TY - GEN
T1 - Analyzing data-centric applications
T2 - 32nd IEEE International Conference on Data Engineering, ICDE 2016
AU - Bourhis, Pierre
AU - Deutch, Daniel
AU - Moskovitch, Yuval
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/6/22
Y1 - 2016/6/22
N2 - We consider in this paper the analysis of complex applications that query and update an underlying database in their operation. We focus on three classes of analytical questions that are important for application owners and users alike: Why was a result generated? What would be the result if the application logic or database is modified in a particular way? How can one interact with the application to achieve a particular goal? Answering these questions efficiently is a fundamental step towards optimizing the application and its use. Noting that provenance was a key component in answering similar questions in the context of database queries, we develop a provenance-based model and efficient algorithms for these problems in the context of data-centric applications. Novel challenges here include the dynamic update of data, combined with the possibly complex workflows allowed by applications. We nevertheless achieve theoretical guarantees for the algorithms performance, and experimentally show their efficiency and usefulness, even in presence of complex applications and large-scale data.
AB - We consider in this paper the analysis of complex applications that query and update an underlying database in their operation. We focus on three classes of analytical questions that are important for application owners and users alike: Why was a result generated? What would be the result if the application logic or database is modified in a particular way? How can one interact with the application to achieve a particular goal? Answering these questions efficiently is a fundamental step towards optimizing the application and its use. Noting that provenance was a key component in answering similar questions in the context of database queries, we develop a provenance-based model and efficient algorithms for these problems in the context of data-centric applications. Novel challenges here include the dynamic update of data, combined with the possibly complex workflows allowed by applications. We nevertheless achieve theoretical guarantees for the algorithms performance, and experimentally show their efficiency and usefulness, even in presence of complex applications and large-scale data.
U2 - 10.1109/ICDE.2016.7498289
DO - 10.1109/ICDE.2016.7498289
M3 - Conference contribution
AN - SCOPUS:84980325711
T3 - 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016
SP - 779
EP - 790
BT - 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 16 May 2016 through 20 May 2016
ER -