TY - GEN
T1 - Detecting the temporal context of queries
AU - Kennedy, Oliver
AU - Yang, Ying
AU - Chomicki, Jan
AU - Fehling, Ronny
AU - Liu, Zhen Hua
AU - Gawlick, Dieter
N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2015
PY - 2015
Y1 - 2015
N2 - Business intelligence and reporting tools rely on a database that accurately mirrors the state of the world. Yet, even if the schema and queries are constructed in exacting detail, assumptions about the data made during extraction, transformation, and schema and query creation of the reporting database may be (accidentally) ignored by end users, or may change as the database evolves over time. As these assumptions are typically implicit (e.g., assuming that a sales record relation is appendonly), it can be hard to even detect that a mistaken assumption has been made. In this paper, we argue that such errors are consequences of unintended contextual dependence, i.e., query outputs dependent on a variable characteristic of the database. We characterize contextual dependence, and explore several strategies for efficiently detecting and quantifying the effects of contextual dependence on query outputs. We present and evaluate our findings in the context of a concrete case study: Detecting temporal dependence using a database management system with versioning capabilities.
AB - Business intelligence and reporting tools rely on a database that accurately mirrors the state of the world. Yet, even if the schema and queries are constructed in exacting detail, assumptions about the data made during extraction, transformation, and schema and query creation of the reporting database may be (accidentally) ignored by end users, or may change as the database evolves over time. As these assumptions are typically implicit (e.g., assuming that a sales record relation is appendonly), it can be hard to even detect that a mistaken assumption has been made. In this paper, we argue that such errors are consequences of unintended contextual dependence, i.e., query outputs dependent on a variable characteristic of the database. We characterize contextual dependence, and explore several strategies for efficiently detecting and quantifying the effects of contextual dependence on query outputs. We present and evaluate our findings in the context of a concrete case study: Detecting temporal dependence using a database management system with versioning capabilities.
UR - https://www.scopus.com/pages/publications/84942645399
U2 - 10.1007/978-3-662-46839-5_7
DO - 10.1007/978-3-662-46839-5_7
M3 - Conference contribution
SN - 9783662468388
T3 - Lecture Notes in Business Information Processing
SP - 97
EP - 113
BT - Enabling Real-Time Business Intelligence - International Workshops, BIRTE 2013 and BIRTE 2014, BIRTE 2014, Revised Selected Papers
A2 - Castellanos, Malu
A2 - Pedersen, Torben Bach
A2 - Tatbul, Nesime
A2 - Dayal, Umeshwar
PB - Springer Verlag
T2 - International Workshops on Business Intelligence for the Real-Time Enterprise, BIRTE 2013 and BIRTE 2014
Y2 - 1 September 2014 through 1 September 2014
ER -