Q. What are some of the challenges associated with ontology-based data access (OBDA) in the context of Big Data?

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Q. What is the expected outcome of raising OBDA to a level where it can be successfully deployed to Big Data?

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Q. What characterizes the data in knowledge- and data-intensive enterprises?

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Q. How do end-users typically interact with data in these enterprises?

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Q. What is the turnaround time like when end-users use predefined queries in specialized applications?

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Q. Why are situations where users need to explore data with ad hoc queries more challenging?

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Q. How does the involvement of IT-skilled personnel affect the turnaround time in exploring data with ad hoc queries?

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Q. What are the important aspects of the end-user data access problem?

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Q. How does the problem of data access become more challenging in Big Data scenarios?

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Q. What is the goal of the Optique project regarding data access for Big Data?

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Q. What does OBDA stand for in the context of data access?

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Q. How does OBDA address the bottleneck in query translation for end-users?

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Q. What challenges do end-users typically face when dealing with Big Data?

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Q. How does the involvement of IT experts affect the turnaround time for information requests?

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Q. What dimensions of data make data access challenging in Big Data scenarios?

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Q. What is the main idea behind ontology-based data access (OBDA)?

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Q. Who typically formulates queries using the terms defined by the ontology in OBDA?

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Q. What does OBDA stand for in the context of data access?

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Q. What is one advantage of using OBDA for end-user data access?

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Q. What does the ontology in OBDA help achieve?

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