowledge Integration and Visualization
  • Data and Knowledge Interoperability and Exchange
  • Data and Knowledge Provenance
  • Knowledge-inspired Data Mining and Machine Learning
  • Knowledge Discovery, Search, and Recommendation
  • Knowledge Analytics Framework and Architecture
  • Privacy Preserving Big Data Collection / Analytics
  • Knowledge Quality Estimation and Uncertainty Handling
  • Use Cases and Applications in Knowledge and Big Data analytics