scholarly journals An Operational Semantics for Network Datalog

10.29007/f89j ◽  
2018 ◽  
Author(s):  
Vivek Nigam ◽  
Limin Jia ◽  
Anduo Wang ◽  
Boon Thau Loo ◽  
Andre Scedrov
Keyword(s):  
Query Language ◽  
Operational Semantics ◽  
Table Entry ◽  
Recursive Programs ◽  
Recursive Query ◽  
Evaluation Algorithm

Network Datalog (<i>NDlog</i>) is a recursive query language that extends Datalog by allowing programs to be distributed in a network. In our initial efforts to formally specify <i>NDlog</i>'s operational semantics, we have found several problems with the current evaluation algorithm being used, including unsound results, unintended multiple derivations of the same table entry, and divergence. In this paper, we make a first step towards correcting these problems by formally specifying a new operational semantics for <i>NDlog</i> and proving its correctness for the fragment of non-recursive programs. We also argue that if termination is guaranteed, then the results also extend to recursive programs. Finally, we identify a number of potential implementation improvements to <i>NDlog</i>.

scholarly journals A Query Language for Workflow Logs

2022 ◽  
Vol 13 (2) ◽  
pp. 1-28
Author(s):  
Yan Tang ◽  
Weilong Cui ◽  
Jianwen Su
Keyword(s):  
Business Process ◽  
Evaluation Method ◽  
Ad Hoc ◽  
Query Language ◽  
Cost Model ◽  
Formal Semantics ◽  
Control Flow ◽  
Query Evaluation ◽  
Evaluation Algorithm ◽  
Laws And Policies

A business process (workflow) is an assembly of tasks to accomplish a business goal. Real-world workflow models often demanded to change due to new laws and policies, changes in the environment, and so on. To understand the inner workings of a business process to facilitate changes, workflow logs have the potential to enable inspecting, monitoring, diagnosing, analyzing, and improving the design of a complex workflow. Querying workflow logs, however, is still mostly an ad hoc practice by workflow managers. In this article, we focus on the problem of querying workflow log concerning both control flow and dataflow properties. We develop a query language based on “incident patterns” to allow the user to directly query workflow logs instead of having to transform such queries into database operations. We provide the formal semantics and a query evaluation algorithm of our language. By deriving an accurate cost model, we develop an optimization mechanism to accelerate query evaluation. Our experiment results demonstrate the effectiveness of the optimization and achieves up to 50× speedup over an adaption of existing evaluation method.

RQL: a recursive query language

1993 ◽  
Vol 5 (3) ◽  
pp. 451-461 ◽  
Author(s):  
R. Ahad ◽  
B. Yao
Keyword(s):  
Query Language ◽  
Recursive Query

scholarly journals On the Designing of Model Checkers for Real-Time Distributed Systems

2015 ◽  
Vol 19 (6) ◽  
pp. 45-56 ◽  
Author(s):  
D. Yu. Volkanov ◽  
V. A. Zakharov ◽  
D. A. Zorin ◽  
I. V. Konnov ◽  
V. V. Podymov
Keyword(s):  
Distributed Systems ◽  
Model Checking ◽  
Real Time ◽  
Query Language ◽  
Formal Model ◽  
Timed Automata ◽  
Operational Semantics ◽  
Real Time Systems ◽  
Conversion Algorithm ◽  
Time Systems

To verify real-time properties of UML statecharts one may apply a UPPAAL, toolbox for model checking of real-time systems. One of the most suitable ways to specify an operational semantics of UML statecharts is to invoke the formal model of Hierarchical Timed Automata. Since the model language of UPPAAL is based on Networks of Timed Automata one has to provide a conversion of Hierarchical Timed Automata to Networks of Timed Automata. In this paper we describe this conversion algorithm and prove that it is correct w.r.t. UPPAAL query language which is based on the subset of Timed CTL.

scholarly journals 4494 Predictors of Reintubation After Cardiac Surgery

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.

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.

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