Bulk types and query language design

This paper offers a new insight into GenSLA classroom research in light of recent developments in the Minimalist Program (MP). Recent research in GenSLA has shown how generative linguistics and acquisition studies can inform the language classroom, mostly focusing on what linguistic aspects of target properties should be integrated as a part of the classroom input. Based on insights from Chomsky’s ‘three factors for language design’ – which bring together the Faculty of Language, input and general principles of economy and efficient computation (the third factor effect) for language development – we put forward a theoretical rationale for how classroom research can offer a unique environment to test the learnability in L2 through the statistical enhancement of the input to which learners are exposed.

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Annotation Language Design for Ada (ANNA).

10.21236/ada140452 ◽

1984 ◽

Author(s):

D. C. Luckham

Keyword(s):

Language Design

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An Ada(Trade Name)/SQL (Structured Query Language) Implementation Kit

10.21236/ada200452 ◽

1988 ◽

Author(s):

Bill R. Brykczynski ◽

Kerry Hilliard

Keyword(s):

Query Language ◽

Language Implementation ◽

Trade Name ◽

Structured Query Language

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4494 Predictors of Reintubation After Cardiac Surgery

Journal of Clinical and Translational Science ◽

10.1017/cts.2020.182 ◽

2020 ◽

Vol 4 (s1) ◽

pp. 50-50

Author(s):

Robert Edward Freundlich ◽

Gen Li ◽

Jonathan P Wanderer ◽

Frederic T Billings ◽

Henry Domenico ◽

...

Keyword(s):

Clinical Trial ◽

Cardiac Surgery ◽

High Risk ◽

Query Language ◽

Risk Estimation ◽

Clinical Care ◽

Model Development ◽

Calibration Plot ◽

Iterative Model ◽

Strong Candidate

OBJECTIVES/GOALS: We modeled risk of reintubation within 48 hours of cardiac surgery using variables available in the electronic health record (EHR). This model will guide recruitment for a prospective, pragmatic clinical trial entirely embedded within the EHR among those at high risk of reintubation. METHODS/STUDY POPULATION: All adult patients admitted to the cardiac intensive care unit following cardiac surgery involving thoracotomy or sternotomy were eligible for inclusion. Data were obtained from operational and analytical databases integrated into the Epic EHR, as well as institutional and departmental-derived data warehouses, using structured query language. Variables were screened for inclusion in the model based on clinical relevance, availability in the EHR as structured data, and likelihood of timely documentation during routine clinical care, in the hopes of obtaining a maximally-pragmatic model. RESULTS/ANTICIPATED RESULTS: A total of 2325 patients met inclusion criteria between November 2, 2017 and November 2, 2019. Of these patients, 68.4% were male. Median age was 63.0. The primary outcome of reintubation occurred in 112/2325 (4.8%) of patients within 48 hours and 177/2325 (7.6%) at any point in the subsequent hospital encounter. Univariate screening and iterative model development revealed numerous strong candidate predictors (ANOVA plot, figure 1), resulting in a model with acceptable calibration (calibration plot, figure 2), c = 0.666. DISCUSSION/SIGNIFICANCE OF IMPACT: Reintubation is common after cardiac surgery. Risk factors are available in the EHR. We are integrating this model into the EHR to support real-time risk estimation and to recruit and randomize high-risk patients into a clinical trial comparing post-extubation high flow nasal cannula with usual care. CONFLICT OF INTEREST DESCRIPTION: REF has received grant funding and consulting fees from Medtronic for research on inpatient monitoring.

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Query Rewriting for Incremental Continuous Query Evaluation in HIFUN

Algorithms ◽

10.3390/a14050149 ◽

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.

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