Ef?cient Query Evaluation over Temporally Correlated Probabilistic Streams

2009 ◽  
Author(s):  
Bhargav Kanagal ◽  
Amol Deshpande
Keyword(s):  
Query Evaluation ◽  
Temporally Correlated

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.

GoFast: Graph-based optimization for efficient and scalable query evaluation

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

scholarly journals A Novel STAP Algorithm for Airborne MIMO Radar Based on Temporally Correlated Multiple Sparse Bayesian Learning

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Hanwei Liu ◽  
Yongshun Zhang ◽  
Yiduo Guo ◽  
Qiang Wang ◽  
Yifeng Wu
Keyword(s):  
Sparse Representation ◽  
Bayesian Learning ◽  
Multiple Input Multiple Output ◽  
Time Spectrum ◽  
Weight Vector ◽  
Mimo Radar ◽  
Sparse Bayesian Learning ◽  
Multiple Measurement Vectors ◽  
Input Multiple Output ◽  
Temporally Correlated

In a heterogeneous environment, to efficiently suppress clutter with only one snapshot, a novel STAP algorithm for multiple-input multiple-output (MIMO) radar based on sparse representation, referred to as MIMOSR-STAP in this paper, is presented. By exploiting the waveform diversity of MIMO radar, each snapshot at the tested range cell can be transformed into multisnapshots for the phased array radar, which can estimate the high-resolution space-time spectrum by using multiple measurement vectors (MMV) technique. The proposed approach is effective in estimating the spectrum by utilizing Temporally Correlated Multiple Sparse Bayesian Learning (TMSBL). In the sequel, the clutter covariance matrix (CCM) and the corresponding adaptive weight vector can be efficiently obtained. MIMOSR-STAP enjoys high accuracy and robustness so that it can achieve better performance of output signal-to-clutter-plus-noise ratio (SCNR) and minimum detectable velocity (MDV) than the single measurement vector sparse representation methods in the literature. Thus, MIMOSR-STAP can deal with badly inhomogeneous clutter scenario more effectively, especially suitable for insufficient independent and identically distributed (IID) samples environment.

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