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SQL query log analysis for identifying user interests and query recommendations

Arzamasova, Natalia

Abstract (englisch):

In the sciences and elsewhere, the use of relational databases has become ubiquitous.
To get maximum profit from a database, one should have in-depth knowledge in both
SQL and a domain (data structure and meaning that a database contains). To assist
inexperienced users in formulating their needs, SQL query recommendation system
(SQL QRS) has been proposed. It utilizes the experience of previous users captured by
SQL query log as well as the user query history to suggest. When constructing such
a system, one should solve related problems: (1) clean the query log and (2) define
appropriate query similarity functions. These two tasks are not only necessary for
building SQL QRS, but they apply to other problems. In what follows, we describe
three scenarios of SQL query log analysis: (1) cleaning an SQL query log, (2) SQL
query log clustering when testing SQL query similarity functions and (3) recommending
SQL queries. We also explain how these three branches are related to each other.
Scenario 1. Cleaning SQL query log as a general pre-processing step
The raw query log is often not suitable for query log analysis tasks such as clustering,
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Volltext §
DOI: 10.5445/IR/1000126161
Veröffentlicht am 26.11.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Programmstrukturen und Datenorganisation (IPD)
Publikationstyp Hochschulschrift
Publikationsdatum 26.11.2020
Sprache Englisch
Identifikator KITopen-ID: 1000126161
Verlag Karlsruher Institut für Technologie (KIT)
Umfang XV, 145 S.
Art der Arbeit Dissertation
Fakultät Fakultät für Informatik (INFORMATIK)
Institut Institut für Programmstrukturen und Datenorganisation (IPD)
Prüfungsdatum 03.02.2020
Schlagwörter SQL query log; Log analysis; User interests; SQL query recommendation
Referent/Betreuer Böhm, K.
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
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