Online aggregation and continuous query support in MapReduce

2010 ◽  
Author(s):  
Tyson Condie ◽  
Neil Conway ◽  
Peter Alvaro ◽  
Joseph M. Hellerstein ◽  
John Gerth ◽  
...  
Keyword(s):  
Continuous Query ◽  
Online Aggregation

scholarly journals Query Rewriting for Incremental Continuous Query Evaluation in HIFUN

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 149
Author(s):  
Petros Zervoudakis ◽  
Haridimos Kondylakis ◽  
Nicolas Spyratos ◽  
Dimitris Plexousakis
Keyword(s):  
Query Optimization ◽  
Query Language ◽  
Computational Cost ◽  
Continuous Queries ◽  
Continuous Query ◽  
Query Rewriting ◽  
Query Evaluation ◽  
Clear Separation ◽  
Complete Dataset ◽  
High Level

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.

A scalable framework for continuous query evaluations over multidimensional, scientific datasets

2015 ◽  
Vol 28 (8) ◽  
pp. 2546-2563 ◽  
Author(s):  
Cameron Tolooee ◽  
Matthew Malensek ◽  
Sangmi Lee Pallickara
Keyword(s):  
Continuous Query ◽  
Scientific Datasets

scholarly journals Online aggregation of unbounded losses using shifting experts with confidence

2018 ◽  
Vol 108 (3) ◽  
pp. 425-444
Author(s):  
Vladimir V’yugin ◽  
Vladimir Trunov
Keyword(s):  
Online Aggregation

A Markov Prediction-Based Privacy Protection Scheme for Continuous Query

2019 ◽  
Vol 28 (09) ◽  
pp. 1950147
Author(s):  
Lei Zhang ◽  
Jing Li ◽  
Songtao Yang ◽  
Yi Liu ◽  
Xu Zhang ◽  
...  
Keyword(s):  
Privacy Protection ◽  
Location Privacy ◽  
Mathematical Proof ◽  
Security Analysis ◽  
Continuous Query ◽  
The Real ◽  
Protection Scheme ◽  
Whole Process ◽  
Prediction Scheme ◽  
Location Privacy Protection

The query probability of a location which the user utilizes to request location-based service (LBS) can be used as background knowledge to infer the real location, and then the adversary may invade the privacy of this user. In order to cope with this type of attack, several algorithms had provided query probability anonymity for location privacy protection. However, these algorithms are all efficient just for snapshot query, and simply applying them in the continuous query may bring hazards. Especially that, continuous anonymous locations which provide query probability anonymity in continuous anonymity are incapable of being linked into anonymous trajectories, and then the adversary can identify the real trajectory as well as the real location of each query. In this paper, the query probability anonymity and anonymous locations linkable are considered simultaneously, then based on the Markov prediction, we provide an anonymous location prediction scheme. This scheme can cope with the shortage of the existing algorithms of query probability anonymity in continuous anonymity locations difficult to be linked, and provide query probability anonymity service for the whole process of continuous query, so this scheme can be used to resist the attack of both of statistical attack as well as the infer attack of the linkable. At last, in order to demonstrate the capability of privacy protection in continuous query and the efficiency of algorithm execution, this paper utilizes the security analysis and experimental evaluation to further confirm the performance, and then the process of mathematical proof as well as experimental results are shown.

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