File access level optimization using page access graph on recursive query evaluation

Author(s):  
Yuki Kusumi ◽  
Shojiro Nishio ◽  
Toshiharu Hasegawa
Author(s):  
Cynthia M. Horne

Chapter 2 explores each of the country cases in this project, namely the Czech Republic, Slovakia, Hungary, Poland, Romania, Bulgaria, Estonia, Latvia, Lithuania, Russia, Ukraine, and Albania. The chapter provides historical details of the transitional justice reforms in all twelve countries from 1989–2013, covering lustration, file access, public disclosures, and truth commissions. This material is then used to place each country case within the typology developed in Chapter 1, according to whether the measures were expansive and included compulsory employment change, limited and included largely voluntary employment change, informal and largely symbolic, or actively rejected. The chapter provides variable conceptualization and operationalization specifics to be used in the subsequent statistical analyses, including three different lustration variables, a truth commission variable, and timing of reform variables. It provides qualitative, comparative historical details to justify the classification of countries according to the primary independent variable, namely lustration and public disclosure programs.


Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 149
Author(s):  
Petros Zervoudakis ◽  
Haridimos Kondylakis ◽  
Nicolas Spyratos ◽  
Dimitris Plexousakis

HIFUN is a high-level query language for expressing analytic queries of big datasets, offering a clear separation between the conceptual layer, where analytic queries are defined independently of the nature and location of data, and the physical layer, where queries are evaluated. In this paper, we present a methodology based on the HIFUN language, and the corresponding algorithms for the incremental evaluation of continuous queries. In essence, our approach is able to process the most recent data batch by exploiting already computed information, without requiring the evaluation of the query over the complete dataset. We present the generic algorithm which we translated to both SQL and MapReduce using SPARK; it implements various query rewriting methods. We demonstrate the effectiveness of our approach in temrs of query answering efficiency. Finally, we show that by exploiting the formal query rewriting methods of HIFUN, we can further reduce the computational cost, adding another layer of query optimization to our implementation.


2021 ◽  
Vol 99 ◽  
pp. 101738
Author(s):  
Ishaq Zouaghi ◽  
Amin Mesmoudi ◽  
Jorge Galicia ◽  
Ladjel Bellatreche ◽  
Taoufik Aguili
Keyword(s):  

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