TY - GEN
T1 - Materialized view selection for aggregate view recommendation
AU - Ehsan, Humaira
AU - Sharaf, Mohamed A.
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - Data analysts arduously rely on data visualizations for drawing insights into huge and complex datasets. However, finding interesting visualizations by manually specifying various parameters such as type, attributes, granularity is a protracted process. Simplification of this process requires systems that can automatically recommend interesting visualizations. Such systems primarily work first by evaluating the utility of all possible visualizations and then recommending the top-k visualizations to the user. However, this process is achieved at the hands of high data processing cost. That cost is further aggravated by the presence of numerical dimensional attributes, as it requires binned aggregations. Therefore, there is a need of recommendation systems that can facilitate data exploration tasks with the increased efficiency, without compromising the quality of recommendations. The most expensive operation while computing the utility of the views is the time spent in executing the query related to the views. To reduce the cost of this particular operation, we propose a novel technique mView, which instead of answering each query related to a view from scratch, reuses results of the already executed queries. This is done by incremental materialization of a set of views in optimal order and answering the queries from the materialized views instead of the base table. The experimental evaluation shows that the mView technique can reduce the cost at least by 25–30% as compared to the previously proposed methods.
AB - Data analysts arduously rely on data visualizations for drawing insights into huge and complex datasets. However, finding interesting visualizations by manually specifying various parameters such as type, attributes, granularity is a protracted process. Simplification of this process requires systems that can automatically recommend interesting visualizations. Such systems primarily work first by evaluating the utility of all possible visualizations and then recommending the top-k visualizations to the user. However, this process is achieved at the hands of high data processing cost. That cost is further aggravated by the presence of numerical dimensional attributes, as it requires binned aggregations. Therefore, there is a need of recommendation systems that can facilitate data exploration tasks with the increased efficiency, without compromising the quality of recommendations. The most expensive operation while computing the utility of the views is the time spent in executing the query related to the views. To reduce the cost of this particular operation, we propose a novel technique mView, which instead of answering each query related to a view from scratch, reuses results of the already executed queries. This is done by incremental materialization of a set of views in optimal order and answering the queries from the materialized views instead of the base table. The experimental evaluation shows that the mView technique can reduce the cost at least by 25–30% as compared to the previously proposed methods.
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U2 - 10.1007/978-3-030-12079-5_8
DO - 10.1007/978-3-030-12079-5_8
M3 - Conference contribution
AN - SCOPUS:85061103160
SN - 9783030120788
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 104
EP - 118
BT - Databases Theory and Applications - 30th Australasian Database Conference, ADC 2019, Proceedings
A2 - Chang, Lijun
A2 - Cao, Xin
A2 - Gan, Junhao
PB - Springer Verlag
T2 - 30th Australasian Database Conference, ADC 2019
Y2 - 29 January 2019 through 1 February 2019
ER -