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SUMMARY:Adaptive Database Systems for Efficient Analytical Query Processin
 g
DTSTART:20190617T100000
DTEND:20190617T120000
DTSTAMP:20260428T214637Z
UID:9116c5be3113b3778f7a1e841f239e701fac9af2a038ad81c7e24b89
CATEGORIES:Conferences - Seminars
DESCRIPTION:Viktor Sanca\nEDIC candidacy exam\nExam president: Prof. Chris
 toph Koch\nThesis advisor: Prof. Anastasia Ailamaki\nCo-examiner: Prof. Ka
 rl Aberer\n\nAbstract\nData integration is one of the most important probl
 ems of data analysis\, since combining multiple data sources provides prev
 iously unknown insights. To analyze the data\, users transform heterogeneo
 us data to a compatible format and load them in the DBMS query engine\, pr
 ocessing data that will never be used. Additionally\, modern data analytic
 s applications require minimal time to insight in presence of ad-hoc workl
 oads. With conflicting requirements of flexibility and performance\, analy
 tical query processing needs to adapt efficiently to evolving workload and
  data characteristics.\n \nIn this proposal we examine an analytical quer
 y processing engine that enables fast queries over raw\, heterogeneous dat
 a [1]. We describe the abstractions and mechanisms it introduces in order 
 to efficiently adapt to queries over a variety of data formats\, and compa
 re the implementation to specialized systems. To address the cardinality e
 stimation errors of queries caused by simplifying assumptions\, we examine
  a data-driven method guided by workload for cardinality estimate adjustme
 nt [2]. Constructing a multitude of fast access methods facilitates query 
 speedup in presence of unknown workloads\, incurring a significant storage
  cost. We present a methodology that proposes to reduce index structure co
 st by automatic tuning based on adapting to data distribution\, and compar
 e its performance to traditional index structures [3].\n \nFinally\, insp
 ired by previous approaches\, we conclude with our research proposal regar
 ding analytical query processing that adapts to evolving data and workload
  characteristics.\n\n\nBackground papers\nFast Queries over Heterogeneous 
 Data Through Engine Customization\, by M. Karpathiotakis\, I. Alagiannis\,
  and A. Ailamaki\,Proc. VLDB Endow.\, vol. 9\, no. 12\, pp. 972–983\, Au
 g. 2016.\nLEO - DB2’s LEarning Optimizer\, by M. Stillger\, G. M. Lohman
 \, V. Markl\, and M. Kandil\, in Proceedings of the 27th International Con
 ference on Very Large Data Bases\, San Francisco\, CA\, USA\, 2001\, pp. 1
 9–28.\nThe Case for Learned Index Structures\, by T. Kraska\, A. Beutel\
 , E. H. Chi\, J. Dean\, and N. Polyzotis\, in Proceedings of the 2018 Inte
 rnational Conference on Management of Data\, New York\, NY\, USA\, 2018\, 
 pp. 489–504.\n 
LOCATION:BC 229 https://plan.epfl.ch/?room==BC%20229
STATUS:CONFIRMED
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