This chapter proposes steps toward the solution to the data access problem that end-users typically face when dealing with Big Data: • They need to pose ad-hoc queries to a collection of data sources, possibly including streaming sources. • They are unable to query these sources on their own, but are dependent on assistance from IT experts. • The turnaround time for information requests is in the range of days, possibly weeks, due to the involvement of the IT personnel. • The volume, complexity, variety, and velocity of the underlying data sources put very high demands on the scalability of the solution.
Scalable end-user access to big data / Giese, M.; Calvanese, D.; Haase, P.; Horrocks, I.; Ioannidis, Y.; Kllapi, H.; Koubarakis, M.; Lenzerini, M.; Moller, R.; Muro, M. R.; Ozcep, O.; Rosati, R.; Schlatte, R.; Schmidt, M.; Soylu, A.; Waaler, A.. - (2013), pp. 205-244. [10.1201/b16014].
Scalable end-user access to big data
Calvanese D.
;Lenzerini M.
;Rosati R.;Soylu A.;
2013
Abstract
This chapter proposes steps toward the solution to the data access problem that end-users typically face when dealing with Big Data: • They need to pose ad-hoc queries to a collection of data sources, possibly including streaming sources. • They are unable to query these sources on their own, but are dependent on assistance from IT experts. • The turnaround time for information requests is in the range of days, possibly weeks, due to the involvement of the IT personnel. • The volume, complexity, variety, and velocity of the underlying data sources put very high demands on the scalability of the solution.File | Dimensione | Formato | |
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