OntologySummit2012: (Track-3) "Challenge: ontology and big data" Community Input (32CJ)
Track Co-Champions: Mr. ErnieLucier & Ms. MaryBrady (338W)
Mission Statement: (32CK)
The mission of this track is to identify appropriate objectives for an "Ontology and Big Data" challenge, prepare problem statements, identify the organizations and people to be advocates, and identify the resources necessary to complete a challenge. The goal will be to select a challenge showing benefits of ontology to big data. T (32DY)
see also: OntologySummit2012_BigDataChallenge_Synthesis (32ED)
Proposed framework for Track 3 Challenge - Ontology and Big Data (32TU)
People in the domains of science, software engineering, computer science, etc. can benefit from a combined knowledge of their domain and application of ontology-based technologies. A combined understanding of these domains and ontology-based technologies may encourage the growth of technology. (32TF)
Problems (32TV)
*Programmers are not able to optimize the use of unstructured data for scientist and engineers (32TH)
*Scientist without ontology training use brute force programming can be inefficient and scientist and engineers are not aware of options and capabilities using ontology-based technologies (32TI)
*Science and ontology-based technology evolution is slow or non-existent (32TJ)
*Is technological growth constrained by the shortage of qualified ontologists? (32TK)
Goals (32TL)
*Enable scientist to make maximum use of big data (32TM)
*Enable scientist to understand the potential of ontology-based systems integration (32TN)
*Enable ontologists to understand scientist needs (32TO)
Needs (32TP)
*The skills most needed today include a combined understanding of a scientific or engineering discipline and knowledge of ontology-based technologies. (32TW)
Output (proposed) (32TQ)
Please feel free to comment (32TT)
Enter your input below ... (please identify yourself and date your entry) (32CL)
- (Rosario Uceda-Sosa, 1/19) In my opinion, there are two dimensions to this track. The first has to do with data volume and dynamicity: how to effectively store/cache/stream large volumes of data under various levels of knowledge of the metadata, ranging from structured data with known metadata to unstructured and unknown subject. This problem may or may not require large ontology models, it's mostly about instance management. The second has to do with large ontology models for complex domains (Smarter Cities, Risk Management in financial markets, etc.) Here, the models may be monolithic, or they may integrate other preexisting models/schemas, or they may have to be authored collaboratively. In a word, it's (mostly) about the models themselves. I'd be (mostly) interested in this second aspect, and see what kinds of ontologies in this area people are working in. (32CM)